├── _config.yml
├── preliminary_proposal.md
├── proposal.md
├── README.md
└── Patent_Classification.ipynb


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1 | theme: jekyll-theme-cayman


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/preliminary_proposal.md:
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 1 | # Patent Classification
 2 | 
 3 | ### Purpose
 4 | Classify patent applications by the US Patent and Trademark Office (USPTO) art unit classification. Currently this is the first step in the processing of a patent application sent to the USPTO. 135 people read each application and assign it to an art unit. The turnaround time is 28 days, and the contracting firm costs about 95 million over 5 years (this might have increased). The art unit is a unit with a technological background to understand the technology in the patent application. 
 5 | 
 6 | ### Problem
 7 | It currently takes too long and costs too much for a patent to be classified and assigned to the correct art unit. Additionally, if patent attorneys can write the patent application with the correct wording to direct the application to one art unit over another, they can increase the likelihood of the patent being granted.
 8 | 
 9 | ### How I will present my work
10 | I would like to have a web app to be used by friends in the legal industry. My minimum viable product would be a presentation.
11 | 
12 | ### Data sources
13 | 1) USPTO
14 | 	- [USPTO bulkdata data dictionary](https://bulkdata.uspto.gov/data/patent/office/actions/bigdata/2017/USPTO%20Patent%20Prosecution%20Research%20Data_Unlocking%20Office%20Action%20Traits.pdf)
15 | 	- [USPTO public pair dataset](https://www.uspto.gov/learning-and-resources/electronic-data-products/patent-examination-research-dataset-public-pair)
16 | 
17 | 2) [Google Big Query](https://cloud.google.com/bigquery/)
18 | 	- patents-public-data dataset
19 | 
20 | ### Next Step
21 | My next step is to construct an SQL query to collect the data I will need from Google Big Query. I will also need to collect additional data from the USPTO office action data bases.
22 | 
23 | #
24 | # Art Price Prediction
25 | 
26 | ### Purpose
27 | Predict the price of art, or predict up and coming artists, specifically for western art. 
28 | 
29 | ### Problem
30 | Predicting what a painting will go for at auction or what artist would be a good investment is difficult even for a professional.
31 | 
32 | ### How I will present my work
33 | I would present a slide show
34 | 
35 | ### Data sources
36 | 1) [Artnet price database](https://www.artnet.com/price-database/)
37 | 2) [Stark Museum of Art](http://starkculturalvenues.org/starkmuseum/) (Specializes in western art and buys western art regularly.)
38 | 3) [Invaluable](https://www.invaluable.com/features/artfact1000.cfm?sgID=3&groupName=American%20Painting%3A%2018th%2F19th%20Century)
39 | 4) [Mutualart](https://www.mutualart.com)
40 | 5) [Mearto](https://www.mearto.com/search)
41 | 
42 | ### Next Step
43 | My next step is to gather the data and speak with experts to get a better idea on features to target for data acquisition.
44 | 
45 | #
46 | # College Football Quarterback Prediction
47 | 
48 | ### Purpose
49 | Predict if a high school football quarterback will perform well at the college level.
50 | 
51 | ### Problem
52 | UT seems to have great difficulty finding a good quarterback even though the Texas high schools are noted as being a top field for recruiting. If UT could more accurately predict if a high school quarterback will perform well at the college level they could more consistently recruit high quality quarterbacks and win more games. 
53 | 
54 | ### How I will present my work
55 | I would present a slide show or visualization.
56 | 
57 | ### Data sources
58 | 1) [Maxpreps](http://www.maxpreps.com)
59 | 2) [Houston Chronical](https://www.chron.com/sports/highschool/scoreboard/)
60 | 3) [ESPN](http://www.espn.com/college-football/statistics)
61 | 4) [CBS Sports](https://www.cbssports.com/collegefootball/stats/playersort/NCAAF/QB)
62 | 5) [Football Outsiders](https://www.footballoutsiders.com/stats/qb)
63 | 
64 | ### Next Step
65 | My next step is to gather the data and speak with experts to get a better idea on features to target for data acquisition.
66 | 


--------------------------------------------------------------------------------
/proposal.md:
--------------------------------------------------------------------------------
 1 | ## Patent Classification Capstone
 2 | 
 3 | ### Purpose
 4 | The purpose of this project is to identify which group of people (art unit) at the US patent office (USPTO) will review a new patent application. The art unit is a group of patent examiners with the technological background to understand the technology in the patent application. Currently, this is the first step in the processing of a patent application sent to the USPTO. The USPTO sends the application to a contractor where 135 people read each application and assign it to an art unit. The turnaround time is [28 days](https://www.serco-na.com/news/press-releases/detail/2194/serco-processes-2-millionth-patent-application-for-u-s), and the USPTO pays the contractor 95 million over 5 years ([Citation](https://www.serco-na.com/news/press-releases/detail/12520/serco-awarded-95-million-patent-classification-contract)). 
 5 | 
 6 | ### Problem
 7 | It currently takes too long and costs too much for a patent to be classified and assigned to the correct art unit. Additionally, if patent attorneys can write the patent application with the correct wording to direct the application to one art unit over another, they can increase the likelihood of the patent being granted.
 8 | 
 9 | **For example**, patent applications that land in art unit 3681, which handles “transportation, construction, electronic commerce, agriculture, national security and license & review,” have about a 29.8% chance of maturing into a patent.  And even if you are lucky enough to be one of the 29.8%, it is likely to take over four years to grant.  But get your patent applications into art unit 3688, which the USPTO describes as handling the exact same subject matter as 3681, and your chances of allowance jump up to about 81%, with an average time to issuance of around 2 years ([Citation](http://www.ipwatchdog.com/2016/04/14/better-way-file-patent-applications/id=68302/)).
10 | 
11 | **[The hardest and easiest art units.](http://www.ipwatchdog.com/2015/05/21/hardest-easiest-art-units/id=57864/):**
12 | 
13 | ![image from ipwatchdog.com](http://www.ipwatchdog.com/wp-content/uploads/2015/05/Figure-1.png)
14 | ![image from ipwatchdog.com](http://www.ipwatchdog.com/wp-content/uploads/2015/05/Figure-2.png)
15 | 
16 | **Alice case law is a major factor preventing software related patents**
17 | Alice Corporation Pty. Ltd. v. CLS Bank International, 573 U.S. ___ (2014) is a case that essentially said any patent that relates to performing an economic activity on a computer is possibly an abstract idea and not patentable. Avoiding Alice rejections is a big concern for patent attorneys. Once you receive an Alice rejection you are less likely to get the patent granted.
18 | 
19 | [**Art units with most Alice rejections**](http://www.ipwatchdog.com/2015/12/14/the-most-likely-art-units-for-alice-rejections/id=63829/)
20 | 
21 | ![image from ipwatchdog.com](http://www.ipwatchdog.com/wp-content/uploads/2015/12/Figure-1.jpg)
22 | 
23 | ### Prior Solutions To This Problem
24 | - [LexisNexis Pathways](https://www.lexisnexisip.com/products/pathways/)
25 | - [Deep learning and IPC codes](https://www.atlantis-press.com/php/download_paper.php?id=25866373)
26 | - [Title and abstract used for classification](https://patinformatics.com/machine-learning-in-patent-analytics-part-2-binary-classification-for-prioritizing-search-results/)
27 | - [WIPO patent applications and IPC codes](http://users.softlab.ntua.gr/facilities/public/AD/Text%20Categorization/Automated%20Categorization%20in%20the%20International%20Patent%20Classification.pdf)
28 | - [Automated Patent Categorization and Guided Patent Search using IPC as Inspired by MeSH and PubMed](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3632996/)
29 | - [Automated Patent Classification using IPC codes](http://cs229.stanford.edu/proj2011/ChristopherLinSpieckermann-AutomatedPatentClassification.pdf)
30 | - [Measuring patent claim breadth using Google Patents Public Datasets](https://cloud.google.com/blog/products/ai-machine-learning/measuring-patent-claim-breadth-using-google-patents-public-datasets)
31 | 
32 | ### My Approach
33 | I will be focusing my classification on art units of the USPTO. The use of classification codes is confusing because they must then be converted into an art unit, which is often used to describe rejection rates. Additionally, the only tools available for lawyers to utilize are behind expensive paywalls.
34 | 
35 | I will first utilize a binary classification such as, does the application belong in art unit 2100 or not. I will then utilize multi-class classification which will determine which of the hundreds of art units the application belongs in. I will utilize numerous algorithms for multi-class classification, including Decision trees, SVM, KNN classifiers, and nural networks.
36 | 
37 | **Baseline Metric**
38 | To determine if the model is an improvement over the current solutions I will use two sources as a range for accuracy. On the high end of the range is the [97% accuracy](https://www.serco-na.com/news/press-releases/detail/2194/serco-processes-2-millionth-patent-application-for-u-s) stated by the contracting firm that currently classifies the patent applications. On the low end I will use an estimate by a patent attorney who estimates that the contractor correctly classifies patent applications [90%](http://www.ipwatchdog.com/2014/03/11/when-uspto-classifies-an-application-incorrectly/id=48457/) of the time.
39 | 
40 | ### Who Cares?
41 | The US is one of the main countries for companies to get patent protection. Because of the expense of patents (ranging from $10,000 - $40,000) it is important for inventors and attorneys to avoid obstacles like a difficult art unit which can increase the cost of the application and lead to an application not getting granted.
42 | 
43 | If successful, attorneys could draft and file their applications with more confidence that it will avoid immediate rejections from the patent office.
44 | 
45 | Additionally, the US Patent office could reduce costs and turnaround time by utilizing the automated model. 
46 | 
47 | ### How I will present my work
48 | I plan to have a classification web app to be used by friends in the legal industry. Visualizations may include word clouds showing the important words in different classes to give information as to what to include to ensure the patent application gets to the art unit the patent attorney wants. It would be for demonstration purposes only until I could ensure no data is recorded and no data is able to be read by anyone other than the lawyer. This is due to issues of client confidentiality and issues relating to publication date of inventions. These issues require a lot of text to explain, but the summary is: the lawyer doesn't want to risk losing their license or losing any pantent rights for their client.
49 | 
50 | ### Data sources
51 | 1) USPTO
52 | 	- [USPTO bulkdata data dictionary](https://bulkdata.uspto.gov/data/patent/office/actions/bigdata/2017/USPTO%20Patent%20Prosecution%20Research%20Data_Unlocking%20Office%20Action%20Traits.pdf)
53 | 	- [USPTO public pair dataset](https://www.uspto.gov/learning-and-resources/electronic-data-products/patent-examination-research-dataset-public-pair)
54 | 
55 | 2) [Google Big Query](https://cloud.google.com/bigquery/)
56 | 	- patents-public-data dataset
57 | 	- SQL table (28gb and 1.4tb)
58 | 	
59 | ### Potential Problems
60 | - Incomplete labeling of art units: I am using USPC codes to assign missing art unit values in the data set. There are hundreds of codes and labels to assign. This could take up a large amount of time.
61 | 
62 | ### Next Step
63 | - Fill in missing art unit values for 3600 and 2100 art units using [USPC codes](https://www.uspto.gov/patents-application-process/patent-search/understanding-patent-classifications/patent-classification).
64 | - Exploratory Data Analysis (EDA) to look for issues of imbalanced classes.
65 | - Build and train models using Google Cloud Platform since I am acquiring the data using SQL from Google BigQuery. I will spend less time moving data between platforms like AWS so I can spend more time on the modeling.
66 | 	- Binary Classification: is it 2100 or not?
67 | 	- Multi-class Classification: is it 2121, 2129, 3689, 3622, 3629, and other art units in the 3600 range.
68 | - Deploy the model as a web app
69 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | ## Patent Classification Capstone
 2 | 
 3 | ### Final Notebook and model can be found in [Patent_Classification.ipynb](https://github.com/rcmckee/dsi-Capstone/blob/master/Patent_Classification.ipynb)
 4 | Also available as a [google colab notebook](https://colab.research.google.com/github/rcmckee/dsi-Capstone/blob/master/Patent_Classification.ipynb)
 5 | 
 6 | A tensorflow version is available in a [google colab notebook](https://colab.research.google.com/drive/1DjOyXyjUF_6kCuTWkow8EMvSiNtD5BIH)
 7 | 
 8 | ### Purpose
 9 | The purpose of this project is to identify which group of people (art unit) at the US patent office (USPTO) will review a new patent application. The art unit is a group of patent examiners with the technological background to understand the technology in the patent application. Currently, this is the first step in the processing of a patent application sent to the USPTO. The USPTO sends the application to a contractor where 135 people read each application and assign it to an art unit. The turnaround time is [28 days](https://www.serco-na.com/news/press-releases/detail/2194/serco-processes-2-millionth-patent-application-for-u-s), and the USPTO pays the contractor 95 million over 5 years ([Citation](https://www.serco-na.com/news/press-releases/detail/12520/serco-awarded-95-million-patent-classification-contract)). 
10 | 
11 | ### Problem
12 | It currently takes too long and costs too much for a patent to be classified and assigned to the correct art unit. Additionally, if patent attorneys can write the patent application with the correct wording to direct the application to one art unit over another, they can increase the likelihood of the patent being granted.
13 | 
14 | **For example**, patent applications that land in art unit 3681, which handles “transportation, construction, electronic commerce, agriculture, national security and license & review,” have about a 29.8% chance of maturing into a patent.  And even if you are lucky enough to be one of the 29.8%, it is likely to take over four years to grant.  But get your patent applications into art unit 3688, which the USPTO describes as handling the exact same subject matter as 3681, and your chances of allowance jump up to about 81%, with an average time to issuance of around 2 years ([Citation](http://www.ipwatchdog.com/2016/04/14/better-way-file-patent-applications/id=68302/)).
