AI-Guided Invention Disclosure Generation From Code And Documents
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Solution Overview
Problem
Traditional methods of generating invention disclosures are time-consuming, prone to errors, and lack consistency, especially for complex technical inventions, leading to inefficiencies and potential delays in patent applications.
Innovation Solution
An AI-guided invention disclosure generation system that integrates programmatic management and AI engines to transform input data and source code into a structured disclosure, using guided and constrained AI prompts to ensure accuracy and completeness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual drafting methods are used to prepare invention disclosures, then inventors can capture technical details and advantages, but the process becomes very time-consuming and may lead to incomplete disclosures
Solution Approach 1:
The patent replaces the manual mechanical drafting process with an automated AI-based system. The system uses natural language processing and machine learning models to automatically generate invention disclosures from technical documentation, code repositories, and project data, eliminating the need for manual writing while maintaining or improving completeness and accuracy.
Solution Approach 2:
The invention disclosure system enables self-service by automatically extracting technical details, advantages, and embodiments from source code and documentation without requiring inventor intervention. The system autonomously performs analysis, synthesis, and drafting, allowing inventors to review and approve rather than manually create disclosures.
2Loss of information
If inventors focus on gathering and compiling detailed information from multiple sources, then the invention disclosure becomes more complete, but the manual formatting and refinement process becomes overwhelming
Solution Approach 1:
The patent segments the invention disclosure generation into distinct automated components: information extraction from source code and documentation, technical detail identification, advantage analysis, embodiment extraction, and structured assembly. Each component handles specific tasks independently, reducing the complexity of the overall process while preserving all technical information.
Solution Approach 2:
The system introduces an AI-based intermediary layer between the source materials (code, documentation) and the final invention disclosure. This intermediary automatically processes raw technical information, extracts relevant details, and transforms them into properly formatted disclosure sections, eliminating the need for inventors to manually manage the complex formatting and refinement process.
3Reliability
If invention disclosures are prepared manually, then inventors can review and refine the content, but errors such as omissions, inconsistencies, and formatting mistakes increase
Solution Approach 1:
The patent implements automated feedback mechanisms where the system continuously validates extracted information against source code and documentation, checks for consistency across different disclosure sections, and verifies formatting compliance. This automated feedback loop identifies and corrects errors such as omissions and inconsistencies before finalizing the disclosure, improving accuracy while maintaining ease of operation.
4Manufacturing precision
If inventors spend significant time formatting and refining invention disclosures, then the quality may improve, but less time remains for focusing on technical aspects of innovation
Solution Approach 1:
The patent replaces the manual mechanical process of formatting and refining disclosures with automated computational processes. AI models handle text generation, formatting, consistency checking, and quality assurance, freeing inventors from these time-consuming tasks and allowing them to focus on technical innovation and creative problem-solving activities.
Data Source
AI summary
An invention disclosure generation system and method transforms information including documents and source code snippets into an invention disclosure. Input data is received from the user interface of an invention disclosure generation platform, where input data includes project documentation, source code, and innovation map. An AI engine analyzes the input data to extract novel ideas. AI engine uses a RAG process that retrieves one or more relevant source code snippets from source code. The AI engine further uses one or more embedding models to generate full context from project documentation. A novelty identification module processes source code snippets and full context to identify novel ideas. A user reviews identified novel ideas to determine alignment with input data. A patentability assessment module generates patentability scores for reviewed novel ideas based on historical patent data and predefined innovation categories.