Knowledge Database Query Interpretation for Product-Service Responses
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Solution Overview
Problem
Existing systems struggle to effectively generate and utilize knowledge from large volumes of data due to issues such as data accuracy and linguistic ambiguities, leading to inefficient information retrieval.
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 and grammatical analysis to provide accurate responses to queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If pattern recognition techniques are used to process text, then the system can automatically interpret text data, but the accuracy of interpretation deteriorates due to linguistic ambiguities and regional dialects
Solution Approach 1:
The patent introduces an intermediary knowledge base that mediates between the automated pattern recognition system and the text data. This knowledge base contains pre-computed relationships between words, phrases, and concepts, allowing the system to resolve ambiguities by consulting stored knowledge rather than relying solely on statistical patterns, thus improving interpretation accuracy while maintaining automation.
Solution Approach 2:
The system incorporates feedback mechanisms where the results of text interpretation are fed back into the knowledge base for refinement. This allows the system to learn from previous interpretations and corrections, gradually improving accuracy. The feedback loop enables continuous optimization of the interpretation models based on actual usage and error correction.
2Quantity of substance
If the volume of available data is increased, then the system has more information to work with, but the difficulty of producing useful information increases due to data volume and accuracy issues
Solution Approach 1:
The patent extracts and isolates the most valuable information from the large volume of data by using the knowledge base to identify and extract only the relevant relationships and facts. Rather than processing all data uniformly, the system extracts specific patterns and knowledge that are most useful for answering queries, thereby maintaining high productivity even with large data volumes.
Solution Approach 2:
The system segments the large data volume into manageable knowledge units stored in the knowledge base. This segmentation allows the system to process information in smaller, more manageable chunks rather than attempting to analyze the entire data volume at once, improving processing efficiency and productivity.
3Stability of the object's composition
If grammatical analysis techniques are used to classify words, then the system can structure text data, but the ability to identify actual word meaning deteriorates due to forced grammatical operations
Solution Approach 1:
The knowledge base serves as an intermediary that bridges grammatical structure and actual word meaning. It contains mappings between grammatical categories and semantic meanings, allowing the system to first establish structural organization through grammatical analysis and then resolve ambiguities by consulting the knowledge base for actual word meanings and relationships.
Data Source
AI summary
A method performed by a computing device includes determining a set of identigens for each word of words of a product-service query to produce sets of identigens. The method further includes interpreting, using identigen pairing rules of a knowledge database, the sets of identigens to determine a most likely meaning interpretation of the product-service query and produce a query entigen group that includes query entigens. The method further includes identifying one or more characteristic entigen categories for a subjective category entigen of the query entigen group. The method further includes recovering a set of response entigens for the product-service query from the knowledge database utilizing the query entigen group and based on the one or more characteristic entigen categories. The set of response entigens provides an answer for the product-service query.