patent_detective.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook provide some static analysis over public data set of patents available on google bigquery. For more knowledge about dataset refer to this article https://cloud.google.com/blog/products/gcp/google-patents-public-datasets-connecting-public-paid-and-private-patent-data. In this notebook, I have used table **publication** to do the analysis. I have also created a big_query helper class which I will be using to fetch the result from big query. Apart from that there is one more python file patent_detective which uses bigquery_helper to fetch data and plot the data using python matplotlib or other libraries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import big_query_helper as bq\n",
    "import patent_detective_analysis as pt\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigquery_patents_research = bq.BigQueryHelper(active_project=\"patents-public-data\",\n",
    "                                    dataset_name=\"google_patents_research\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['publications',\n",
       " 'publications_201710',\n",
       " 'publications_201802',\n",
       " 'publications_201809']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This will get us all the tables under dataset = google_patents_reseach\n",
    "bigquery_patents_research.get_tables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Next we will be using `publications` table for our analysis purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "patent_detective = pt.PatentDetective(active_project='patents-public-data', dataset_name = 'google_patents_research', table_name = 'publications')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis on inventors of publications\n",
    "Below we are fetching avg_inventors per country per year from google big query. And then we will be using this to plot various chart from the extracted data. This provides the number of inventor for each country per year. Wel will be using this to plot a simple bar chart and heatmap. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f0_</th>\n",
       "      <th>cnt</th>\n",
       "      <th>country_code</th>\n",
       "      <th>filing_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>2.888767</td>\n",
       "      <td>7369566</td>\n",
       "      <td>CN</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          f0_      cnt country_code  filing_year\n",
       "767  2.888767  7369566           CN         2015"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_inventors = patent_detective.avg_inventors_per_country()\n",
    "avg_inventor_sorted = avg_inventors.sort_values(by='cnt', ascending=False)\n",
    "avg_inventor_sorted[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts_sorted = avg_inventor_sorted['cnt'][:100].tolist()\n",
    "country_codes_sorted = avg_inventor_sorted['country_code'][:100].tolist()\n",
    "plt.bar(country_codes_sorted, counts_sorted, align='edge', width=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above chart provides the bar chart of number of inventor for each country where CN is having leading all other country with large margin and then being followed by US and JP which are almost equal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# below is the bar plot for patents count for each country from top 100 row of avg_inventors\n",
    "counts = avg_inventors['cnt'][:100].tolist()\n",
    "country_codes = avg_inventors['country_code'][:100].tolist()\n",
    "plt.bar(country_codes, counts, align='edge', width=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we will be plotting a heatmap depicting the change in number of invetors over a period of time from 1970 to 2015. And we have used 10 countries for this ananlysis which are ('US', 'FR', 'GB', 'AT', 'CH', 'CA', 'AU', 'DE', 'DK', 'SU'). For few countries, number of inventors in not available in google bigquery dataset for some years. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# unique() method will return the unique country_codes from dataframe avg_inventors\n",
    "# avg_inventors.country_code.unique()\n",
    "countries=['US', 'FR', 'GB', 'AT', 'CH', 'CA', 'AU', 'DE', 'DK', 'SU']\n",
    "filing_years = [1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015]\n",
    "truncated_data = avg_inventors.loc[avg_inventors['country_code'].isin(countries) & avg_inventors['filing_year'].isin(filing_years) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f0_</th>\n",
       "      <th>cnt</th>\n",
       "      <th>country_code</th>\n",
       "      <th>filing_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>1.703795</td>\n",
       "      <td>239645</td>\n",
       "      <td>DE</td>\n",
       "      <td>1970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>2.099878</td>\n",
       "      <td>9041</td>\n",
       "      <td>AT</td>\n",
       "      <td>1970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>1.779474</td>\n",
       "      <td>7532</td>\n",
       "      <td>GB</td>\n",
       "      <td>1970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>1.623402</td>\n",
       "      <td>328544</td>\n",
       "      <td>US</td>\n",
       "      <td>1970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>1.746557</td>\n",
       "      <td>46042</td>\n",
       "      <td>AU</td>\n",
       "      <td>1970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>3.253608</td>\n",
       "      <td>23560</td>\n",
       "      <td>SU</td>\n",
       "      <td>1970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>1.723110</td>\n",
