Semantic search and summarization for electronic documents

This invention describes a system for finding specific information within electronic documents. It works by taking a question you ask in plain language, converting it into a computer-readable code (an 'embedding'), and then matching that code against similar codes representing parts of documents to find the most relevant sections. One specific version of this system is designed to search within unsigned electronic contracts that contain special markers for signatures.

Why it matters: Filed in 2023, this system relies on contextualized embeddings and natural language processing. By 2026, the rapid advancements in large language models (LLMs) have made generating high-quality embeddings and performing sophisticated summarization significantly more accessible and efficient, potentially simplifying implementation and improving performance.

Status: Under examinationA pending application. It has not been granted, and it has not been abandoned.
How hard to build: SpecializedAI/ML software development, document indexing

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