Movement Path Analytics for Context-Aware Mobile Entity Profiling
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Solution Overview
Problem
Current service providers largely ignore the rich contextual information embedded in location data, focusing only on time-independent location information, thus missing the path traveled by mobile devices over time, which limits the adaptability of services.
Innovation Solution
An apparatus and method that analyze time-series of location data points to determine attributes and create profiles of target entities, incorporating accuracy determination, session grouping, clustering, and annotation information to provide contextual insights for personalized services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If service providers use only time-independent location information, then the service implementation is simple, but the service adaptability and personalization are limited
Solution Approach 1:
The patent segments location data into distinct components: time-independent location information and time-series movement data. By separating these elements, the system can process complex temporal patterns independently while maintaining simple service logic for basic location-based functions, thus improving adaptability without proportionally increasing overall system complexity.
Solution Approach 2:
The patent adds the time dimension to traditional location data by incorporating time-series data points that track movement paths. This transforms static 2D location information into dynamic 4D data (latitude, longitude, time, sequence), enabling services to adapt to user behavior patterns over time while maintaining a structured approach that manages complexity through dimensional organization.
2Loss of information
If service providers analyze time-series location data to extract contextual information, then service personalization improves, but data processing time and computational resources increase
Solution Approach 1:
The patent extracts specific contextual attributes from time-series location data such as movement patterns, frequently visited locations, and behavioral characteristics. By selectively extracting only the most relevant contextual information rather than processing all raw data, the system recovers valuable contextual insights while minimizing the time and computational resources required for analysis.
Solution Approach 2:
The patent performs preliminary processing of time-series location data by pre-computing and storing contextual attributes such as user profiles and movement patterns. This preliminary action allows the system to have contextual information readily available when needed for service personalization, reducing the processing time required during actual service delivery.
3Productivity
If service providers ignore movement paths and focus only on current location, then processing speed is fast, but service relevance and user experience deteriorate
Solution Approach 1:
The patent creates simplified representations or copies of complex movement path data in the form of aggregated contextual attributes and user profiles. These copied representations capture the essential behavioral patterns without requiring full processing of detailed path information, enabling fast processing speeds while maintaining service relevance through the use of pre-computed behavioral characteristics.
Data Source
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AI summary
The present disclosure relates to apparatus, systems, and methods for providing a location information analytics mechanism. The location information analytics mechanism is configured to analyze location information to extract contextual information (e.g., profile) about a mobile device or a user of a mobile device, collectively referred to as a target entity. The location information analytics mechanism can include analyzing location data points associated with a target entity to determine features associated with the target entity, and using the features to predict attributes associated with the target entity. The set of predicted attributes can form a profile of the target entity.