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Welcome to PatentBot! 🚀🤖

Automating Patent Documentation with Natural Language Processing

Inspiration

Patents play a crucial role in protecting and incentivizing innovation by granting exclusive rights to inventors. However, the process of creating and documenting patents can be time-consuming and complex, often requiring legal expertise and specialized knowledge. The inspiration behind creating PatentBot stemmed from recognizing the challenges and complexities involved in the patenting process. Several factors contributed to the development of PatentBot:

  • Time-consuming and labor-intensive nature: Drafting patent documentation requires significant time and effort, involving technical expertise, legal knowledge, and adherence to specific guidelines. The manual process often leads to delays and increases costs for inventors and organizations.

  • Vast amount of information: Patent offices receive a tremendous volume of patent applications and scientific publications, making it a challenge to sift through this vast amount of information to identify prior art that may be relevant to a particular invention,

  • Evolving technology landscape: Technological advancements are rapidly changing and expanding across multiple domains. Keeping up with the latest developments and ensuring comprehensive coverage in the search for prior art becomes increasingly challenging.

  • Legal expertise requirement: Patent law can be intricate, and understanding the legal nuances and requirements is crucial for successful patent applications. Many inventors and small businesses lack access to specialized legal counsel, making the process even more daunting.

  • Complexity of patent language: Patent documents often use complex technical and legal jargon, making them challenging for inventors, researchers, and even legal professionals to comprehend fully. Clear and concise communication of inventions becomes crucial to ensure accurate representation.

PatentBot harnesses the power of NLP, a branch of artificial intelligence (AI) that focuses on the interaction between computers and human language, to extract and analyze information from various sources and generate patent documentation efficiently. By employing advanced algorithms and machine learning techniques, PatentBot aims to significantly reduce the manual effort and time required for patent drafting, while ensuring accuracy, consistency, and compliance with legal requirements.

What it does

  • Automated Drafting: Leveraging its language understanding capabilities, PatentBot can automatically generate patent claims, abstracts, descriptions, and other sections of the patent document. This automation frees inventors and patent attorneys from the tedious task of manual drafting, allowing them to focus on the core aspects of their inventions.

  • Prior Art Search: PatentBot utilizes NLP techniques to conduct comprehensive searches for prior art references, which are crucial to assess the novelty and inventiveness of a patent. By analyzing vast repositories of patents databases, PatentBot assists in identifying potential conflicts or similar inventions, enhancing the quality of the patent documentation.

  • Patent Comparison: The comparison results generated by PatentBot are presented in a clear and concise manner, allowing users to visualize the relationships between patents and identify areas of overlap or potential conflicts.

How we built it

PatentBot is an advanced patent documentation tool that integrates various technologies to streamline the patenting process. Let's dive into how PatentBot utilizes each component:

  • HuggingFace Dataset: PatentBot leverages the HuggingFace dataset, specifically the HUPD (HuggingFace Unified Patent Dataset), which provides a vast collection of patent documents. This dataset serves as a valuable resource for training and fine-tuning PatentBot's language understanding capabilities.

  • Cohere for Embeddings: PatentBot utilizes Cohere, an AI platform, to generate embeddings for patent documents. By using the CohereEmbeddings API, PatentBot can convert patent texts into numerical representations that capture the semantic information and context of the documents. These embeddings capture the essence of the patents and form the basis for similarity calculations.

  • Pinecone Vector DB and Indexing: PatentBot stores the generated embeddings in Pinecone, a vector database. Pinecone enables efficient storage and retrieval of high-dimensional vectors, making it suitable for managing the embeddings generated by Cohere. PatentBot creates an index in Pinecone based on the embeddings, enabling fast and accurate retrieval of similar patents during search and comparison operations.

  • LangChain for Conversational Bot: PatentBot integrates with LangChain, a conversational AI platform, to handle chat interactions. LangChain provides the necessary interfaces to connect with OpenAI's ChatGPT API, allowing PatentBot to generate responses and engage in interactive conversations with users. This enables PatentBot to provide real-time assistance, answer questions, and guide users through the patenting process in a conversational manner.

  • ChainLit for Frontend Integration: To tie all the components together and create a user-friendly frontend, PatentBot utilizes ChainLit, a frontend development framework. ChainLit enables seamless integration of the various technologies and components into a cohesive and intuitive user interface. It facilitates the design and implementation of the frontend, allowing users to interact with PatentBot effortlessly.

  • AWS for Deployment: PatentBot is deployed on the cloud infrastructure provided by Amazon Web Services (AWS). AWS offers a scalable and reliable cloud environment that ensures PatentBot's availability, performance, and security. By leveraging AWS services, PatentBot can handle varying user loads, scale resources as needed, and provide a seamless experience for users.

  • ChatGPT for Documentation Creation: PatentBot also incorpores ChatGPT, a powerful language model developed by OpenAI, to assist in creating the project documentation. ChatGPT leverages its language generation capabilities to generate clear and concise descriptions of its own functionalities.

Challenges we ran into

There were two main challenges we ran into when developing PatentBot:

  1. Evaluating the quality of the generated embeddings. There are multiple ways to generate embeddings however evaluating their suitability for an specific task and quantify can be challenging. We approached this problem with a categorization metric in which we used CPCs as labels and using a reference text for each CPC we query the most similar embeddings in the database compare their CPC to the one of the text used as reference. Hence, we can quantify it as a classification task. Nevertheless, due to issues with ID for each patent and lack of metadata we decided to migrate from Big Patent as primary dataset to The Harvard USPTO Patent Dataset changing the requirements for evaluation and the proper definition of a categorization metric.

  2. Proper source referencing for the tasks of the PatentBot was also a challenge since during some stages of the development process, id referencing in our bot was inaccurate and in sometimes retrieved ids not present in the dataset.

Accomplishments that we're proud of

We built a Bot with multiple capabilities able to tackle three main common challenges when developing a patent: the search for prior works, patent drafting and comparing. The automation of these tasks can help reduce costs and increase efficiency in patent processes. Our bot has the potential of becoming an scalable application with high impact, that in this first version accomplished great results.

What's next for PatentBot

The main functionality that considered could be added to the PatentBot is the possibility of having users with different permissions and capabilities inside the application. This will increase robustness of the app and secure flow of information.

In addition, a further research with attorneys and eventual users of the application can give us ideas on what are the key points to enhance and functionalities to add so that PatentBot can tackle as many issues in the patent processes as possible.