Contextual-Causal Document Analysis for Duplication and Gap Detection
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Solution Overview
Problem
Conventional document comparison methods relying on keyword matching fail to understand the substantial content of documents, leading to inaccurate responses due to lexical and structural variability, hindering effective duplication and gap detection.
Innovation Solution
A method involving contextual and causal analysis of documents using transformer-based models and Graph Neural Networks for retrieval, capsule networks for hierarchical understanding, Siamese networks for duplication detection, and zero-shot learning for gap detection, generating responses through large language models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If keyword matching is used for document comparison, then the method is simple and fast, but it cannot understand the substantial content and leads to inaccurate responses
Solution Approach 1:
The patent replaces the mechanical keyword matching system with an AI-based contextual analysis system using transformer models. This substitution enables the system to understand semantic meaning and context rather than just matching keywords, thereby improving accuracy while maintaining efficiency through automated AI processing
Solution Approach 2:
The patent changes the fundamental parameters of document comparison from keyword-based to context-based analysis. By using transformer models to generate contextual representations and compare semantic meanings, the system transforms the comparison mechanism to handle lexical variability and structural differences accurately
2Ease of manufacture
If simple keyword matching is used, then the system is easy to implement, but it fails to handle lexical variability and document structural variability
Solution Approach 1:
The patent implements a universal document comparison system using transformer models that can handle multiple types of variability (lexical, structural, semantic) within a single framework. The model serves multiple functions: understanding context, identifying synonyms, detecting structural relationships, and generating accurate comparisons, replacing the need for multiple specialized systems
Solution Approach 2:
The patent introduces transformer models as an intermediary between the input documents and the comparison output. This intermediary layer processes and interprets the complex variability in documents, translating them into comparable contextual representations that enable accurate duplication and gap detection despite differences in wording and structure
Data Source
AI summary
The present disclosure relates to a method, a device, and a computer program product for generating a response about a document. The method includes: according to a first document, retrieving multiple second documents associated with the first document; determining duplication between the first document and the multiple second documents through contextual analysis of the first document and the multiple second documents; determining gaps between the first document and the multiple second documents through causal analysis of the first document and the multiple second documents; and generating a response about the first document according to the duplication and the gaps using a language model. In this way, by considering both the context and the causality in various documents, it is possible to conduct duplication and gap detection for more complex documents, and thus generate a more accurate response about the document.