Document Sequence Optimization for Cross-Document Retrieval
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Solution Overview
Problem
Document retrieval processes in various industries face inefficiencies and bottlenecks due to the need to gather and analyze information from multiple sources in a sequential manner, leading to time-consuming and resource-intensive operations, even when automated systems are used.
Innovation Solution
The implementation of machine learning models, such as entity-document correlation and document sequence optimization models, to identify and optimize the sequence of related document objects, enabling intelligent inference of annotative and sequential relationships across documents, thereby streamlining the retrieval process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sequential gathering and analysis of information from multiple document sources is performed, then comprehensive information retrieval is achieved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing documents to extract entities, relationships, and metadata before retrieval is needed. Document embeddings are pre-computed and stored, allowing the retrieval system to quickly query pre-processed information without performing full sequential analysis during the actual retrieval operation, thus reducing time consumption while maintaining comprehensive information retrieval.
Solution Approach 2:
The patent replaces the mechanical sequential gathering and analysis process with machine learning-based automated systems. Entity recognition models, relationship extraction models, and embedding-based retrieval systems substitute manual or rule-based sequential processing, enabling parallel computation and significantly reducing time consumption while maintaining or improving retrieval comprehensiveness.
2Reliability
If sequential gathering and analysis of information from multiple document sources is performed, then comprehensive information retrieval is achieved, but operational complexity and resource intensity increase
Solution Approach 1:
The system merges multiple separate operations (entity extraction, relationship identification, document analysis) into an integrated machine learning pipeline. By combining these functions into unified models that process documents end-to-end, the system reduces operational complexity while maintaining comprehensive information retrieval capabilities.
Solution Approach 2:
The system implements self-service through automated entity recognition and relationship extraction without requiring manual configuration or intervention. The machine learning models autonomously identify entities, infer relationships, and structure information from unstructured documents, reducing operational complexity while ensuring comprehensive retrieval.
3Productivity
If machine learning models are used to identify and optimize document sequences, then retrieval efficiency is enhanced, but computational costs increase
Solution Approach 1:
The system applies partial action by using machine learning models selectively - primarily for generating initial document sequences and embeddings, rather than continuously re-processing all documents. Once embeddings are generated, retrieval operations leverage these pre-computed representations through efficient similarity searches, reducing computational costs while maintaining high retrieval efficiency.
Solution Approach 2:
The patent uses copying by creating document embeddings - compressed numerical representations of document content - that can be stored and reused multiple times. Instead of re-processing original documents for each retrieval query, the system copies and reuses pre-computed embeddings, significantly reducing computational costs while maintaining retrieval efficiency.
4Reliability
If machine learning models infer annotative relationships between documents, then document retrieval reliability is improved, but device complexity increases
Solution Approach 1:
The system segments the complex task of relationship inference into distinct machine learning components: entity recognition models identify specific entities in text, relationship extraction models identify connections between entities, and embedding models capture semantic relationships. This segmentation allows each component to be optimized independently while working together to improve document retrieval reliability.
Data Source
AI summary
Systems and methods are configured to generate a set of related document objects for a predictive entity and/or to generate an optimal document sequence for a set of related document objects. In one embodiment, a set of related document objects for a predictive entity is generated by processing entity metadata features associated with the predictive entity using an entity-document correlation machine learning model, and an optimal document sequence is generated for the set of related document objects by processing the set of related document objects using a document sequence optimization machine learning model.