Ambient Sensing Assistant Actions for Low-Input Task Automation
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Solution Overview
Problem
Existing automated assistants require explicit user inputs for routine tasks, leading to increased interaction duration and resource consumption.
Innovation Solution
An ambient sensing machine learning model processes sensor data from assistant devices to infer user ambient states and generate suggested actions, which can be proactively suggested or automatically performed, reducing the need for explicit user inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated assistants require explicit user inputs for routine tasks, then task accuracy is ensured, but interaction duration increases and resource consumption increases
Solution Approach 1:
The system performs preliminary sensing and inference of user ambient states before explicit user input occurs. Sensors continuously monitor environmental data and the ML model predicts user intentions in advance, allowing the assistant to proactively suggest or automatically perform tasks before the user needs to explicitly request them, thereby reducing interaction duration while maintaining reliability through confidence threshold validation
2Ease of operation
If automated assistants require explicit user inputs for routine tasks, then user control is maintained, but productivity decreases
Solution Approach 1:
The system dynamically adjusts the level of automation based on ML model confidence thresholds. When confidence is high, tasks are automatically performed with full user control implied through prior consent. When confidence is lower, the system transitions to suggesting actions that require explicit user confirmation, thereby maintaining user control while maximizing productivity through intelligent adaptive automation
3Productivity
If automated assistants process sensor data continuously to infer ambient states, then proactive task suggestion improves, but computational resources increase
Solution Approach 1:
The system applies partial processing by continuously monitoring sensor data at low computational intensity and only performing full ML model inference when ambient state changes are detected or confidence thresholds are met. This selective processing approach enables proactive task suggestion capability while minimizing computational resource consumption by avoiding excessive continuous full-model processing
4Loss of time
If automated assistants automatically perform tasks based on inferred ambient states, then interaction duration is reduced, but risk of incorrect action increases
Solution Approach 1:
The system implements feedback through confidence threshold validation before automatically performing tasks. The ML model continuously evaluates ambient state data and only triggers automatic task performance when the inferred ambient state meets predetermined confidence thresholds. This feedback mechanism reduces interaction duration for high-confidence scenarios while maintaining action accuracy by preventing automatic execution of low-confidence inferences
Data Source
AI summary
Implementations can determine an ambient state that reflects a state of a user and/or an environment of the user based on an instance of sensor data. The ambient state can be processed, using an ambient sensing machine learning (ML) model, to generate suggested action(s) that are suggested to be performed, on behalf of the user, by an automated assistant. In some implementations, a corresponding representation of the suggested action(s) can be provided for presentation to the user, and the suggested action(s) can be performed by the automated assistant in response to a user selection of the suggested action(s). In additional or alternative implementations, the suggested action(s) can be automatically performed by the automated assistant. Implementations can additionally or alternatively generate training instances for training the ambient sensing ML model based on interactions with the automated assistant.