patentgpt-extract 0.1.5

A package for downloading and analysisng us patents.

PatentGPT

This repository hosts a prototype for patent analysis with a particular focus on extracting specific technical measurements and their associated values

Installation

from patentgpt.main import main

Quick Start

1. Clone this repository.
2. Install the required Python packages by running !pip install -r requirements.txt.
3. Run the Jupyter notebook quick_start.ipynb.
4. Authenticate and add your OpenAI token. Then answer these questions::
   • Enter a date in the format 'YYYY-MM-DD': exampe is 2023-01-12
   • Enter the number of patents you want to analyze: example is 5 (this randomly select 5 parsed patents)
   • Do you want to log the results? (yes/no)
   • Select a model for analysis: 1. gpt-3.5-turbo 2. gpt-4
5. JSON results will be saved in the output folder.

System Design

Below is an image that illustrates the main design and workflow of the patent analysis system

The project involves the following steps:

1. Downloading a ZIP archive that contains granted patent full-text data (without images) from https://bulkdata.uspto.gov/.
2. Reading the contained patents from XML files and extracting individual XML file, parsing it to text file and saving it in data.
3. Implementing an approach based on a large language model (LLM) to extract measurements from the patents using vector store Chroma. The measurements are returned in a structured format (such as JSON).
4. Analyzing the model's results, and discussing the results/output of the models, including typical errors and points for improvement.

Directory Structure

• root/
  • data/
  • output/
  • src/
  • requirements.txt
  • LICENSE
  • README.md
  • images

Requirements

• Python 3.10+
• See requirements.txt for Python packages and versions.

License

This project is licensed under the terms of the MIT license. See LICENSE file.

Additional Resources

For further questions or if you encounter any problems, please do not hesitate to open an issue.

• My Website - More details about me and my projects.
• LinkedIn - See the post related to machine learning and software development on LinkedIn.
• Medium - See my posts related to ML/AI, algorithms, and system design.