Federated Product Pedigree Framework with Graph Database Anomaly Detection
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Solution Overview
Problem
Current product pedigree verification systems are fragmented and siloed, leading to missing or reduced visibility and inability to ensure supply chain authenticity and integrity, making them susceptible to fraudulent data and compromised products.
Innovation Solution
A framework that generates a federated product pedigree list by creating data elements for each touchpoint in a product's lifecycle, linking them using cryptographic hashes, and storing them in a graph database, with a machine learning model to detect anomalies and ensure authenticity and integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If product pedigree information is tracked through fragmented siloed systems, then each system can maintain its own data, but comprehensive visibility and supply chain authenticity cannot be ensured
Solution Approach 1:
The patent merges fragmented pedigree data from multiple siloed systems into a unified federated pedigree list. Data elements from different touchpoints (manufacturing, distribution, retail) are combined into a single comprehensive structure that provides end-to-end supply chain visibility while maintaining the organizational autonomy of each participant.
Solution Approach 2:
The patent creates a universal pedigree data structure that can accommodate information from diverse sources and industries. The federated pedigree list serves multiple functions: tracking product history, verifying authenticity, detecting anomalies, and providing visibility across different organizational boundaries simultaneously.
2Reliability
If traditional pedigree verification methods are used, then implementation is simpler, but the system cannot detect fraudulent activities or anomalies
Solution Approach 1:
The patent implements feedback mechanisms through anomaly detection algorithms that continuously monitor the pedigree list for suspicious patterns. When anomalies are detected (such as unexpected touchpoints or irregular timing), the system generates alerts that trigger further investigation, creating a closed-loop verification process that actively detects and responds to potential fraud.
Solution Approach 2:
The patent performs preliminary verification by establishing cryptographic hash links between data elements as they are added to the pedigree list. This preliminary structuring with immutable hash references ensures data integrity from the outset, making later fraud detection more effective without requiring complex retrospective analysis.
3Reliability
If data elements are stored in traditional databases, then storage is straightforward, but tamper-proof verification and immutable linking cannot be achieved
Solution Approach 1:
The patent replaces traditional mechanical database storage with a cryptographic hash-based linking structure. Each data element contains a hash reference to the previous element, creating an immutable chain that resists tampering. This substitution of cryptographic mechanisms for traditional database relationships ensures data integrity while maintaining manageable complexity.
4Loss of information
If comprehensive touchpoint data is collected for all product interactions, then complete pedigree verification is possible, but data management becomes more complex
Solution Approach 1:
The patent segments comprehensive pedigree information into discrete, standardized data elements, each representing a specific touchpoint in the product lifecycle. This segmentation allows complete traceability while managing complexity through modular data structures that can be independently validated and processed.
Solution Approach 2:
The patent transforms unstructured or semi-structured touchpoint data into standardized parameters with consistent formats and schemas. By changing the parameter representation of pedigree information into a unified structure, the system achieves complete data collection while simplifying management through standardization.
Data Source
AI summary
In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, retrieving touchpoint data of a product and creating a data element based on the retrieved touchpoint data, the data element includes information about a touchpoint of the product by a stakeholder. The method also includes, by the computing device, adding the created data element to a product pedigree list, wherein the product pedigree list is a pedigree list which contains data elements of the product. The method may further include, by the computing device, storing the product pedigree list in a graph database. The method may further include, by the computing device, predicting, using a machine learning (ML) model (e.g., an autoencoder), whether a state of the product pedigree list is normal or anomalous.