Predictive Assistant Action Scheduling for Incomplete Context Data
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Solution Overview
Problem
Existing machine-learned assistant systems face inefficiencies due to reliance on synchronous user inputs and static scheduling parameters, leading to errors and wasted computational resources when executing tasks with incomplete data.
Innovation Solution
Implementing a machine-learned assistant system that uses scheduling queries to determine optimal execution times based on dynamic context data availability, deferring actions until sufficient data is available and continuously updating task schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system executes actions synchronously based on static scheduling parameters, then the system operation is simple, but errors and computational waste increase due to incomplete data
Solution Approach 1:
The patent implements dynamic scheduling by replacing static scheduling parameters with a machine-learned model that continuously predicts optimal execution times based on evolving context data availability. The system dynamically adjusts action schedules according to real-time data conditions, transitioning from rigid synchronous execution to flexible predictive scheduling that adapts to changing data availability patterns.
Solution Approach 2:
The system incorporates feedback mechanisms where the machine-learned model continuously monitors context data availability and adjusts action scheduling accordingly. The model learns from historical patterns of data availability and action execution, using this feedback to improve future scheduling decisions and reduce errors caused by incomplete data.
2Reliability
If the system waits for complete data before executing actions, then execution accuracy improves, but time consumption increases
Solution Approach 1:
The system performs preliminary actions by predicting future data availability times using the machine-learned model, rather than waiting passively for complete data. The model proactively identifies optimal execution windows based on historical patterns, allowing the system to prepare and schedule actions in advance while maintaining high data completeness at execution time.
Solution Approach 2:
The patent changes the parameter of execution timing from fixed synchronous moments to predicted optimal times based on data availability patterns. By transforming the scheduling parameter from static to predictive, the system can execute actions at the optimal moment when data is most likely to be complete, balancing time waiting with execution reliability.
3Productivity
If the system uses static scheduling parameters, then computational resources are consumed efficiently, but energy consumption increases due to redundant computations
Solution Approach 1:
The system changes the scheduling parameter from static to dynamic predictions based on machine learning models that analyze context data availability patterns. This parameter transformation enables the system to identify optimal execution times that maximize data completeness while minimizing redundant computational attempts, thereby reducing energy consumption without sacrificing productivity.
Solution Approach 2:
The machine-learned model continuously feedbacks on data availability patterns and adjusts scheduling decisions to avoid redundant computations. By learning from historical execution outcomes and data availability patterns, the system eliminates wasteful computational attempts that would occur with static scheduling, optimizing both productivity and energy efficiency.
Data Source
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AI summary
An example method includes generating, using a machine-learned assistant system and based on a first input, an action object comprising data describing an assistant action to be performed by the machine-learned assistant system. The example method includes generating, using the machine-learned assistant system and based on a second input, a time for executing the assistant action, wherein the second input is based on at least a portion of a cache of available context data. The example method includes queuing the action object for execution. The example method includes executing the assistant action associated with the action object using the machine-learned assistant system.