Contextual Data Interpretation via Multi-Dimensional Token Coordinates
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Solution Overview
Problem
Natural language processing techniques inaccurately classify and interpret documents, leading to resource-intensive computational loads and memory inefficiencies due to the retrieval of irrelevant documents, which hampers performance and display capabilities.
Innovation Solution
A system comprising a data processing arrangement that analyzes documents to determine specific domains, tokenizes sentences, determines token coordinates in a multi-dimensional space, and interprets contextual meaning using an analyzer, tokenizer, and interpreter module, leveraging an ontological databank to efficiently classify and retrieve relevant documents based on domain and language.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language processing technique is used to interpret documents, then document classification and interpretation can be performed, but classification accuracy deteriorates due to incorrect interpretation of contextual meaning
Solution Approach 1:
The patent transforms the interpretation problem from traditional natural language processing into a geometric problem by mapping tokens to coordinates in a multi-dimensional space. This dimensional transformation allows the system to capture contextual relationships that NLP alone cannot, thereby improving both classification accuracy and interpretation reliability simultaneously.
Solution Approach 2:
The patent introduces an intermediary ontological databank that serves as a bridge between raw document tokens and their contextual meanings. This databank stores pre-defined relationships and concepts, allowing the system to accurately interpret tokens by referencing their positions and relationships in the multi-dimensional space, thus resolving the accuracy-reliability contradiction.
2Quantity of substance
If natural language processing technique processes large amounts of documents, then comprehensive information can be retrieved, but resource consumption increases due to high computational load
Solution Approach 1:
The patent performs preliminary action by pre-defining the ontological databank with domain concepts, relationships, and multi-dimensional space mappings before actual document processing. This pre-computation allows the system to efficiently retrieve and interpret documents by simply querying the pre-structured space, significantly reducing computational resource consumption during actual processing while maintaining comprehensive document retrieval capability.
3Quantity of substance
If irrelevant documents are retrieved by natural language processing, then comprehensive search results can be provided, but memory efficiency deteriorates due to unnecessary RAM occupation
Solution Approach 1:
The patent replaces the traditional mechanical NLP-based filtering system with a geometric query system operating in multi-dimensional space. By representing documents and queries as points and regions in this space, the system can efficiently identify and retrieve only relevant documents through geometric operations, eliminating the need to load and process irrelevant documents in memory, thus improving memory efficiency while maintaining comprehensive search coverage.
Data Source
AI summary
A data processing arrangement is configured to obtain plurality of documents including sentences, analyze sentences of plurality of documents to determine specific domain associated with each of plurality of documents, tokenize sentences in each of plurality of documents to obtain plurality of tokens for each of plurality of documents, determine token coordinates of each of plurality of tokens, and interpret contextual meaning of each of tokens of plurality of tokens for each of plurality of documents.