15 | 
16 | **[The hardest and easiest art units.](http://www.ipwatchdog.com/2015/05/21/hardest-easiest-art-units/id=57864/):**
17 | 
18 | ![image from ipwatchdog.com](http://www.ipwatchdog.com/wp-content/uploads/2015/05/Figure-1.png)
19 | ![image from ipwatchdog.com](http://www.ipwatchdog.com/wp-content/uploads/2015/05/Figure-2.png)
20 | 
21 | **Alice case law is a major factor preventing software related patents**
22 | Alice Corporation Pty. Ltd. v. CLS Bank International, 573 U.S. ___ (2014) is a case that essentially said any patent that relates to performing an economic activity on a computer is possibly an abstract idea and not patentable. Avoiding Alice rejections is a big concern for patent attorneys. Once you receive an Alice rejection you are less likely to get the patent granted.
23 | 
24 | [**Art units with most Alice rejections**](http://www.ipwatchdog.com/2015/12/14/the-most-likely-art-units-for-alice-rejections/id=63829/)
25 | 
26 | ![image from ipwatchdog.com](http://www.ipwatchdog.com/wp-content/uploads/2015/12/Figure-1.jpg)
27 | 
28 | ### Prior Solutions To This Problem
29 | - [LexisNexis Pathways](https://www.lexisnexisip.com/products/pathways/)
30 | - [Deep learning and IPC codes](https://www.atlantis-press.com/php/download_paper.php?id=25866373)
31 | - [Title and abstract used for classification](https://patinformatics.com/machine-learning-in-patent-analytics-part-2-binary-classification-for-prioritizing-search-results/)
32 | - [WIPO patent applications and IPC codes](http://users.softlab.ntua.gr/facilities/public/AD/Text%20Categorization/Automated%20Categorization%20in%20the%20International%20Patent%20Classification.pdf)
33 | - [Automated Patent Categorization and Guided Patent Search using IPC as Inspired by MeSH and PubMed](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3632996/)
34 | - [Automated Patent Classification using IPC codes](http://cs229.stanford.edu/proj2011/ChristopherLinSpieckermann-AutomatedPatentClassification.pdf)
35 | - [Measuring patent claim breadth using Google Patents Public Datasets](https://cloud.google.com/blog/products/ai-machine-learning/measuring-patent-claim-breadth-using-google-patents-public-datasets)
36 | 
37 | ### My Approach
38 | I will be focusing my classification on art units of the USPTO. The use of classification codes is confusing because they must then be converted into an art unit, which is often used to describe rejection rates. Additionally, the only tools available for lawyers to utilize are behind expensive paywalls.
39 | 
40 | I will first utilize a binary classification such as, does the application belong in art unit 2100 or not. I will then utilize multi-class classification which will determine which of the hundreds of art units the application belongs in. I will utilize numerous algorithms for multi-class classification, including Decision trees, SVM, KNN classifiers, and nural networks.
41 | 
42 | **Baseline Metric**
43 | To determine if the model is an improvement over the current solutions I will use two sources as a range for accuracy. On the high end of the range is the [97% accuracy](https://www.serco-na.com/news/press-releases/detail/2194/serco-processes-2-millionth-patent-application-for-u-s) stated by the contracting firm that currently classifies the patent applications. On the low end I will use an estimate by a patent attorney who estimates that the contractor correctly classifies patent applications [90%](http://www.ipwatchdog.com/2014/03/11/when-uspto-classifies-an-application-incorrectly/id=48457/) of the time.
44 | 
45 | ### Who Cares?
46 | The US is one of the main countries for companies to get patent protection. Because of the expense of patents (ranging from $10,000 - $40,000) it is important for inventors and attorneys to avoid obstacles like a difficult art unit which can increase the cost of the application and lead to an application not getting granted.
47 | 
48 | If successful, attorneys could draft and file their applications with more confidence that it will avoid immediate rejections from the patent office.
49 | 
50 | Additionally, the US Patent office could reduce costs and turnaround time by utilizing the automated model. 
51 | 
52 | ### How I will present my work
53 | I plan to have a classification web app to be used by friends in the legal industry. Visualizations may include word clouds showing the important words in different classes to give information as to what to include to ensure the patent application gets to the art unit the patent attorney wants. It would be for demonstration purposes only until I could ensure no data is recorded and no data is able to be read by anyone other than the lawyer. This is due to issues of client confidentiality and issues relating to publication date of inventions. These issues require a lot of text to explain, but the summary is: the lawyer doesn't want to risk losing their license or losing any pantent rights for their client.
54 | 
55 | ### Data sources
56 | 1) USPTO
57 | 	- [USPTO bulkdata data dictionary](https://bulkdata.uspto.gov/data/patent/office/actions/bigdata/2017/USPTO%20Patent%20Prosecution%20Research%20Data_Unlocking%20Office%20Action%20Traits.pdf)
58 | 	- [USPTO public pair dataset](https://www.uspto.gov/learning-and-resources/electronic-data-products/patent-examination-research-dataset-public-pair)
59 | 
60 | 2) [Google Big Query](https://cloud.google.com/bigquery/)
61 | 	- patents-public-data dataset
62 | 	- SQL table (28gb and 1.4tb)
63 | 	
64 | ### Potential Problems
65 | - Incomplete labeling of art units: I am using USPC codes to assign missing art unit values in the data set. There are hundreds of codes and labels to assign. This could take up a large amount of time.
66 | 
67 | ### Next Step
68 | - Fill in missing art unit values for 3600 and 2100 art units using [USPC codes](https://www.uspto.gov/patents-application-process/patent-search/understanding-patent-classifications/patent-classification).
69 | - Exploratory Data Analysis (EDA) to look for issues of imbalanced classes.
70 | - Build and train models using Google Cloud Platform since I am acquiring the data using SQL from Google BigQuery. I will spend less time moving data between platforms like AWS so I can spend more time on the modeling.
71 | 	- Binary Classification: is it 2100 or not?
72 | 	- Multi-class Classification: is it 2121, 2129, 3689, 3622, 3629, and other art units in the 3600 range.
73 | - Deploy the model as a web app
74 | 


--------------------------------------------------------------------------------
/Patent_Classification.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |   "nbformat": 4,
   3 |   "nbformat_minor": 0,
   4 |   "metadata": {
   5 |     "colab": {
   6 |       "name": "Patent Classification.ipynb",
   7 |       "version": "0.3.2",
   8 |       "provenance": [],
   9 |       "include_colab_link": true
  10 |     },
  11 |     "kernelspec": {
  12 |       "name": "python3",
  13 |       "display_name": "Python 3"
  14 |     }
  15 |   },
  16 |   "cells": [
  17 |     {
  18 |       "cell_type": "markdown",
  19 |       "metadata": {
  20 |         "id": "view-in-github",
  21 |         "colab_type": "text"
  22 |       },
  23 |       "source": [
  24 |         "<a href=\"https://colab.research.google.com/github/rcmckee/dsi-Capstone/blob/master/Patent_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
  25 |       ]
  26 |     },
  27 |     {
  28 |       "metadata": {
  29 |         "id": "YMXrGSVuFK0i",
  30 |         "colab_type": "code",
  31 |         "colab": {}
  32 |       },
  33 |       "cell_type": "code",
  34 |       "source": [
  35 |         "import pandas as pd\n",
  36 |         "import numpy as np\n",
  37 |         "import scipy.stats as scs\n",
  38 |         "import statsmodels.api as sm\n",
  39 |         "import matplotlib.pyplot as plt\n",
  40 |         "\n",
  41 |         "%matplotlib inline\n",
  42 |         "%config InlineBackend.figure_format='retina'"
  43 |       ],
  44 |       "execution_count": 0,
  45 |       "outputs": []
  46 |     },
  47 |     {
  48 |       "metadata": {
  49 |         "id": "QJ8RnnDnGfVl",
  50 |         "colab_type": "code",
  51 |         "colab": {
  52 |           "base_uri": "https://localhost:8080/",
  53 |           "height": 51
  54 |         },
  55 |         "outputId": "8bb32283-2c6a-4ee4-c96f-beb047dc11cf"
  56 |       },
  57 |       "cell_type": "code",
  58 |       "source": [
  59 |         "# Code to read csv file into Colaboratory:\n",
  60 |         "!pip install -U -q PyDrive\n",
  61 |         "from pydrive.auth import GoogleAuth\n",
  62 |         "from pydrive.drive import GoogleDrive\n",
  63 |         "from google.colab import auth\n",
  64 |         "from oauth2client.client import GoogleCredentials\n"
  65 |       ],
  66 |       "execution_count": 3,
  67 |       "outputs": [
  68 |         {
  69 |           "output_type": "stream",
  70 |           "text": [
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  72 |             "\u001b[?25h  Building wheel for PyDrive (setup.py) ... \u001b[?25ldone\n",
  73 |             "\u001b[?25h"
  74 |           ],
  75 |           "name": "stdout"
  76 |         }
  77 |       ]
  78 |     },
  79 |     {
  80 |       "metadata": {
  81 |         "id": "QcA3OHBhGsVC",
  82 |         "colab_type": "code",
  83 |         "colab": {}
  84 |       },
  85 |       "cell_type": "code",
  86 |       "source": [
  87 |         "# Authenticate and create the PyDrive client.\n",
  88 |         "auth.authenticate_user()\n",
  89 |         "gauth = GoogleAuth()\n",
  90 |         "gauth.credentials = GoogleCredentials.get_application_default()\n",
  91 |         "drive = GoogleDrive(gauth)"
  92 |       ],
  93 |       "execution_count": 0,
  94 |       "outputs": []
  95 |     },
  96 |     {
  97 |       "metadata": {
  98 |         "id": "NLQS3r_uGswm",
  99 |         "colab_type": "code",
 100 |         "colab": {}
 101 |       },
 102 |       "cell_type": "code",
 103 |       "source": [
 104 |         "downloaded = drive.CreateFile({'id':'1UcoQDxQe5MGruMUoD4013HrTz346OH8i'}) \n",
 105 |         "downloaded.GetContentFile('small_700_through_710_descr_clm_code.csv')  \n",
 106 |         "df = pd.read_csv('small_700_through_710_descr_clm_code.csv')\n",
 107 |         "# Dataset is now stored in a Pandas Dataframe\n"
 108 |       ],
 109 |       "execution_count": 0,
 110 |       "outputs": []
 111 |     },
 112 |     {
 113 |       "metadata": {
 114 |         "id": "ESVoFcmdH6Uo",
 115 |         "colab_type": "code",
 116 |         "colab": {
 117 |           "base_uri": "https://localhost:8080/",
 118 |           "height": 204
 119 |         },
 120 |         "outputId": "24bd29af-01f2-4f8d-e514-5d558fbb1502"
 121 |       },
 122 |       "cell_type": "code",
 123 |       "source": [
 124 |         "df.head()"
 125 |       ],
 126 |       "execution_count": 14,
 127 |       "outputs": [
 128 |         {
 129 |           "output_type": "execute_result",
 130 |           "data": {
 131 |             "text/html": [
 132 |               "<div>\n",
 133 |               "<style scoped>\n",
 134 |               "    .dataframe tbody tr th:only-of-type {\n",
 135 |               "        vertical-align: middle;\n",
 136 |               "    }\n",
 137 |               "\n",
 138 |               "    .dataframe tbody tr th {\n",
 139 |               "        vertical-align: top;\n",
 140 |               "    }\n",
 141 |               "\n",
 142 |               "    .dataframe thead th {\n",
 143 |               "        text-align: right;\n",
 144 |               "    }\n",
 145 |               "</style>\n",
 146 |               "<table border=\"1\" class=\"dataframe\">\n",
 147 |               "  <thead>\n",
 148 |               "    <tr style=\"text-align: right;\">\n",
 149 |               "      <th></th>\n",
 150 |               "      <th>Unnamed: 0</th>\n",
 151 |               "      <th>descr</th>\n",
 152 |               "      <th>clm</th>\n",
 153 |               "      <th>code</th>\n",
 154 |               "    </tr>\n",
 155 |               "  </thead>\n",
 156 |               "  <tbody>\n",
 157 |               "    <tr>\n",
 158 |               "      <th>0</th>\n",
 159 |               "      <td>0</td>\n",
 160 |               "      <td>This application claims priority under 35 U.S....</td>\n",
 161 |               "      <td>What is claimed is: \\n     \\n         1 . A pr...</td>\n",
 162 |               "      <td>700</td>\n",
 163 |               "    </tr>\n",
 164 |               "    <tr>\n",
 165 |               "      <th>1</th>\n",
 166 |               "      <td>1</td>\n",
 167 |               "      <td>BACKGROUND \\n     1. Field of Invention \\n    ...</td>\n",
 168 |               "      <td>What is claimed is: \\n     \\n         1 . A st...</td>\n",
 169 |               "      <td>700</td>\n",
 170 |               "    </tr>\n",
 171 |               "    <tr>\n",
 172 |               "      <th>2</th>\n",
 173 |               "      <td>2</td>\n",
 174 |               "      <td>CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...</td>\n",
 175 |               "      <td>What is claimed is: \\n     \\n         1 . A me...</td>\n",
 176 |               "      <td>700</td>\n",
 177 |               "    </tr>\n",
 178 |               "    <tr>\n",
 179 |               "      <th>3</th>\n",
 180 |               "      <td>3</td>\n",
 181 |               "      <td>FIELD OF THE INVENTION \\n     The present inve...</td>\n",
 182 |               "      <td>1 . A method for state-transition-controlled p...</td>\n",
 183 |               "      <td>700</td>\n",
 184 |               "    </tr>\n",
 185 |               "    <tr>\n",
 186 |               "      <th>4</th>\n",
 187 |               "      <td>4</td>\n",
 188 |               "      <td>RELATED APPLICATION \\n     This application cl...</td>\n",
 189 |               "      <td>What is claimed is: \\n     \\n         1 . A me...</td>\n",
