User Profiling Using Time-Based Clustering for ROI Data
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Solution Overview
Problem
Current technologies fail to effectively generate region of interest (ROI) data meaningful to users by utilizing image data and user profiles, particularly in the context of event occurrence times and geographic locations, leading to inadequate personalized services.
Innovation Solution
A method involving the acquisition of source data, clustering based on time data, generating user profiles through neural networks, and creating ROI data that includes location information, using a communication module, memory, and processor to determine relevant geographic regions of interest to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user profiling is performed using traditional methods without time data, then the profile generation process is simpler, but the accuracy and personalization of the generated ROI data deteriorates
Solution Approach 1:
The user profiling process is segmented into multiple stages: data acquisition with time stamps, time-based clustering of source data, feature extraction from clusters, and neural network-based profile generation. This segmentation allows the system to handle complexity in a structured manner while improving ROI data accuracy through progressive refinement at each stage.
Solution Approach 2:
Time data is introduced as an additional dimension for clustering user source data. By organizing data into time-based clusters (e.g., morning, afternoon, evening activities), the system captures temporal patterns that significantly enhance the accuracy of ROI generation without requiring fundamental changes to the overall system architecture.
2Adaptability or versatility
If time data is collected and processed for user profiling, then the personalization of services is improved, but the data processing time and computational resources increase
Solution Approach 1:
Source data is pre-clustered based on time information during data acquisition phases, and feature values are extracted from these pre-formed clusters before being input to the neural network. This preliminary organization of data by time patterns reduces the computational burden during the actual ROI generation process, enabling personalized services without excessive processing delays.
3Reliability
If comprehensive source data is collected for user profiling, then the quality of generated profiles is improved, but the quantity of data to be processed increases
Solution Approach 1:
Instead of processing all raw source data directly, the system extracts representative feature values from time-based clusters of source data. These extracted features capture the essential characteristics of user behavior patterns while reducing the data volume significantly, maintaining profile quality without the computational overhead of processing every individual data point.
Data Source
AI summary
The present disclosure comprise: acquiring source data for generating a profile of a user and time data related with generation of the source data; clustering the source data based on the time data related with the generation of the source data as a category; generating a profile of the user by using the cluster generated through the clustering; and generating region of interest data including information of a geographic region that may be determined to be of interest to the user based on the profile of the user, and wherein the ROI data may include location information of the user, and the profile of the user associated with the time data may be labeled. The intelligent device of the present disclosure may be associated with an artificial intelligence module, drone (unmanned aerial vehicle, UAV), robot, augmented reality (AR) devices, virtual reality (VR) devices, devices related to 5G services, and the like.