Contextual Automatic Grouping for Shared Event Detection
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Solution Overview
Problem
Existing systems lack an efficient method to automatically detect shared events among users without predefined event definitions, leading to unnecessary computations and reduced system bandwidth.
Innovation Solution
A computing system analyzes data signals from user devices to identify connections and contexts, determining the occurrence of shared events by comparing data signals from associated users, such as location and ambient noise data, to indicate when a shared event is happening or has occurred.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses predefined event definitions to detect shared events, then the detection accuracy is improved, but the device complexity and processing overhead increase significantly
Solution Approach 1:
The system enables user devices to autonomously detect shared events by comparing their own context data with that of other users, without requiring centralized predefined event definitions. Each device independently determines event occurrence based on contextual similarities, eliminating the need for complex system-wide event management infrastructure.
Solution Approach 2:
Context data serves as an intermediary that indirectly indicates event occurrence. Instead of directly defining and detecting specific events, the system uses contextual parameters (location, time, activity) as mediators to infer shared event participation, simplifying the detection mechanism while maintaining accuracy.
2Reliability
If the system analyzes context data from all users to detect shared events, then the event detection completeness is improved, but the loss of time and processing bandwidth increase
Solution Approach 1:
The system segments the user base into relevant subsets by identifying connections (social relationships, contact lists) before context comparison. This segmentation allows the system to focus analysis only on connected users who are likely to share events, rather than analyzing all users universally, thereby reducing processing time while maintaining detection completeness.
Solution Approach 2:
The system performs preliminary identification of connected users and their relationships before conducting context data comparison. By pre-establishing which users are relevant to each other through connection analysis, the system prepares the groundwork for efficient event detection, reducing the time required during actual event occurrence analysis.
3Measurement precision
If the system compares context data from connected users, then the event detection accuracy is improved, but the quantity of data processing increases
Solution Approach 1:
The system applies different processing strategies to different user groups based on their connection types. Connected users (social connections) receive more intensive context comparison, while contact list users receive lighter processing. This localized quality approach ensures high detection accuracy for closely connected users while reducing overall data processing volume across the entire system.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing event detection are disclosed. In one aspect, a method a computing system that receives data from a first computing device associated with a first user that indicates a current context of the first user. The method includes identifying a subset of users associated with the first user based on the current context of the first user, and receiving data indicating a current context of the at least one other user. The method compares the current context of the first user with the current context of the at least one other user and determines that a shared event is presently occurring or has occurred. The shared event can be an event associated with the first user and the at least one other user of the subset of users. The method then indicates, at least to the first user, that the shared event is presently occurring or has occurred.