Venue Data Prefetching for Mobile Location Accuracy
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Solution Overview
Problem
Mobile devices face challenges in efficiently storing and downloading venue data due to resource constraints and high cellular data costs, especially when unreliable wireless connections are involved, making it impractical to store detailed maps of all potential venues.
Innovation Solution
The technique involves prefetching venue data based on predicted user behavior by identifying routine locations and requesting data from a location server, allowing the device to store only necessary maps and location fingerprints, which can then be used to determine the user's location within a venue without the need for real-time downloads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If venue data for all potential venues is stored on the mobile device, then location determination accuracy is improved, but storage space is consumed
Solution Approach 1:
The system performs preliminary actions by prefetching venue data for locations the user is predicted to visit before actually arriving there. The mobile device requests and stores venue data in advance during periods of high network availability, so that when the user enters a venue, the data is already available for immediate location determination without requiring continuous storage of all possible venue data.
Solution Approach 2:
The system applies local quality by storing venue data selectively based on predicted user behavior patterns. Instead of uniformly storing data for all venues, the system identifies specific locations of interest based on user routines and only prefetches data for those particular venues, making the storage strategy adaptive to individual user needs rather than applying a one-size-fits-all approach.
2Quantity of substance
If venue data is downloaded in real time when the user enters a venue, then storage space is saved, but data transmission reliability deteriorates due to unreliable wireless connections
Solution Approach 1:
The system downloads venue data in advance during periods when network conditions are favorable, rather than waiting until the user actually enters a venue. This preliminary action ensures that data is available offline when needed, eliminating the risk of failed downloads due to unreliable wireless connections at the moment of venue entry.
Solution Approach 2:
The system provides beforehand cushioning by prefetching venue data before the user needs it, creating a buffer that protects against potential network failures. By having data already stored locally, the system cushions against the unreliability of real-time wireless connections, ensuring that location determination can proceed without interruption even if network conditions are poor when the user enters the venue.
3Loss of energy
If venue data is downloaded when Wi-Fi is available, then data transmission cost is reduced, but the complexity of managing data downloads increases
Solution Approach 1:
The system implements self-service by automatically detecting network conditions and autonomously deciding when to prefetch venue data. The mobile device monitors Wi-Fi availability and user location patterns, then automatically initiates data downloads without requiring user intervention or complex manual management. This self-managing approach handles the complexity internally while keeping the user experience simple.
Solution Approach 2:
The system uses feedback mechanisms by continuously monitoring network conditions (Wi-Fi availability) and user behavior patterns, then using this feedback to intelligently trigger data prefetching operations. The system adapts its data download behavior based on real-time feedback about network quality and predicted user needs, optimizing cost efficiency while managing complexity through automated decision-making rather than manual control.
Data Source
AI summary
Methods, systems, and computer program product for prefetching location data based on predicted user behavior. A mobile device can request, from a user routine subsystem of the mobile device, a list of locations that a user of the mobile device routinely visits while the user carries the mobile device. The mobile device can determine a cluster of these locations that are within a specified distance between one another. The mobile device can request location data for these locations from a location server, even if the user is not at one of these locations. The location data can include a venue map and a venue location fingerprint. Upon detecting that the user entered a venue at one of these locations, the mobile device can determine a location of the user inside of the venue using the venue location fingerprint. The mobile device can then display the location on a venue map.