Group Identification and Action for Crowd Control
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Solution Overview
Problem
Existing location-based services and data structures fail to effectively identify and manage user behavior when users are traveling in groups, leading to inefficiencies in crowd control and service optimization in venues.
Innovation Solution
A system that examines data from traveling users with associated mobile devices to determine group formations using machine logic, processes historical data to decide actions, and provides outputs for optimizing user behavior and service delivery, such as notifications and order management, through a manager system that integrates location data, social media, and machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If location-based services examine data from multiple traveling users to identify groups, then crowd control and service efficiency are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system segments users into distinct groups based on location data analysis, allowing targeted service delivery to each group. This segmentation enables the system to manage complexity by processing data in manageable units (individual groups) rather than handling all users simultaneously, thereby improving service efficiency while controlling system complexity.
Solution Approach 2:
The patent introduces an intermediary processing layer that analyzes location data from multiple users to identify groups before services are delivered. This intermediary layer abstracts the complexity of multi-user data processing, allowing the system to efficiently identify groups and deliver targeted services without directly managing the full complexity of individual user tracking.
2Adaptability or versatility
If the system processes historical data to determine group activities, then service personalization is improved, but loss of time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis of historical data to identify group patterns and preferences before actual service delivery occurs. By pre-processing historical data to establish group behaviors and preferences, the system reduces real-time processing requirements and enables faster, more personalized service responses without significant time loss.
Solution Approach 2:
The patent implements feedback mechanisms where historical data analysis continuously refines group profiles and service recommendations. This feedback loop allows the system to learn from past data processing and improve efficiency over time, reducing computational resources and processing time while enhancing service personalization through iterative optimization.
Data Source
AI summary
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: examining data of a plurality of traveling users, wherein at least some of the plurality of traveling users have associated mobile client computer devices, and determining, by machine logic, based on the examining that certain traveling users of the plurality of traveling users are traveling in a group; deciding, by machine logic, based on the determining that one or more action is to be performed, wherein the deciding is in dependence on a processing of historical data, the historical data specifying activities of the certain traveling users when the certain traveling users travel in a group; and providing one or more output for performance of the one or more action.