Hierarchical Capability Search for Accurate AI Query Matching
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Solution Overview
Problem
Existing keyword-based search methods for identifying AI productivity tool-enablable software applications lack context and keyword relevance, leading to inefficiencies and resource-intensive data saturation when matching user queries with appropriate capabilities.
Innovation Solution
A hierarchical capabilities decision tree combined with TF-IDF and cosine similarity search algorithms is used to analyze user queries, prioritizing comparisons based on parent-child relationships and keyword relevance, reducing data saturation and enhancing search efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If keyword-based search methods are used to match user queries with software application capabilities, then the search process is simple to implement, but the search accuracy and context relevance deteriorate
Solution Approach 1:
The patent transforms the search approach by changing the parameter from simple keyword matching to TF-IDF weighted semantic similarity scoring. This involves calculating term frequency-inverse document frequency weights for keywords and combining them with cosine similarity measurements, thereby improving search accuracy while maintaining computational feasibility through structured parameter transformation
Solution Approach 2:
The patent adds a hierarchical dimension to the search process by organizing capabilities into parent-child relationships. The search first evaluates parent capabilities at a higher level, then drills down to child capabilities only when relevant, creating a multi-dimensional search space that improves accuracy without linearly increasing complexity
2Quantity of substance
If comprehensive keyword matching is performed across all capabilities, then the search coverage is complete, but the processing resources and time consumption increase
Solution Approach 1:
The patent segments the capability set into hierarchical groups (parent capabilities and child capabilities). Instead of processing all capabilities uniformly, the system first evaluates parent capabilities as broader categories, then selectively processes child capabilities only when the parent shows relevance to the user query, thereby reducing overall processing load while maintaining comprehensive coverage
Solution Approach 2:
The patent implements partial action by performing TF-IDF weighted semantic similarity calculations only on parent capabilities first, and only on child capabilities when their parent capabilities show relevance. This selective processing approach ensures complete search coverage is maintained while significantly reducing the total number of computations required compared to evaluating all capabilities exhaustively
3Measurement precision
If hierarchical parent-child relationships are incorporated into the search, then the context relevance improves, but the system complexity increases
Solution Approach 1:
The patent adds a hierarchical dimension to organize capabilities into parent-child relationships, where parent capabilities represent broader categories and child capabilities represent specific functions. This hierarchical structuring improves context relevance by enabling the system to match user queries at appropriate levels of abstraction while maintaining manageable system complexity through organized categorization
Solution Approach 2:
The patent performs preliminary evaluation of parent capabilities before evaluating child capabilities. By first calculating TF-IDF weighted semantic similarity scores for parent capabilities and identifying relevant matches, the system prepares a filtered set of candidate child capabilities for further evaluation, thereby reducing the overall computational burden despite the added hierarchical complexity
4Measurement precision
If TF-IDF weighted semantic similarity search is performed, then the keyword relevance improves, but the computational complexity increases
Solution Approach 1:
The patent enhances simple keyword matching by transforming it into TF-IDF weighted semantic similarity scoring. This involves calculating term frequency weights and inverse document frequency weights for each keyword, then combining these parameters with cosine similarity measurements. This parameter transformation significantly improves keyword relevance while keeping computational complexity manageable through efficient mathematical operations
Solution Approach 2:
The patent segments the computational process into distinct phases: first calculating TF-IDF weights for keywords in the user query, then calculating cosine similarity between the weighted query and capability descriptions, and finally combining these scores. This segmentation of the computational process makes the complex TF-IDF weighted semantic similarity search more manageable and efficient by breaking it into reusable, optimized sub-routines
Data Source
AI summary
An information handling system for an on-the-box artificial intelligence productivity tool may comprise a memory storing descriptions of capabilities associated with software applications in a decision tree with each capability and a generated capability intent value stored as a capability node grouped under one of several branches in parent-child relationships, and a hardware processor executing machine readable code instructions to generate a query input intent value from a user query input requesting action by one of the software applications, comparing the capability intent values of the capability nodes along a branch in the decision tree with a weighted by a TF-IDF comparison between the user query input and each of the descriptions of capabilities to identify a best match capability node having a highest TF-IDF weighted cosine semantic similarity search score, and executing an associated best match capability for a first software application having the best match capability node.