Ambient Sensing ML for Proactive Assistant Action Inference
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing automated assistants require explicit user inputs for routine tasks, leading to increased interaction duration and resource consumption, as they lack the ability to infer ambient states and proactively suggest or perform actions.
Innovation Solution
An ambient sensing machine learning model processes sensor data from assistant devices to determine ambient states and generate suggested actions, which can be presented to the user or automatically performed based on confidence thresholds, with feedback mechanisms to refine the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated assistants require explicit user inputs for routine tasks, then the assistant can accurately perform requested actions, but the interaction duration increases and computational resources are consumed
Solution Approach 1:
The system performs preliminary analysis of ambient sensor data to predict user needs before explicit commands are given. By continuously monitoring environmental context (audio, video, sensor data) and pre-processing this information through machine learning models, the assistant can anticipate required actions and prepare responses in advance, reducing the time users need to spend formulating and issuing commands.
Solution Approach 2:
The automated assistant monitors its own operational context and autonomously determines when to suggest or perform actions without waiting for explicit user requests. By self-activating based on ambient state analysis, the system eliminates the need for users to initiate each interaction, thereby reducing interaction duration while maintaining action accuracy through confidence threshold evaluation.
2Reliability
If automated assistants require explicit user inputs for routine tasks, then the assistant can perform requested actions, but computational and network resources are consumed
Solution Approach 1:
Instead of performing complete action processing for all potential tasks, the system applies partial processing by evaluating only the most probable actions based on ambient context. The machine learning model ranks potential actions by confidence score, and only those exceeding a threshold are pursued further. This partial processing approach reduces computational and network resource consumption while maintaining reliable action performance for high-confidence predictions.
Solution Approach 2:
The system dynamically adjusts the confidence threshold parameter based on operational conditions, user preferences, and resource availability. By changing this parameter, the assistant can balance between performing more actions autonomously (reducing user inputs) and conserving computational resources. This adaptive parameter adjustment allows the system to optimize resource consumption while maintaining reliable action performance.
3Productivity
If the automated assistant infers ambient state and proactively suggests actions, then explicit user inputs are reduced, but the complexity of the system increases
Solution Approach 1:
The system segments the complex ambient sensing and action prediction task into distinct functional modules: audio processing module, video processing module, sensor data processing module, machine learning inference module, and action generation module. Each module handles a specific aspect of the workflow, allowing for independent optimization, easier maintenance, and reduced overall system complexity through modular architecture.
Solution Approach 2:
The patent introduces an ambient state representation as an intermediary data structure that bridges raw sensor data and predicted user actions. This intermediary layer abstracts the complexity of multi-sensor fusion and ML model processing, providing a standardized interface between the sensing subsystem and the action generation subsystem. By using this intermediary representation, the system manages complexity while maintaining high interaction efficiency.
4Productivity
If the automated assistant automatically performs actions based on confidence thresholds, then user inputs are eliminated, but the risk of incorrect actions increases
Solution Approach 1:
The system implements feedback mechanisms where user responses to suggested actions are captured and used to refine the machine learning model. When users confirm, reject, or modify suggested actions, this feedback is fed back into the training data, allowing the model to learn from actual user behavior patterns. This continuous feedback loop improves action accuracy over time, reducing the risk of incorrect automated actions while maintaining high automation levels.
Solution Approach 2:
Before automatically performing actions, the system performs preliminary verification by evaluating multiple candidate actions and selecting only those with confidence scores above a dynamic threshold. This preliminary filtering process reduces the risk of incorrect actions by ensuring that only high-confidence predictions are executed automatically. The system can also present top candidates to users for confirmation when confidence is moderate, further mitigating the risk of errors.
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.