 190 |               "      <td>700</td>\n",
 191 |               "    </tr>\n",
 192 |               "  </tbody>\n",
 193 |               "</table>\n",
 194 |               "</div>"
 195 |             ],
 196 |             "text/plain": [
 197 |               "   Unnamed: 0                                              descr  \\\n",
 198 |               "0           0  This application claims priority under 35 U.S....   \n",
 199 |               "1           1  BACKGROUND \\n     1. Field of Invention \\n    ...   \n",
 200 |               "2           2  CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...   \n",
 201 |               "3           3  FIELD OF THE INVENTION \\n     The present inve...   \n",
 202 |               "4           4  RELATED APPLICATION \\n     This application cl...   \n",
 203 |               "\n",
 204 |               "                                                 clm  code  \n",
 205 |               "0  What is claimed is: \\n     \\n         1 . A pr...   700  \n",
 206 |               "1  What is claimed is: \\n     \\n         1 . A st...   700  \n",
 207 |               "2  What is claimed is: \\n     \\n         1 . A me...   700  \n",
 208 |               "3  1 . A method for state-transition-controlled p...   700  \n",
 209 |               "4  What is claimed is: \\n     \\n         1 . A me...   700  "
 210 |             ]
 211 |           },
 212 |           "metadata": {
 213 |             "tags": []
 214 |           },
 215 |           "execution_count": 14
 216 |         }
 217 |       ]
 218 |     },
 219 |     {
 220 |       "metadata": {
 221 |         "id": "MDJWwaFtFdxz",
 222 |         "colab_type": "code",
 223 |         "colab": {
 224 |           "base_uri": "https://localhost:8080/",
 225 |           "height": 204
 226 |         },
 227 |         "outputId": "f226c74a-7d23-492c-8080-f3db770a0a33"
 228 |       },
 229 |       "cell_type": "code",
 230 |       "source": [
 231 |         "df.drop('Unnamed: 0',axis=1, inplace=True)\n",
 232 |         "df = df[(df['code']==705)|(df['code']==706)|(df['code']==700)]\n",
 233 |         "df['descr_clm'] = df.descr + df.clm\n",
 234 |         "df.drop(['descr','clm'],axis=1, inplace=True)\n",
 235 |         "df['code'] = df['code'].astype('category')\n",
 236 |         "df.head()"
 237 |       ],
 238 |       "execution_count": 15,
 239 |       "outputs": [
 240 |         {
 241 |           "output_type": "execute_result",
 242 |           "data": {
 243 |             "text/html": [
 244 |               "<div>\n",
 245 |               "<style scoped>\n",
 246 |               "    .dataframe tbody tr th:only-of-type {\n",
 247 |               "        vertical-align: middle;\n",
 248 |               "    }\n",
 249 |               "\n",
 250 |               "    .dataframe tbody tr th {\n",
 251 |               "        vertical-align: top;\n",
 252 |               "    }\n",
 253 |               "\n",
 254 |               "    .dataframe thead th {\n",
 255 |               "        text-align: right;\n",
 256 |               "    }\n",
 257 |               "</style>\n",
 258 |               "<table border=\"1\" class=\"dataframe\">\n",
 259 |               "  <thead>\n",
 260 |               "    <tr style=\"text-align: right;\">\n",
 261 |               "      <th></th>\n",
 262 |               "      <th>code</th>\n",
 263 |               "      <th>descr_clm</th>\n",
 264 |               "    </tr>\n",
 265 |               "  </thead>\n",
 266 |               "  <tbody>\n",
 267 |               "    <tr>\n",
 268 |               "      <th>0</th>\n",
 269 |               "      <td>700</td>\n",
 270 |               "      <td>This application claims priority under 35 U.S....</td>\n",
 271 |               "    </tr>\n",
 272 |               "    <tr>\n",
 273 |               "      <th>1</th>\n",
 274 |               "      <td>700</td>\n",
 275 |               "      <td>BACKGROUND \\n     1. Field of Invention \\n    ...</td>\n",
 276 |               "    </tr>\n",
 277 |               "    <tr>\n",
 278 |               "      <th>2</th>\n",
 279 |               "      <td>700</td>\n",
 280 |               "      <td>CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...</td>\n",
 281 |               "    </tr>\n",
 282 |               "    <tr>\n",
 283 |               "      <th>3</th>\n",
 284 |               "      <td>700</td>\n",
 285 |               "      <td>FIELD OF THE INVENTION \\n     The present inve...</td>\n",
 286 |               "    </tr>\n",
 287 |               "    <tr>\n",
 288 |               "      <th>4</th>\n",
 289 |               "      <td>700</td>\n",
 290 |               "      <td>RELATED APPLICATION \\n     This application cl...</td>\n",
 291 |               "    </tr>\n",
 292 |               "  </tbody>\n",
 293 |               "</table>\n",
 294 |               "</div>"
 295 |             ],
 296 |             "text/plain": [
 297 |               "  code                                          descr_clm\n",
 298 |               "0  700  This application claims priority under 35 U.S....\n",
 299 |               "1  700  BACKGROUND \\n     1. Field of Invention \\n    ...\n",
 300 |               "2  700  CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
 301 |               "3  700  FIELD OF THE INVENTION \\n     The present inve...\n",
 302 |               "4  700  RELATED APPLICATION \\n     This application cl..."
 303 |             ]
 304 |           },
 305 |           "metadata": {
 306 |             "tags": []
 307 |           },
 308 |           "execution_count": 15
 309 |         }
 310 |       ]
 311 |     },
 312 |     {
 313 |       "metadata": {
 314 |         "id": "5etELzCVJEGg",
 315 |         "colab_type": "text"
 316 |       },
 317 |       "cell_type": "markdown",
 318 |       "source": [
 319 |         "# Data exploration"
 320 |       ]
 321 |     },
 322 |     {
 323 |       "metadata": {
 324 |         "id": "6KDJrO7fIs8m",
 325 |         "colab_type": "code",
 326 |         "colab": {
 327 |           "base_uri": "https://localhost:8080/",
 328 |           "height": 85
 329 |         },
 330 |         "outputId": "13bc5313-1a35-4334-9930-797816a22529"
 331 |       },
 332 |       "cell_type": "code",
 333 |       "source": [
 334 |         "df['code'].value_counts()"
 335 |       ],
 336 |       "execution_count": 16,
 337 |       "outputs": [
 338 |         {
 339 |           "output_type": "execute_result",
 340 |           "data": {
 341 |             "text/plain": [
 342 |               "706    1000\n",
 343 |               "705    1000\n",
 344 |               "700    1000\n",
 345 |               "Name: code, dtype: int64"
 346 |             ]
 347 |           },
 348 |           "metadata": {
 349 |             "tags": []
 350 |           },
 351 |           "execution_count": 16
 352 |         }
 353 |       ]
 354 |     },
 355 |     {
 356 |       "metadata": {
 357 |         "id": "JD8bXdGTIsrW",
 358 |         "colab_type": "code",
 359 |         "colab": {}
 360 |       },
 361 |       "cell_type": "code",
 362 |       "source": [
 363 |         "df['category_id'] = df['code'].factorize()[0]"
 364 |       ],
 365 |       "execution_count": 0,
 366 |       "outputs": []
 367 |     },
 368 |     {
 369 |       "metadata": {
 370 |         "id": "EcJP6L6jIyEk",
 371 |         "colab_type": "code",
 372 |         "colab": {
 373 |           "base_uri": "https://localhost:8080/",
 374 |           "height": 85
 375 |         },
 376 |         "outputId": "c4692e5a-f1f3-424a-cc0a-c02bf0d107d4"
 377 |       },
 378 |       "cell_type": "code",
 379 |       "source": [
 380 |         "df['category_id'].value_counts()"
 381 |       ],
 382 |       "execution_count": 18,
 383 |       "outputs": [
 384 |         {
 385 |           "output_type": "execute_result",
 386 |           "data": {
 387 |             "text/plain": [
 388 |               "1    1000\n",
 389 |               "2    1000\n",
 390 |               "0    1000\n",
 391 |               "Name: category_id, dtype: int64"
 392 |             ]
 393 |           },
 394 |           "metadata": {
 395 |             "tags": []
 396 |           },
 397 |           "execution_count": 18
 398 |         }
 399 |       ]
 400 |     },
 401 |     {
 402 |       "metadata": {
 403 |         "id": "MZp3DjrlI1Ak",
 404 |         "colab_type": "code",
 405 |         "colab": {}
 406 |       },
 407 |       "cell_type": "code",
 408 |       "source": [
 409 |         "category_id_df = df[['code', 'category_id']].drop_duplicates().sort_values('category_id')\n",
 410 |         "category_to_id = dict(category_id_df.values)\n",
 411 |         "id_to_category = dict(category_id_df[['category_id', 'code']].values)"
 412 |       ],
 413 |       "execution_count": 0,
 414 |       "outputs": []
 415 |     },
 416 |     {
 417 |       "metadata": {
 418 |         "id": "GIw0FGdxI3sm",
 419 |         "colab_type": "code",
 420 |         "colab": {
 421 |           "base_uri": "https://localhost:8080/",
 422 |           "height": 34
 423 |         },
 424 |         "outputId": "a7dcf91a-9b69-428b-b071-f4b450351120"
 425 |       },
 426 |       "cell_type": "code",
 427 |       "source": [
 428 |         "id_to_category"
 429 |       ],
 430 |       "execution_count": 20,
 431 |       "outputs": [
 432 |         {
 433 |           "output_type": "execute_result",
 434 |           "data": {
 435 |             "text/plain": [
 436 |               "{0: 700, 1: 705, 2: 706}"
 437 |             ]
 438 |           },
 439 |           "metadata": {
 440 |             "tags": []
 441 |           },
 442 |           "execution_count": 20
 443 |         }
 444 |       ]
 445 |     },
 446 |     {
 447 |       "metadata": {
 448 |         "id": "1aH1wZ_yI6tV",
 449 |         "colab_type": "code",
 450 |         "colab": {
 451 |           "base_uri": "https://localhost:8080/",
 452 |           "height": 204
 453 |         },
 454 |         "outputId": "400ef8fe-be54-41cf-8f28-4823c2af6f34"
 455 |       },
 456 |       "cell_type": "code",
 457 |       "source": [
 458 |         "df.sample(5, random_state=0)"
 459 |       ],
 460 |       "execution_count": 21,
 461 |       "outputs": [
 462 |         {
 463 |           "output_type": "execute_result",
 464 |           "data": {
 465 |             "text/html": [
 466 |               "<div>\n",
 467 |               "<style scoped>\n",
 468 |               "    .dataframe tbody tr th:only-of-type {\n",
 469 |               "        vertical-align: middle;\n",
 470 |               "    }\n",
 471 |               "\n",
 472 |               "    .dataframe tbody tr th {\n",
 473 |               "        vertical-align: top;\n",
 474 |               "    }\n",
 475 |               "\n",
 476 |               "    .dataframe thead th {\n",
 477 |               "        text-align: right;\n",
 478 |               "    }\n",
 479 |               "</style>\n",
 480 |               "<table border=\"1\" class=\"dataframe\">\n",
 481 |               "  <thead>\n",
 482 |               "    <tr style=\"text-align: right;\">\n",
 483 |               "      <th></th>\n",
 484 |               "      <th>code</th>\n",
 485 |               "      <th>descr_clm</th>\n",
 486 |               "      <th>category_id</th>\n",
 487 |               "    </tr>\n",
 488 |               "  </thead>\n",
 489 |               "  <tbody>\n",
 490 |               "    <tr>\n",
 491 |               "      <th>2077</th>\n",
 492 |               "      <td>700</td>\n",
 493 |               "      <td>CROSS-REFERENCE TO RELATED APPLICATION(S) \\n  ...</td>\n",
 494 |               "      <td>0</td>\n",
 495 |               "    </tr>\n",
 496 |               "    <tr>\n",
 497 |               "      <th>3401</th>\n",
 498 |               "      <td>706</td>\n",
 499 |               "      <td>BACKGROUND \\n     Many systems are instrumente...</td>\n",
 500 |               "      <td>2</td>\n",
 501 |               "    </tr>\n",
 502 |               "    <tr>\n",
 503 |               "      <th>4393</th>\n",
 504 |               "      <td>706</td>\n",
 505 |               "      <td>FIELD \\n     The subject matter disclosed here...</td>\n",
 506 |               "      <td>2</td>\n",
 507 |               "    </tr>\n",
 508 |               "    <tr>\n",
 509 |               "      <th>10648</th>\n",
 510 |               "      <td>705</td>\n",
 511 |               "      <td>This application is a continuation of U.S. pat...</td>\n",
 512 |               "      <td>1</td>\n",
 513 |               "    </tr>\n",
 514 |               "    <tr>\n",
 515 |               "      <th>10687</th>\n",
 516 |               "      <td>705</td>\n",
 517 |               "      <td>BACKGROUND \\n     1. Technical Field \\n     Em...</td>\n",
 518 |               "      <td>1</td>\n",
 519 |               "    </tr>\n",
 520 |               "  </tbody>\n",
 521 |               "</table>\n",
 522 |               "</div>"
 523 |             ],
 524 |             "text/plain": [
 525 |               "      code                                          descr_clm  category_id\n",
 526 |               "2077   700  CROSS-REFERENCE TO RELATED APPLICATION(S) \\n  ...            0\n",
 527 |               "3401   706  BACKGROUND \\n     Many systems are instrumente...            2\n",
 528 |               "4393   706  FIELD \\n     The subject matter disclosed here...            2\n",
 529 |               "10648  705  This application is a continuation of U.S. pat...            1\n",
 530 |               "10687  705  BACKGROUND \\n     1. Technical Field \\n     Em...            1"
 531 |             ]
 532 |           },
 533 |           "metadata": {
 534 |             "tags": []
 535 |           },
 536 |           "execution_count": 21
 537 |         }
 538 |       ]
 539 |     },
 540 |     {
 541 |       "metadata": {
 542 |         "id": "xJNaURyuJLcg",
 543 |         "colab_type": "code",
