Sensor Fingerprint Venue Identification
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Solution Overview
Problem
Existing location-aware systems struggle to accurately distinguish between nearby venues due to limited sensor accuracy and require pre-existing venue data, failing to provide relevant information based on the user's current activity or event.
Innovation Solution
The method utilizes sensor fingerprints generated from multiple sensor readings, including physical sensors like cameras, microphones, and thermometers, to identify specific venues or categories, allowing for venue identification and categorization even without prior knowledge of the location, by comparing current sensor data to stored fingerprints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS positioning sensor is used to determine location, then geo-spatial location can be obtained, but the accuracy is insufficient to distinguish between nearby venues
Solution Approach 1:
The patent combines multiple sensor types (GPS, accelerometer, microphone, camera, thermometer, barometer, altimeter) into an integrated sensor system that collects data from all sources and processes them together through a single processor to generate a unified sensor fingerprint for venue identification
Solution Approach 2:
The patent creates a composite sensor fingerprint by aggregating readings from multiple different sensor types, where each sensor contributes a different component of the overall fingerprint profile, similar to how composite materials combine different substances to achieve desired properties
2Adaptability or versatility
If location-aware systems use pre-existing venue databases, then venue information can be provided, but venues without pre-stored data cannot be identified
Solution Approach 1:
The system performs preliminary actions by collecting sensor data and generating sensor fingerprints for venues during initial visits or when users check-in, storing these fingerprints in advance so that future visits can be identified even without pre-existing detailed venue information
Solution Approach 2:
The system enables venues to self-identify by comparing their unique sensor fingerprints against the database, allowing venues without pre-stored information to be automatically identified and categorized based on their environmental characteristics
3Measurement precision
If sensor readings are collected continuously, then accurate venue identification is achieved, but energy consumption increases
Solution Approach 1:
The system implements periodic action by collecting sensor data at specific intervals or triggered by events (such as user check-in, location changes, or scheduled times) rather than continuously, reducing energy consumption while maintaining accurate venue identification capability
Solution Approach 2:
The system applies partial action by selectively activating only the necessary sensors for a given measurement cycle based on current context, and using aggregation to combine multiple partial readings into a complete venue fingerprint, achieving accurate identification with reduced overall data collection
Data Source
AI summary
When a mobile device is known to be at a venue, readings from one or more sensors of the device are used to generate a sensor fingerprint of the venue and/or a venue category corresponding to the venue. The sensor fingerprint indicates typical sensor readings for the one or more physical sensors for devices that are located at the venue and/or venues of the same category. Sensor fingerprints can also be generated for other circumstances associated with the mobile device, such as the activity being undertaken by the corresponding user and a type of event currently occurring at the venue. Fingerprints can also be generated for combinations of circumstances, such as a particular activity at a certain category of venue. When location data cannot distinguish at which venue a mobile device is located, sensor fingerprints are compared to sensor readings received from the device to resolve this ambiguity.