NLU Meaning Search Elastic Thresholds
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Solution Overview
Problem
Natural Language Understanding (NLU) systems face challenges with static search scopes and resource-intensive searches, leading to premature pruning of potential meaning matches and depth-first tree traversal biases, which limits the effectiveness of meaning searches.
Innovation Solution
The implementation of a dynamic focus threshold and progressive search-criteria prediction functions, along with elastic thresholds and forcible diversity, to manage resource consumption and prevent premature pruning, allowing for a more comprehensive search of meaning representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a static search scope is used in NLU systems, then the search process is simple and resource-efficient, but potential meaning matches are prematurely pruned and search effectiveness is limited
Solution Approach 1:
The patent implements dynamic search scope adjustment by introducing a focus threshold that adapts during the search process. The search scope expands or contracts based on the quality of matches found and resource availability, transforming the static search parameter into a dynamic one that responds to search progress and system state
Solution Approach 2:
The system changes the search scope parameter dynamically during execution. The focus threshold serves as a controllable parameter that adjusts the breadth of search, allowing the system to transition between narrow and wide search scopes based on match quality and resource constraints
2Productivity
If a comprehensive search of all meaning representations is performed, then more potential matches are found, but resource consumption increases significantly
Solution Approach 1:
The system performs partial search by selectively exploring meaning representations based on the focus threshold. Instead of exhaustively searching all possibilities, it concentrates computational effort on the most promising search paths while pruning less likely matches, achieving acceptable completeness with reduced resource usage
Solution Approach 2:
The search intensity dynamically adjusts based on resource availability and match quality. When resources are constrained or good matches are found, the search scope contracts. When resources are abundant or matches are scarce, the scope expands, creating a dynamic balance between completeness and efficiency
3Productivity
If depth-first tree traversal is used in meaning search, then the search follows a focused path, but biases the search toward certain branches and may miss alternative meanings
Solution Approach 1:
The patent introduces dynamic switching between search strategies. The system can transition from depth-first traversal to breadth-first exploration based on the focus threshold and search progress, allowing it to adapt its traversal behavior rather than being locked into a single fixed approach
Solution Approach 2:
The search behavior parameter changes dynamically. The focus threshold controls the degree of exploration versus exploitation, allowing the system to adjust its traversal strategy to balance between following promising paths and exploring alternative branches
Data Source
AI summary
The present disclosure is directed to an agent automation framework that is capable of extracting meaning from user utterances and suitably responding using a search-based natural language understanding (NLU) framework. The NLU framework includes a meaning extraction subsystem capable of detecting multiple alternative meaning representations for a given natural language utterance. Furthermore, the NLU framework includes a meaning search subsystem that enables elastic confidence thresholds (e.g., elastic beam-width meaning searches), forced diversity, and cognitive construction grammar (CCG)-based predictive scoring functions to provide an efficient and effective meaning search. As such, the disclosed meaning extraction subsystem and meaning search subsystem improve the performance, the domain specificity, the inference quality, and/or the efficiency of the NLU framework.