Point-of-Sale Device Event Trigger for Sensor Data Capture
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Solution Overview
Problem
Fraud and abuse in the retail industry, particularly payment card theft and fraud, result in significant monetary losses for merchants due to the difficulty in identifying offenders and the small value of individual transactions, making existing prevention technologies inadequate.
Innovation Solution
Systems and methods for capturing sensor data at retail locations using point-of-sale devices, which generate event triggers to collect data from various sensors, including cameras, and store it on remote servers for analysis, enabling fraud detection and prevention by reviewing transactional context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor data is captured and stored for fraud detection, then fraud detection capability is improved, but device complexity and data management requirements increase
Solution Approach 1:
The system segments sensor data collection by triggering captures only at specific moments (transaction events) rather than continuously monitoring. The point-of-sale device captures data only when transactions occur, dividing the monitoring function into discrete event-based segments that reduce overall system complexity while maintaining fraud detection capability.
Solution Approach 2:
The system performs preliminary actions by capturing sensor data at the moment of transaction events before fraud can occur. By triggering captures at the precise moment of purchase, the system prepares evidence for potential fraud detection without requiring continuous complex monitoring systems.
2Loss of information
If multiple sensors are deployed to capture comprehensive data, then information completeness is improved, but loss of information increases due to data volume
Solution Approach 1:
The system extracts only the necessary sensor data relevant to transaction events. By triggering captures only when transactions occur and selecting specific sensors (camera, microphone) based on the event type, the system removes unnecessary data collection, reducing overall data volume while maintaining complete information about actual transactions.
Solution Approach 2:
The system applies partial action by capturing data from only the most relevant sensors at the moment of transaction rather than all sensors continuously. This selective partial capture maintains sufficient information for fraud detection while avoiding excessive data collection that would increase storage and processing requirements.
3Speed
If real-time data capture is implemented, then response time to fraud is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic action by triggering sensor captures only at specific intervals when transactions occur, rather than continuous real-time monitoring. This event-driven periodic capture maintains rapid response to actual transactions while significantly reducing energy consumption compared to constant monitoring.
Solution Approach 2:
The system uses the transaction event itself as the trigger for data capture, allowing the business process to initiate the monitoring action. The transaction occurrence naturally triggers the capture mechanism, eliminating the need for separate continuous monitoring systems that would consume additional energy.
Data Source
AI summary
Disclosed are systems and methods of capturing sensor data associated with a retail location. The systems and methods further include a point-of-sale device for processing customer transactions and generating an event trigger for capturing sensor data from one or more of the sensors at the retail location. In response to the event trigger, the systems and methods capture data from the one or more sensors at the retail location and provide access to the stored sensor data and event triggers via a user interface of the point-of-sale device for review of the stored sensor data and event trigger.