Shared Event Detection for Timely Activity Suggestions
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Solution Overview
Problem
Existing systems lack the ability to automatically detect shared events among users and provide actionable suggestions related to those events, leading to inefficient processing and unnecessary computations.
Innovation Solution
A computing system that analyzes data signals from user devices to detect shared events and selects appropriate activity suggestions using machine-learning algorithms, eliminating the need for predefined templates and optimizing processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system uses predefined templates for all possible events, then it can provide comprehensive activity suggestions, but the device complexity and processing overhead increase significantly
Solution Approach 1:
The system employs machine learning algorithms that automatically learn and adapt to different event types from data patterns, eliminating the need for manual template creation and maintenance. The algorithms self-optimize by identifying event characteristics and generating appropriate activity suggestions without human intervention, reducing the burden of system configuration and updates.
Solution Approach 2:
The patent transforms the static template-based approach into a dynamic parameter-driven system. Instead of relying on fixed templates, the system uses machine learning models that adjust their parameters based on input data characteristics, enabling flexible adaptation to various event types while maintaining computational efficiency through optimized model parameters.
2Reliability
If the system processes all data signals from all users continuously, then it can detect any shared event, but the system bandwidth and energy consumption increase
Solution Approach 1:
The system performs preliminary filtering and preprocessing of data signals before main analysis. It identifies and eliminates irrelevant or redundant data early in the processing pipeline, reducing the volume of data that requires intensive computation. This preliminary action maintains detection accuracy by preserving critical event indicators while discarding unnecessary information.
Solution Approach 2:
The patent implements selective processing where the system applies full analysis only to data signals that show potential event characteristics, while applying lighter processing to other signals. This partial action approach maintains reliability for critical detections while reducing overall system bandwidth consumption by avoiding exhaustive processing of all incoming data.
3Ease of operation
If the system provides activity suggestions for all detected events, then user engagement increases, but the loss of time for processing and generating suggestions increases
Solution Approach 1:
The system generates activity suggestions selectively based on event confidence levels and user preferences. For high-confidence shared events with strong user interest indicators, the system provides comprehensive suggestions. For lower-confidence or less relevant events, it provides minimal or no suggestions, reducing processing time while maintaining user engagement for the most valuable cases.
Solution Approach 2:
The patent implements periodic updates of activity suggestions rather than continuous generation. Once suggestions are provided for an event, the system monitors for significant changes in event status or user behavior before generating new suggestions. This periodic action reduces processing time by avoiding redundant suggestion generation while keeping users engaged through timely updates when relevant.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing actionable suggestions are disclosed. In one aspect, a method includes receiving (i) an indication that an event detection module has determined that a shared event of a particular type is presently occurring or has occurred, and (ii) data referencing an attribute associated with the shared event. The method includes selecting, from among multiple output templates that are each associated with a different type of shared event, a particular output template associated with the particular type of shared event detected by the module. The method generates a notification for output using at least (i) the selected particular output template, and (ii) the data referencing the attribute associated with the shared event. The method then provides, for output to a user device, the notification that is generated.