User Profiling via Spatio-Temporal Data Analysis
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Solution Overview
Problem
Existing location-based services cannot provide unique descriptions of individuals based on their geolocation alone, as they fail to account for past and present locations, demographics, and activities, leading to inappropriate service delivery.
Innovation Solution
A data-processing system that receives and evaluates spatial and temporal data points to estimate user descriptions by considering demographic, commercial, activity, and travel patterns, enabling unique user profiling and customizable services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If location-based services use only current geolocation data to describe users, then the system is simple and fast, but the user description is not unique and cannot distinguish between different users at the same location
Solution Approach 1:
The patent transitions from two-dimensional current location data to three-dimensional spatio-temporal data by adding the time dimension. It collects location data at multiple time points (first location at first time, second location at second time) to create a temporal sequence that uniquely identifies user patterns, resolving the ambiguity of multiple users being at the same location simultaneously.
Solution Approach 2:
The system performs preliminary data collection and evaluation of spatio-temporal patterns before delivering services. It continuously receives and stores location data over time, establishing a historical record of user movements and patterns that can be referenced when determining appropriate services, rather than reacting only to current location.
2Measurement precision
If the system collects and evaluates spatio-temporal data points to create unique user descriptions, then user profiling accuracy improves, but data processing complexity and time requirements increase
Solution Approach 1:
The system continuously receives and processes spatio-temporal data points in an ongoing manner rather than performing batch processing. This continuous evaluation allows the system to maintain updated user profiles without requiring large time investments for processing, as data is processed in real-time or near-real-time as it is collected.
Solution Approach 2:
The patent extracts and evaluates specific characteristics from the spatio-temporal data points, such as movement patterns, temporal sequences, and location transitions. By focusing on extracting only the relevant pattern information rather than processing all raw data, the system achieves accurate user profiling with reduced processing overhead.
3Productivity
If location-based services consider only present location context, then service delivery is fast and simple, but service relevance decreases when multiple users are at the same location
Solution Approach 1:
The system dynamically adapts service delivery based on the user's spatio-temporal pattern history rather than using a static approach based solely on current location. It evaluates the sequence and timing of locations to determine the user's likely intentions and preferences, allowing the same location to trigger different services for different users based on their individual patterns.
Data Source
AI summary
A description of a user is estimated based on the context of a user's past and present locations. A disclosed data-processing system continually receives data points for each user that represent spatial and/or temporal events. These events represent, for example, presence of a person at a specific geographic location such as a geographic area or point of interest (POI). The data-processing system evaluates the received data points in relation to one or more of the geographic locations, yielding results that are also based on the demographic characteristics of each visited location and the commercial characteristics of each visited location. The data-processing system evaluates the data points also to determine patterns exhibited in each user's activity or inactivity, and patterns exhibited in the distance traveled and the type of travel. The data-processing system bases the user descriptions on the results of these evaluations.