Hierarchical Capability Search Using Breadth-First Semantic Matching
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Solution Overview
Problem
Existing keyword-based search algorithms, such as TF-IDF, struggle to accurately match user queries with appropriate AI productivity tool-enablable software applications due to their inability to consider context and synonyms in natural language inputs, leading to potential mismatches and reduced accuracy in identifying the best matching capability.
Innovation Solution
A breadth-first tree-traversal methodology combined with TF-IDF weighted cosine semantic similarity search is employed to analyze hierarchical capabilities decision trees, considering a wider array of nodes and critical keywords to enhance the accuracy of matching user intents with the most relevant AI productivity tool-enablable software applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword-based search algorithms (TF-IDF) are used, then the search process is simple and fast, but the accuracy of matching user queries with appropriate capabilities is poor due to inability to consider context and synonyms
Solution Approach 1:
The patent segments the search process into two distinct phases: (1) a breadth-first tree traversal that identifies candidate capabilities by examining parent-child relationships in the capability hierarchy, and (2) a semantic similarity search using cosine similarity on embedding vectors that evaluates the semantic match between query and candidate capabilities. This segmentation allows the system to combine the structural advantages of hierarchical search with the semantic advantages of vector-based matching, resolving the contradiction between simplicity and accuracy.
Solution Approach 2:
The patent introduces capability embedding vectors as an intermediary representation that bridges the gap between keyword-based search and semantic understanding. These embeddings capture the semantic meaning of capabilities in a vector space, allowing the cosine similarity search to measure semantic proximity without requiring complex natural language processing. This intermediary enables accurate semantic matching while keeping the overall system architecture relatively simple.
2Measurement precision
If a breadth-first tree-traversal methodology is employed to analyze hierarchical capabilities, then the accuracy of identifying best matching capability is improved by considering a wider array of nodes, but the computational time and resources increase
Solution Approach 1:
The patent performs preliminary action by conducting the breadth-first tree traversal first to identify a set of candidate capabilities that are structurally relevant to the query. This preliminary filtering based on hierarchical relationships narrows down the search space before the more computationally intensive semantic similarity search is applied. By pre-identifying candidates through the tree traversal, the system avoids performing semantic comparisons on all possible capabilities, thus reducing overall computational time while maintaining high accuracy.
3Reliability
If TF-IDF weighted cosine semantic similarity search is used, then the semantic search performance is enhanced by considering critical keywords and context, but the complexity of the search mechanism increases
Solution Approach 1:
The patent changes the parameter of similarity measurement from traditional keyword matching to cosine similarity on embedding vectors. This parameter change allows the system to capture semantic relationships and context by measuring the angular distance between query and capability vectors in a high-dimensional space. The TF-IDF weighting is applied to the embedding computation to emphasize critical keywords while still capturing semantic meaning. This parameter transformation achieves reliable semantic matching while keeping the implementation relatively straightforward by leveraging existing embedding models.
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 each of the capability nodes in the decision tree, as weighted by a lexical comparison between the user query input and each of the descriptions of capabilities to conduct a breadth-first search to identify a best match childless capability node having a highest lexical weighted cosine semantic similarity search score, and executing the associated best match capability from the best match childless capability node.