 544 |         "colab": {
 545 |           "base_uri": "https://localhost:8080/",
 546 |           "height": 287
 547 |         },
 548 |         "outputId": "69ca4d0f-0fd8-4150-ce3d-b5d725a9c9c6"
 549 |       },
 550 |       "cell_type": "code",
 551 |       "source": [
 552 |         "df.groupby('code').descr_clm.count().plot.bar(ylim=0);"
 553 |       ],
 554 |       "execution_count": 22,
 555 |       "outputs": [
 556 |         {
 557 |           "output_type": "display_data",
 558 |           "data": {
 559 |             "image/png": 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8xZzzY9XF1ZnHqT1re3z39vje6nZjjBtdhVoAADg0DjrQv6nNsps7H+Pc0eB99EbYN1Zn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\nQmCC\n",
 560 |             "text/plain": [
 561 |               "<Figure size 432x288 with 1 Axes>"
 562 |             ]
 563 |           },
 564 |           "metadata": {
 565 |             "tags": [],
 566 |             "image/png": {
 567 |               "width": 378,
 568 |               "height": 270
 569 |             }
 570 |           }
 571 |         }
 572 |       ]
 573 |     },
 574 |     {
 575 |       "metadata": {
 576 |         "id": "o__UXblgJTYT",
 577 |         "colab_type": "text"
 578 |       },
 579 |       "cell_type": "markdown",
 580 |       "source": [
 581 |         "# Tf-idf Vectorizer"
 582 |       ]
 583 |     },
 584 |     {
 585 |       "metadata": {
 586 |         "id": "TjikdGnQJN04",
 587 |         "colab_type": "code",
 588 |         "colab": {
 589 |           "base_uri": "https://localhost:8080/",
 590 |           "height": 34
 591 |         },
 592 |         "outputId": "63382c28-dbda-4afd-e866-cc5761bcefae"
 593 |       },
 594 |       "cell_type": "code",
 595 |       "source": [
 596 |         "from sklearn.feature_extraction.text import TfidfVectorizer\n",
 597 |         "\n",
 598 |         "tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5, norm='l2', encoding='latin-1', ngram_range=(1, 2), stop_words='english')\n",
 599 |         "\n",
 600 |         "features = tfidf.fit_transform(df['descr_clm']).toarray()\n",
 601 |         "labels = df['category_id']\n",
 602 |         "features.shape"
 603 |       ],
 604 |       "execution_count": 23,
 605 |       "outputs": [
 606 |         {
 607 |           "output_type": "execute_result",
 608 |           "data": {
 609 |             "text/plain": [
 610 |               "(3000, 110325)"
 611 |             ]
 612 |           },
 613 |           "metadata": {
 614 |             "tags": []
 615 |           },
 616 |           "execution_count": 23
 617 |         }
 618 |       ]
 619 |     },
 620 |     {
 621 |       "metadata": {
 622 |         "id": "-uCj_tMaJiIz",
 623 |         "colab_type": "text"
 624 |       },
 625 |       "cell_type": "markdown",
 626 |       "source": [
 627 |         "**3000 applications, represented by 110325 features, representing the tf-idf score for different unigrams and bigrams.**"
 628 |       ]
 629 |     },
 630 |     {
 631 |       "metadata": {
 632 |         "id": "hnb2et8SKA8a",
 633 |         "colab_type": "text"
 634 |       },
 635 |       "cell_type": "markdown",
 636 |       "source": [
 637 |         "# Top 10 Uni-Grams and Bi-Grams for each Patent class"
 638 |       ]
 639 |     },
 640 |     {
 641 |       "metadata": {
 642 |         "id": "xoxJm48wJc5S",
 643 |         "colab_type": "code",
 644 |         "colab": {}
 645 |       },
 646 |       "cell_type": "code",
 647 |       "source": [
 648 |         "from sklearn.feature_selection import chi2"
 649 |       ],
 650 |       "execution_count": 0,
 651 |       "outputs": []
 652 |     },
 653 |     {
 654 |       "metadata": {
 655 |         "id": "GwFF_gUtJ-z0",
 656 |         "colab_type": "code",
 657 |         "colab": {
 658 |           "base_uri": "https://localhost:8080/",
 659 |           "height": 1190
 660 |         },
 661 |         "outputId": "a7d6525f-de58-4005-d8a7-7ca2f1d5d448"
 662 |       },
 663 |       "cell_type": "code",
 664 |       "source": [
 665 |         "N = 10\n",
 666 |         "for code, category_id in sorted(category_to_id.items()):\n",
 667 |         "  features_chi2 = chi2(features, labels == category_id)\n",
 668 |         "  indices = np.argsort(features_chi2[0])\n",
 669 |         "  feature_names = np.array(tfidf.get_feature_names())[indices]\n",
 670 |         "  unigrams = [v for v in feature_names if len(v.split(' ')) == 1]\n",
 671 |         "  bigrams = [v for v in feature_names if len(v.split(' ')) == 2]\n",
 672 |         "  print(\"# '{}':\".format(code))\n",
 673 |         "  print(\"  . Most correlated unigrams:\\n       . {}\".format('\\n       . '.join(unigrams[-N:])))\n",
 674 |         "  print(\"  . Most correlated bigrams:\\n       . {}\".format('\\n       . '.join(bigrams[-N:])))"
 675 |       ],
 676 |       "execution_count": 26,
 677 |       "outputs": [
 678 |         {
 679 |           "output_type": "stream",
 680 |           "text": [
 681 |             "# '700':\n",
 682 |             "  . Most correlated unigrams:\n",
 683 |             "       . temperature\n",
 684 |             "       . direction\n",
 685 |             "       . motor\n",
 686 |             "       . position\n",
 687 |             "       . sensor\n",
 688 |             "       . controlling\n",
 689 |             "       . power\n",
 690 |             "       . robot\n",
 691 |             "       . control\n",
 692 |             "       . controller\n",
 693 |             "  . Most correlated bigrams:\n",
 694 |             "       . electric power\n",
 695 |             "       . control signal\n",
 696 |             "       . robot according\n",
 697 |             "       . power supply\n",
 698 |             "       . control method\n",
 699 |             "       . method controlling\n",
 700 |             "       . controller configured\n",
 701 |             "       . control unit\n",
 702 |             "       . perspective view\n",
 703 |             "       . control device\n",
 704 |             "# '705':\n",
 705 |             "  . Most correlated unigrams:\n",
 706 |             "       . card\n",
 707 |             "       . price\n",
 708 |             "       . sale\n",
 709 |             "       . financial\n",
 710 |             "       . transactions\n",
 711 |             "       . credit\n",
 712 |             "       . purchase\n",
 713 |             "       . merchant\n",
 714 |             "       . transaction\n",
 715 |             "       . payment\n",
 716 |             "  . Most correlated bigrams:\n",
 717 |             "       . debit card\n",
 718 |             "       . financial transaction\n",
 719 |             "       . account number\n",
 720 |             "       . payment transaction\n",
 721 |             "       . mobile device\n",
 722 |             "       . account associated\n",
 723 |             "       . goods services\n",
 724 |             "       . service provider\n",
 725 |             "       . credit card\n",
 726 |             "       . point sale\n",
 727 |             "# '706':\n",
 728 |             "  . Most correlated unigrams:\n",
 729 |             "       . model\n",
 730 |             "       . classifier\n",
 731 |             "       . neurons\n",
 732 |             "       . neuron\n",
 733 |             "       . artificial\n",
 734 |             "       . prediction\n",
 735 |             "       . probability\n",
 736 |             "       . neural\n",
 737 |             "       . training\n",
 738 |             "       . learning\n",
 739 |             "  . Most correlated bigrams:\n",
 740 |             "       . learning algorithm\n",
 741 |             "       . model based\n",
 742 |             "       . training set\n",
 743 |             "       . artificial neural\n",
 744 |             "       . knowledge base\n",
 745 |             "       . artificial intelligence\n",
 746 |             "       . neural networks\n",
 747 |             "       . training data\n",
 748 |             "       . machine learning\n",
 749 |             "       . neural network\n"
 750 |           ],
 751 |           "name": "stdout"
 752 |         }
 753 |       ]
 754 |     },
 755 |     {
 756 |       "metadata": {
 757 |         "id": "SiCi9DuFK9Rg",
 758 |         "colab_type": "text"
 759 |       },
 760 |       "cell_type": "markdown",
 761 |       "source": [
 762 |         "# Model training and evaluation"
 763 |       ]
 764 |     },
 765 |     {
 766 |       "metadata": {
 767 |         "id": "wzY8wzPVK3-w",
 768 |         "colab_type": "code",
 769 |         "colab": {}
 770 |       },
 771 |       "cell_type": "code",
 772 |       "source": [
 773 |         "from sklearn.linear_model import LogisticRegression\n",
 774 |         "from sklearn.ensemble import RandomForestClassifier\n",
 775 |         "from sklearn.naive_bayes import MultinomialNB\n",
 776 |         "from sklearn.model_selection import cross_val_score"
 777 |       ],
 778 |       "execution_count": 0,
 779 |       "outputs": []
 780 |     },
 781 |     {
 782 |       "metadata": {
 783 |         "id": "iMSeFmdLLCov",
 784 |         "colab_type": "code",
 785 |         "colab": {
 786 |           "base_uri": "https://localhost:8080/",
 787 |           "height": 377
 788 |         },
 789 |         "outputId": "c8e476e3-9580-4241-a2b9-a2463564194f"
 790 |       },
 791 |       "cell_type": "code",
 792 |       "source": [
 793 |         "models = [\n",
 794 |         "    RandomForestClassifier(n_estimators=400, max_depth=50, random_state=0),\n",
 795 |         "    MultinomialNB(alpha=0.01),\n",
 796 |         "    LogisticRegression(random_state=0, C=0.9),\n",
 797 |         "]\n",
 798 |         "CV = 5\n",
 799 |         "cv_df = pd.DataFrame(index=range(CV * len(models)))\n",
 800 |         "entries = []\n",
 801 |         "for model in models:\n",
 802 |         "  model_name = model.__class__.__name__\n",
 803 |         "  accuracies = cross_val_score(model, features, labels, scoring='accuracy', cv=CV)\n",
 804 |         "  for fold_idx, accuracy in enumerate(accuracies):\n",
 805 |         "    entries.append((model_name, fold_idx, accuracy))\n",
 806 |         "cv_df = pd.DataFrame(entries, columns=['model_name', 'fold_idx', 'accuracy'])"
 807 |       ],
 808 |       "execution_count": 70,
 809 |       "outputs": [
 810 |         {
 811 |           "output_type": "stream",
 812 |           "text": [
 813 |             "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
 814 |             "  FutureWarning)\n",
 815 |             "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
 816 |             "  \"this warning.\", FutureWarning)\n",
 817 |             "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
 818 |             "  FutureWarning)\n",
 819 |             "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
 820 |             "  \"this warning.\", FutureWarning)\n",
 821 |             "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
 822 |             "  FutureWarning)\n",
 823 |             "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
 824 |             "  \"this warning.\", FutureWarning)\n",
 825 |             "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
 826 |             "  FutureWarning)\n",
 827 |             "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
 828 |             "  \"this warning.\", FutureWarning)\n",
 829 |             "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
 830 |             "  FutureWarning)\n",
 831 |             "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
 832 |             "  \"this warning.\", FutureWarning)\n"
 833 |           ],
 834 |           "name": "stderr"
 835 |         }
 836 |       ]
 837 |     },
 838 |     {
 839 |       "metadata": {
 840 |         "id": "Zpd2tHqDLKD_",
 841 |         "colab_type": "code",
 842 |         "colab": {}
 843 |       },
 844 |       "cell_type": "code",
 845 |       "source": [
 846 |         "import seaborn as sns"
 847 |       ],
 848 |       "execution_count": 0,
 849 |       "outputs": []
 850 |     },
 851 |     {
 852 |       "metadata": {
 853 |         "id": "_2E8rXiDLPA-",
 854 |         "colab_type": "code",
 855 |         "colab": {
 856 |           "base_uri": "https://localhost:8080/",
 857 |           "height": 333
 858 |         },
 859 |         "outputId": "1d82f2fe-f7ea-44e0-856b-903ff68a61d6"
 860 |       },
 861 |       "cell_type": "code",
 862 |       "source": [
 863 |         "sns.boxplot(x='model_name', y='accuracy', data=cv_df)\n",
 864 |         "sns.stripplot(x='model_name', y='accuracy', data=cv_df, \n",
 865 |         "              size=8, jitter=True, edgecolor=\"gray\", linewidth=2);"
 866 |       ],
 867 |       "execution_count": 71,
 868 |       "outputs": [
 869 |         {
 870 |           "output_type": "stream",
 871 |           "text": [
 872 |             "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:454: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
 873 |             "  box_data = remove_na(group_data)\n"
 874 |           ],
 875 |           "name": "stderr"
 876 |         },
 877 |         {
 878 |           "output_type": "display_data",
 879 |           "data": {
 880 |             "image/png": 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48c6+wrYN3k9kGkQmA47Xo9C+lewdo/EFqTrk2OFsssiY5CZdOt/YR9ebLbjd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\nwCBJ6nOzZr3S5nPrWoM6vWzLwFZa11i53fVJknqOAYMkqc8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fXLPeR65tOqtB88QLyQHD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\nkkr9f/eE0O3JN+MAAAAAAElFTkSuQmCC\n",
 881 |             "text/plain": [
 882 |               "<Figure size 432x288 with 1 Axes>"
 883 |             ]
 884 |           },
 885 |           "metadata": {
 886 |             "tags": [],
 887 |             "image/png": {
 888 |               "width": 390,
 889 |               "height": 262
 890 |             }
