Client Device Venue Identification Using Visual Machine Learning
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Solution Overview
Problem
Existing geolocation technologies, such as consumer-grade GPS, struggle to accurately determine the precise venue of a device due to environmental obstructions and protocol limitations, leading to inaccurate venue identification.
Innovation Solution
A client device leverages machine learning schemes, including convolutional neural networks, to analyze visual cues from images or videos to filter and select the correct venue from a set of possible locations, combining GPS data with visible IP networks and a server's venue database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS data is used to determine device location, then location information can be obtained, but venue identification accuracy deteriorates when multiple venues are nearby or environmental obstructions are present
Solution Approach 1:
The patent combines multiple data sources including GPS coordinates, visible IP networks, and machine learning image classification to determine device location. The system merges these different types of data through a processing pipeline that uses neural networks to analyze images and cross-reference with venue databases, thereby achieving accurate venue identification even when GPS alone is insufficient due to environmental obstructions or proximity to multiple venues.
Solution Approach 2:
The patent introduces machine learning image classification as an intermediary mechanism between raw GPS data and final venue identification. The system uses neural networks to process images captured by the device, extract visual features, and match them against venue databases. This intermediary processing step enables the system to disambiguate locations where GPS data alone would be insufficient, particularly when multiple venues are nearby or when environmental factors obstruct direct GPS accuracy.
2Measurement precision
If machine learning image classification is applied to determine venue, then venue identification accuracy improves, but device complexity increases
Solution Approach 1:
The patent segments the venue determination process into distinct functional modules: image capture, image classification using neural networks, venue database querying, and location verification. By dividing the complex task into separate processing stages, the system manages complexity through modular architecture while maintaining high accuracy. Each module can be independently optimized and processed sequentially, reducing the computational burden on any single component.
Solution Approach 2:
The patent replaces traditional mechanical GPS-based location determination with a computational approach using machine learning image classification. Instead of relying solely on hardware-based GPS signals that fail in certain environments, the system uses software-based neural network models to analyze visual data and determine location. This substitution of mechanical reliance with computational intelligence improves accuracy in challenging environments while managing system complexity through algorithmic efficiency.
Data Source
AI summary
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.