Semantic Association Between Location and Ambient Profiles
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Solution Overview
Problem
Existing location-based services require determining a geographic location before accessing information about nearby venues, parcels, or buildings, and do not allow for the update or expansion of information based on insights from one type of data to correct or enhance the other, leading to inefficiencies.
Innovation Solution
A location information management application establishes semantic associations between location profiles and ambient profiles, such as beacon or sensor profiles, independent of geographic location, allowing for the retrieval and updating of information without requiring a specific device location, and enabling the propagation of information between these profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If geographic location is determined by mobile device before accessing venue information, then location-based services can be provided, but the system requires additional location determination step and cannot access information independently
Solution Approach 1:
The system pre-establishes associations between beacon profiles and location profiles before runtime operations. By performing the association building in advance (extracting patterns from beacon profiles and linking them to location profiles), the system eliminates the need for location determination at query time, allowing direct access to venue information through beacon identification alone.
Solution Approach 2:
The patent introduces beacon profiles as an intermediary between the mobile device and location information. Instead of directly determining geographic location and then accessing venue data, the system uses beacon profiles as a mediator that contains extracted patterns (such as Wi-Fi network identifiers) to directly link observed beacons to location profiles, bypassing the traditional GPS-based location determination step.
2Ease of manufacture
If location profiles and ambient profiles are maintained separately, then data organization is simple, but insights from one data type cannot be used to update or expand the other
Solution Approach 1:
The system merges location profiles and ambient (beacon) profiles into a unified association structure. By combining these previously separate data types through established associations, the system enables cross-pollination of information where insights from beacon observations can update location profiles and vice versa, while maintaining the organizational simplicity of separate profile structures through defined relationship links.
Solution Approach 2:
The patent implements feedback mechanisms where information from beacon profiles flows back to update location profiles and vice versa. The association database enables bidirectional information flow: observed beacon patterns update location profile accuracy, and location profile information refines beacon profile associations, creating a self-improving system that maintains data freshness and accuracy.
3Measurement precision
If geographic location determination is required for every information access, then location accuracy can be maintained, but system efficiency and response time decrease
Solution Approach 1:
The system extracts the essential identifying patterns from beacon profiles (such as Wi-Fi network SSIDs, BSSIDs, or other unique identifiers) and places them directly into the association database alongside location profile information. This extraction allows the system to match observed beacons to locations using these extracted patterns without performing full geographic location determination, maintaining accuracy while dramatically improving access efficiency.
Data Source
AI summary
In one embodiment, techniques are provided to establish and use semantic associations between location profiles and ambient profiles. One or more location profiles are selected from a location database. A first plurality of ambient profiles is selected for a first area surrounding one or more geographic locations of the location profiles. One or more patterns are extracted from the first plurality of ambient profiles and are used to generate associations between location profiles and ambient profiles in an association database which semantically associates location profiles with ambient profiles independent of geographic location. The associations may be used, among other things, to service requests from mobile devices and/or update ambient profiles or location profiles.