 891 |           }
 892 |         }
 893 |       ]
 894 |     },
 895 |     {
 896 |       "metadata": {
 897 |         "id": "MVxml5fBLShS",
 898 |         "colab_type": "code",
 899 |         "colab": {
 900 |           "base_uri": "https://localhost:8080/",
 901 |           "height": 102
 902 |         },
 903 |         "outputId": "9542c5f6-7167-4dd5-ce44-cab57c04ad4f"
 904 |       },
 905 |       "cell_type": "code",
 906 |       "source": [
 907 |         "cv_df.groupby('model_name').accuracy.mean()"
 908 |       ],
 909 |       "execution_count": 72,
 910 |       "outputs": [
 911 |         {
 912 |           "output_type": "execute_result",
 913 |           "data": {
 914 |             "text/plain": [
 915 |               "model_name\n",
 916 |               "LogisticRegression        0.856000\n",
 917 |               "MultinomialNB             0.847000\n",
 918 |               "RandomForestClassifier    0.850667\n",
 919 |               "Name: accuracy, dtype: float64"
 920 |             ]
 921 |           },
 922 |           "metadata": {
 923 |             "tags": []
 924 |           },
 925 |           "execution_count": 72
 926 |         }
 927 |       ]
 928 |     },
 929 |     {
 930 |       "metadata": {
 931 |         "id": "jCpJmGQhLXs-",
 932 |         "colab_type": "text"
 933 |       },
 934 |       "cell_type": "markdown",
 935 |       "source": [
 936 |         "#  LogisticRegression Model Interpretation "
 937 |       ]
 938 |     },
 939 |     {
 940 |       "metadata": {
 941 |         "id": "o7iDRuz8LWwE",
 942 |         "colab_type": "code",
 943 |         "colab": {}
 944 |       },
 945 |       "cell_type": "code",
 946 |       "source": [
 947 |         "from sklearn.model_selection import train_test_split"
 948 |       ],
 949 |       "execution_count": 0,
 950 |       "outputs": []
 951 |     },
 952 |     {
 953 |       "metadata": {
 954 |         "id": "fMATkCVQLdCq",
 955 |         "colab_type": "code",
 956 |         "colab": {
 957 |           "base_uri": "https://localhost:8080/",
 958 |           "height": 105
 959 |         },
 960 |         "outputId": "4c9917af-9db5-4a8b-c1c0-2e9feb5fccf3"
 961 |       },
 962 |       "cell_type": "code",
 963 |       "source": [
 964 |         "model = LogisticRegression(random_state=0, C=0.9)\n",
 965 |         "\n",
 966 |         "X_train, X_test, y_train, y_test, indices_train, indices_test = train_test_split(features, labels, df.index, test_size=0.33, random_state=0, stratify=df['code'])\n",
 967 |         "\n",
 968 |         "model.fit(X_train, y_train)\n",
 969 |         "y_pred_proba = model.predict_proba(X_test)\n",
 970 |         "y_pred = model.predict(X_test)"
 971 |       ],
 972 |       "execution_count": 81,
 973 |       "outputs": [
 974 |         {
 975 |           "output_type": "stream",
 976 |           "text": [
 977 |             "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
 978 |             "  FutureWarning)\n",
 979 |             "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
 980 |             "  \"this warning.\", FutureWarning)\n"
 981 |           ],
 982 |           "name": "stderr"
 983 |         }
 984 |       ]
 985 |     },
 986 |     {
 987 |       "metadata": {
 988 |         "id": "KEY-nE8aLtYi",
 989 |         "colab_type": "code",
 990 |         "colab": {
 991 |           "base_uri": "https://localhost:8080/",
 992 |           "height": 85
 993 |         },
 994 |         "outputId": "d8407e64-1e58-40f8-8bea-9c7c0f5a2c2f"
 995 |       },
 996 |       "cell_type": "code",
 997 |       "source": [
 998 |         "y_train.value_counts()"
 999 |       ],
1000 |       "execution_count": 82,
1001 |       "outputs": [
1002 |         {
1003 |           "output_type": "execute_result",
1004 |           "data": {
1005 |             "text/plain": [
1006 |               "2    670\n",
1007 |               "1    670\n",
1008 |               "0    670\n",
1009 |               "Name: category_id, dtype: int64"
1010 |             ]
1011 |           },
1012 |           "metadata": {
1013 |             "tags": []
1014 |           },
1015 |           "execution_count": 82
1016 |         }
1017 |       ]
1018 |     },
1019 |     {
1020 |       "metadata": {
1021 |         "id": "Z_xwuIj-kh-v",
1022 |         "colab_type": "code",
1023 |         "colab": {
1024 |           "base_uri": "https://localhost:8080/",
1025 |           "height": 85
1026 |         },
1027 |         "outputId": "951e83bd-0501-4a38-9559-7f1214703697"
1028 |       },
1029 |       "cell_type": "code",
1030 |       "source": [
1031 |         "y_test.value_counts()"
1032 |       ],
1033 |       "execution_count": 83,
1034 |       "outputs": [
1035 |         {
1036 |           "output_type": "execute_result",
1037 |           "data": {
1038 |             "text/plain": [
1039 |               "2    330\n",
1040 |               "1    330\n",
1041 |               "0    330\n",
1042 |               "Name: category_id, dtype: int64"
1043 |             ]
1044 |           },
1045 |           "metadata": {
1046 |             "tags": []
1047 |           },
1048 |           "execution_count": 83
1049 |         }
1050 |       ]
1051 |     },
1052 |     {
1053 |       "metadata": {
1054 |         "id": "FivCi71Xkmkx",
1055 |         "colab_type": "code",
1056 |         "colab": {}
1057 |       },
1058 |       "cell_type": "code",
1059 |       "source": [
1060 |         "\n",
1061 |         "from sklearn.metrics import confusion_matrix"
1062 |       ],
1063 |       "execution_count": 0,
1064 |       "outputs": []
1065 |     },
1066 |     {
1067 |       "metadata": {
1068 |         "id": "GPUXs0XPkpR7",
1069 |         "colab_type": "code",
1070 |         "colab": {
1071 |           "base_uri": "https://localhost:8080/",
1072 |           "height": 282
1073 |         },
1074 |         "outputId": "318e66ef-3db5-4dd0-f692-a48326434d1a"
1075 |       },
1076 |       "cell_type": "code",
1077 |       "source": [
1078 |         "conf_mat = confusion_matrix(y_test, y_pred)\n",
1079 |         "sns.heatmap(conf_mat, annot=True, fmt='d',\n",
1080 |         "            xticklabels=category_id_df.code.values, yticklabels=category_id_df.code.values)\n",
1081 |         "plt.ylabel('Actual')\n",
1082 |         "plt.xlabel('Predicted');"
1083 |       ],
1084 |       "execution_count": 84,
1085 |       "outputs": [
1086 |         {
1087 |           "output_type": "display_data",
1088 |           "data": {
1089 |             "image/png": 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IfykYEEL4awihYwjhaeBQ4P7yHqSSCnaXkw/lxadf5apzbig0wQaoUbMG1apVK/RaterV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\n",
1090 |             "text/plain": [
1091 |               "<Figure size 432x288 with 2 Axes>"
1092 |             ]
1093 |           },
1094 |           "metadata": {
1095 |             "tags": [],
1096 |             "image/png": {
1097 |               "width": 364,
1098 |               "height": 265
1099 |             }
1100 |           }
1101 |         }
1102 |       ]
1103 |     },
1104 |     {
1105 |       "metadata": {
1106 |         "id": "4fqoNRKAk3mr",
1107 |         "colab_type": "text"
1108 |       },
1109 |       "cell_type": "markdown",
1110 |       "source": [
1111 |         "## Analyze incorrectly labeled data"
1112 |       ]
1113 |     },
1114 |     {
1115 |       "metadata": {
1116 |         "id": "dYyNiHeYkr_J",
1117 |         "colab_type": "code",
1118 |         "colab": {
1119 |           "base_uri": "https://localhost:8080/",
1120 |           "height": 4071
1121 |         },
1122 |         "outputId": "5524d5f5-cc33-4a33-b682-87ec02622e45"
1123 |       },
1124 |       "cell_type": "code",
1125 |       "source": [
1126 |         "from IPython.display import display\n",
1127 |         "\n",
1128 |         "for predicted in category_id_df.category_id:\n",
1129 |         "  for actual in category_id_df.category_id:\n",
1130 |         "    if predicted != actual and conf_mat[actual, predicted] >= 2:\n",
1131 |         "      print(\"'{}' predicted as '{}' : {} examples.\".format(id_to_category[actual], id_to_category[predicted], conf_mat[actual, predicted]))\n",
1132 |         "      display(df.loc[indices_test[(y_test == actual) & (y_pred == predicted)]][['descr_clm']])\n",
1133 |         "      print('')"
1134 |       ],
1135 |       "execution_count": 85,
1136 |       "outputs": [
1137 |         {
1138 |           "output_type": "stream",
1139 |           "text": [
1140 |             "'705' predicted as '700' : 14 examples.\n"
1141 |           ],
1142 |           "name": "stdout"
1143 |         },
1144 |         {
1145 |           "output_type": "display_data",
1146 |           "data": {
1147 |             "text/html": [
1148 |               "<div>\n",
1149 |               "<style scoped>\n",
1150 |               "    .dataframe tbody tr th:only-of-type {\n",
1151 |               "        vertical-align: middle;\n",
1152 |               "    }\n",
1153 |               "\n",
1154 |               "    .dataframe tbody tr th {\n",
1155 |               "        vertical-align: top;\n",
1156 |               "    }\n",
1157 |               "\n",
1158 |               "    .dataframe thead th {\n",
1159 |               "        text-align: right;\n",
1160 |               "    }\n",
1161 |               "</style>\n",
1162 |               "<table border=\"1\" class=\"dataframe\">\n",
1163 |               "  <thead>\n",
1164 |               "    <tr style=\"text-align: right;\">\n",
1165 |               "      <th></th>\n",
1166 |               "      <th>descr_clm</th>\n",
1167 |               "    </tr>\n",
1168 |               "  </thead>\n",
1169 |               "  <tbody>\n",
1170 |               "    <tr>\n",
1171 |               "      <th>1902</th>\n",
1172 |               "      <td>CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...</td>\n",
1173 |               "    </tr>\n",
1174 |               "    <tr>\n",
1175 |               "      <th>10656</th>\n",
1176 |               "      <td>TECHNICAL FIELD \\n     The present invention r...</td>\n",
1177 |               "    </tr>\n",
1178 |               "    <tr>\n",
1179 |               "      <th>558</th>\n",
1180 |               "      <td>CROSS-REFERENCES TO RELATED APPLICATIONS \\n   ...</td>\n",
1181 |               "    </tr>\n",
1182 |               "    <tr>\n",
1183 |               "      <th>532</th>\n",
1184 |               "      <td>CROSS-REFERENCE TO RELATED PATENT APPLICATION ...</td>\n",
1185 |               "    </tr>\n",
1186 |               "    <tr>\n",
1187 |               "      <th>1896</th>\n",
1188 |               "      <td>CROSS REFERENCE TO RELATED APPLICATIONS \\n    ...</td>\n",
1189 |               "    </tr>\n",
1190 |               "    <tr>\n",
1191 |               "      <th>2345</th>\n",
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1193 |               "    </tr>\n",
1194 |               "    <tr>\n",
1195 |               "      <th>2378</th>\n",
1196 |               "      <td>BACKGROUND OF THE INVENTION \\n     1. Field of...</td>\n",
1197 |               "    </tr>\n",
1198 |               "    <tr>\n",
1199 |               "      <th>7992</th>\n",
1200 |               "      <td>CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...</td>\n",
1201 |               "    </tr>\n",
1202 |               "    <tr>\n",
1203 |               "      <th>4298</th>\n",
1204 |               "      <td>CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...</td>\n",
1205 |               "    </tr>\n",
1206 |               "    <tr>\n",
1207 |               "      <th>1872</th>\n",
1208 |               "      <td>This application claims priority to U.S. Provi...</td>\n",
1209 |               "    </tr>\n",
1210 |               "    <tr>\n",
1211 |               "      <th>8021</th>\n",
1212 |               "      <td>RELATED PATENT APPLICATIONS \\n     This applic...</td>\n",
1213 |               "    </tr>\n",
1214 |               "    <tr>\n",
1215 |               "      <th>4229</th>\n",
1216 |               "      <td>RELATED APPLICATIONS \\n     The present invent...</td>\n",
1217 |               "    </tr>\n",
1218 |               "    <tr>\n",
1219 |               "      <th>8083</th>\n",
1220 |               "      <td>BACKGROUND \\n     Entities such as data center...</td>\n",
1221 |               "    </tr>\n",
1222 |               "    <tr>\n",
1223 |               "      <th>8106</th>\n",
1224 |               "      <td>CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...</td>\n",
1225 |               "    </tr>\n",
1226 |               "  </tbody>\n",
1227 |               "</table>\n",
1228 |               "</div>"
1229 |             ],
1230 |             "text/plain": [
1231 |               "                                               descr_clm\n",
1232 |               "1902   CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1233 |               "10656  TECHNICAL FIELD \\n     The present invention r...\n",
1234 |               "558    CROSS-REFERENCES TO RELATED APPLICATIONS \\n   ...\n",
1235 |               "532    CROSS-REFERENCE TO RELATED PATENT APPLICATION ...\n",
1236 |               "1896   CROSS REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1237 |               "2345   FIELD OF THE INVENTION \\n     Embodiments gene...\n",
1238 |               "2378   BACKGROUND OF THE INVENTION \\n     1. Field of...\n",
1239 |               "7992   CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1240 |               "4298   CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1241 |               "1872   This application claims priority to U.S. Provi...\n",
1242 |               "8021   RELATED PATENT APPLICATIONS \\n     This applic...\n",
1243 |               "4229   RELATED APPLICATIONS \\n     The present invent...\n",
1244 |               "8083   BACKGROUND \\n     Entities such as data center...\n",
1245 |               "8106   CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ..."
