Personalized Location Services via Mobility Pattern Analysis
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Solution Overview
Problem
Current location-based services (LBS) lack the ability to effectively identify and provide personalized services to users based on their mobility patterns and interests, particularly in a cellular data service network, which limits the serendipitous discovery of relevant locations and interactions with similar users.
Innovation Solution
A method and system that analyze user mobility patterns and application affiliations by classifying data packets to associate user locations with pre-determined application categories, enabling the provision of location-based services tailored to individual interests and enhancing user experiences through enhanced quality of service and information caching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If location-based services are provided using basic location estimation methods (cell tower lookup, triangulation, GPS), then users can access general LBS functions, but the services cannot be effectively personalized based on user mobility patterns and interests
Solution Approach 1:
The system performs preliminary analysis of accounting data packets and application data packets to determine user mobility patterns and application affiliations before providing location-based services. This advance preparation enables personalized service delivery without adding complexity during real-time service provision
Solution Approach 2:
The patent introduces an intermediary analysis layer that processes accounting data and application data to extract mobility patterns and application affiliations. This intermediary layer acts as a mediator between raw network data and personalized LBS delivery, shielding the complexity of pattern recognition algorithms from the service delivery mechanism
2Adaptability or versatility
If user mobility patterns and application data are analyzed in real-time to provide personalized services, then service personalization is improved, but processing time and computational resources increase
Solution Approach 1:
The system analyzes accounting data packets and application data packets in advance to determine user mobility patterns and application affiliations before actual service delivery. This preliminary analysis stores processed information that can be quickly retrieved during service provision, avoiding real-time processing delays
Solution Approach 2:
The patent implements a dynamic approach where the level of analysis and personalization adapts based on service requirements and available resources. The system can adjust the granularity of mobility pattern matching and application category classification to balance personalization quality with processing time constraints
3Measurement precision
If comprehensive data packets (accounting and application data) are collected and analyzed to determine user patterns, then accuracy of user profiling is improved, but data processing complexity and resource consumption increase
Solution Approach 1:
The patent extracts only the essential and relevant features from comprehensive data packets - specifically mobility patterns from accounting data and application affiliations from application data. This selective extraction focuses processing on key characteristics that drive personalization decisions, filtering out redundant information
Solution Approach 2:
The system applies different levels of analysis to different data types based on their specific characteristics and contribution to personalization. Accounting data receives mobility pattern analysis while application data receives application category classification, with each processed according to its local requirements rather than uniform complex processing
Data Source
AI summary
A method for providing location based service in a cellular data service network (CDSN) by analyzing accounting data packets of the CDSN to determine a user mobility pattern, classifying application data packets of the CDSN into pre-determined application categories, analyzing the accounting data packets and the application data packets to associate the user mobility pattern and one of the pre-determined application category, comparing a newly received accounting data packet and the user mobility pattern to identify a match, and providing, in response to identifying the match, the location based service to a user based on the pre-determined application category.