Snap inc. (20240331244). DEVICE LOCATION BASED ON MACHINE LEARNING CLASSIFICATIONS simplified abstract
Contents
DEVICE LOCATION BASED ON MACHINE LEARNING CLASSIFICATIONS
Organization Name
Inventor(s)
Ebony James Charlton of London (GB)
Sumant Milind Hanumante of Fremont CA (US)
Dhritiman Sagar of New York NY (US)
DEVICE LOCATION BASED ON MACHINE LEARNING CLASSIFICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240331244 titled 'DEVICE LOCATION BASED ON MACHINE LEARNING CLASSIFICATIONS
The abstract of the patent application describes a system where a client device can request location information from a server, which then returns multiple nearby venues to the client device. The client device can use machine learning schemes to determine its specific location within the possible venues, and the venue system can select imagery for presentation based on this location information. The presentation may be published as an ephemeral message on a network platform.
- The system allows a client device to request location information from a server.
- The server returns multiple venues that are near the client device.
- Machine learning schemes, such as convolutional neural networks, are used to determine the specific venue where the client device is located.
- The venue system selects imagery for presentation based on the client device's location.
- The presentation may be published as an ephemeral message on a network platform.
Potential Applications: - Location-based advertising - Augmented reality experiences - Social media check-ins
Problems Solved: - Providing accurate location information to client devices - Enhancing user experience with personalized imagery - Facilitating targeted marketing strategies
Benefits: - Improved user engagement - Enhanced location-based services - Increased efficiency in delivering relevant content
Commercial Applications: Title: Location-Based Marketing and Personalized Content Delivery This technology can be used in mobile marketing campaigns, social media platforms, and location-based services to provide users with personalized content based on their specific location.
Prior Art: Prior art related to this technology may include patents or research papers on location-based services, machine learning algorithms for location detection, and image selection based on user location.
Frequently Updated Research: Researchers may be exploring advancements in machine learning algorithms for more accurate location detection, improvements in image recognition technology for better imagery selection, and the impact of personalized content delivery on user engagement.
Questions about the technology: 1. How does the system ensure the privacy and security of user location data? 2. What are the potential challenges in implementing machine learning schemes for location detection in real-time scenarios?
Original Abstract Submitted
a venue system of a client device can submit a location request to a server, which returns multiple venues that are near the client device. the client device can use one or more machine learning schemes (e.g., convolutional neural networks) to determine that the client device is located in one of specific venues of the possible venues. the venue system can further select imagery for presentation based on the venue selection. the presentation may be published as ephemeral message on a network platform.
