Location Analytics Profiling From Time-Series Movement Data
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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 over time, limiting 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
1Adaptability or versatility
If service providers use only time-independent location information, then the system complexity is low, but the service personalization capability is limited
Solution Approach 1:
The patent segments time-independent location information into multiple time-points and further divides the time-series data into sessions and clusters. This segmentation enables extraction of movement patterns and contextual information while maintaining manageable system complexity through structured data organization.
Solution Approach 2:
The patent adds the time dimension to traditional location information by analyzing time-series of location data points. This transforms static location data into dynamic movement trajectories, enabling service providers to understand user behavior patterns without proportionally increasing system complexity.
2Loss of information
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 segments time-series location data into sessions (continuous movement sequences) and clusters (grouped sessions with similar characteristics). This segmentation reduces processing complexity by organizing raw data into meaningful units that can be analyzed for contextual information such as user habits and preferences.
Solution Approach 2:
The patent performs preliminary processing of location data by pre-segmenting it into sessions and clusters before detailed analysis. This preliminary action prepares the data structure in advance, reducing the computational complexity of subsequent contextual information extraction and attribute determination.
3Adaptability or versatility
If service providers ignore movement paths, then the processing speed is high, but the service adaptability deteriorates
Solution Approach 1:
The patent extracts essential movement pattern information from complete time-series location data by identifying key sessions and clusters. This extraction approach captures the necessary contextual information for service adaptability while discarding redundant data, thereby maintaining processing speed.
Solution Approach 2:
The patent applies partial action by analyzing only the most significant movement patterns (key sessions and clusters) rather than processing every single location data point in detail. This selective analysis maintains processing speed while providing sufficient contextual information for service adaptability.
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.