1246 |             ]
1247 |           },
1248 |           "metadata": {
1249 |             "tags": []
1250 |           }
1251 |         },
1252 |         {
1253 |           "output_type": "stream",
1254 |           "text": [
1255 |             "\n",
1256 |             "'706' predicted as '700' : 12 examples.\n"
1257 |           ],
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1259 |         },
1260 |         {
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1330 |               "    <tr>\n",
1331 |               "      <th>3351</th>\n",
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1334 |               "  </tbody>\n",
1335 |               "</table>\n",
1336 |               "</div>"
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1339 |               "                                              descr_clm\n",
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1350 |               "3303  FIELD AND BACKGROUND OF THE INVENTION \\n     T...\n",
1351 |               "3351  TECHNICAL FIELD \\n     The present invention r..."
1352 |             ]
1353 |           },
1354 |           "metadata": {
1355 |             "tags": []
1356 |           }
1357 |         },
1358 |         {
1359 |           "output_type": "stream",
1360 |           "text": [
1361 |             "\n",
1362 |             "'700' predicted as '705' : 22 examples.\n"
1363 |           ],
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1365 |         },
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1455 |               "    </tr>\n",
1456 |               "    <tr>\n",
1457 |               "      <th>5129</th>\n",
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1459 |               "    </tr>\n",
1460 |               "    <tr>\n",
1461 |               "      <th>2074</th>\n",
1462 |               "      <td>This utility patent application is a continuat...</td>\n",
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1464 |               "    <tr>\n",
1465 |               "      <th>2084</th>\n",
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1468 |               "    <tr>\n",
1469 |               "      <th>5191</th>\n",
1470 |               "      <td>The current application claims a priority to t...</td>\n",
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1472 |               "    <tr>\n",
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1475 |               "    </tr>\n",
1476 |               "    <tr>\n",
1477 |               "      <th>3710</th>\n",
1478 |               "      <td>CROSS REFERENCE TO RELATED APPLICATION \\n     ...</td>\n",
1479 |               "    </tr>\n",
1480 |               "  </tbody>\n",
1481 |               "</table>\n",
1482 |               "</div>"
1483 |             ],
1484 |             "text/plain": [
1485 |               "                                              descr_clm\n",
1486 |               "3972  This application claims the benefit of U.S. pr...\n",
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1488 |               "3707  CROSS REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1489 |               "3850  This application claims the benefit of U.S. Pr...\n",
1490 |               "757   CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1491 |               "3706  BACKGROUND \\n     Shipments of items are often...\n",
1492 |               "3694  CROSS REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1493 |               "6112  CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1494 |               "3748  CLAIM OF BENEFIT TO PRIOR APPLICATION \\n     T...\n",
1495 |               "3761  CROSS REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1496 |               "3700  BACKGROUND \\n     Retailers, wholesalers, and ...\n",
1497 |               "3971  TECHNICAL FIELD \\n     This disclosure relates...\n",
1498 |               "760   CROSS-REFERENCE TO RELATED APPLICATION \\n     ...\n",
1499 |               "5149  PRIORITY CLAIM \\n     This application is a co...\n",
1500 |               "788   BACKGROUND \\n     Three-dimensional (3D) print...\n",
1501 |               "5186  CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1502 |               "5129  BACKGROUND \\n     Shipments of items are often...\n",
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1506 |               "3746  CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1507 |               "3710  CROSS REFERENCE TO RELATED APPLICATION \\n     ..."
1508 |             ]
1509 |           },
1510 |           "metadata": {
1511 |             "tags": []
1512 |           }
1513 |         },
1514 |         {
1515 |           "output_type": "stream",
1516 |           "text": [
1517 |             "\n",
1518 |             "'706' predicted as '705' : 16 examples.\n"
1519 |           ],
1520 |           "name": "stdout"
1521 |         },
1522 |         {
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1608 |               "    <tr>\n",
1609 |               "      <th>4491</th>\n",
1610 |               "      <td>CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...</td>\n",
1611 |               "    </tr>\n",
1612 |               "  </tbody>\n",
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1614 |               "</div>"
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1617 |               "                                              descr_clm\n",
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1624 |               "4430  FIELD OF THE DISCLOSURE \\n     The present dis...\n",
1625 |               "3298  CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1626 |               "3304  CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1627 |               "3287  CONTINUATION-IN-PART \\n     This application i...\n",
1628 |               "3174  CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1629 |               "4656  TECHNICAL FIELD \\n     The present disclosure ...\n",
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1631 |               "594   CROSS REFERENCE TO OTHER APPLICATIONS \\n     T...\n",
1632 |               "4396  CROSS REFERENCE TO RELATED APPLICATION \\n     ...\n",
1633 |               "4491  CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ..."
1634 |             ]
1635 |           },
1636 |           "metadata": {
1637 |             "tags": []
1638 |           }
1639 |         },
1640 |         {
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1643 |             "\n",
1644 |             "'700' predicted as '706' : 21 examples.\n"
1645 |           ],
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1663 |               "        text-align: right;\n",
1664 |               "    }\n",
1665 |               "</style>\n",
1666 |               "<table border=\"1\" class=\"dataframe\">\n",
1667 |               "  <thead>\n",
1668 |               "    <tr style=\"text-align: right;\">\n",
1669 |               "      <th></th>\n",
1670 |               "      <th>descr_clm</th>\n",
1671 |               "    </tr>\n",
1672 |               "  </thead>\n",
1673 |               "  <tbody>\n",
1674 |               "    <tr>\n",
1675 |               "      <th>5227</th>\n",
1676 |               "      <td>RELATED APPLICATIONS \\n     This application c...</td>\n",
1677 |               "    </tr>\n",
1678 |               "    <tr>\n",
1679 |               "      <th>784</th>\n",
1680 |               "      <td>TECHNICAL FIELD \\n     The present disclosure ...</td>\n",
1681 |               "    </tr>\n",
1682 |               "    <tr>\n",
1683 |               "      <th>2076</th>\n",
1684 |               "      <td>CLAIM OF PRIORITY \\n     This application is a...</td>\n",
1685 |               "    </tr>\n",
1686 |               "    <tr>\n",
1687 |               "      <th>2092</th>\n",
1688 |               "      <td>CROSS REFERENCE TO RELATED APPLICATIONS \\n    ...</td>\n",
1689 |               "    </tr>\n",
1690 |               "    <tr>\n",
1691 |               "      <th>2117</th>\n",
1692 |               "      <td>CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...</td>\n",
1693 |               "    </tr>\n",
1694 |               "    <tr>\n",
1695 |               "      <th>2090</th>\n",
1696 |               "      <td>CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...</td>\n",
1697 |               "    </tr>\n",
1698 |               "    <tr>\n",
1699 |               "      <th>2107</th>\n",
1700 |               "      <td>CROSS REFERENCE TO RELATED APPLICATION \\n     ...</td>\n",
1701 |               "    </tr>\n",
1702 |               "    <tr>\n",
1703 |               "      <th>5312</th>\n",
1704 |               "      <td>FIELD OF THE INVENTION \\n     This description...</td>\n",
1705 |               "    </tr>\n",
1706 |               "    <tr>\n",
1707 |               "      <th>5318</th>\n",
1708 |               "      <td>RELATED APPLICATIONS \\n     This application c...</td>\n",
1709 |               "    </tr>\n",
1710 |               "    <tr>\n",
1711 |               "      <th>2036</th>\n",
1712 |               "      <td>FIELD \\n     Embodiments taught herein relate ...</td>\n",
1713 |               "    </tr>\n",
1714 |               "    <tr>\n",
1715 |               "      <th>3641</th>\n",
1716 |               "      <td>FIELD OF THE INVENTION \\n     The present inve...</td>\n",
1717 |               "    </tr>\n",
1718 |               "    <tr>\n",
1719 |               "      <th>5061</th>\n",
1720 |               "      <td>FIELD OF THE INVENTION \\n     The present inve...</td>\n",
1721 |               "    </tr>\n",
1722 |               "    <tr>\n",
1723 |               "      <th>5082</th>\n",
1724 |               "      <td>CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...</td>\n",
1725 |               "    </tr>\n",
1726 |               "    <tr>\n",
1727 |               "      <th>5072</th>\n",
1728 |               "      <td>CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...</td>\n",
1729 |               "    </tr>\n",
1730 |               "    <tr>\n",
1731 |               "      <th>2085</th>\n",
1732 |               "      <td>TECHNICAL FIELD \\n     This disclosure relates...</td>\n",
1733 |               "    </tr>\n",
1734 |               "    <tr>\n",
1735 |               "      <th>5300</th>\n",
1736 |               "      <td>REFERENCE TO RELATED APPLICATION \\n     This a...</td>\n",
1737 |               "    </tr>\n",
1738 |               "    <tr>\n",
1739 |               "      <th>785</th>\n",
1740 |               "      <td>CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...</td>\n",
1741 |               "    </tr>\n",
1742 |               "    <tr>\n",
1743 |               "      <th>3961</th>\n",
1744 |               "      <td>TECHNICAL FIELD \\n     The present disclosure ...</td>\n",
1745 |               "    </tr>\n",
1746 |               "    <tr>\n",
1747 |               "      <th>774</th>\n",
1748 |               "      <td>RELATED APPLICATIONS \\n     This application c...</td>\n",
1749 |               "    </tr>\n",
1750 |               "    <tr>\n",
1751 |               "      <th>5341</th>\n",
1752 |               "      <td>CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...</td>\n",
1753 |               "    </tr>\n",
1754 |               "    <tr>\n",
1755 |               "      <th>5083</th>\n",
1756 |               "      <td>TECHNICAL FIELD \\n     Embodiments of the subj...</td>\n",
1757 |               "    </tr>\n",
1758 |               "  </tbody>\n",
1759 |               "</table>\n",
1760 |               "</div>"
1761 |             ],
1762 |             "text/plain": [
1763 |               "                                              descr_clm\n",
1764 |               "5227  RELATED APPLICATIONS \\n     This application c...\n",
1765 |               "784   TECHNICAL FIELD \\n     The present disclosure ...\n",
1766 |               "2076  CLAIM OF PRIORITY \\n     This application is a...\n",
1767 |               "2092  CROSS REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1768 |               "2117  CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1769 |               "2090  CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1770 |               "2107  CROSS REFERENCE TO RELATED APPLICATION \\n     ...\n",
1771 |               "5312  FIELD OF THE INVENTION \\n     This description...\n",
1772 |               "5318  RELATED APPLICATIONS \\n     This application c...\n",
1773 |               "2036  FIELD \\n     Embodiments taught herein relate ...\n",
1774 |               "3641  FIELD OF THE INVENTION \\n     The present inve...\n",
1775 |               "5061  FIELD OF THE INVENTION \\n     The present inve...\n",
1776 |               "5082  CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1777 |               "5072  CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1778 |               "2085  TECHNICAL FIELD \\n     This disclosure relates...\n",
1779 |               "5300  REFERENCE TO RELATED APPLICATION \\n     This a...\n",
1780 |               "785   CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1781 |               "3961  TECHNICAL FIELD \\n     The present disclosure ...\n",
1782 |               "774   RELATED APPLICATIONS \\n     This application c...\n",
1783 |               "5341  CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1784 |               "5083  TECHNICAL FIELD \\n     Embodiments of the subj..."
