Automated Call Series Analysis for Repeat Caller Identification
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Solution Overview
Problem
Call centers face challenges in identifying and addressing repeat callers effectively, leading to increased operational costs, customer dissatisfaction, and lost revenue due to inadequate first call resolution.
Innovation Solution
A computer-implemented method that processes audio recordings and metadata to identify repeat callers and analyze call series, generating reports on common topics and correlation metrics to pinpoint issues driving repeat calls, using content-based analysis and phonetic representation techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of audio recordings is used to identify repeat callers, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The patent replaces manual mechanical analysis of audio recordings with automated content-based analysis using speech recognition and natural language processing technologies. The system automatically transcribes audio recordings to text, extracts entities and topics, and identifies repeat callers without human intervention, thereby maintaining measurement precision while dramatically improving productivity.
Solution Approach 2:
The patent introduces an intermediary layer of metadata extraction and content analysis between the raw audio recordings and the final identification of repeat callers. This intermediary process converts unstructured audio data into structured information including transcripts, entities, topics, and call patterns, enabling both accurate identification and efficient processing at scale.
2Reliability
If comprehensive analysis of all call data is performed, then reliability is improved, but loss of time worsens
Solution Approach 1:
The patent performs preliminary content-based analysis on all call recordings to extract and store metadata including transcripts, entities, topics, and call patterns in advance. This preliminary processing creates a ready-to-query database of structured information, allowing rapid retrieval and analysis when needed, thus maintaining high reliability for first call resolution while minimizing analysis time during critical moments.
Solution Approach 2:
The patent segments the comprehensive call data analysis into distinct modular components: audio transcription, entity extraction, topic identification, pattern recognition, and metric calculation. Each segment can be processed independently and in parallel, enabling thorough analysis of all call data while reducing overall processing time through distributed computation and selective querying.
3Measurement precision
If detailed content-based analysis is applied to identify repeat caller patterns, then measurement precision is improved, but device complexity worsens
Solution Approach 1:
The patent implements a universal content-based analysis platform that handles multiple analysis tasks through a single integrated system. The same speech recognition, natural language processing, and entity extraction engines used for identifying repeat callers are also applied to analyze call topics, sentiment, and other metrics, reducing overall system complexity while maintaining high measurement precision through reusable modular components.
Data Source
AI summary
Some general aspects of the invention relate to systems and methods of processing data, for example, to improve customer interactions. One aspect, in particular, relates to a computer-implemented method that includes accepting user input for analysis of a database having media data and metadata. The media data includes a group of audio recordings and the metadata includes descriptive information of the group of audio recordings. A representation of a set of call series is formed based on user input, and processed to generate an analysis report. A visual representation of the analysis report is formed for presentation to a user.