Neural Network Conversation Description Generation
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Solution Overview
Problem
Current manual systems for generating conversation descriptions are time-consuming, prone to errors, and costly, often resulting in incomplete or inaccurate summaries, especially when customer service representatives are rushed or overwhelmed.
Innovation Solution
An automated system using neural networks processes conversation messages to identify key events and generate concise descriptions, excluding non-essential details, and updates descriptions in real-time to ensure accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual systems are used to generate conversation descriptions, then the descriptions can be customized and reviewed, but the process is time-consuming and costly
Solution Approach 1:
The patent replaces the manual mechanical process of writing conversation descriptions with an automated neural network system. The neural network processes conversation data and generates descriptions automatically, eliminating the need for human operators to manually compose summaries while maintaining high accuracy through trained models.
Solution Approach 2:
The system enables self-service by allowing the conversation description generation to occur automatically without human intervention. The neural network independently analyzes conversation data, identifies key events, and produces descriptions on its own, freeing human operators from this repetitive task.
2Productivity
If customer service representatives are rushed to meet workload demands, then productivity increases, but description quality deteriorates
Solution Approach 1:
The patent replaces the human representative's manual description-writing process with an automated neural network system. This substitution allows representatives to focus entirely on handling conversations while the neural network generates accurate descriptions automatically, maintaining both high productivity and description quality without the trade-off that previously existed.
3Loss of information
If manual description generation is required at the end of each session, then complete coverage is achieved, but operational cost increases
Solution Approach 1:
The patent replaces the costly manual process with an automated neural network system that processes conversation data and generates descriptions without human intervention. This automation maintains complete coverage of conversation details while significantly reducing operational costs by eliminating the need for human labor in description generation.
Solution Approach 2:
The system achieves self-service by automatically capturing and processing conversation data through the neural network. The system independently generates comprehensive descriptions without requiring human representatives to allocate time and resources to this task, thereby reducing operational costs while maintaining complete information capture.
4Productivity
If a time lag occurs between conversation end and description entry, then representatives have time to handle other tasks, but omissions increase
Solution Approach 1:
The patent replaces the delayed manual description entry process with real-time automated generation using a neural network. The system processes conversation data and generates descriptions immediately as conversations occur, eliminating time lags and ensuring complete accuracy while allowing representatives to maintain high productivity without the risk of omissions.
Data Source
AI summary
A description of a conversation may be generated to allow a person to understand important aspects of the conversation without needing to review the conversation. The conversation description may be generated by identifying one or more events that occurred in the conversation and then generating the description using the identified events. A set of possible events may be determined in advance for a particular application. The events may be identified by using an event neural network for each event. Each event neural network may process the messages of the conversation to generate an event score that indicates a match between the conversation and the corresponding event. The event scores may then be used to select one or more events. Message scores from the event neural network of a selected event may then be used to select one or more messages of the conversation as a rationale for the selected event.