Location Prediction Using Wireless Signals on Social Networks
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Solution Overview
Problem
Current social networking systems face challenges in predicting future geographic locations users will visit and accurately determining their current location, especially with ambiguous GPS data and filtering high-quality information from diverse sources in place-entities graphs.
Innovation Solution
Implementing a machine learning model that uses embeddings to predict the next geographic location a user will visit based on previous locations, and employing background signal information to disambiguate user location, while filtering out low-quality place-entities by calculating cluster-quality scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS data is used to determine user location, then location information is obtained, but the data may be ambiguous and inaccurate
Solution Approach 1:
The patent introduces wireless signal data (Wi-Fi, Bluetooth, cellular) as intermediary information to bridge the gap between ambiguous GPS data and accurate location determination. By combining GPS coordinates with surrounding wireless signal characteristics, the system creates a more reliable location profile that can disambiguate GPS errors through cross-validation with multiple data sources.
Solution Approach 2:
The system employs multiple location determination methods (GPS, wireless signal triangulation, crowd-sourced location data) that can function independently or in combination. This multi-functional approach ensures that when GPS data is ambiguous or unavailable, alternative methods can provide location information, thereby improving overall reliability without sacrificing the speed and simplicity of GPS-based solutions.
2Productivity
If machine learning models predict future user locations, then computational resources are reduced by preemptive information delivery, but the complexity of the system increases
Solution Approach 1:
The patent implements location prediction using machine learning models that analyze historical user movement patterns to forecast future locations before users actually arrive there. This preliminary action allows the system to pre-load and prepare location-based content, advertisements, and information, delivering them to users proactively rather than reactively, thereby improving information delivery efficiency while managing complexity through automated predictive algorithms.
Solution Approach 2:
The system incorporates feedback loops where predicted locations are continuously validated against actual user location data. This feedback mechanism allows the machine learning models to refine their predictions over time, improving accuracy while maintaining manageable system complexity through iterative optimization rather than requiring overly complex initial models.
3Loss of information
If diverse place-entity information is collected from multiple sources, then information completeness is improved, but the difficulty of filtering high-quality information increases
Solution Approach 1:
The patent implements quality filtering mechanisms that operate autonomously to evaluate and rank place-entity information from diverse sources. The system uses multiple criteria (user ratings, verification status, recency, source reliability) to automatically assess information quality without requiring manual intervention, thereby maintaining information completeness while managing the filtering difficulty through self-service evaluation algorithms.
Solution Approach 2:
The system introduces intermediary validation layers that act as mediators between raw place-entity data from multiple sources and the final information presented to users. These intermediaries include verification systems, cross-referencing mechanisms, and quality scoring algorithms that filter and rank information based on multiple quality dimensions, making the filtering process more manageable while preserving comprehensive information.
Data Source
AI summary
In one embodiment, a method includes receiving from a client system of a user, background signal-information identifying one or more first wireless signals within wireless communication range of the client system; accessing a place-entity database, wherein the place-entity database comprises information indicating that a first place-entity corresponds to one or more second wireless signals; determining that the client system is located at a geographic location associated with the first place-entity based on determining that the one or more first wireless signals match the one or more second wireless signals and further based on the information indicating that the first place-entity corresponds to the one or more second wireless signals; and sending, to the client system, information associated with the first place-entity automatically without a query from the user of the client system, wherein the query is related to the first place-entity.