Automated Intent Discovery from Unstructured Customer Conversations
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Solution Overview
Problem
Current solutions for analyzing customer queries and answers from unstructured customer interaction data are inefficient, requiring significant human intervention and skilled manpower, and struggle with conversational noise, multiple message event windows, and intermingled topics in chat conversations.
Innovation Solution
The system employs separate Intent Discovery and Answer Discovery computing modules to automatically extract customer questions and intents, and corresponding answers from unstructured data sources like chat logs and phone transcripts, using novel clustering algorithms and noise filtering techniques, without the need for human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human review is used to extract customer interaction data, then analysis accuracy can be maintained, but productivity is prohibitively low and costs are high
Solution Approach 1:
The patent replaces manual human review with automated machine learning models and natural language processing algorithms. The system uses trained ML models to automatically extract customer questions, intents, and answers from unstructured interaction data, eliminating the need for human reviewers while maintaining extraction accuracy through algorithmic pattern recognition and semantic analysis.
2Adaptability or versatility
If traditional query listing methods are used, then database structure is simple, but adaptability to cover highest possible number of questions is limited
Solution Approach 1:
The system enables automatic self-learning through unsupervised clustering algorithms that autonomously discover and organize customer questions without manual intervention. The ML models automatically identify patterns, group similar queries into intent clusters, and extract answers from conversation data, allowing the system to adapt to new query types automatically as data accumulates.
Solution Approach 2:
The patent segments the large volume of unstructured customer interaction data into distinct intent clusters using unsupervised learning algorithms. Each cluster represents a specific customer intent or question type, allowing the system to organize and manage diverse queries in a structured manner while maintaining high adaptability to cover numerous question variations.
3Reliability
If skilled manpower is used for answer curation, then answer quality can be ensured, but the process is expensive and not scalable
Solution Approach 1:
The patent replaces skilled human manpower with automated machine learning systems for answer curation. The system uses natural language processing to automatically extract answers from customer service conversations, phone transcripts, and support tickets. ML algorithms verify answer quality through consistency checks and confidence scoring, enabling scalable processing of large datasets without proportional increases in human resources.
4Loss of information
If manual analysis of unstructured data is performed, then data privacy can be maintained through control, but loss of time is significant
Solution Approach 1:
The patent replaces time-consuming manual analysis with automated machine learning pipelines that process unstructured customer interaction data rapidly. The system extracts and analyzes data while maintaining privacy controls through automated data governance mechanisms, eliminating the time loss associated with manual review while preserving data protection through systematic access control and processing protocols.
Data Source
AI summary
A computerized method of populating one or more structured databases includes performing, by a computing device, the steps of: receiving customer message data from one or more data sources; extracting, from the customer message data, data sets representative of a set of customer questions; pre-processing, the data sets representative of the set of customer questions using one or more filters, thereby producing pre-processed data sets representative of customer questions; extracting, from the pre-processed data sets representative of customer questions, a set of customer expression data sets; grouping, the customer expression data sets into a set of clusters, each cluster representing one customer intent data set, each customer intent data set corresponding to one or more customer expression data sets; and storing, the customer intent data sets and the customer expression data sets in the structured database(s), the structured database(s) in electronic communication with the computing device.