Digital Assistant Error Detection via User Interaction Feedback
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Solution Overview
Problem
Digital assistants face challenges in identifying and addressing errors in speech interactions, as users often receive limited feedback on errors, making it difficult to determine when mistakes occur and what specific issues need correction.
Innovation Solution
The system detects user interactions indicative of problems, such as dissatisfaction expressed through speech inputs, volume, or physical actions, and stores information in a repository for error analysis, allowing adjustments to speech-to-text and natural language processing modules to prevent similar errors in the future.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If speech-to-text processing and natural language processing are used to enable natural language interaction, then user interaction capability is improved, but error detection capability deteriorates due to limited feedback mechanisms
Solution Approach 1:
The system implements feedback by monitoring user responses and interactions after the digital assistant provides its output. It detects signals such as repeated questions, expressions of confusion, or lack of expected user actions to infer that an error may have occurred. This feedback mechanism enables the system to identify potential errors despite the inherent limitations of natural language processing feedback.
2Productivity
If the digital assistant performs complex tasks autonomously, then productivity is improved, but reliability deteriorates due to increased opportunities for errors in speech processing and task execution
Solution Approach 1:
The system monitors user responses and interaction patterns to detect potential errors in task execution. By analyzing whether users repeat themselves, express confusion, or fail to take expected actions, the system can identify when autonomous task execution may have erred, enabling corrective measures while maintaining high productivity.
Solution Approach 2:
The system stores information about user requests and assistant responses in a repository for later error analysis. This preliminary recording of interactions allows the system to review and learn from past operations, improving reliability of future autonomous task execution without reducing current productivity.
3Ease of operation
If speech synthesis is used to convert text to speech output, then user interface accessibility is improved, but measurement precision deteriorates due to incorrect word pronunciation
Solution Approach 1:
The system detects user responses that indicate pronunciation problems, such as requests for repetition, expressions of misunderstanding, or failure to respond as expected. This feedback allows the system to identify speech synthesis errors and potentially correct them, maintaining accessibility while improving pronunciation accuracy.
Data Source
AI summary
The method is performed at an electronic device with one or more processors and memory storing one or more programs for execution by the one or more processors. A speech input containing a request is received from a user. At least one action in furtherance of satisfying the request is performed. A user interaction is detected, such as a speech input to a digital assistant or a physical interaction with a device. It is determined whether the user interaction is indicative of a problem in the performing of the at least one action. Upon determining that the user interaction is indicative of a problem, information relating to the request is stored in a repository for error analysis.