Assistant Action Correlation Using Dynamic Multi-Modal User Context
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing automated assistants struggle with versatility in multi-user environments, leading to incorrect assignment of user commands and wastage of computational resources due to unclear user identification, especially when guest users attempt to modify actions.
Innovation Solution
The automated assistant dynamically selects modalities to correlate user inputs with circumstantial conditions, allowing for efficient identification and modification of actions without explicit authentication, thereby preserving computational resources and reducing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the automated assistant uses explicit authentication for each user input, then user identification accuracy is improved, but response latency increases and computational resources are wasted
Solution Approach 1:
The system performs preliminary user identification using circumstantial conditions (location, device, time, activity) before processing the actual user input. This preliminary action creates a pre-established context that allows subsequent commands to be processed without repeated authentication, reducing latency while maintaining identification accuracy.
Solution Approach 2:
The automated assistant uses the circumstantial data environment (device sensors, location services, activity recognition) to automatically identify users without requiring explicit authentication actions. The system serves itself by leveraging existing contextual information rather than requiring users to provide additional verification.
2Measurement precision
If the automated assistant uses multiple modalities for user identification, then user identification accuracy is improved, but device complexity increases
Solution Approach 1:
The user identification system is segmented into multiple independent modalities (geolocation matching, device identification, activity recognition, time-based context) that operate independently but contribute to the overall identification decision. This modular approach improves accuracy while managing complexity by allowing selective activation of modalities.
Solution Approach 2:
The circumstantial conditions framework serves multiple functions simultaneously: user identification, action correlation, context maintenance, and authentication bypass decisions. This multi-functionality reduces the need for separate systems for each function, thereby managing complexity while improving identification accuracy.
3Productivity
If the automated assistant correlates user inputs with circumstantial conditions, then computational resource efficiency is improved, but reliability of action assignment may worsen
Solution Approach 1:
The system continuously monitors and updates circumstantial conditions, providing feedback loops that refine user identification over time. When actions are correlated with circumstantial data, the system validates assignments and can adjust or request clarification if confidence thresholds are not met, maintaining reliability while improving efficiency.
Solution Approach 2:
The system dynamically adjusts the parameters and thresholds for circumstantial condition matching based on context, user history, and confidence levels. This allows the system to be more efficient in high-confidence scenarios while maintaining reliability by lowering thresholds or requiring additional verification when uncertainty is detected.
Data Source
Figure 1A
Figure 1B
Figure 2
AI summary
Implementations set forth herein relate to an automated assistant that uses circumstantial condition data, generated based on circumstantial conditions of an input, to determine whether the input should affect an action been initialized by a particular user. The automated assistant can allow each user to manipulate their respective ongoing action without necessitating interruptions for soliciting explicit user authentication. For example, when an individual in a group of persons interacts with the automated assistant to initialize or affect a particular ongoing action, the automated assistant can generate data that correlates that individual to the particular ongoing action. The data can be generated using a variety of different input modalities, which can be dynamically selected based on changing circumstances of the individual. Therefore, different sets of input modalities can be processed each time a user provides an input for modifying an ongoing action and/or initializing another action.