Conversation Flow Signature Classification for Support Knowledge Retention
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Solution Overview
Problem
Maintaining high subject matter expertise in customer support roles is challenging due to employee turnover, as organizations face difficulties in effectively documenting and retaining knowledge of past customer service requests and resolution steps.
Innovation Solution
A method and system that generate a conversation flow signature from communication transcripts between customers and support agents, classify it using a machine learning classifier trained on customer support records, and output the associated steps for configuring or repairing products, thereby aiding in resolving similar requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If detailed documentation of past customer service requests is maintained, then knowledge retention is improved, but documentation maintenance complexity increases
Solution Approach 1:
The system automatically generates conversation flow signatures and classifies support requests without human intervention. The machine learning classifier autonomously processes communication transcripts, extracts key features, and categorizes requests into predefined categories, eliminating the need for manual documentation maintenance while preserving knowledge.
Solution Approach 2:
The patent replaces manual documentation processes with an automated machine learning system. Instead of humans manually creating and maintaining documentation, the system uses NLP techniques to automatically analyze communication transcripts, generate signatures, and classify requests, substituting mechanical human labor with computational processes.
2Reliability
If manual documentation of resolution steps is maintained, then expertise transfer is improved, but time consumption increases
Solution Approach 1:
The system pre-processes communication transcripts during and after support interactions, automatically generating conversation flow signatures and storing them in the database. This preliminary action ensures that when similar requests occur, the classification and resolution steps can be quickly retrieved without time-consuming manual documentation.
Solution Approach 2:
The system creates structured copies of support interactions in the form of conversation flow signatures that capture essential features of the communication. These signatures serve as compressed representations that can be quickly classified and matched against similar requests, enabling rapid expertise transfer without reproducing entire documentation sets.
3Measurement precision
If comprehensive support records are stored, then classification accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system extracts only the most relevant features from communication transcripts to create conversation flow signatures. Instead of processing entire transcripts, the NLP engine identifies and extracts key entities, intents, and contextual features, storing only these essential elements in the database for efficient classification while maintaining accuracy.
Solution Approach 2:
The patent segments the support request processing into distinct components: transcript processing, signature generation, feature extraction, and classification. Each component handles a specific aspect of the data, breaking down the complex task of analyzing comprehensive support records into manageable segments that can be processed independently and efficiently.
Data Source
AI summary
A method including: generating a conversation flow signature based on a set of communication transcripts, each of the communication transcripts being associated with a support request for a product, each of the communication transcripts being a text transcript of a communication between a respective customer and a respective customer support agent; classifying the conversation flow signature into one of a plurality of categories, the conversation flow signature being classified by using a machine learning classifier that is trained based on customer support records, each of the plurality of categories corresponding to a respective set of steps for configuring or repairing the product; and outputting an indication of the respective set of steps that is associated with the category in which the conversation flow signature is classified.