Human-Machine Interaction Waiting Time Reduction
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Solution Overview
Problem
Human-machine interaction systems face challenges in reducing the negative effects of service waiting time, particularly in scenarios where displaying a progress bar is not practical or desirable, such as with humanoid robots in question and answer dialogs, which can negatively impact user satisfaction and experience.
Innovation Solution
A method and system that utilize a local cache for frequently asked questions and dynamic behavior selection to minimize perceived waiting time, including the use of natural language classification and prior-behaviors like dialog outputs to engage users while waiting for cloud service responses, with the option to provide alternative actions if responses are not received within predetermined time periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system waits for cloud service responses without providing feedback, then the processing accuracy is improved, but the user satisfaction deteriorates due to perceived waiting time
Solution Approach 1:
The system implements feedback mechanisms by providing users with status updates and estimated waiting times during the cloud service processing period. This maintains processing accuracy while reducing perceived waiting time through continuous information flow to the user.
Solution Approach 2:
The system performs preliminary actions by preparing local cache content and pre-processing data before cloud service calls. This reduces the actual waiting time users experience while maintaining the accuracy of cloud-based processing when needed.
2Loss of time
If the system uses local cache for all questions, then the response time is reduced, but the answer accuracy deteriorates for non-cached questions
Solution Approach 1:
The system segments questions into cached and non-cached categories, applying different response strategies. Frequently asked questions use local cache for immediate responses, while other questions route to cloud services, optimizing both response time and accuracy for each segment.
Solution Approach 2:
The system applies partial caching strategy where only frequently asked questions are cached locally. This partial action reduces response time for common queries while maintaining accuracy for unique questions by routing them to cloud services for fresh processing.
3Object-affected harmful factors
If the system selects behaviors with longer execution time, then the perceived waiting time is reduced, but the actual productivity deteriorates
Solution Approach 1:
The system uses periodic feedback actions during waiting periods, providing status updates at regular intervals. This creates the perception of active processing without extending the actual processing time, maintaining productivity while reducing perceived waiting time.
Solution Approach 2:
The system introduces intermediary feedback messages as mediators between the actual processing and user perception. These intermediaries provide reassurance and status information without interfering with the core processing productivity.
Data Source
AI summary
A query including a question from a user is received by a device. It is determined that the question and an associated answer is not found in a local cache of the device. At least one category for the question is determined. At least one cloud service for answering the question is determined. The at least one cloud service is in communication with the device via a network. A request including the question is sent to the at least one cloud service. Selecting a behavior is selected based upon the at least one category. The behavior includes a feedback to the user to reduce a perceived waiting time by the user. The behavior is executed.