User Profile Clustering for Accurate Region of Interest Generation
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Solution Overview
Problem
Current big data technologies lack the ability to effectively generate a region of interest (ROI) meaningful to users and create appropriate user profiles, which are essential for providing personalized services.
Innovation Solution
A method and device that acquire source data to create user profiles by clustering location information and event data, using artificial neural networks to extract feature values and generate ROI data labeled with user profiles, including geographic regions of interest based on user behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional big data technologies are used to analyze user data, then data processing capability is maintained, but the ability to generate meaningful ROI and create accurate user profiles deteriorates
Solution Approach 1:
The patent segments user data into multiple dimensions including location information, event data, visit history, and demographic information. By dividing the comprehensive user data into distinct segments, the system can analyze each dimension separately and combine them to create more accurate user profiles and identify meaningful ROIs, thereby improving measurement precision without losing information.
Solution Approach 2:
The patent introduces temporal dimensions by analyzing visit history and event timing, and spatial dimensions by processing location information. This multi-dimensional approach transforms traditional single-dimensional data analysis into a comprehensive multi-dimensional user profiling system, enabling better ROI generation and profile accuracy.
2Measurement precision
If comprehensive source data is collected for user profiling, then profile accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent divides the complex data processing system into modular components: location information processing module, event data processing module, visit history analysis module, and profile generation module. Each module handles specific data types independently, reducing overall system complexity while maintaining comprehensive analysis capability for accurate user profiling.
Solution Approach 2:
The patent introduces intermediate data structures such as event data formats and visit history records that serve as mediators between raw source data and final user profiles. These intermediaries organize and standardize data from multiple sources, simplifying the processing pipeline and making the system more manageable despite handling comprehensive data.
3Measurement precision
If location information and event data are clustered together, then ROI generation accuracy improves, but computational requirements increase
Solution Approach 1:
The patent segments the clustering process into location-based clustering and event-based clustering that are performed separately and then integrated. This segmentation allows the system to apply appropriate algorithms to each data type and reduces the computational burden compared to clustering all data together, while still achieving accurate ROI identification through the combined results.
Solution Approach 2:
The patent applies clustering algorithms selectively to the most relevant data subsets rather than processing all user data uniformly. By focusing computational resources on key data elements that most influence ROI accuracy, the system achieves high identification accuracy while optimizing energy consumption through partial processing action.
Data Source
AI summary
Disclosed are an artificial device and a method for controlling the same. In the method for controlling the artificial device according to an embodiment of the present specification, source data for creating a profile of a user is acquired and a cluster including location information of the user and data on an event associated with the location information is created. In addition, the profile of the user is created by using the cluster, and region of interest (ROI) data is generated based on the profile of the user. According to the method, the ROI data including information on a region of interest to the user may be generated. The intelligent device of the present disclosure can be associated with artificial intelligence modules, drones (unmanned aerial vehicles (UAVs)), robots, augmented reality (AR) devices, virtual reality (VR) devices, devices related to 5G service, etc.