Personal Assistant Module Non-Deterministic Task Initiation
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Solution Overview
Problem
Personal assistant modules often require additional user input to determine the correct action for ambiguous declarations due to insufficient intelligence in recalling user preferences and contexts, leading to increased computational resource expenditure and user inconvenience, especially in situations like driving.
Innovation Solution
A personal assistant module is provided with access to a user's interaction history across multiple devices, allowing it to initiate a non-deterministic task initiation process that identifies candidate responsive actions and determines their probabilities based on past interactions and contexts, selecting a single action stochastically to minimize user input and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the personal assistant module solicits additional information from the user to determine the correct action, then the accuracy of action selection is improved, but the computational resource expenditure and user inconvenience increase
Solution Approach 1:
The system performs preliminary analysis of the ambiguous declaration against the interaction history database before soliciting user input. By pre-computing candidate actions and their probabilities based on historical data, the system reduces the need for extensive back-and-forth information gathering, thereby improving accuracy while minimizing computational resource expenditure during active user interaction.
Solution Approach 2:
The personal assistant module autonomously determines candidate actions and their probabilities by self-service analysis of the interaction history, without requiring user intervention. The system serves itself by automatically recalling relevant historical contexts and computing action probabilities, thereby improving accuracy while avoiding the computational overhead of repeated user solicitation cycles.
2Measurement precision
If the personal assistant module solicits additional information from the user, then the accuracy of action selection is improved, but the user convenience deteriorates
Solution Approach 1:
The system performs preliminary analysis of the ambiguous declaration against the interaction history database before soliciting user input. By pre-computing candidate actions and their probabilities based on historical data, the system reduces the need for extensive back-and-forth information gathering, thereby improving accuracy while minimizing user inconvenience.
Solution Approach 2:
The system uses interaction history as feedback to improve future action selections. By analyzing past user responses and preferences, the personal assistant learns to predict user intent more accurately, reducing the frequency and need for additional information solicitation, thereby improving both accuracy and user convenience over time.
3Measurement precision
If the personal assistant module identifies multiple candidate responsive actions, then the accuracy of action selection is improved, but the device complexity increases
Solution Approach 1:
The system extracts only the most relevant features and contexts from the interaction history database that are necessary for determining candidate actions. By selectively extracting pertinent historical data rather than processing the entire database, the system improves action selection accuracy while reducing processing complexity and computational overhead.
Solution Approach 2:
The system changes parameters by computing probabilities for each candidate action based on historical analysis, then uses these probability parameters to rank and select the most likely intended action. This parameter-based approach transforms a complex multi-candidate evaluation problem into a more manageable probability comparison task, improving accuracy while controlling complexity.
Data Source
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AI summary
Techniques are described herein for reducing the need by a personal assistant module to solicit additional information from a user to identify which responsive action to take in response to a received user declaration. In various implementations, upon identifying a user declaration received at a computing device, a plurality of candidate responsive actions that can be initiated by the computing device in response to the user declaration may be identified. A single candidate responsive action may then be non-deterministically (e.g., randomly, stochastically) selected to be exclusively initiated on the computing device in response to the user declaration.