Response Change Reason Identification in Adaptive AI Devices
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Solution Overview
Problem
Users of electronic devices face confusion when a response changes unexpectedly due to changes in the user's environment or updates in AI models, as they lack information on why the response has changed, making it difficult to control the device to retrieve their preferred response.
Innovation Solution
An electronic device method and system that identifies and provides information on why a response has changed by comparing the history of responses to a current input with a previous input of similar intention, identifying changed modules, and inferring the cause of the change, allowing users to understand and control the device effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the electronic device outputs a response adaptively determined in consideration of a change of user's surrounding environment or using a newly updated AI model, then the response accuracy and adaptability are improved, but the user cannot know the reason why the response has changed from the previous response
Solution Approach 1:
The system implements feedback by providing users with information about why responses have changed. The processor generates and outputs reason information that explains the cause of response changes, allowing users to understand the adaptive adjustments made by the system based on environmental changes or AI model updates.
Solution Approach 2:
The system introduces an intermediary mechanism that bridges the gap between the adaptive response system and the user. This intermediary (the reason information generation and output module) translates internal system changes into understandable explanations for users, maintaining both adaptability and user comprehension.
2Reliability
If the electronic device uses a newly updated AI model to generate responses, then the response quality is improved, but the device complexity increases
Solution Approach 1:
The system segments the complexity management by separating the AI model processing from the explanation generation. The processor independently handles the adaptive response generation and separately generates reason information about changes, allowing the system to maintain high response quality while managing complexity through functional separation.
Data Source
AI summary
Provided is a method of providing, by an electronic device, information about a reason why a response has changed, the method including: outputting a first response by processing at least one module according to a first input; obtaining information about a history of a second response output according to a second input having a same intention as an intention of the first input, the second response being different from the first response; identifying, as a changed module, a module operated differently from at least one module operated according to the second input from among at least one module operated according to the first input, based on the obtained information; and providing information about the reason why the first response has changed from the second response based on the changed module.