Location Analytics Using Movement Paths for User Profiling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current location-based services primarily utilize time-independent location information, neglecting the rich contextual information embedded in the path traveled by mobile devices, which limits their ability to provide personalized and adaptive services.
Innovation Solution
A location information analytics mechanism that analyzes time-series of location data points to determine attributes and profiles of target entities, using session and cluster segmentation, annotation information, and machine learning techniques to predict future behaviors and provide personalized services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If service providers use only time-independent location information, then the service implementation is simple, but the service personalization and adaptability are limited
Solution Approach 1:
The patent segments location information into multiple dimensions: time-independent location data and time-dependent path data. By dividing the location information analysis into separate temporal components, the system can process simple location-based services using only static location data while enabling personalized services by incorporating temporal path patterns, thus resolving the contradiction between implementation simplicity and service personalization capability
Solution Approach 2:
The patent adds a temporal dimension to traditional location information by analyzing the path traveled over time. This transforms static 2D location coordinates into dynamic 4D spatiotemporal trajectories, enabling services to distinguish between different users visiting the same location through their unique movement patterns, thereby enhancing service personalization without abandoning simple location-based services
2Adaptability or versatility
If service providers analyze time-series location data to extract contextual information, then service personalization improves, but data processing complexity increases
Solution Approach 1:
The patent extracts only the necessary contextual features from time-series location data, such as frequently visited locations, path patterns, and temporal behaviors, rather than processing the entire raw dataset. This selective extraction of relevant attributes reduces computational complexity while maintaining the ability to provide personalized services
Solution Approach 2:
The patent performs preliminary processing of location data by pre-computing and storing aggregated path patterns and frequently visited locations. This preliminary analysis creates processed features that can be quickly retrieved and used for service personalization, reducing the computational burden during actual service delivery
Data Source
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.