Semantic-Temporal Content Mapping for Complex Document Relationships
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Solution Overview
Problem
Existing methods for identifying and analyzing relationships between documents, particularly in large collections such as patent families, are laborious, subjective, and inaccurate, often failing to recognize relevance and requiring manual review of numerous documents.
Innovation Solution
An AI-based system for content semantic and temporal analytics that leverages generative Large Language Models (LLMs) to automatically identify and organize documents based on semantic and temporal relationships, providing visualizations and interactive interfaces for user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional manual review methods are used to identify and analyze document relationships, then subjectivity and inaccuracy are reduced, but labor intensity and time consumption increase significantly
Solution Approach 1:
The patent replaces manual mechanical review processes with an automated AI-based system that performs semantic analysis, temporal relationship detection, and citation network analysis. The system automatically identifies document relationships, extracts entities, and generates visualizations without human intervention, thereby eliminating time consumption while maintaining or improving accuracy through consistent algorithmic application.
Solution Approach 2:
The patent introduces an AI-based intermediary system that acts as a mediator between the large collection of documents and the user. This intermediary automatically processes documents, identifies relationships, and presents synthesized information, eliminating the need for users to manually review numerous documents while providing accurate relationship identification through sophisticated natural language processing and machine learning algorithms.
2Reliability
If comprehensive citation network analysis is performed to capture all temporal relationships, then completeness of relationship identification is improved, but complexity of analysis and computational resources increase
Solution Approach 1:
The patent segments the complex citation network analysis into distinct modular components: temporal relationship detection, semantic similarity analysis, entity extraction, and visualization generation. Each module handles a specific aspect of the analysis independently, reducing overall system complexity while maintaining comprehensive relationship capture through the coordinated operation of these specialized sub-systems.
Solution Approach 2:
The patent creates a multi-functional AI system that simultaneously performs multiple types of analysis including semantic similarity computation, temporal relationship detection, citation network traversal, and visual presentation. This universal system handles diverse analytical tasks through integrated machine learning models, reducing the need for separate specialized systems while maintaining comprehensive relationship identification.
3Measurement precision
If detailed review of individual claim sets and specifications is conducted to determine relevance, then accuracy of relevance determination is improved, but productivity and efficiency decrease
Solution Approach 1:
The patent replaces the mechanical process of detailed manual review of claim sets and specifications with automated AI-based semantic analysis. The system uses natural language processing to compute semantic similarity between documents, extract key entities and concepts, and determine relevance automatically, thereby maintaining high accuracy in relevance determination while dramatically improving productivity by eliminating time-consuming manual analysis.
Solution Approach 2:
The patent performs preliminary automated filtering and semantic analysis of patent documents before detailed human review. The AI system pre-processes large volumes of patent data, identifies potentially relevant documents through semantic similarity computation, and organizes them by relationship types, allowing users to focus only on pre-screened candidates. This preliminary action maintains high accuracy while improving overall productivity by reducing the volume of documents requiring detailed examination.
Data Source
AI summary
The present teaching relates to identifying related content and significance thereof with respect to some specified goal. Initial content is obtained based on a specified goal. Associated content is identified based on the semantics of the initial content and semantic-based temporal information. Different temporal relations are determined based on the specified goal and are extracted from content comprising the initial and associated content. With respect to each extracted temporal relation, determine a semantic analysis to be performed based on the specified goal. A summary is automatically generated for each of the semantic analysis results and is visualized to reveal semantic and temporal relations among different related content pieces. The visualization can be adjusted according to user input.