AI System Semantic Vector Mapping for Natural Language Understanding
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Solution Overview
Problem
Existing artificial intelligence systems fail to provide intelligent and responsive interactions due to their limitations in understanding different phrasings, terminology, sentence structure, and speech patterns, leading to inefficiencies in customer service, user interface, and interaction with individuals, particularly in areas like customer service, computing, and portable electronics.
Innovation Solution
An artificial intelligence system that extracts concepts from text and speech, using numeric representations to bypass language constraints, allowing for dynamic output generation and understanding of various patterns, enabling language-independent interaction and quick processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing automated systems use pre-prepared responses based on detected words or phrases, then the system structure is simple and easy to implement, but the system fails to understand contextual meaning and cannot properly respond to diverse user input
Solution Approach 1:
The patent transforms the system from using discrete word/phrase matching to a continuous semantic space representation where concepts are mapped to numerical vectors. This parameter change allows the system to capture contextual meaning and relationships between concepts, enabling it to understand diverse phrasings and generate appropriate responses without requiring complex rule-based structures.
Solution Approach 2:
The patent replaces the mechanical keyword-matching system with a computational semantic analysis system using numerical representations. Instead of mechanically comparing input strings against predefined patterns, the system uses vector arithmetic and geometric relationships in semantic space to understand and generate responses, achieving higher adaptability with comparable implementation complexity.
2Reliability
If the system presents a menu of options to the user, then the system can handle specific predefined scenarios, but the user must listen to all options until finding the relevant one, increasing call time and costs
Solution Approach 1:
The patent performs preliminary semantic analysis of the user's input to identify the core concept and intent before presenting any menu options. By pre-processing the input to extract meaningful concepts and their relationships, the system can directly navigate to relevant options or generate appropriate responses without requiring the user to listen through entire menus, significantly reducing call time while maintaining reliable scenario handling.
3Measurement precision
If the system requires users to respond with specific words or phrases, then the system can accurately interpret user intent, but the user experience becomes rigid and frustrating when natural language variations are used
Solution Approach 1:
The patent creates a universal semantic representation system that can process and interpret multiple linguistic expressions for the same intent. By mapping diverse words, phrases, and sentence structures to common conceptual vectors, the system achieves both precise intent interpretation and ease of operation, allowing users to speak naturally while maintaining accurate understanding of their intentions.
4Adaptability or versatility
If existing systems process natural language input directly, then the system can understand various phrasings and patterns, but the processing becomes slow and computationally intensive
Solution Approach 1:
The patent extracts only the essential semantic features and core concepts from natural language input, representing them as compact numerical vectors. By taking out and retaining only the critical meaning-bearing elements while discarding redundant linguistic details, the system achieves both comprehensive language understanding and efficient processing, improving productivity without sacrificing adaptability.
Data Source
AI summary
An artificial intelligence system and method for interpreting input from a user and generating a response to the user. The input is converted into an array of concepts which are compared to a database of interrelated concepts. A response is generated based on the concepts in the database and their relationship to the concepts in the input array. The system and method may be implemented in a number of electronic or computer devices to interact with humans or computer systems.