Associative Memory Query Module for Ambiguity Detection
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Solution Overview
Problem
Ambiguous data in associative memory systems leads to confusion, errors, and inaccurate analysis, as existing technologies struggle to distinguish between multiple category associations and often require manual investigation, which is time-consuming and prone to errors.
Innovation Solution
A system and method that utilize an associative memory with a query module to perform open queries across multiple perspectives, identifying and displaying potential ambiguities by analyzing data through direct and indirect relationships, allowing for automatic detection and resolution of ambiguities within the memory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual investigation is used to identify ambiguous data, then accuracy of ambiguity detection can be improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The patent replaces manual mechanical investigation with an automated computer-based system that uses algorithms to detect ambiguous data. The system automatically queries databases, compares data across multiple categories, and identifies ambiguities without human intervention, thereby maintaining detection accuracy while dramatically reducing time consumption.
Solution Approach 2:
The system enables self-service by allowing the database to query itself across multiple categories and perspectives. The automated ambiguity detection system independently identifies ambiguous data points by comparing them against multiple classification schemes, eliminating the need for external manual verification while maintaining high accuracy.
2Reliability
If comprehensive multi-perspective querying is performed to identify all potential ambiguities, then completeness of ambiguity detection is improved, but system complexity and computational resources increase
Solution Approach 1:
The patent segments the ambiguity detection process into distinct modular components: data retrieval modules for different database tables, comparison modules for different category pairs, and analysis modules for different ambiguity types. This segmentation allows the system to perform comprehensive multi-perspective querying while managing complexity through organized, reusable components.
Solution Approach 2:
The system employs universal query structures and comparison algorithms that can be applied across multiple database tables and category types. The same core ambiguity detection logic works universally for different data perspectives, reducing overall system complexity while maintaining comprehensive detection capability.
3Productivity
If automated ambiguity detection system is implemented, then productivity and speed of ambiguity identification are improved, but initial system development cost and technical complexity increase
Solution Approach 1:
The patent implements preliminary action by pre-defining category relationships, query structures, and comparison criteria before actual ambiguity detection begins. The system is pre-configured with knowledge of available database categories and their relationships, allowing rapid automated detection without requiring complex real-time decision-making, thus improving productivity while managing development complexity.
Data Source
AI summary
A system comprising an associative memory, an input module, a query module, and a display module. The input module is configured to receive a value within a first perspective of the associative memory. The query module is configured to perform an open query of the associative memory using the value, perform the open query within at least one of an insert perspective and a second perspective of the associative memory. The at least one of the insert perspective and the second perspective has as many or more category associations for the value relative to the first perspective. The display module is configured to display a result of the query and to display a list of one or more potential ambiguities that result from the open query.