Symbolic Query Interpretation Using Equation Packages and Semantic Memory
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Solution Overview
Problem
Existing data processing systems face challenges in generating useful information from large volumes of data due to issues such as data accuracy and variance in word interpretation across languages and dialects, leading to inefficiencies in pattern recognition and grammatical analysis.
Innovation Solution
A computing system that utilizes AI servers to ingest content, extract knowledge, and interact with user devices to facilitate the generation and utilization of knowledge, including pattern recognition techniques that classify words into grammatical types and apply statistical reasoning to provide accurate responses to queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pattern recognition techniques and statistical reasoning are used to process text, then the system can attempt to overcome word ambiguities, but the accuracy of interpretation remains insufficient due to variance in word meaning across languages and dialects
Solution Approach 1:
The patent introduces a knowledge base as an intermediary component that stores predefined relationships between words, concepts, and their meanings. This knowledge base mediates between the raw text input and the interpretation output, providing a structured reference that reduces ambiguity without requiring overly complex pattern recognition algorithms. The knowledge base acts as a mediator that translates varied linguistic expressions into standardized conceptual representations.
Solution Approach 2:
The system changes the approach from purely statistical pattern recognition to a hybrid approach that incorporates semantic parameter relationships. By representing words and concepts with their semantic parameters and relationships in the knowledge base, the system can interpret text more accurately by considering the semantic context and relationships rather than just statistical patterns, thereby improving interpretation accuracy without proportionally increasing computational complexity.
2Quantity of substance
If the system processes large volumes of data, then more information is available, but data accuracy and interpretation consistency deteriorate due to variance in word usage
Solution Approach 1:
The patent extracts and pre-processes key information from large volumes of data into a structured knowledge base. Rather than processing all raw data directly, the system extracts essential relationships, word meanings, and conceptual connections and stores them in the knowledge base. This extraction process filters out noise and inconsistencies, allowing the system to handle large data volumes while maintaining high accuracy by relying on the pre-processed, validated knowledge representations.
Solution Approach 2:
The system performs preliminary action by pre-processing and structuring data into the knowledge base before actual query processing. This preliminary organization of data into meaningful relationships and concepts allows the system to quickly and accurately interpret new data without re-analyzing everything from scratch, thereby maintaining high accuracy even when processing large volumes of data.
3Ease of operation
If grammatical analysis is used to classify words into types, then the system can construct grammatical sentences, but it cannot accurately identify what words are trying to describe
Solution Approach 1:
The knowledge base serves multiple functions simultaneously: it provides grammatical structure information, semantic meaning relationships, and contextual interpretation guidelines. By making the system multi-functional, it can both construct grammatical sentences and accurately identify word meanings without requiring separate specialized systems, thereby improving word meaning identification while maintaining ease of operation.
Solution Approach 2:
The knowledge base acts as an intermediary that bridges grammatical analysis and semantic interpretation. It translates grammatical structures into meaningful conceptual representations by providing the semantic context and relationships between words. This mediator allows the system to move beyond mere grammatical construction to accurate meaning identification by incorporating semantic information from the knowledge base.
Data Source
AI summary
A method performed by a processor includes identifying a symbolic representation of a query of a topic to produce a plurality of tokens. The method further includes generating a first equation package for the plurality of tokens that corresponds to a first permutation of a plurality of permutations of interpretation of the plurality of tokens based on one or more different meanings of the symbolic representations of the query. The method further includes updating, utilizing a symbolic representation memory, the first equation package for the plurality of tokens that optimizes an interpretation confidence level for the plurality of tokens to produce a second equation package that includes a sequence of second selected equation elements that corresponds to a second permutation of the plurality of permutations of interpretation of the plurality of tokens representing a most likely interpretation of the query.