IVD Kit R&D Proposal Generation Using RAG and Language Models
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Solution Overview
Problem
Existing methods for developing in-vitro diagnostic kits are inefficient and labor-intensive, requiring manual review of vast amounts of biological papers and patents to generate research and development proposals, which is impractical due to the high volume and real-time updates of published literature.
Innovation Solution
A server-based system utilizing a pre-trained language model, such as a generative model trained by RLHF, automatically generates a research and development proposal for in-vitro diagnostic kits by integrating intended use information, technical specifications, regulatory requirements, and marketability data, leveraging Retrieval-Augmented Generation (RAG) to synthesize information from various documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual review of biological papers and patents is performed to generate R&D proposals, then comprehensive information can be obtained, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated information processing system that uses natural language processing and machine learning algorithms to extract and analyze information from biological papers and patents, eliminating the need for manual review while maintaining comprehensive information gathering
Solution Approach 2:
The patent introduces an automated information processing system as an intermediary between the raw literature data and the R&D proposal generation, which automatically collects, processes, and synthesizes information from multiple sources to produce comprehensive proposals without direct human intervention in the review process
2Measurement precision
If manual review of vast amounts of literature is performed, then accurate R&D proposals can be generated, but the process becomes impractical due to high volume and real-time updates
Solution Approach 1:
The patent replaces manual literature review with an automated system that uses natural language processing and machine learning to accurately extract information from vast amounts of literature, maintaining precision while enabling practical processing of large volumes and real-time updates
Solution Approach 2:
The patent performs preliminary automated processing and filtering of literature data before proposal generation, pre-organizing and validating information sources so that accurate proposals can be generated efficiently without manual review of every document
3Reliability
If comprehensive document review is conducted to ensure proposal quality, then proposal accuracy improves, but development time increases
Solution Approach 1:
The patent replaces manual quality review processes with automated validation and verification systems that use machine learning models to assess proposal quality, ensuring comprehensive document review is performed rapidly and accurately without extending development time
Data Source
AI summary
A method for automatically generating a research and development proposal for an in vitro diagnostic kit, performed by a server for automatically generating the research and development proposal, includes: acquiring an initial proposal including intended use information for the kit; providing the acquired initial proposal to a pre-trained language model; acquiring a specification (spec) for the kit from the language model receiving the initial proposal, wherein the acquired specification for the kit includes at least one of a technical specification to be used for the research and development of the kit, a resource specification for the research and development, a design specification for the kit, a regulatory approval specification that is needed to meet for approval, and a performance specification for the kit; and controlling to generate the research and development proposal for the kit including the acquired specification and the intended use information for the kit.