Discover imoisharma/U.S.-Patents-Claims Open Source project

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Created about 5 years ago
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imoisharma

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I am using USPTO ( United States Patent and Trademark Office) SOURCE LINK WHICH is the federal agency for granting U.S. patents and registering trademarks. They also provide open data about U.S. patents. So, today we will work with one open dataset they provided on their website. (Source- download the dataset from here-Patent grant full-text data/XML) Dataset Description - Patent grant full-text data (no images) (JAN 1976 - present) (1976–2001 Automated Patent System (APS) format is Green Book) Contains the full text of each patent grant issued weekly (Tuesdays) from January 1, 1976, to present (excludes images/drawings and reexaminations). The dataset contains (JAN 2002 - present) the full text of each patent grant issued weekly (Tuesdays) (excludes images/drawings and reexaminations). The file format is eXtensible Markup Language (XML) in accordance with the Patent Grant International Common Element (ICE) Document Type Definition (DTD).

Python-Web-Scraping-

When performing data science tasks, it's common to want to use data found on the internet. You'll usually be able to access this data in csv format, or via an Application Programming Interface (API). However, there are times when the data you want can only be accessed as part of a web page. In cases like this, you'll want to use a technique called web scraping to get the data from the web page into a format you can work with in your analysis.

Jupyter Notebook

Text--Summarization

Automatic summarization is the process of shortening a text document with software, in order to create a summary with the major points of the original document. Technologies that can make a coherent summary take into account variables such as length, writing style and syntax.

Python

aws-scripts

AWS Shell Scripts for most of the AWS Services. Any issues contact via [email protected]

Shell

aws-codepipeline-s3-codedeploy-linux-2.0

Use this sample when creating a simple pipeline in AWS CodePipeline while following the Simple Pipeline Walkthrough tutorial.

Shell