Deep Learning Zone Detection in Unstructured Support Transcripts
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Solution Overview
Problem
Conventional solutions for analyzing customer support transcripts are ineffective due to acronyms, shorthand notation, varying word choices, and lack of grammatical structure, making it difficult to identify specific data points and zones of interest.
Innovation Solution
A deep learning framework using heuristics, natural language processing, and clustering models to identify and highlight zones of interest in text transcripts, such as call reasons, resolutions, and sentiment, by applying regex patterns and classification models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search solutions are used on text transcripts, then the system is simple and easy to implement, but the ability to identify specific data points and zones of interest deteriorates due to acronyms, shorthand notation, and varying word choices
Solution Approach 1:
The patent segments the transcript analysis process into multiple specialized components: heuristic rules for pattern matching, clustering algorithms for grouping similar statements, and classification models for categorizing zones. This segmentation allows each component to handle specific aspects of the unstructured text, improving overall identification accuracy while managing complexity through modular architecture
Solution Approach 2:
The patent introduces intermediary processing layers between the raw transcript and the search function. These intermediaries include normalized statement representations, clustered topic groups, and classified zone categories that bridge the gap between unstructured text and structured search queries, enabling accurate identification despite variations in wording and notation
2Productivity
If manual analysis of transcripts is performed to identify zones of interest, then the system remains simple, but the time required to process and retrieve information increases significantly
Solution Approach 1:
The patent performs preliminary processing of transcripts by applying heuristic rules and clustering algorithms to pre-identify and categorize zones of interest before actual search operations. This preliminary action creates structured representations and topic clusters in advance, enabling rapid information retrieval during subsequent searches without requiring manual analysis each time
Solution Approach 2:
The patent replaces manual mechanical analysis with automated computational systems including machine learning models and clustering algorithms. These systems automatically process transcripts, identify patterns, and retrieve information at speeds far exceeding manual capability, dramatically improving productivity while reducing time loss
3Ease of operation
If the transcript structure is maintained in its original unformatted state, then the data remains authentic and complete, but the difficulty of searching and identifying specific information increases
Solution Approach 1:
The patent applies local quality enhancement by maintaining the original transcript structure and wording while adding localized structural markers and classifications to specific zones. Heuristic rules and clustering algorithms identify and tag important segments without altering the underlying text, preserving contextual nuance while improving searchability through selective structural enhancement
Data Source
AI summary
Systems, methods, and computer program products for identifying zones of interest in text transcripts. An application may receive input specifying a text statement type and determine a plurality of heuristics for identifying statements of the statement type in transcripts. The application may determine, based on a first heuristic, a first text statement of the statement type. The application may generate, based on a clustering algorithm, a plurality of additional statements of the statement type. The application may receive a first text transcript. The application may identify, based on a second heuristic, a first text statement in the first text transcript, where the first text transcript statement is of the statement type. The application may generate a graphical indication that the first text transcript statement is of the statement type, and display the first transcript statement and the graphical indication on a display.