Dynamic Geolocation Event Detection for Content Targeting
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Solution Overview
Problem
Existing content distribution systems struggle to effectively target and deliver content to geolocations, particularly at transient events where crowds form, due to unreliable wireless media and the challenge of distinguishing between routine and special events, leading to inefficiencies and false positives.
Innovation Solution
A system that dynamically detects events by analyzing data from mobile devices to infer crowd presence and proximity, allowing for the selective provision of content to users based on event criteria, such as time-limited, trigger-based, or post-event offers, without requiring pre-defined geofences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content targeting systems use pre-defined geofences and labeled geographic areas, then content delivery accuracy is improved, but the system cannot detect transient events where crowds form quickly enough
Solution Approach 1:
The system transitions from static pre-defined geofences to dynamic crowd detection by continuously monitoring mobile device geolocations and identifying transient gatherings in real-time, allowing the system to adapt to changing event conditions
Solution Approach 2:
The system performs preliminary crowd detection and event identification by analyzing aggregated geolocation data before content delivery is needed, enabling proactive content distribution to emerging events
2Reliability
If users repeatedly seek access to wireless media at events, then content delivery reliability is improved, but battery consumption increases significantly
Solution Approach 1:
The system uses passive geolocation data from mobile devices to identify crowds and events without requiring active user participation or repeated wireless connections, allowing the network to self-organize content delivery based on detected event patterns
3Productivity
If wireless media access is increased to improve content delivery, then content distribution speed is improved, but wireless media reliability deteriorates due to crowding
Solution Approach 1:
The system introduces an intermediary crowd detection layer that aggregates geolocation data to identify event patterns, enabling content delivery decisions without requiring direct wireless communication between all users and content servers
4Area of stationary object
If the system targets content to all crowded areas, then content delivery coverage is improved, but false positives increase due to routine crowds
Solution Approach 1:
The system applies different analysis criteria to different geographic contexts by examining local patterns of crowd formation, duration, and density to distinguish between routine gatherings and special events warranting content delivery
Data Source
AI summary
Provided is a process of selectively providing content to computing devices based on geographic proximity to dynamically detected events drawing crowds, the process including: obtaining, with one or more computers, data indicative of current geolocations of more than 5,000 mobile computing devices based on information reported by an application executing on the mobile computing devices; inferring, with one or more computers, that an event with a crowd is occurring based on the data indicative of the geolocations indicating an amount of people and a proximity of the people; selecting, with one or more computers, content in response to the inference; and sending, with one or more computers, the selected content to one or more user computing devices for presentation based on proximity between the one or more user computing devices and a geographic location of the event with the crowd.