Document Entity Comparison for Patent Dependency Analysis
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Solution Overview
Problem
Existing search algorithms for identifying entities in documents, particularly in patent searching, lack the ability to provide comprehensive insights into entity dependencies and correlations between documents, which hinders effective assessment of patentability and validity.
Innovation Solution
A document comparison system utilizing a processor and memory device to identify and compare entities across documents, employing a hierarchical model and dictionary to determine entity dependencies and correlations, facilitating assessments of patentability and validity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional search algorithms are used to identify entities in documents, then the search process is simple and fast, but the ability to provide comprehensive insights into entity dependencies and correlations is insufficient
Solution Approach 1:
The system segments the analysis process into distinct modules: entity identification module, entity dependency analysis module, and correlation assessment module. This segmentation allows the system to handle complex analysis tasks through manageable components, preserving entity dependency information while maintaining operational feasibility
Solution Approach 2:
The system introduces intermediary data structures including entity graphs, dependency matrices, and correlation vectors that mediate between simple entity identification and comprehensive dependency analysis. These intermediaries preserve detailed entity relationship information without requiring the entire system to become overly complex
2Measurement precision
If comprehensive entity dependency analysis is performed across documents, then the accuracy of patentability assessment is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary entity identification and extraction before conducting full dependency analysis. By pre-processing documents to identify key entities and their basic relationships, the system reduces the computational burden of subsequent comprehensive analysis, maintaining high assessment accuracy while reducing overall processing time
Solution Approach 2:
The system implements a tiered analysis approach where only critical entity dependencies are analyzed in full detail, while less important relationships receive simplified processing. This partial action strategy maintains patentability assessment accuracy for key innovations while reducing unnecessary computational overhead
3Loss of information
If detailed entity comparison is conducted between documents, then the insights into document relationships are enhanced, but the complexity of the comparison process increases
Solution Approach 1:
The system applies different levels of comparison detail to different entity pairs based on their local importance and context. Critical entities receive comprehensive dependency analysis while less important entities use simplified comparison methods, preserving essential document correlation information while managing system complexity
Data Source
AI summary
The present disclosure relates to a document comparison system (100) for comparing a first document (D1) with a second document (D2). The first document (D1) includes at least a first section having at least one first section entity (CEn), and the second document (D2) includes at least one second document entity (CEn). The document comparison system includes a processor (110) and a memory device (120). The processor is configured to identify the at least one first section entity (CEn) in the first section of the first document (D1). The processor conducts a search of the second document (D2) to identify the presence or absence of one or more second document entity (CEn) corresponding to or equivalent to each first section entity (CEn) identified in the first section. The present disclosure also relates to a dictionary generating system; a document comparison method; a dictionary populating method; and non-transitory computer-readable medium.