Predictive Action Scheduling in Machine-Learned Assistants

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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 inaccuracies and computational waste when executing tasks with incomplete data.

Innovation Solution

Implementing machine-learned models to evaluate context data availability and predict optimal execution times, allowing deferred execution until sufficient data is available, and dynamically updating task schedules based on new context.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If machine-learned assistant systems use synchronous user inputs and static scheduling parameters, then the system operation is simple, but the task execution accuracy deteriorates due to incomplete data

Engineering Contradiction:

Improvetask execution accuracyVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent implements dynamic scheduling by replacing static scheduling parameters with a machine-learned model that continuously evaluates context data availability and predicts optimal execution times. The system dynamically adjusts task schedules based on real-time data availability, transitioning from rigid static scheduling to flexible dynamic scheduling that adapts to changing conditions.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system incorporates feedback mechanisms where the machine-learned model continuously monitors context data availability and uses this feedback to predict when tasks should be executed. The model learns from historical data and performance metrics, adjusting its predictions to improve task execution accuracy over time.

Inventive Principle:

Principle #23Feedback

2Loss of energy

If the system executes tasks immediately without checking data availability, then the response time is short, but computational waste increases due to incomplete data

Engineering Contradiction:

Improvecomputational wasteVSAvoidtask execution delay

Core Design Contradiction:

Loss of energyVSLoss of time

Solution Approach 1:

The system performs preliminary evaluation of context data availability before task execution using a machine-learned model. This preliminary action predicts whether sufficient data exists for accurate task execution, allowing the system to defer execution until data is available rather than attempting immediate execution with incomplete data, thereby avoiding computational waste.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent changes the parameter of task execution timing from immediate or fixed-time execution to data-driven timing based on predicted data availability. The system adjusts execution parameters dynamically, waiting until sufficient context data is available before initiating task execution, thus optimizing the balance between response time and computational efficiency.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If the system uses static scheduling parameters, then the implementation is simple, but the adaptability to changing context data deteriorates

Engineering Contradiction:

Improveadaptability to context dataVSAvoidscheduling mechanism complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent replaces the mechanical/static scheduling system with a machine-learned predictive model. Instead of using fixed scheduling parameters, the system substitutes a data-driven machine-learned model that evaluates context data availability and predicts optimal execution times, enabling adaptive scheduling that responds to changing conditions.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system transforms static scheduling parameters into dynamic, data-driven execution timing predictions. The machine-learned model continuously monitors and adapts to changes in context data availability, adjusting task schedules in real-time to match actual data conditions rather than relying on predetermined static parameters.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20260003672A1Machine-Learned Assistant Systems with Predictive Action Scheduling

Publication Date: 2026.01.01 GOOGLE LLC

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.