1785 |             ]
1786 |           },
1787 |           "metadata": {
1788 |             "tags": []
1789 |           }
1790 |         },
1791 |         {
1792 |           "output_type": "stream",
1793 |           "text": [
1794 |             "\n",
1795 |             "'705' predicted as '706' : 33 examples.\n"
1796 |           ],
1797 |           "name": "stdout"
1798 |         },
1799 |         {
1800 |           "output_type": "display_data",
1801 |           "data": {
1802 |             "text/html": [
1803 |               "<div>\n",
1804 |               "<style scoped>\n",
1805 |               "    .dataframe tbody tr th:only-of-type {\n",
1806 |               "        vertical-align: middle;\n",
1807 |               "    }\n",
1808 |               "\n",
1809 |               "    .dataframe tbody tr th {\n",
1810 |               "        vertical-align: top;\n",
1811 |               "    }\n",
1812 |               "\n",
1813 |               "    .dataframe thead th {\n",
1814 |               "        text-align: right;\n",
1815 |               "    }\n",
1816 |               "</style>\n",
1817 |               "<table border=\"1\" class=\"dataframe\">\n",
1818 |               "  <thead>\n",
1819 |               "    <tr style=\"text-align: right;\">\n",
1820 |               "      <th></th>\n",
1821 |               "      <th>descr_clm</th>\n",
1822 |               "    </tr>\n",
1823 |               "  </thead>\n",
1824 |               "  <tbody>\n",
1825 |               "    <tr>\n",
1826 |               "      <th>7101</th>\n",
1827 |               "      <td>CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...</td>\n",
1828 |               "    </tr>\n",
1829 |               "    <tr>\n",
1830 |               "      <th>10682</th>\n",
1831 |               "      <td>BACKGROUND \\n     Advertisers increasingly see...</td>\n",
1832 |               "    </tr>\n",
1833 |               "    <tr>\n",
1834 |               "      <th>7030</th>\n",
1835 |               "      <td>This application is a continuation of U.S. Ser...</td>\n",
1836 |               "    </tr>\n",
1837 |               "    <tr>\n",
1838 |               "      <th>7113</th>\n",
1839 |               "      <td>BACKGROUND \\n     Product lifecycle management...</td>\n",
1840 |               "    </tr>\n",
1841 |               "    <tr>\n",
1842 |               "      <th>7095</th>\n",
1843 |               "      <td>FIELD OF THE INVENTION \\n     The present inve...</td>\n",
1844 |               "    </tr>\n",
1845 |               "    <tr>\n",
1846 |               "      <th>8074</th>\n",
1847 |               "      <td>RELATED APPLICATIONS \\n     This application i...</td>\n",
1848 |               "    </tr>\n",
1849 |               "    <tr>\n",
1850 |               "      <th>8069</th>\n",
1851 |               "      <td>TECHNICAL FIELD \\n     The present disclosure ...</td>\n",
1852 |               "    </tr>\n",
1853 |               "    <tr>\n",
1854 |               "      <th>10624</th>\n",
1855 |               "      <td>FIELD OF THE INVENTION \\n     The invention re...</td>\n",
1856 |               "    </tr>\n",
1857 |               "    <tr>\n",
1858 |               "      <th>7029</th>\n",
1859 |               "      <td>TECHNICAL FIELD \\n     Embodiments of the pres...</td>\n",
1860 |               "    </tr>\n",
1861 |               "    <tr>\n",
1862 |               "      <th>7039</th>\n",
1863 |               "      <td>CLAIM OF PRIORITY \\n     This application is a...</td>\n",
1864 |               "    </tr>\n",
1865 |               "    <tr>\n",
1866 |               "      <th>530</th>\n",
1867 |               "      <td>This application claims the benefit of U.S. Pr...</td>\n",
1868 |               "    </tr>\n",
1869 |               "    <tr>\n",
1870 |               "      <th>8042</th>\n",
1871 |               "      <td>REFERENCE TO RELATED APPLICATIONS \\n     This ...</td>\n",
1872 |               "    </tr>\n",
1873 |               "    <tr>\n",
1874 |               "      <th>9311</th>\n",
1875 |               "      <td>RELATED APPLICATIONS \\n     This patent applic...</td>\n",
1876 |               "    </tr>\n",
1877 |               "    <tr>\n",
1878 |               "      <th>542</th>\n",
1879 |               "      <td>CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...</td>\n",
1880 |               "    </tr>\n",
1881 |               "    <tr>\n",
1882 |               "      <th>1903</th>\n",
1883 |               "      <td>TECHNICAL FIELD OF THE INVENTION \\n     The in...</td>\n",
1884 |               "    </tr>\n",
1885 |               "    <tr>\n",
1886 |               "      <th>572</th>\n",
1887 |               "      <td>CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...</td>\n",
1888 |               "    </tr>\n",
1889 |               "    <tr>\n",
1890 |               "      <th>7034</th>\n",
1891 |               "      <td>RELATED APPLICATIONS \\n     This application c...</td>\n",
1892 |               "    </tr>\n",
1893 |               "    <tr>\n",
1894 |               "      <th>10632</th>\n",
1895 |               "      <td>TECHNICAL FIELD \\n     The present technology ...</td>\n",
1896 |               "    </tr>\n",
1897 |               "    <tr>\n",
1898 |               "      <th>10643</th>\n",
1899 |               "      <td>PRIORITY \\n     This application claims priori...</td>\n",
1900 |               "    </tr>\n",
1901 |               "    <tr>\n",
1902 |               "      <th>9356</th>\n",
1903 |               "      <td>TECHNICAL FIELD \\n     The present disclosure ...</td>\n",
1904 |               "    </tr>\n",
1905 |               "    <tr>\n",
1906 |               "      <th>9327</th>\n",
1907 |               "      <td>CROSS REFERENCE TO OTHER APPLICATIONS \\n     T...</td>\n",
1908 |               "    </tr>\n",
1909 |               "    <tr>\n",
1910 |               "      <th>8046</th>\n",
1911 |               "      <td>PRIORITY DATA \\n     This application claims p...</td>\n",
1912 |               "    </tr>\n",
1913 |               "    <tr>\n",
1914 |               "      <th>9344</th>\n",
1915 |               "      <td>CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...</td>\n",
1916 |               "    </tr>\n",
1917 |               "    <tr>\n",
1918 |               "      <th>7105</th>\n",
1919 |               "      <td>BACKGROUND \\n     This disclosure relates gene...</td>\n",
1920 |               "    </tr>\n",
1921 |               "    <tr>\n",
1922 |               "      <th>7112</th>\n",
1923 |               "      <td>BACKGROUND \\n     Online social networks allow...</td>\n",
1924 |               "    </tr>\n",
1925 |               "    <tr>\n",
1926 |               "      <th>562</th>\n",
1927 |               "      <td>BACKGROUND \\n     Unless otherwise indicated h...</td>\n",
1928 |               "    </tr>\n",
1929 |               "    <tr>\n",
1930 |               "      <th>7119</th>\n",
1931 |               "      <td>CROSS REFERENCE TO RELATED APPLICATIONS \\n    ...</td>\n",
1932 |               "    </tr>\n",
1933 |               "    <tr>\n",
1934 |               "      <th>4255</th>\n",
1935 |               "      <td>This application is a Continuation of U.S. app...</td>\n",
1936 |               "    </tr>\n",
1937 |               "    <tr>\n",
1938 |               "      <th>7972</th>\n",
1939 |               "      <td>FIELD OF THE INVENTION \\n     The present inve...</td>\n",
1940 |               "    </tr>\n",
1941 |               "    <tr>\n",
1942 |               "      <th>9335</th>\n",
1943 |               "      <td>This application relates to U.S. provisional p...</td>\n",
1944 |               "    </tr>\n",
1945 |               "    <tr>\n",
1946 |               "      <th>566</th>\n",
1947 |               "      <td>TECHNICAL FIELD \\n     The present invention r...</td>\n",
1948 |               "    </tr>\n",
1949 |               "    <tr>\n",
1950 |               "      <th>9349</th>\n",
1951 |               "      <td>CROSS REFERENCE TO RELATED APPLICATION \\n     ...</td>\n",
1952 |               "    </tr>\n",
1953 |               "    <tr>\n",
1954 |               "      <th>8044</th>\n",
1955 |               "      <td>CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...</td>\n",
1956 |               "    </tr>\n",
1957 |               "  </tbody>\n",
1958 |               "</table>\n",
1959 |               "</div>"
1960 |             ],
1961 |             "text/plain": [
1962 |               "                                               descr_clm\n",
1963 |               "7101   CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1964 |               "10682  BACKGROUND \\n     Advertisers increasingly see...\n",
1965 |               "7030   This application is a continuation of U.S. Ser...\n",
1966 |               "7113   BACKGROUND \\n     Product lifecycle management...\n",
1967 |               "7095   FIELD OF THE INVENTION \\n     The present inve...\n",
1968 |               "8074   RELATED APPLICATIONS \\n     This application i...\n",
1969 |               "8069   TECHNICAL FIELD \\n     The present disclosure ...\n",
1970 |               "10624  FIELD OF THE INVENTION \\n     The invention re...\n",
1971 |               "7029   TECHNICAL FIELD \\n     Embodiments of the pres...\n",
1972 |               "7039   CLAIM OF PRIORITY \\n     This application is a...\n",
1973 |               "530    This application claims the benefit of U.S. Pr...\n",
1974 |               "8042   REFERENCE TO RELATED APPLICATIONS \\n     This ...\n",
1975 |               "9311   RELATED APPLICATIONS \\n     This patent applic...\n",
1976 |               "542    CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1977 |               "1903   TECHNICAL FIELD OF THE INVENTION \\n     The in...\n",
1978 |               "572    CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1979 |               "7034   RELATED APPLICATIONS \\n     This application c...\n",
1980 |               "10632  TECHNICAL FIELD \\n     The present technology ...\n",
1981 |               "10643  PRIORITY \\n     This application claims priori...\n",
1982 |               "9356   TECHNICAL FIELD \\n     The present disclosure ...\n",
1983 |               "9327   CROSS REFERENCE TO OTHER APPLICATIONS \\n     T...\n",
1984 |               "8046   PRIORITY DATA \\n     This application claims p...\n",
1985 |               "9344   CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1986 |               "7105   BACKGROUND \\n     This disclosure relates gene...\n",
1987 |               "7112   BACKGROUND \\n     Online social networks allow...\n",
1988 |               "562    BACKGROUND \\n     Unless otherwise indicated h...\n",
1989 |               "7119   CROSS REFERENCE TO RELATED APPLICATIONS \\n    ...\n",
1990 |               "4255   This application is a Continuation of U.S. app...\n",
1991 |               "7972   FIELD OF THE INVENTION \\n     The present inve...\n",
1992 |               "9335   This application relates to U.S. provisional p...\n",
1993 |               "566    TECHNICAL FIELD \\n     The present invention r...\n",
1994 |               "9349   CROSS REFERENCE TO RELATED APPLICATION \\n     ...\n",
1995 |               "8044   CROSS-REFERENCE TO RELATED APPLICATIONS \\n    ..."
1996 |             ]
1997 |           },
1998 |           "metadata": {
1999 |             "tags": []
2000 |           }
2001 |         },
2002 |         {
2003 |           "output_type": "stream",
2004 |           "text": [
2005 |             "\n"
2006 |           ],
2007 |           "name": "stdout"
2008 |         }
2009 |       ]
2010 |     },
2011 |     {
2012 |       "metadata": {
2013 |         "id": "x2ewl40Vkwq5",
2014 |         "colab_type": "code",
2015 |         "colab": {
2016 |           "base_uri": "https://localhost:8080/",
2017 |           "height": 1190
2018 |         },
2019 |         "outputId": "92b93def-183a-488a-8abb-b8dc32bec787"
2020 |       },
2021 |       "cell_type": "code",
2022 |       "source": [
2023 |         "# model.fit(features, labels)\n",
2024 |         "from sklearn.feature_selection import chi2\n",
2025 |         "\n",
2026 |         "N = 10\n",
2027 |         "for category, category_id in sorted(category_to_id.items()):\n",
2028 |         "  indices = np.argsort(model.coef_[category_id])\n",
2029 |         "  feature_names = np.array(tfidf.get_feature_names())[indices]\n",
2030 |         "  unigrams = [v for v in reversed(feature_names) if len(v.split(' ')) == 1][:N]\n",
2031 |         "  bigrams = [v for v in reversed(feature_names) if len(v.split(' ')) == 2][:N]\n",
2032 |         "  print(\"# '{}':\".format(category))\n",
2033 |         "  print(\"  . Top unigrams:\\n       . {}\".format('\\n       . '.join(unigrams)))\n",
2034 |         "  print(\"  . Top bigrams:\\n       . {}\".format('\\n       . '.join(bigrams)))"
2035 |       ],
2036 |       "execution_count": 86,
2037 |       "outputs": [
2038 |         {
2039 |           "output_type": "stream",
2040 |           "text": [
2041 |             "# '700':\n",
2042 |             "  . Top unigrams:\n",
2043 |             "       . control\n",
2044 |             "       . controller\n",
2045 |             "       . power\n",
2046 |             "       . controlling\n",
2047 |             "       . sensor\n",
2048 |             "       . robot\n",
2049 |             "       . temperature\n",
2050 |             "       . position\n",
2051 |             "       . energy\n",
2052 |             "       . controlled\n",
2053 |             "  . Top bigrams:\n",
2054 |             "       . control device\n",
2055 |             "       . control unit\n",
2056 |             "       . controller configured\n",
2057 |             "       . perspective view\n",
2058 |             "       . method controlling\n",
2059 |             "       . control signal\n",
2060 |             "       . control method\n",
2061 |             "       . power supply\n",
2062 |             "       . configured control\n",
2063 |             "       . according claim\n",
2064 |             "# '705':\n",
2065 |             "  . Top unigrams:\n",
2066 |             "       . payment\n",
2067 |             "       . transaction\n",
2068 |             "       . price\n",
2069 |             "       . financial\n",
2070 |             "       . customer\n",
2071 |             "       . provider\n",
2072 |             "       . purchase\n",
2073 |             "       . transactions\n",
2074 |             "       . account\n",
2075 |             "       . sale\n",
2076 |             "  . Top bigrams:\n",
2077 |             "       . service provider\n",
2078 |             "       . mobile device\n",
2079 |             "       . point sale\n",
2080 |             "       . credit card\n",
2081 |             "       . method claim\n",
2082 |             "       . implemented method\n",
2083 |             "       . goods services\n",
2084 |             "       . readable medium\n",
2085 |             "       . account associated\n",
2086 |             "       . computer executable\n",
2087 |             "# '706':\n",
2088 |             "  . Top unigrams:\n",
2089 |             "       . learning\n",
2090 |             "       . probability\n",
2091 |             "       . model\n",
2092 |             "       . training\n",
2093 |             "       . rule\n",
2094 |             "       . neural\n",
2095 |             "       . prediction\n",
2096 |             "       . knowledge\n",
2097 |             "       . predicting\n",
2098 |             "       . artificial\n",
2099 |             "  . Top bigrams:\n",
2100 |             "       . machine learning\n",
2101 |             "       . neural network\n",
2102 |             "       . training data\n",
2103 |             "       . knowledge base\n",
2104 |             "       . artificial intelligence\n",
2105 |             "       . artificial neural\n",
2106 |             "       . neural networks\n",
2107 |             "       . data set\n",
2108 |             "       . input data\n",
2109 |             "       . readable storage\n"
2110 |           ],
2111 |           "name": "stdout"
2112 |         }
2113 |       ]
2114 |     },
2115 |     {
2116 |       "metadata": {
2117 |         "id": "3vnSfRNAmp1s",
2118 |         "colab_type": "text"
2119 |       },
2120 |       "cell_type": "markdown",
2121 |       "source": [
2122 |         "### Do phrases appear in 706 and 705?"
