Automated Assistant Muted Response Adjustment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users often fail to audibly perceive responses from automated assistants due to muted device settings, leading to repeated queries and increased resource burden on hardware and networks.
Innovation Solution
An objective metric is used to determine the criticality of audible perception for specific queries, allowing for automatic adjustment of response volume settings or prompting users to increase volume, thereby ensuring audible responses are received without unnecessary resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the automated assistant processes repeated queries from users who muted the response volume, then the system continues to generate responses, but hardware resources (battery, processing, memory) and network resources are burdened unnecessarily
Solution Approach 1:
The system performs preliminary detection of the response volume setting state before processing the query. By checking whether the device is in a muted state prior to full query processing, the system can avoid consuming hardware and network resources on queries that will not be audibly perceived, thus preventing unnecessary energy loss and resource burden
Solution Approach 2:
The system utilizes feedback from the response volume setting state to dynamically adjust query processing behavior. When the muted state is detected, the system modifies its processing workflow to skip or reduce processing, creating a feedback loop that optimizes resource allocation based on the actual user perception state
2Reliability
If the response volume setting is muted, then users cannot perceive audible responses, but automatically adjusting the volume for all queries may disrupt user preferences for non-critical queries
Solution Approach 1:
The system applies different volume adjustment strategies to different query types based on their criticality. For critical queries where audible response is essential, the system overrides the muted setting. For non-critical queries, the system respects the user's muted preference. This localized differentiation resolves the contradiction by applying volume adjustment only where necessary
Solution Approach 2:
The system changes the response volume parameter dynamically based on query criticality assessment. Rather than maintaining a fixed volume setting or uniformly overriding user preferences, the system adjusts the volume parameter selectively for critical queries, thereby ensuring reliable audible delivery when needed while preserving user control for other cases
3Device complexity
If the system processes all queries uniformly regardless of volume setting, then processing logic remains simple, but user experience deteriorates due to repeated queries and resource waste
Solution Approach 1:
The system performs a preliminary check of the response volume setting state before committing to full query processing. This early detection step adds minimal complexity but prevents the much larger resource expenditure of processing queries that will not be heard, thereby improving dialog efficiency with minimal increase in processing logic complexity
Solution Approach 2:
The system serves itself by automatically detecting and responding to its own muted state condition. Rather than requiring complex external control or user intervention, the system autonomously adjusts its processing behavior based on the volume setting, maintaining simplicity while improving productivity
Data Source
AI summary
Techniques enable an automatic adjustment of a muted response setting of an automated assistant based on a determination of an expectation by a user to hear an audible response to their query, despite the muted setting. Determination of the expectation may be based on historical, empirical data uploaded from multiple users over time for a given response scenario. For example, the system may determine from the historical data that a certain type of query has been associated with a user both repeating their query and increasing a response volume setting within a given timeframe. Metrics may be generated, stored, and invoked in response to attributes associated with identifiable types of queries and query scenarios. Automated response characteristics meant to reduce inefficiencies may be associated with certain queries that can otherwise collectively burden network bandwidth and processing resources.