Digital Assistant Training via Impasse Detection and Parameter Adjustment
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Solution Overview
Problem
Digital assistant systems often fail to provide satisfactory responses to user requests due to imperfect speech recognition, unrecognized terms, and inadequate capabilities, leading to user dissatisfaction and inefficiencies in task execution.
Innovation Solution
The system implements a method for training digital assistants by detecting impasses during user interactions, establishing learning sessions, and adjusting parameters for speech-to-text processing, natural language processing, and task execution based on user feedback and clarification inputs to improve response accuracy and user satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the digital assistant uses standard speech recognition and natural language processing, then the system can operate with basic functionality, but the response accuracy and user satisfaction remain insufficient
Solution Approach 1:
The system performs preliminary actions by establishing a learning session before final task execution. When an impasse is detected, the system proactively initiates a learning session to gather clarification inputs and adjust parameters, rather than waiting for repeated failures. This preliminary learning phase improves response accuracy by preparing the system with corrected understanding before attempting the task again.
Solution Approach 2:
The system implements continuous feedback loops by detecting impasses during dialogue, establishing learning sessions, and adjusting parameters based on clarification inputs. The system monitors task execution outcomes and uses this feedback to refine speech-to-text processing, natural language processing, and task execution parameters, thereby improving response accuracy through iterative learning.
2Reliability
If the digital assistant adjusts parameters frequently to improve response accuracy, then user satisfaction increases, but the processing time and system overhead increase
Solution Approach 1:
The system applies partial adjustment by only modifying parameters when impasses are detected, rather than continuously adjusting all parameters. The learning session focuses specifically on the aspects that caused the impasse, adjusting only the necessary speech-to-text or natural language processing parameters. This selective approach maintains user satisfaction while minimizing unnecessary processing time overhead.
3Measurement precision
If the digital assistant implements comprehensive learning sessions with multiple clarification inputs, then the understanding of user intent improves, but the interaction duration increases
Solution Approach 1:
The system obtains only the necessary clarification inputs required to resolve the specific impasse, rather than gathering excessive information. The learning session is tailored to the particular misunderstanding detected, requesting only the minimal clarification needed to improve intent inference accuracy for that specific case, thereby limiting dialogue duration while maintaining precision.
Solution Approach 2:
The system serves itself by automatically detecting impasses and initiating learning sessions without requiring external intervention. The system autonomously manages the clarification process, adjusting parameters based on user feedback within the learning session, and then independently applies these adjustments to improve future responses, reducing the need for extended manual interaction.
Data Source
AI summary
An electronic device with one or more processors and memory includes a procedure for training a digital assistant. In some embodiments, the device detects an impasse in a dialog between the digital assistant and a user including a speech input. During a learning session, the device utilizes a subsequent clarification input from the user to adjust intent inference or task execution associated with the speech input to produce a satisfactory response. In some embodiments, the device identifies a pattern of success or failure associated with an aspect previously used to complete a task and generates a hypothesis regarding a parameter used in speech recognition, intent inference or task execution as a cause for the pattern. Then, the device tests the hypothesis by altering the parameter for a subsequent completion of the task and adopts or rejects the hypothesis based on feedback information collected from the subsequent completion.