2123 |       ]
2124 |     },
2125 |     {
2126 |       "metadata": {
2127 |         "id": "skrSqDfXlL03",
2128 |         "colab_type": "code",
2129 |         "colab": {
2130 |           "base_uri": "https://localhost:8080/",
2131 |           "height": 85
2132 |         },
2133 |         "outputId": "384cc934-1a4d-438f-ce78-ac3705f144c1"
2134 |       },
2135 |       "cell_type": "code",
2136 |       "source": [
2137 |         "df[df.descr_clm.str.lower().str.contains('machine learning')].code.value_counts()"
2138 |       ],
2139 |       "execution_count": 87,
2140 |       "outputs": [
2141 |         {
2142 |           "output_type": "execute_result",
2143 |           "data": {
2144 |             "text/plain": [
2145 |               "706    178\n",
2146 |               "705     17\n",
2147 |               "700      8\n",
2148 |               "Name: code, dtype: int64"
2149 |             ]
2150 |           },
2151 |           "metadata": {
2152 |             "tags": []
2153 |           },
2154 |           "execution_count": 87
2155 |         }
2156 |       ]
2157 |     },
2158 |     {
2159 |       "metadata": {
2160 |         "id": "gijkozQumnCY",
2161 |         "colab_type": "code",
2162 |         "colab": {
2163 |           "base_uri": "https://localhost:8080/",
2164 |           "height": 85
2165 |         },
2166 |         "outputId": "9289e3b9-6ae8-48b7-b968-4c22480bfd44"
2167 |       },
2168 |       "cell_type": "code",
2169 |       "source": [
2170 |         "df[df.descr_clm.str.lower().str.contains('linear regression')].code.value_counts()"
2171 |       ],
2172 |       "execution_count": 88,
2173 |       "outputs": [
2174 |         {
2175 |           "output_type": "execute_result",
2176 |           "data": {
2177 |             "text/plain": [
2178 |               "706    32\n",
2179 |               "700     7\n",
2180 |               "705     6\n",
2181 |               "Name: code, dtype: int64"
2182 |             ]
2183 |           },
2184 |           "metadata": {
2185 |             "tags": []
2186 |           },
2187 |           "execution_count": 88
2188 |         }
2189 |       ]
2190 |     },
2191 |     {
2192 |       "metadata": {
2193 |         "id": "iwEMrTwvm43b",
2194 |         "colab_type": "code",
2195 |         "colab": {
2196 |           "base_uri": "https://localhost:8080/",
2197 |           "height": 85
2198 |         },
2199 |         "outputId": "4c5d6264-9e88-4c5e-fb06-34e9ac8048cf"
2200 |       },
2201 |       "cell_type": "code",
2202 |       "source": [
2203 |         "df[df.descr_clm.str.lower().str.contains('statistical analysis')].code.value_counts()"
2204 |       ],
2205 |       "execution_count": 89,
2206 |       "outputs": [
2207 |         {
2208 |           "output_type": "execute_result",
2209 |           "data": {
2210 |             "text/plain": [
2211 |               "706    32\n",
2212 |               "700    11\n",
2213 |               "705     7\n",
2214 |               "Name: code, dtype: int64"
2215 |             ]
2216 |           },
2217 |           "metadata": {
2218 |             "tags": []
2219 |           },
2220 |           "execution_count": 89
2221 |         }
2222 |       ]
2223 |     },
2224 |     {
2225 |       "metadata": {
2226 |         "id": "eBnm0SJvm9AJ",
2227 |         "colab_type": "code",
2228 |         "colab": {
2229 |           "base_uri": "https://localhost:8080/",
2230 |           "height": 85
2231 |         },
2232 |         "outputId": "dcf3d278-7190-4b68-be56-e38a28e217c3"
2233 |       },
2234 |       "cell_type": "code",
2235 |       "source": [
2236 |         "df[df.descr_clm.str.lower().str.contains('statistics')].code.value_counts()"
2237 |       ],
2238 |       "execution_count": 90,
2239 |       "outputs": [
2240 |         {
2241 |           "output_type": "execute_result",
2242 |           "data": {
2243 |             "text/plain": [
2244 |               "706    86\n",
2245 |               "705    50\n",
2246 |               "700    11\n",
2247 |               "Name: code, dtype: int64"
2248 |             ]
2249 |           },
2250 |           "metadata": {
2251 |             "tags": []
2252 |           },
2253 |           "execution_count": 90
2254 |         }
2255 |       ]
2256 |     },
2257 |     {
2258 |       "metadata": {
2259 |         "id": "WK9MNL-3nALj",
2260 |         "colab_type": "code",
2261 |         "colab": {
2262 |           "base_uri": "https://localhost:8080/",
2263 |           "height": 85
2264 |         },
2265 |         "outputId": "11aebc86-5908-4e5b-dd35-d701a1ebedee"
2266 |       },
2267 |       "cell_type": "code",
2268 |       "source": [
2269 |         "df[df.descr_clm.str.lower().str.contains('probability')].code.value_counts()"
2270 |       ],
2271 |       "execution_count": 91,
2272 |       "outputs": [
2273 |         {
2274 |           "output_type": "execute_result",
2275 |           "data": {
2276 |             "text/plain": [
2277 |               "706    270\n",
2278 |               "705     58\n",
2279 |               "700     44\n",
2280 |               "Name: code, dtype: int64"
2281 |             ]
2282 |           },
2283 |           "metadata": {
2284 |             "tags": []
2285 |           },
2286 |           "execution_count": 91
2287 |         }
2288 |       ]
2289 |     },
2290 |     {
2291 |       "metadata": {
2292 |         "id": "ccSVD9POnKvZ",
2293 |         "colab_type": "text"
2294 |       },
2295 |       "cell_type": "markdown",
2296 |       "source": [
2297 |         "**Statistics and probability appear in both 706 and 705, meaning, there is possible opportunity to describe those processes in a different way so that it is read as a machine learning patent application instead of a business method patent (high risk of Alice rejection).**"
2298 |       ]
2299 |     },
2300 |     {
2301 |       "metadata": {
2302 |         "id": "w-eNQBPznuec",
2303 |         "colab_type": "text"
2304 |       },
2305 |       "cell_type": "markdown",
2306 |       "source": [
2307 |         "# Test model on new patent application"
2308 |       ]
2309 |     },
2310 |     {
2311 |       "metadata": {
2312 |         "id": "6ssQAsHtnHNU",
2313 |         "colab_type": "code",
2314 |         "colab": {
2315 |           "base_uri": "https://localhost:8080/",
2316 |           "height": 88
2317 |         },
2318 |         "cellView": "both",
2319 |         "outputId": "d4fb8403-d15f-4e2c-bf72-2102105f6796"
2320 |       },
2321 |       "cell_type": "code",
2322 |       "source": [
2323 |         "#@title Default title text\n",
2324 |         "texts = \"1. A system comprising:a memory that stores computer executable components a processor that executes computer executable components stored in the memory wherein the computer executable components comprise:a snapshot component that generates a first sequence of multi-dimensional time series data and a second sequence of multi-dimensional time series data from multi-dimensional time series data associated with at least two different data types generated by a data system over a consecutive period of time and a machine learning component that analyzes the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data using a convolutional neuralnetwork system to predict an event associated with the multi-dimensional time series data. 2. The system of claim 1 wherein the snapshot component generates a data matrix associated with the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data and wherein the machine learning component analyzes the data matrix using the convolutional neural network system. 3. The system of claim 1 wherein the machine learning component analyzes the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data using a parallel network of processing units associated with the convolutional neural network system and wherein performance of the processor to predict the event associated with the multi-dimensional time series data is improved by employing the convolutional neural network system. 4. The system of claim 1 wherein the snapshot component determines a size of the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data based on data associated with the convolutional neural network system. 5. The system of claim 1 wherein the snapshot component determines a set of parameters for the convolutional neuralnetwork system based on a classification of data associated with the convolutional neural network system. 6. The system of claim 1 wherein a portion of data from the first sequence of multi-dimensional time series data corresponds to the second sequence of multi-dimensional time series data and wherein the machine learning component analyzes the portion of the data. 7. The system of claim 1 wherein at least the first sequence of multi-dimensional time series data comprises dynamic data that is related to other data included in the first sequence of multi-dimensional time series data or the second sequence of multi-dimensional time series data and wherein the machine learning component analyzes the dynamic data. 8. The system of claim 1 wherein the snapshot component generates the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data based on feedback data indicative of information for tuning the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data. 9. The system of claim 1 wherein the machine learning component adjusts the convolutional neural network system based on feedback data indicative of information for tuning the convolutional neural network system. 10. The system of claim 1 wherein the convolutional neuralnetwork system is associated with nonlinear processing of features associated with the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data. 11. The system of claim 1 further comprising:a display component that generates a user interface to display output data associated with the event in a human interpretable format. 12-19. (canceled) 20. A computer program product for machine learning the computer program product comprising a computer readable storage medium having program instructions embodied therewith the program instructions executable by processor to cause the processor to:generate by the processor a data matrix based on first time series data associated with a first data source and second time series data associated with a second data source analyze by the processor the data matrix associated with the first time series data and the second time series data using a convolutional neural network system and generate by the processor prediction data that comprises a predicted event associated with the first time series data and the second time series data based on data generated by the convolutional neural network system. 21. The computer program product of claim 20 wherein the program instructions are further executable by the processor to cause the processor to:modify by the processor the data matrix based on the data generated by the convolutional neural network system. 22. The computer program product of claim 20 wherein the program instructions are further executable by the processor to cause the processor to:modify by the processor a convolutional neural networkassociated with the convolutional neural network system based on the data generated by the convolutional neuralnetwork system. 23. A computer program product for machine learning the computer program product comprising a computer readable storage medium having program instructions embodied therewith the program instructions executable by processor to cause the processor to:generate by the processor a data matrix based on multi-dimensional time series data associated with at least two different data types perform by the processor a convolutional neural network process based on the data matrix associated with the multi-dimensional time series data and generate by the processor prediction data that comprises a predicted event associated with the multi-dimensional time series data based on the convolutional neural network process. 24. The computer program product of claim 23 wherein the program instructions are further executable by the processor to cause the processor to:modify by the processor the data matrix based on the prediction data. 25. The computer program product of claim 23 wherein the program instructions are further executable by the processor to cause the processor to:modify by the processor a convolutional neural networkassociated with the convolutional neural network process based on the prediction data.\\\"\" #@param {type:\"string\"}\n",
2325 |         "texts = [texts]\n",
2326 |         "# text from patent https://patentimages.storage.googleapis.com/e2/3f/75/2f32412145e159/US20180260697A1.pdf\n",
2327 |         "\n",
2328 |         "text_features = tfidf.transform(texts)\n",
2329 |         "predictions = model.predict(text_features)\n",
2330 |         "for text, predicted in zip(texts, predictions):\n",
2331 |         "  print(\"  - Predicted as: '{}'\".format(id_to_category[predicted]))\n",
2332 |         "  print(\"\")\n",
2333 |         "  print('\"{}\"'.format(text))"
2334 |       ],
2335 |       "execution_count": 94,
2336 |       "outputs": [
2337 |         {
2338 |           "output_type": "stream",
2339 |           "text": [
2340 |             "  - Predicted as: '706'\n",
2341 |             "\n",
2342 |             "\"1. A system comprising:a memory that stores computer executable components a processor that executes computer executable components stored in the memory wherein the computer executable components comprise:a snapshot component that generates a first sequence of multi-dimensional time series data and a second sequence of multi-dimensional time series data from multi-dimensional time series data associated with at least two different data types generated by a data system over a consecutive period of time and a machine learning component that analyzes the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data using a convolutional neuralnetwork system to predict an event associated with the multi-dimensional time series data. 2. The system of claim 1 wherein the snapshot component generates a data matrix associated with the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data and wherein the machine learning component analyzes the data matrix using the convolutional neural network system. 3. The system of claim 1 wherein the machine learning component analyzes the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data using a parallel network of processing units associated with the convolutional neural network system and wherein performance of the processor to predict the event associated with the multi-dimensional time series data is improved by employing the convolutional neural network system. 4. The system of claim 1 wherein the snapshot component determines a size of the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data based on data associated with the convolutional neural network system. 5. The system of claim 1 wherein the snapshot component determines a set of parameters for the convolutional neuralnetwork system based on a classification of data associated with the convolutional neural network system. 6. The system of claim 1 wherein a portion of data from the first sequence of multi-dimensional time series data corresponds to the second sequence of multi-dimensional time series data and wherein the machine learning component analyzes the portion of the data. 7. The system of claim 1 wherein at least the first sequence of multi-dimensional time series data comprises dynamic data that is related to other data included in the first sequence of multi-dimensional time series data or the second sequence of multi-dimensional time series data and wherein the machine learning component analyzes the dynamic data. 8. The system of claim 1 wherein the snapshot component generates the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data based on feedback data indicative of information for tuning the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data. 9. The system of claim 1 wherein the machine learning component adjusts the convolutional neural network system based on feedback data indicative of information for tuning the convolutional neural network system. 10. The system of claim 1 wherein the convolutional neuralnetwork system is associated with nonlinear processing of features associated with the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data. 11. The system of claim 1 further comprising:a display component that generates a user interface to display output data associated with the event in a human interpretable format. 12-19. (canceled) 20. A computer program product for machine learning the computer program product comprising a computer readable storage medium having program instructions embodied therewith the program instructions executable by processor to cause the processor to:generate by the processor a data matrix based on first time series data associated with a first data source and second time series data associated with a second data source analyze by the processor the data matrix associated with the first time series data and the second time series data using a convolutional neural network system and generate by the processor prediction data that comprises a predicted event associated with the first time series data and the second time series data based on data generated by the convolutional neural network system. 21. The computer program product of claim 20 wherein the program instructions are further executable by the processor to cause the processor to:modify by the processor the data matrix based on the data generated by the convolutional neural network system. 22. The computer program product of claim 20 wherein the program instructions are further executable by the processor to cause the processor to:modify by the processor a convolutional neural networkassociated with the convolutional neural network system based on the data generated by the convolutional neuralnetwork system. 23. A computer program product for machine learning the computer program product comprising a computer readable storage medium having program instructions embodied therewith the program instructions executable by processor to cause the processor to:generate by the processor a data matrix based on multi-dimensional time series data associated with at least two different data types perform by the processor a convolutional neural network process based on the data matrix associated with the multi-dimensional time series data and generate by the processor prediction data that comprises a predicted event associated with the multi-dimensional time series data based on the convolutional neural network process. 24. The computer program product of claim 23 wherein the program instructions are further executable by the processor to cause the processor to:modify by the processor the data matrix based on the prediction data. 25. The computer program product of claim 23 wherein the program instructions are further executable by the processor to cause the processor to:modify by the processor a convolutional neural networkassociated with the convolutional neural network process based on the prediction data.\"\"\n"
2343 |           ],
2344 |           "name": "stdout"
2345 |         }
2346 |       ]
2347 |     }
2348 |   ]
2349 | }


--------------------------------------------------------------------------------