Hierarchical Semantic Capability Search for AI Query Matching
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Solution Overview
Problem
Existing keyword-based search methods for identifying AI productivity tool-enablable software applications fail to account for contextual relevance and synonyms, leading to inefficiencies and increased processing resource consumption.
Innovation Solution
Implementing a semantic similarity search using a cosine similarity algorithm within a hierarchical capabilities decision tree to match user queries with the most relevant AI productivity tool capabilities, limiting comparisons based on parent-child relationships to reduce data saturation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-based search methods are used to identify AI productivity tool capabilities, then the search process is simple to implement, but contextual relevance and synonyms are not accounted for leading to reduced accuracy
Solution Approach 1:
The patent transforms the search approach from keyword-based to semantic-based by changing the parameter of comparison from literal string matching to cosine similarity scores. This allows the system to capture contextual relevance and synonyms while maintaining a structured comparison framework through vector embeddings.
Solution Approach 2:
The patent replaces the mechanical keyword-matching system with a semantic similarity system using cosine similarity algorithms. This substitution enables the system to understand contextual relationships and synonyms, significantly improving search accuracy without requiring complex manual configuration.
2Measurement precision
If semantic similarity search is implemented across all capabilities, then contextual relevance and synonyms are accounted for improving accuracy, but processing resource consumption increases
Solution Approach 1:
The patent segments the capabilities into hierarchical groups using parent-child relationships. This segmentation allows the semantic similarity search to be performed selectively within relevant subsets rather than across all capabilities, reducing processing resource consumption while maintaining search accuracy.
Solution Approach 2:
The patent performs semantic similarity search only on a subset of capabilities that are relevant to the user query, rather than exhaustively searching all capabilities. This partial action approach reduces processing resource consumption while still achieving high accuracy for the most relevant matches.
3Measurement precision
If semantic similarity search is performed across all capabilities without hierarchical filtering, then comprehensive matching is achieved, but data saturation increases processing time
Solution Approach 1:
The patent divides the capability set into hierarchical segments with parent-child relationships. This segmentation enables the search to progressively narrow down from broader categories to specific capabilities, achieving comprehensive matching without processing time proportional to the total number of capabilities.
Solution Approach 2:
The patent performs preliminary filtering using parent capabilities to identify relevant subsets before conducting detailed semantic similarity search. This preliminary action reduces the data volume for the main search operation, decreasing processing time while maintaining match comprehensiveness.
4Productivity
If hierarchical parent-child relationships are used to limit comparisons, then processing efficiency is improved, but the complexity of the search structure increases
Solution Approach 1:
The patent organizes capabilities into hierarchical segments with clear parent-child relationships. This segmentation improves search efficiency by enabling progressive filtering, while the hierarchical structure itself provides a natural and intuitive organization that does not significantly increase operational complexity.
Solution Approach 2:
The hierarchical structure serves multiple functions: it organizes capabilities, enables efficient filtering, and provides contextual relationships. This multi-functionality justifies the structural complexity by delivering substantial productivity improvements through a single integrated framework.
Data Source
AI summary
A system and method for executing computer readable code instructions for an on-the-box (OTB) artificial intelligence (AI) productivity tool comprising a hardware processor accessing capabilities associated with each of a plurality of AI productivity tool-enablable software applications, a natural language capabilities database memory to store natural language descriptions of the capabilities and capability intent values generated from the natural language descriptions in a capabilities decision tree with each capability node grouped under a branch of the capabilities decision tree according to logical topics in hierarchical parent-child relationships, the hardware processor generating a query input intent value from a user query input and performing a cosine semantic similarity search comparing the capability intent values of the capability nodes along the branch of the capabilities decision tree for identifying a best match capability node having a highest cosine semantic similarity search score, and the hardware processor executing the best match capability.