Query Coverage Analysis via Vector Space Modeling
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Solution Overview
Problem
Current methods for finding missing search queries in call center media, such as transcripts, are labor-intensive and require extensive manual listening, making it difficult to automatically analyze the correspondence between text search queries and topics in large datasets.
Innovation Solution
A computer-based system and method that assigns queries to topics by calculating query vectors and topic vectors from search results, generating a report or visualization to show query coverage, allowing for automatic analysis and identification of uncovered topics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual listening methods are used to find missing queries, then query coverage analysis can be performed, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual listening (mechanical human operation) with automatic speech recognition and text analysis systems. The system uses ASR to convert audio to text, then employs natural language processing and vector space models to automatically analyze query coverage, eliminating the need for manual listening while maintaining analysis accuracy.
Solution Approach 2:
The patent introduces intermediate processing steps including automatic speech recognition transcripts, text preprocessing, and vector space representations as mediators between the original audio data and the final coverage analysis. These intermediaries enable automated analysis by transforming unstructured audio into structured text data that can be processed computationally.
2Loss of information
If extensive manual listening is performed to identify uncovered topics, then missing queries can be found, but the complexity and cost of the process increases
Solution Approach 1:
The patent replaces complex manual analysis processes with automated computational systems. Vector space models and similarity calculations automatically identify uncovered topics by comparing query vectors against transcript vectors, eliminating the need for manual listening while reducing process complexity through standardized algorithms.
Solution Approach 2:
The patent transforms the analysis problem by changing parameters from temporal (listening duration) to mathematical (vector similarity metrics). By representing queries and transcripts as vectors in a shared semantic space, the system can automatically measure coverage through distance or similarity calculations rather than manual evaluation.
3Productivity
If automated vector-based analysis is used, then query coverage can be quickly analyzed, but the system requires sophisticated processing infrastructure
Solution Approach 1:
The patent performs preliminary processing by pre-computing vector representations of queries and transcripts before the actual coverage analysis. This includes pre-processing text, calculating vector embeddings, and storing them for efficient retrieval and comparison, which enables rapid analysis without requiring complex real-time processing infrastructure.
Solution Approach 2:
The patent creates simplified copies or representations of the original data in vector form. Instead of processing raw audio or unstructured text during analysis, the system uses pre-computed vector copies that capture the essential semantic information, enabling efficient similarity calculations with reduced computational requirements.
Data Source
AI summary
A computer based system and method for assigning queries to topics and/or visualizing or analyzing query coverage may include, using a computer processor, searching, using a set of queries, over a set of text documents, to produce for each query a set of search results for the query. Each search result may include a subset of text from a text document of the set of text documents. For each query, a query vector may be calculated based on the set of search results for the query, and for each of a set of topics describing the set of text documents, a topic vector may be calculated. A report or visualization may be generated including the set of queries and the set of topics using the topic vectors and the query vectors.