Mobile Visual Locator Using Image and Orientation Data
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Solution Overview
Problem
Current location determination techniques for promotional offers, such as GPS and cell tower triangulation, have limited accuracy and are not suitable for all environments, especially indoors, leading to ineffective targeting of consumers with location-based promotions.
Innovation Solution
A system that uses servers to receive location, image, and orientation data from consumer devices to programmatically extract merchant identifiers and perform fuzzy searches, allowing for accurate merchant identification and personalized promotional communications based on consumer location and orientation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS or cell tower triangulation is used for location determination, then the system can provide location-based promotional offers, but the accuracy and precision of location determination is limited
Solution Approach 1:
The system divides location determination into multiple independent components: GPS for outdoor location, cell tower triangulation for supplementary positioning, and image recognition with orientation data for precise merchant identification. Each component operates independently and contributes to the overall location accuracy without interfering with others.
Solution Approach 2:
The system merges multiple location determination techniques (GPS, cell tower triangulation, image recognition, orientation data from accelerometers) into a unified approach. By combining these diverse methods, the system achieves both high accuracy and broad environmental suitability, overcoming the limitations of any single technique.
2Measurement precision
If multiple data sources (location, image, orientation) are processed to identify merchants, then the accuracy of merchant identification improves, but the device complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary processing of image data to extract merchant identifiers and logos before the fuzzy search process. Orientation data from accelerometers is pre-captured and stored alongside location data. This preliminary preparation reduces the complexity of the main identification process by having data ready in advance.
Solution Approach 2:
The system introduces a fuzzy search algorithm as an intermediary process that bridges the gap between raw data (images, location, orientation) and merchant identification. This intermediary handles the complexity of matching multiple data sources against merchant databases, making the overall system more manageable and modular.
3Measurement precision
If the system requests user confirmation for merchant selection, then the accuracy of promotional targeting improves, but the time required to deliver promotions increases
Solution Approach 1:
The system applies partial confirmation by requesting user input only when necessary (e.g., when multiple merchants match the criteria or when confidence is low). In cases where the fuzzy search returns a single clear match or high-confidence results, the system proceeds without additional confirmation, thus reducing time loss while maintaining accuracy where needed.
Solution Approach 2:
The system uses feedback from the fuzzy search results to dynamically determine whether user confirmation is needed. If the search returns ambiguous or multiple results, feedback triggers a confirmation request. If results are clear and confident, no feedback loop is initiated, saving time while maintaining accuracy.
Data Source
AI summary
Techniques for providing remote messages to mobile devices based on image data and other sensor data are discussed herein. Some embodiments may include one or more servers configured to: receive, from a consumer device via a network, location data indicating a consumer device location of a consumer device; receive, from the consumer device via the network, image data captured by a camera of the consumer device; receive, from the consumer device via the network, orientation data defining an orientation of the camera when the image data was captured, wherein the orientation data is captured by an accelerometer of the consumer device; attempt to extract a merchant identifier from the image based on programmatically processing the image data; determine one or more merchants based on a fuzzy search of available ones of the location data, the merchant identifier, and the orientation data.