Multi-Objective Patent Claim Interpretation for Infringement Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing patent documents require human experts for interpretation, leading to inefficiencies in tasks such as determining claim infringement.
Innovation Solution
A computer-implemented method using language models to optimize the interpretation of patent claims by minimizing an objective function through multiple interpretations, evaluating sub-objective functions with product and prior art data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human experts interpret patent claims, then interpretation accuracy is maintained, but processing time and cost increase significantly
Solution Approach 1:
The patent replaces the mechanical system of human expert interpretation with an automated computer-implemented method using language models. The system generates multiple interpretations of patent claims and evaluates them against product data and prior art to determine infringement, substituting human cognitive processing with automated AI-based analysis.
Solution Approach 2:
The patent creates multiple copies or variants of patent claim interpretations by generating a plurality of interpretations through language models. These multiple interpretations are then evaluated against each other and against product data, allowing the system to replicate and compare human-like analytical reasoning without requiring actual human experts.
2Reliability
If multiple interpretations are generated and evaluated, then analysis thoroughness improves, but computational complexity increases
Solution Approach 1:
The patent segments the complex analysis task into distinct sub-objective functions: generating multiple interpretations, evaluating each interpretation against product data, evaluating against prior art, and determining infringement. This segmentation allows the system to manage computational complexity by processing tasks in manageable, discrete steps rather than as a single monolithic operation.
Solution Approach 2:
The patent employs partial action by generating a limited number of interpretations (e.g., top 5 interpretations) rather than exhaustively analyzing all possible interpretations. This approach balances analysis thoroughness with computational efficiency, evaluating sufficient interpretations to achieve reliable results without the excessive computational burden of complete exhaustive analysis.
Data Source
AI summary
A computer-implemented method: (A) receives product data identifying a product; (B) (B) receives patent data identifying a patent claim; (C) receives prior art data identifying a set of prior art, (D) executes an optimization process to minimize an objective function, the objective function including: a first sub-objective function and a second sub-objective function, the executing the optimization process comprising: (D)(1) generating a plurality of interpretations of the patent claim, (D)(2) for each particular interpretation in the plurality of interpretations of the patent claim: (D)(2)(a) using a first language model to evaluate the first sub-objective function with the product data and the particular interpretation as inputs to the first sub-objective function, (D)(2)(b) using a second language model to evaluate the second sub-objective function with the prior art data and the particular interpretation as inputs to the second sub-objective function.