Edge-Based AI Validity Determination for Multi-Jurisdictional IP Rights
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Solution Overview
Problem
Determining the validity or enforceability of intellectual property rights is a time-consuming and laborious process due to the complexity of statutes, regulations, and operational rules across multiple jurisdictions.
Innovation Solution
A method and system that utilize AI-driven, multi-jurisdictional, edge-based validity determination by converting logic conditions from spoken language into predicates conforming to first-order predicate calculus, training these predicates with specific facts, and deploying them to an edge device for logical inference processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual due diligence is performed to determine validity or enforceability of intellectual property rights, then accuracy and thoroughness of evaluation is improved, but time consumption and labor requirements increase significantly
Solution Approach 1:
The patent replaces manual legal analysis with an AI-driven automated system that uses natural language processing to extract logic conditions from statutes and regulations, and employs machine learning models to evaluate IP right validity. This substitution of mechanical manual processing with automated AI systems achieves both high accuracy and reduced time consumption.
Solution Approach 2:
The system enables self-service evaluation by allowing users to input IP right information and automatically receiving validity assessments without requiring manual legal expert intervention. The automated AI system performs the evaluation independently, making the process efficient and accessible.
2Reliability
If comprehensive analysis of multiple statutes, regulations, and operational rules across jurisdictions is performed, then evaluation thoroughness is improved, but process complexity and labor requirements increase
Solution Approach 1:
The patent creates a universal AI system that can handle multiple jurisdictions, statutes, regulations, and operational rules through a single integrated platform. The system uses multi-lingual natural language processing and unified machine learning models to evaluate IP rights across different legal frameworks, reducing process complexity while maintaining comprehensive analysis.
Solution Approach 2:
The system introduces AI algorithms as intermediaries between the complex legal frameworks and the evaluation process. The natural language processing and machine learning models act as mediators that automatically interpret and apply multiple statutes and regulations, simplifying the overall process while ensuring thorough evaluation.
3Productivity
If automated systems are used to evaluate intellectual property rights, then processing speed and efficiency are improved, but system complexity and development requirements increase
Solution Approach 1:
The patent employs preliminary action by pre-training machine learning models on extensive legal datasets and pre-processing statutes and regulations into structured logic conditions. This preparation enables the system to quickly evaluate new IP rights without requiring complex real-time processing, thus achieving high productivity while managing system complexity.
Solution Approach 2:
The system segments the complex evaluation process into distinct modules: natural language processing for extracting logic conditions, machine learning for pattern recognition, and validation algorithms for determining IP right validity. This segmentation allows each component to be optimized independently, improving overall efficiency while making the system more manageable.
Data Source
AI summary
A method for ascertaining the validity or enforceability of an intellectual property right is disclosed. In one embodiment, such a method identifies a jurisdiction associated with an acquired intellectual property right and identifies one or more logic conditions of the jurisdiction deemed to affect validity or enforceability of the acquired intellectual property right. The logic conditions may be encoded in a spoken language associated with the jurisdiction. The method converts the logic conditions from the spoken language into one or more predicates conforming to first order predicate calculus. The method trains, on a computing device, the one or more predicates with a particular set of facts to validate and verify a proper logical outcome. The one or more predicates are then deployed to an edge device to execute on a logical inference processor implemented on the edge device. A corresponding system and computer program product are also disclosed.