Proactive Task Planning With Trigger-Based User Action Prediction
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Solution Overview
Problem
Existing systems lack the ability to predict user actions proactively and deliver personalized experiences without requiring runtime inference loops, failing to account for individual user interests and preferences.
Innovation Solution
A centralized action prediction system utilizing a generative model to anticipate user actions based on explicit and inferred interests, preferences, and various data sources, generating proactive task plans that are executed upon trigger events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a centralized action prediction system is implemented, then system complexity is reduced and resource utilization is improved, but the ability to predict user actions proactively and deliver personalized experiences is limited without runtime inference loops
Solution Approach 1:
The system performs preliminary action by pre-computing action predictions during offline training phases using generative models. User action predictions are generated in advance based on historical data and user profiles, stored as proactive task plans with trigger events, eliminating the need for complex runtime inference loops while maintaining personalized prediction capabilities.
2Reliability
If multiple systems perform independent action prediction and planning, then system autonomy is maintained, but resource utilization is inefficient and redundancy increases
Solution Approach 1:
The patent merges multiple independent action prediction and planning systems into a single centralized system. The centralized action prediction system consolidates generative models, user profile data, and task planning functions into one unified architecture, eliminating redundant computations across multiple systems while maintaining the autonomy to make independent predictions through centralized intelligence.
3Adaptability or versatility
If runtime inference loops are used for action prediction, then personalized user experiences can be delivered, but system latency increases and real-time performance deteriorates
Solution Approach 1:
The system performs preliminary action by pre-computing personalized action predictions during offline training phases. User-specific task plans with associated trigger events are generated in advance and stored for rapid retrieval. When trigger events occur, the system executes pre-computed predictions immediately without requiring time-consuming runtime inference loops, thus delivering personalized experiences with minimal latency.
Data Source
AI summary
Techniques for predicting an action(s) to perform for a user and, optionally, delivering proactive experiences are described. A system receives data usable to determine a predicted action of a user and invokes a generative model to process the data and determine the predicted action. The system may thereafter determine a system-performable action corresponding to the predicted action and determine a task(s) for executing the system-performable action. The system may also invoke the or another generative model to determine a trigger event(s) for triggering performance of the task(s). The system may receive an event indicating the trigger event(s) has occurred and, based thereon, perform the task(s). Alternatively, a generative model may determine proactive content is to be output during a dialog with the user and, based thereon, the system may perform the task(s).