Conversational Moment Detection for Automated Support Actions
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Solution Overview
Problem
Current customer support tools in call centers are labor-intensive and time-consuming for generating recommended responses, as they require manual identification and creation of responses for each word, phrase, or sentence structure, lacking efficiency and scalability.
Innovation Solution
A conversation monitoring system that identifies conversational moments through machine learning, allowing users to define and customize conditions and actions via a user interface or API, with a machine learning model training to enhance detection and response generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual identification and creation of responses for each word, phrase, or sentence structure is used, then recommended responses can be generated, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces the manual mechanical process of identifying and creating responses with an automated speech recognition and natural language processing system. The system automatically transcribes conversation audio to text, identifies keywords and phrases, and generates recommended responses without human intervention, thus resolving the contradiction between response quality and generation speed.
Solution Approach 2:
The system enables self-service by automatically performing the entire workflow from speech transcription to response generation. The computer system autonomously processes conversations, identifies relevant moments, and provides recommended responses, eliminating the need for manual labor while maintaining high productivity and reliability.
2Reliability
If comprehensive scripts with multiple words, phrases, and sentence structures are created, then customer support quality improves, but the development time and effort increase significantly
Solution Approach 1:
The system performs preliminary action by pre-processing and analyzing conversation data to identify relevant moments and patterns before actual customer interactions occur. The speech recognition system and natural language processing algorithms are pre-configured to automatically detect and respond to various conversational scenarios, eliminating the need for time-consuming manual script development.
Solution Approach 2:
The system introduces dynamics by making the response generation process adaptive and flexible. Instead of static pre-written scripts, the system dynamically generates recommended responses based on real-time conversation analysis, allowing it to handle diverse customer queries without requiring comprehensive manual scripting for every possible scenario.
3Reliability
If detailed conversation analysis with multiple conditions is implemented, then interaction quality improves, but system complexity increases
Solution Approach 1:
The patent applies universality by designing a multi-functional system that integrates speech recognition, natural language processing, conversation analysis, and response generation into a single unified platform. This universal system handles multiple conversational conditions and scenarios through a common architecture, reducing the complexity that would arise from implementing separate systems for each function.
Solution Approach 2:
The system introduces an intermediary layer of natural language processing that mediates between the raw speech input and the response generation process. This intermediary automatically translates spoken language into structured data, identifies conversational moments and keywords, and prepares information for response generation, thereby simplifying the overall system architecture while maintaining high interaction quality.
Data Source
AI summary
Techniques for initiating system actions based on conversational content are disclosed. A system identifies a first conversational moment type. The first conversational moment type is defined by a first set of one or more conversational conditions. The system receives a user-selected action to be performed by the system in response to detecting conversational moments of the first conversational moment type. The system stores the user-selected action in association with the first conversational moment type. The system performs the user-selected action in response to detecting the conversational moments of the first conversational moment type.