Electronic Device Personalized Health Recommendation via User Data Segmentation
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Solution Overview
Problem
Existing electronic devices lack an effective method to provide personalized health-related services by leveraging user data and social relationships, limiting their ability to promote healthy behaviors through competition and encouragement.
Innovation Solution
An electronic device equipped with a communication module, processor, and memory that gathers user data, sends requests to an external server, and provides information about similar users within specific categories, enabling personalized health recommendations and competitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If electronic devices provide general health services without personalization, then service coverage is broad, but user engagement and effectiveness are limited
Solution Approach 1:
The patent segments user data into multiple categories (health data, activity data, social data) and processes each category separately to identify similar users. This segmentation approach enables personalized service without overwhelming the system with undifferentiated data, resolving the contradiction between personalization capability and data processing complexity
Solution Approach 2:
The patent changes the parameter of user matching from general demographics to multiple specific dimensions including health metrics, activity patterns, and social characteristics. This multi-parameter approach enables precise personalization while the systematic organization of these parameters prevents processing complexity from becoming unmanageable
2Measurement precision
If electronic devices collect and process extensive user data for personalized services, then recommendation accuracy improves, but data privacy concerns and processing time increase
Solution Approach 1:
The patent performs preliminary categorization and organization of user data before the actual matching process. By pre-structuring health data, activity data, and social data into standardized formats, the system reduces the time required for subsequent matching operations while maintaining high accuracy in identifying similar users
Solution Approach 2:
The patent extracts only the essential and relevant features from extensive user data for the matching process, rather than processing all available data. This extraction of key characteristics (health metrics, activity patterns, social attributes) maintains matching accuracy while significantly reducing processing time and data privacy exposure
Data Source
AI summary
An electronic device includes a housing, a communication module positioned inside the housing, a processor positioned inside the housing and operatively connected with the communication module, a sensor module operatively connected with the processor, and a memory positioned inside the housing and operatively connected with the communication module, the sensor module, and the processor. The memory stores instructions configured to, when executed, enable the processor to gather data related to a first user, send a request for a user group corresponding to a first category among a plurality of categories to an external server using the communication module, obtain the user group corresponding to the first category based on at least part of the data related to the first user from the external server using the communication module, and provide information about at least one second user in the obtained user group.