AI Patentability Evaluation Using Positive-Negative Opinion Generation
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Solution Overview
Problem
Existing techniques for determining the possibility of acquiring intellectual property rights fail to sufficiently enhance user satisfaction with the determination result, as they rely solely on ranks and degrees of coincidence with similar documents.
Innovation Solution
An information processing apparatus and method utilizing three large language models to generate positive and negative opinions and a conclusion regarding the possibility of acquiring intellectual property rights, based on analysis target information and related art, enhancing user satisfaction through comprehensive evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If only rank and degree of coincidence with similar documents are used for determination, then the evaluation process is simple and fast, but user satisfaction with the determination result is insufficient
Solution Approach 1:
The evaluation process is segmented into multiple independent modules: related art acquisition unit, positive opinion generation unit, negative opinion generation unit, and conclusion generation unit. Each module performs a specific function, allowing the system to maintain speed while improving comprehensiveness through modular architecture.
Solution Approach 2:
The patent merges multiple evaluation perspectives (positive and negative opinions) with traditional evaluation methods (rank and degree of coincidence) to create a comprehensive determination system. This combination allows the system to leverage the speed of automated retrieval while adding the depth of multi-perspective analysis through large language models.
2Reliability
If comprehensive multi-perspective evaluation is implemented using multiple large language models, then user satisfaction with determination results is enhanced, but device complexity and processing time increase
Solution Approach 1:
The patent employs multiple large language models that can be configured to perform different functions (positive opinion generation, negative opinion generation, conclusion generation). This multi-functionality approach allows a single system architecture to handle multiple evaluation tasks, reducing overall system complexity while maintaining comprehensive evaluation capabilities.
Solution Approach 2:
The related art information acts as an intermediary between the analysis target information and the large language models. By first acquiring and preparing related art information, the system creates a structured intermediate representation that simplifies the input to the models, thereby reducing the complexity of the evaluation process.
3Measurement precision
If multiple large language models are used to generate balanced positive and negative opinions, then determination accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The related art acquisition unit performs preliminary action by pre-acquiring and organizing related art information before the evaluation process begins. This preparation step reduces the computational burden on the large language models during the actual evaluation, thereby decreasing processing time while maintaining high determination accuracy through comprehensive multi-perspective analysis.
Data Source
AI summary
An information processing apparatus includes a related art acquisition unit that acquires related art information indicating related art related to an analysis target intellectual property based on analysis target information, a positive opinion generation unit that generates a positive opinion regarding a possibility of acquiring a right to the analysis target intellectual property based on the analysis target information and the related art information by using a first model, a negative opinion generation unit that generates a negative opinion regarding the possibility based on the analysis target information and the related art information by using a second model, and a conclusion generation unit that generates a conclusion regarding the possibility based on the positive opinion and the negative opinion by using a third model. This information processing apparatus enhances patentability evaluation through AI-driven decision making support.