Computational Model Operation Using Multiple Subject Representations
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Solution Overview
Problem
Current computing systems are limited in representing individual pieces of information, leading to increased time and network bandwidth requirements for users to find answers to queries, as they often need to read multiple documents to locate relevant information.
Innovation Solution
The development of computational models, such as tensor-product representation, equation-system, and graph models, allows computing devices to determine and operate on representations of statements and queries, providing relevant information directly in response to user inquiries by modeling relationships between subjects and predicates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If computing systems use traditional document representation methods, then system simplicity is maintained, but user time and network bandwidth increase when finding answers
Solution Approach 1:
The patent segments information representation into multiple computational models (tensor-product representation, equation-system, graph models) that can independently process different aspects of queries. This allows the system to divide complex information retrieval tasks into manageable segments, improving response time without requiring a single monolithic complex system.
Solution Approach 2:
The patent introduces multiple representation dimensions for the same information (document, statement, query) by using different computational models simultaneously. This multi-dimensional representation allows the system to access and process information more efficiently, reducing user search time while managing complexity through structured organization.
2Adaptability or versatility
If computing systems represent only documents and document locations, then implementation simplicity is maintained, but information representation capability deteriorates
Solution Approach 1:
The patent creates a universal computational framework where the same system architecture can handle multiple types of information representation (documents, statements, queries) using unified computational models. This multi-functional approach enhances adaptability while avoiding the need for separate specialized systems for each information type.
Solution Approach 2:
The patent introduces computational models as intermediary structures that mediate between raw information (documents, statements) and user queries. These intermediaries transform and represent information in multiple ways, enabling versatile information representation without directly increasing system structural complexity.
3Loss of information
If users read multiple documents to find answers, then search comprehensiveness is improved, but network bandwidth and time consumption increase
Solution Approach 1:
The patent implements feedback mechanisms where computational models process queries and return targeted information based on user needs. The system receives query input, processes it through computational models, and provides feedback responses that directly address user information needs, reducing unnecessary data transmission and saving network bandwidth while maintaining information completeness.
Solution Approach 2:
The patent extracts relevant information directly from computational models that have already processed and organized data, rather than requiring users to read through entire documents. This extraction approach provides complete information without the network bandwidth and time costs of transmitting and processing full document contents.
Data Source
AI summary
A processing unit can determine multiple representations associated with a statement, e.g., subject or predicate representations. In some examples, the representations can lack representation of semantics of the statement. The computing device can determine a computational model of the statement based at least in part on the representations. The computing device can receive a query, e.g., via a communications interface. The computing device can determine at least one query representation, e.g., a subject, predicate, or entity representation. The computing device can then operate the model using the query representation to provide a model output. The model output can represent a relationship between the query representations and information in the model. The computing device can, e.g., transmit an indication of the model output via the communications interface. The computing device can determine mathematical relationships between subject representations and attribute representations for multiple statements, and determine the model using the relationships.