Digital Assistant Sensor Gating for Ambiguous Voice Requests
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Solution Overview
Problem
Digital assistants struggle to determine appropriate responses to ambiguous user requests, particularly in computer-generated reality technologies, due to the difficulty in interpreting deictic references and the need for additional contextual data.
Innovation Solution
An electronic device with a digital assistant analyzes spoken input semantically to determine the likelihood of needing additional contextual data, and if the likelihood exceeds a threshold, it enables cameras and other sensors to capture data for a more precise response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the digital assistant continuously enables all sensors to capture contextual data, then the response accuracy to ambiguous requests is improved, but the power consumption increases and battery life decreases
Solution Approach 1:
The system dynamically adjusts sensor enablement based on real-time analysis of request ambiguity. Sensors are enabled only when the digital assistant determines that additional contextual data is needed to satisfy an ambiguous request, rather than remaining continuously active. This dynamic adaptation resolves the contradiction by making sensor usage conditional and responsive to actual information needs.
Solution Approach 2:
The system changes the operational state parameter of sensors from a static 'always on' mode to a conditional mode based on the determined likelihood that contextual data is required. When the likelihood exceeds a threshold, sensors are enabled; otherwise, they remain disabled. This parameter change allows the system to optimize between response accuracy and power consumption based on actual operational context.
2Use of energy by moving object
If the digital assistant enables sensors selectively based on semantic analysis, then the power usage is reduced and battery life is improved, but the complexity of the system increases
Solution Approach 1:
The system performs preliminary semantic analysis on user requests to determine ambiguity and the likelihood that contextual data is needed before enabling sensors. This preliminary action allows the system to make informed decisions about sensor enablement, reducing unnecessary sensor activation and associated complexity. The semantic analysis framework is established in advance and applied systematically to each request.
Solution Approach 2:
The system uses feedback from the semantic analysis process to control sensor enablement decisions. The digital assistant analyzes the request, determines the likelihood of needing contextual data, and uses this feedback to selectively enable or disable sensors. This feedback loop manages system complexity by using intelligent decision-making rather than continuous sensor activation, balancing power savings with the need for contextual information.
3Reliability
If the digital assistant captures data from sensors only when necessary, then the privacy benefits are enhanced, but the ability to respond to ambiguous requests may be compromised
Solution Approach 1:
The system changes the data capture parameter from continuous recording to on-demand capture based on the determined likelihood that contextual data is required. When the likelihood exceeds a threshold, the camera and other sensors are enabled to capture necessary data; otherwise, no data is captured. This parameter change maintains privacy benefits while ensuring response accuracy when actually needed.
Solution Approach 2:
The system dynamically adjusts data capture behavior based on real-time analysis of each request. The digital assistant evaluates whether additional contextual data is needed to satisfy ambiguous requests and enables sensors only in those specific instances. This dynamic approach balances privacy protection with the ability to provide accurate responses when contextual information is genuinely required.
Data Source
AI summary
Systems and processes for operating a digital assistant are provided. An example process for determining a response includes, at an electronic device having one or more processors and memory, receiving a spoken input including a request, performing a semantic analysis on the spoken input, determining, based on the semantic analysis, a likelihood that the electronic device requires additional contextual data to satisfy the request, and in accordance with the determined likelihood exceeding a threshold, enabling a camera of the electronic device and determining a response to the request based on data captured by the camera of the electronic device.