Query Response Generation Using Knowledge Database Intermediary
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Solution Overview
Problem
Current computing systems face challenges in effectively extracting useful information from large datasets due to issues like data volume, accuracy, and linguistic ambiguities, where similar words in different languages or dialects can represent different concepts, making it difficult to generate accurate query responses.
Innovation Solution
A computing system that utilizes AI servers to ingest content, extract knowledge, and interact with user devices by facilitating the generation and utilization of knowledge through a network, involving modules like collections, identigen entigen intelligence, and query modules to interpret queries, gather content, and provide responses, using techniques such as pattern recognition and statistical reasoning to overcome ambiguities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pattern recognition techniques are used to process text, then the system can attempt to overcome linguistic ambiguities, but the accuracy of query responses deteriorates due to similar words representing different concepts in different languages or dialects
Solution Approach 1:
The patent introduces an intermediary knowledge base that mediates between the query and the stored data. This knowledge base contains pre-processed relationships and contextual information that helps disambiguate similar words across different languages and dialects, enabling more accurate query responses without requiring overly complex pattern recognition algorithms.
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing data in a knowledge base before actual query processing. This includes pre-establishing relationships between concepts, pre-translating common terms across languages, and pre-organizing data structures that facilitate quick and accurate matching during query execution, thereby improving response accuracy without real-time computational complexity.
2Quantity of substance
If the volume of available data is increased, then more information is available for analysis, but the difficulty of extracting useful information increases due to data volume and variances in text interpretation
Solution Approach 1:
The patent segments the large volume of data into manageable units within the knowledge base, organizing information by categories, relationships, and contextual metadata. This segmentation allows the system to process and extract useful information from large datasets more efficiently by breaking down complex data structures into smaller, more manageable components that can be queried and analyzed systematically.
Solution Approach 2:
The system changes parameters by transforming raw text data into structured knowledge representations with standardized attributes and relationships. This parameter transformation includes normalizing text formats, establishing consistent entity identifiers, and creating standardized relationship schemas, which simplifies the extraction of useful information from voluminous data while maintaining accuracy across different languages and dialects.
3Ease of operation
If grammar based techniques are used to classify words into grammatical types, then the system can force words to support grammatical operations, but the ability to identify what a word is actually trying to describe deteriorates
Solution Approach 1:
The knowledge base acts as an intermediary that bridges grammatical structure and semantic meaning. It stores pre-established relationships between words, phrases, and concepts, allowing the system to go beyond grammatical classification to identify the actual meaning and intent of words in context, thereby improving precision of word meaning identification while maintaining ease of operation through pre-processed data structures.
Data Source
AI summary
A method performed by a computing device includes identifying a first set of sentiment identigens of a plurality of sets of identigens for a query that includes a string of words, where the first set of sentiment identigens represents different meanings of a first sentiment word of the string of words. The method further includes selecting an entigen group from a knowledge database based on the plurality of sets of identigens, where a first sentiment entigen of the entigen group corresponds to the first set of sentiment identigens. The method further includes generating a response entigen group based on the entigen group, where a response entigen of the response entigen group corresponds to a selected identigen from a set of identigens of the plurality of sets of identigens regarding a word of the string of words.