Hierarchical Grid Location Tracking for Mobile Devices
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Solution Overview
Problem
Current location determination systems, such as GPS, provide insufficient information about a mobile device's location in terms of whether it is within specific geographical regions like stores, homes, or commercial districts, and are computationally intensive due to the use of methods like Ray Casting.
Innovation Solution
A computationally efficient method using a hierarchical grid system to determine if a mobile device is within a predetermined geographical boundary by converting its coordinates to cell identifiers and searching for matches in a database of cell identifiers associated with regions, reducing the number of cells and improving precision through different resolution levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Ray Casting method is used to determine whether a point is within a polygon, then location determination accuracy is improved, but computational complexity increases significantly
Solution Approach 1:
The patent divides the geographical space into a hierarchical grid system where the earth's surface is segmented into cells of varying resolutions. Each cell is identified by a unique cell identifier, transforming the continuous geographical space into discrete, manageable units. This segmentation allows the system to determine location by simple cell identifier matching rather than complex geometric calculations.
Solution Approach 2:
The patent creates a simplified representation of geographical regions by storing cell identifiers that correspond to predefined regions in a database. Instead of working with complex polygon geometries, the system uses a copy or representation of the region boundaries in terms of cell identifiers, enabling efficient lookup and comparison operations.
2Measurement precision
If high resolution grid system is used to improve location precision, then measurement precision is improved, but the number of cells increases leading to increased computational complexity
Solution Approach 1:
The patent implements a dynamic resolution system where the grid resolution is adjusted based on the specific location and requirements. Different regions can have different resolution levels, allowing the system to maintain high precision where needed while using coarser grids in other areas, thus optimizing the balance between precision and computational complexity.
Solution Approach 2:
The patent employs a hierarchical grid structure where finer resolution grids are nested within coarser resolution grids. This nested structure allows the system to first identify a general region using a coarse grid, then progressively refine the location using nested finer grids only where necessary, reducing the overall number of cells that need to be processed.
3Loss of information
If comprehensive location information is tracked to improve user profiling accuracy, then information completeness is improved, but data processing complexity increases
Solution Approach 1:
The patent extracts only the essential location information needed for user profiling by mapping device locations to cell identifiers and then to predefined regions. Instead of processing complete geographical coordinates and boundary geometries, the system extracts and stores only the relevant region identifiers, significantly reducing data processing complexity while maintaining information completeness for profiling purposes.
Data Source
AI summary
Systems and methods including mobile devices determining their locations using location determination units, such as GPS receivers. Locations of the mobile devices and predefined locations/regions that have been visited by the mobile devices during a period of time are efficiently organized into a location graph that identifies the neighbors of predefined locations, distances to neighbors, and visitation counts of mobile devices to the predefined locations during the period of time. Keyword values for keywords representing user attributes are assigned to mobile devices and the predefined locations. Based on the location graph, the keyword values are propagated via the location graph, from neighbors to predefined locations, from mobile devices to predefined locations, and from predefined locations to mobile devices, to incrementally improve the accuracy of the keyword values over a number of time periods.