Chatbot Session Analysis for Escalation Cause Identification
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Solution Overview
Problem
Existing methods for analyzing chatbot communication sessions to identify causes of escalation are inefficient, relying on manual processing that wastes resources and is prone to human error, leading to inaccurate results and improper chatbot operation.
Innovation Solution
A device that analyzes session data from chatbot communication sessions to determine the cause of escalations by identifying relevant entries in the session data, using machine learning models to train the chatbot to address these causes and prevent future escalations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processing of session data is used to identify causes of escalation, then human analysis can be performed, but computing resources are wasted and human error leads to inaccurate results
Solution Approach 1:
The patent replaces manual mechanical processing of session data with an automated machine learning-based system. The machine learning model automatically analyzes session data to identify escalation causes, eliminating human error and improving both accuracy and efficiency simultaneously.
Solution Approach 2:
The system uses machine learning models that can autonomously analyze session data without human intervention. The model self-trains on historical data and automatically identifies patterns and causes of escalation, making the system self-sufficient and highly efficient.
2Productivity
If manual processing of session data is used, then analysis can be performed with simple tools, but computing resources are wasted and the process is time-consuming
Solution Approach 1:
The patent replaces slow manual mechanical analysis with fast automated machine learning processing. The machine learning model processes session data much faster than human analysts, significantly improving productivity while the automated nature of the process optimizes computing resource usage compared to inefficient manual methods.
3Reliability
If manual analysis of session data is performed, then flexibility in analysis approach is maintained, but human error and lack of consistency lead to improper chatbot operation
Solution Approach 1:
The patent replaces variable manual analysis approaches with a consistent machine learning-based system. The machine learning model provides uniform, repeatable analysis results that are not affected by human error or variability in analyst approach, significantly improving reliability and consistency.
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning model continuously learns from historical session data and escalation patterns. This feedback loop allows the model to improve its accuracy and consistency over time, adapting to changing patterns while maintaining reliable and consistent analysis results.
Data Source
AI summary
A device may receive session data of a communication session between an artificial intelligence (AI) communication device and a first user device. The device may analyze the session data to determine one or more portions of the session data and may identify a portion, of the one or more portions, for processing based on one or more criteria associated with the portion. The portion may include a plurality of entries including communications from the first user device and from the AI communication device. The device may analyze the plurality of entries to identify an entry including information regarding an escalation and may analyze one or more additional entries, of the plurality of entries, to determine a category associated with a cause of the escalation. The device may cause the AI communication device to be configured to address the cause based on the one or more additional entries and the category.