       "      <td>32558</td>\n",
       "      <td>CH</td>\n",
       "      <td>1970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>1.760020</td>\n",
       "      <td>106363</td>\n",
       "      <td>CA</td>\n",
       "      <td>1970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>1.917405</td>\n",
       "      <td>16030</td>\n",
       "      <td>DK</td>\n",
       "      <td>1970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>1.086631</td>\n",
       "      <td>3740</td>\n",
       "      <td>FR</td>\n",
       "      <td>1970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          f0_     cnt country_code  filing_year\n",
       "168  1.703795  239645           DE         1970\n",
       "169  2.099878    9041           AT         1970\n",
       "173  1.779474    7532           GB         1970\n",
       "176  1.623402  328544           US         1970\n",
       "178  1.746557   46042           AU         1970\n",
       "182  3.253608   23560           SU         1970\n",
       "191  1.723110   32558           CH         1970\n",
       "197  1.760020  106363           CA         1970\n",
       "198  1.917405   16030           DK         1970\n",
       "199  1.086631    3740           FR         1970"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "truncated_data[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a0ee208>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "truncated_data_pivoted = truncated_data.pivot('country_code','filing_year','cnt')\n",
    "sns.heatmap(truncated_data_pivoted, linewidths=.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we have created a heat map for countries and count of inventors over period of 45 years. This data can also be plotted as an image where our data will act as an image matrix with count being the number of pixel at that particular location. Below is the sample for this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tr_np_array = truncated_data_pivoted.to_numpy()\n",
    "plt.imshow(tr_np_array, cmap='viridis')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below analysis is one of the question being asked on indorse platform where we need to find the top term used in 1000 most recent patents. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('method', 203),\n",
       " ('process', 85),\n",
       " ('means', 65),\n",
       " ('cells', 48),\n",
       " ('cell', 41),\n",
       " ('water', 40),\n",
       " ('ml', 39),\n",
       " ('characterized', 38),\n",
       " ('solution', 36),\n",
       " ('mixture', 35)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fetch top terms from project patents-public-data and dataset = google-patents-research \n",
    "# project with table = publications and this is with limit of 1000 on publications. \n",
    "top_terms = patent_detective.top_terms()\n",
    "top_terms[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot top 10 top terms\n",
    "patent_detective.plot_top_n_terms(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, each publication has been cited number of other publication. In google bigquery this data is present as an array of publication which has cited that particular publication. So below is the analysis on publication citation  as to find the most cited publication. We are getting publciation_number and number of times publication has been cited as *cnt*. To get the more details about the publication, we can use publication_number on https://patents.google.com/ to get the details of publication. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>publication_number</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US-4683202-A</td>\n",
       "      <td>9709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US-4683202-B1</td>\n",
       "      <td>9709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US-4816567-A</td>\n",
       "      <td>8136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US-4683195-B1</td>\n",
       "      <td>7758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US-4683195-A</td>\n",
       "      <td>7758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>US-5523520-A</td>\n",
       "      <td>4856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>US-5892900-A</td>\n",
       "      <td>4698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>US-4946778-A</td>\n",
       "      <td>4651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>US-4733665-A</td>\n",
       "      <td>4639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>US-4733665-B1</td>\n",
       "      <td>4639</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  publication_number   cnt\n",
       "0       US-4683202-A  9709\n",
       "1      US-4683202-B1  9709\n",
       "2       US-4816567-A  8136\n",
       "3      US-4683195-B1  7758\n",
       "4       US-4683195-A  7758\n",
       "5       US-5523520-A  4856\n",
       "6       US-5892900-A  4698\n",
       "7       US-4946778-A  4651\n",
       "8       US-4733665-A  4639\n",
       "9      US-4733665-B1  4639"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data around most cited publications\n",
    "most_cited_publications = patent_detective.most_cited_publications()\n",
    "most_cited_publications[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot top 100 most cited publications\n",
    "patent_detective.plot_most_cited_publication(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Char for top 10 most cited publications\n",
    "patent_detective.plot_most_cited_publication(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}