Chatbot Feedback Loop for Reducing Text-to-Voice Network Traffic
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Solution Overview
Problem
Conventional chatbot systems fail to understand user requests, leading to increased network traffic due to the need for users to switch to voice-based communication when text-based queries remain unresolved.
Innovation Solution
A system that identifies unresolved user queries in text-based communication and shares this information with a voice-based communication system to update the chatbot's machine learning model, optimizing it to handle similar queries in future text-based interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a conventional chatbot system is used for text-based communication, then the system structure is simple, but the chatbot fails to understand user requests leading to increased network traffic
Solution Approach 1:
The patent implements feedback mechanisms where the chatbot system receives responses from voice-based communication systems and uses them to update and improve its machine learning models. This feedback loop enables the chatbot to learn from unresolved queries and improve its understanding capability over time, directly addressing the reliability issue while maintaining a manageable system complexity through iterative improvement rather than complete system redesign
Solution Approach 2:
The patent introduces an intermediary information sharing mechanism between the text-based chatbot system and voice-based communication systems. This intermediary layer allows the chatbot to access insights from voice interactions without requiring direct integration or complex coupling between systems, thereby improving understanding capability while keeping system complexity relatively low
2Productivity
If the chatbot fails to understand user requests, then the text-based communication system remains simple, but users must switch to voice-based communication increasing network traffic
Solution Approach 1:
The patent applies preliminary action by proactively improving the chatbot's understanding capability through continuous learning from voice-based communication data. By preparing and training the machine learning models in advance with feedback from voice interactions, the system resolves queries more efficiently before users need to switch communication modes, thereby reducing network traffic and energy loss
Solution Approach 2:
The feedback mechanism allows the system to learn from unresolved queries that result in voice-based communication transitions. By analyzing these cases and incorporating them into model training, the system improves query resolution efficiency for future interactions, reducing the frequency of failed text-based communications and associated network traffic
3Reliability
If the machine learning model is updated with feedback from voice-based communication, then chatbot performance improves, but the system complexity increases
Solution Approach 1:
The patent implements a structured feedback mechanism where information from voice-based communications is systematically collected, processed, and used to update machine learning models. This feedback loop improves chatbot performance by enabling continuous learning from real-world interaction data, while the automated nature of the feedback process helps manage system complexity through standardized procedures rather than manual intervention
Solution Approach 2:
The machine learning model updates are performed automatically through self-service mechanisms where the system autonomously processes feedback data and retrains models without requiring extensive manual configuration or intervention. This self-service approach improves chatbot performance while keeping system complexity manageable by reducing the need for complex manual management processes
Data Source
AI summary
Methods and systems for reducing network traffic between a service and client devices. In some aspects, the system receives a first data stream for a first type of communication between a service and a client device for a user. In response to determining that the first data stream includes an unresolved user query, the system determines that a second type of communication occurred between the service and the user. The system processes a second data stream for the second type of communication to determine that the second type of communication includes the unresolved user query and a service response to the unresolved user query. The system provides the unresolved user query and the service response to update a machine learning model used by the service to generate service responses to one or more user queries during a future communication of the first type.