POS Data Anonymization and Timestamp Synchronization
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Solution Overview
Problem
Conventional point of sale (POS) systems are vulnerable to fraudulent activities such as 'pass-throughs' and 'sweethearting', where items are not scanned, leading to financial losses, and existing detection methods like scan-gap analysis are impractical due to noise and variability in legitimate scan-gaps. Additionally, reviewing POS transaction data with video recordings is challenging due to unsynchronized system clocks, making it difficult to reconcile item scan data with video frames.
Innovation Solution
A computer system that monitors POS transactions, anonymizes private information, and synchronizes event timestamps across networks to identify and tag fraudulent activities in video recordings, enabling effective detection and review of fraudulent behavior by correlating POS event data with video frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If scan-gap analysis is used to detect fraudulent activities, then detection capability is improved, but reliability deteriorates due to noise and variability in legitimate scan-gaps
Solution Approach 1:
The patent introduces video recordings as an intermediary to verify scan-gap detections. When scan-gap analysis identifies a potential fraudulent event, the system retrieves corresponding video frames to visually confirm whether an item was actually missed. This intermediary verification step filters out false positives caused by legitimate variability in scanning patterns, thereby maintaining high detection capability while improving reliability.
2Adaptability or versatility
If system clocks are not synchronized across networks, then network independence is maintained, but ability to reconcile item scan data with video frames deteriorates
Solution Approach 1:
The patent performs preliminary timestamp correction by calculating the clock skew between POS system and video system clocks before retrieving video frames. The system determines the time offset using known reference points (such as when items are placed on the conveyor) and applies this correction to synchronize timestamps. This preliminary action enables accurate correlation of scan data with video frames while maintaining network independence, as the skew calculation uses only local timing information.
3Loss of information
If private information is retained in POS data, then data completeness is improved, but security deteriorates when data is transmitted outside private network
Solution Approach 1:
The patent extracts and removes private information (such as customer PII, payment details, and internal system identifiers) from POS transaction data before transmitting it outside the private network. The system identifies sensitive fields using pattern recognition and regulatory guidelines, then either redacts or anonymizes this information while preserving the structural integrity and analytical value of the dataset. This extraction approach maintains data completeness for fraud detection purposes while eliminating security risks associated with transmitting sensitive information.
Data Source
AI summary
A computer system monitors a private network for presence of data traffic. In one embodiment, the private network is a retail environment in which a customer purchases an item scanned at a point-of-sale terminal. The data traffic can be point-of-sale transaction information generated in response to purchase of the item at the point-of-sale terminal. Subsequent to detecting presence of the data traffic in the network, the computer system analyzes the data traffic to identify private information in the data traffic. To protect the private information from improper disclosure to unauthorized entities, the computer system modifies the data traffic. Thus, techniques herein can include detecting occurrence of one or more point-of-sale events in a private network, anonymizing the event information (e.g., data traffic), and forwarding the anonymized event information associated with the point-of-sale events out of the private network to a remote resource.