Anchor Event Detection for Executable Action Suggestions
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Solution Overview
Problem
Existing digital assistants lack the ability to intelligently suggest executable actions based on user behavior patterns detected through electronic device interactions, leading to suboptimal user experiences.
Innovation Solution
A predictive model is trained to detect anchor events and suggest actions based on user behavior patterns, using supervised or unsupervised machine learning to identify correlations between event occurrences and actions, and provide suggested actions within a temporal offset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a digital assistant provides basic command execution, then operational functionality is maintained, but user experience remains suboptimal due to lack of intelligent action suggestions
Solution Approach 1:
The system performs preliminary analysis of user behavior patterns and event correlations before the user needs actions suggested. The predictive model is trained in advance on historical data to identify relationships between anchor events and subsequent user actions, enabling the system to proactively suggest relevant actions before the user explicitly requests them.
Solution Approach 2:
The digital assistant serves itself by automatically analyzing its own operational data and user interaction patterns to generate predictions. The system uses its accumulated knowledge base and trained predictive models to autonomously suggest actions without requiring external configuration or manual programming of specific user behaviors.
2Measurement precision
If the digital assistant analyzes all possible user actions and events, then prediction accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The system segments the vast space of possible user actions and events into manageable categories. It identifies and focuses on 'anchor events' - specific trigger events that have strong correlations with subsequent actions. The predictive model is trained separately on different action types and event categories, allowing the system to achieve high accuracy for specific predictions without needing to process all possible combinations simultaneously.
Solution Approach 2:
The system applies different analysis depths and model complexities to different event-action pairs based on their importance and correlation strength. High-frequency, high-correlation event-action pairs receive more sophisticated analysis and modeling, while less important pairs use simpler heuristics. This localized quality adjustment optimizes overall prediction accuracy while controlling computational resources.
Data Source
AI summary
Systems and processes for providing, via an electronic device, suggested user actions. The suggested actions are provided in response to detecting an occurrence of a predefined event occurring in the user's day. The occurrence of the anchor is encoded in signals generated by the electronic device. The occurrence of the anchor is detectable via monitoring and analysis of electronic signals. Based on the user's previous interactions with the device, the occurrence of the anchor is indicative of user behavior and/or action taken in response to the anchor. Machine learning (ML) is employed to train an anchor model to associate actions taken in response to anchor occurrences. The trained anchor model is employed to detect anchors and provide suggested actions in response to the detected anchor occurrence. The suggested action is based on a type of anchor occurrence and contextual conditions of the anchor occurrences.