Reasoning Interface Layer for Contextual Query Enhancement
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Solution Overview
Problem
Current natural language understanding (NLU) systems fail to consider and process contextual information, such as user preferences and environmental conditions, leading to incomplete or inaccurate query responses.
Innovation Solution
A method and system that determine candidate reasoners based on received queries and contextual information, generating additional query fields using a reasoning interface layer and an intelligent knowledge layer, which merges and prioritizes these fields based on confidence scores to provide enhanced queries, allowing for dynamic selection of sources and consideration of multiple contextual factors like location, time, and user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If NLU systems translate user speech directly into a query without considering contextual information, then the system operation is simple and fast, but the query response is incomplete or inaccurate
Solution Approach 1:
The patent introduces a reasoning interface layer as an intermediary between the NLU system and the query processing system. This layer receives the initial query, determines candidate reasoners based on the query and contextual information, generates additional query fields, and merges results with confidence scores. This intermediary structure enables accurate contextual reasoning without requiring complete system redesign, thus improving query response accuracy while managing system complexity.
2Measurement precision
If multiple candidate reasoners are determined and additional query fields are generated for each, then the query processing becomes more comprehensive and accurate, but the processing time and computational resources increase
Solution Approach 1:
The patent implements partial action by determining a limited number of candidate reasoners (e.g., top 3) rather than processing all possible reasoners. The system generates additional query fields for each candidate reasoner but merges them with confidence scores, allowing the system to process multiple perspectives while limiting computational overhead through selective processing and confidence-based filtering.
Solution Approach 2:
The system performs preliminary action by determining candidate reasoners and generating additional query fields before executing the final query processing. This preliminary structuring of reasoning paths allows the system to prepare multiple potential solutions in advance, then efficiently select and merge the most relevant ones using confidence scores, reducing overall processing time.
3Adaptability or versatility
If contextual information from sensors and user settings is incorporated into query processing, then the results are more personalized and accurate, but the system complexity and data processing requirements increase
Solution Approach 1:
The reasoning interface layer serves multiple functions: it determines candidate reasoners, generates additional query fields, merges results with confidence scores, and handles contextual information from various sources. This multi-functional design allows the system to incorporate diverse contextual data (sensor information, user settings, preferences) through a single unified interface, enabling personalization without proportionally increasing overall system complexity.
Data Source
AI summary
In an embodiment, a method includes determining, based on a received query and contextual information, candidate reasoners to respond to a received query to select a candidate reasoner. A reasoner or candidate reasoner is a module that translates information from a sensor, user settings, or other source, into additional or revised fields for a query. The method further includes generating, at each candidate reasoner determined, additional or revised query fields based on the contextual information and a rule of a rule database. The method further includes merging the additional query fields for each candidate reasoner based on a confidence score or other metric of each corresponding candidate reasoner. The confidence score can be based on applicability of the contextual information to the received query. The method further includes providing an enhanced query having the additional or revised query fields. Such a method enhances natural language understanding by supplementing queries with contextual information.