Automatic Movement Classification via Geolocation Analysis
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Solution Overview
Problem
Current systems for tracking movement and activity require manual input and classification, which is time-consuming and burdensome, leading to system overload and inefficiencies.
Innovation Solution
Computer-implemented systems and methods that automatically classify movements using geolocation data and timestamps, applying algorithms based on historical criteria to identify and classify trips without user intervention, and provide information to output destinations automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual input and classification methods are used for tracking movement data, then users can provide detailed information about their activities, but the system experiences overload and reduced performance due to the time-consuming and burdensome nature of manual input
Solution Approach 1:
The system automatically classifies movements by analyzing geolocation data patterns itself, without requiring manual user input. The classification algorithm processes location data, timestamps, and movement characteristics to autonomously categorize activities, making the system self-sufficient in data classification while improving performance and reducing user burden
Solution Approach 2:
The patent replaces the mechanical manual input process with an automated computational system that uses algorithms to analyze geolocation data and automatically assign movement classifications, substituting human manual operations with automated information processing
2Loss of information
If manual classification of movements is required, then detailed movement data can be captured, but user burden increases and system responsiveness decreases
Solution Approach 1:
The system performs automatic movement classification using algorithms that analyze geolocation data, timestamps, and movement patterns to assign classifications without user intervention, making the system serve itself in the classification task while maintaining data completeness and improving user convenience
3Productivity
If automated classification algorithms are implemented, then system load is reduced and performance improves, but complexity of the classification system increases
Solution Approach 1:
The classification system is divided into distinct functional modules: a movement identification module that detects movements from geolocation data, a classification module that applies classification rules, and a data provision module that outputs classified data. This segmentation manages complexity by organizing the automated classification process into manageable, independent components that work together to improve system efficiency
Data Source
AI summary
Computer-implemented systems and methods for classifying movement of an object are provided. Geolocation data for an object and timestamps associated with the geolocation data are processed to automatically identify a movement of the object. The movement is characterized by at least (i) timing data, and (ii) location data indicative of starting and ending locations of the object. One or more criteria for classifying the identified movement are accessed, where the one or more criteria are based on historical data for previous movements. An algorithm that evaluates the timing data and the location data of the identified movement against the one or more criteria is applied. The algorithm is configured to automatically assign a classification of a plurality of classifications to the identified movement based on the evaluation. A determination of whether to provide information on the identified movement to an output destination is made based on the assigned classification.