Derivative works — US Patent 5768384

Defensive Disclosure: Derivative Works of US Patent 5,768,384

Publication Date: April 28, 2026
Reference Patent: US 5,768,384 ("System for identifying authenticating and tracking manufactured articles")
Purpose: This document discloses a series of derivative inventions, technical variations, and new applications based on the core claims of US patent 5,768,384. The intent is to place these concepts into the public domain, thereby establishing prior art against future patent applications for similar, incremental improvements.

Derivative Variations on Core System Claim (Claim 1)

The following disclosures describe variations on the system for identifying and authenticating articles, focusing on the "manufacturing meter" and its associated components.

1.1. Material & Component Substitution: Virtual Secure Meter with DNA Ink

Enabling Description: The physical, tamper-resistant "manufacturing meter" hardware is replaced by a Virtual Secure Element (VSE) operating within a trusted execution environment (TEE), such as Intel SGX or ARM TrustZone, on a hardened industrial computer located on the manufacturing floor. The VSE software performs all cryptographic operations, including key management and encryption of manufacturing data (operator ID, timestamp, machine telemetry). The encrypted data is then encoded and used to synthesize a unique DNA sequence. This sequence is embedded in an ink or varnish using synthetic DNA taggants from a provider like DNATrax. The printer component is an aerosol jet or inkjet system that applies the DNA-laced ink to the article. Authentication is performed by taking a sample, sequencing the DNA using a portable MinION-type sequencer, and comparing the resulting data payload against a secure database record of the associated manifest.

Mermaid.js Diagram:

graph TD
    subgraph Industrial PC with TEE
        A[Manufacturing Data Input: Operator ID, Batch #] --> B{Virtual Secure Element};
        C[Machine Telemetry] --> B;
        B --> D[Encryption Engine];
    end
    D --> E{DNA Sequence Synthesis};
    E --> F[DNA-laced Ink Reservoir];
    F --> G[Aerosol Jet Printer];
    G --> H((Manufactured Article));
    subgraph Authentication
        I[Sample Collection from Article] --> J[Portable DNA Sequencer];
        J --> K{Decrypted Manufacturing Data};
        L[Shipping Manifest Data] --> M{Comparison Logic};
        K --> M;
        M --> N[Authentication Result: Valid/Invalid];
    end

1.2. Component Substitution: Physical Unclonable Function (PUF) Enrollment Meter

Enabling Description: This variation eliminates the printed code entirely. For electronic components, the intrinsic, unique variations of a Physical Unclonable Function (PUF) circuit within the component's silicon (e.g., an SRAM PUF or Ring Oscillator PUF) are used as the unique identifier. The "manufacturing meter" is a specialized enrollment station. The article (e.g., a microcontroller) is placed in the station, which powers it and applies a set of challenges to the PUF circuit, recording the unique challenge-response pairs (CRPs). This CRP data, along with operator/manufacturing data, is encrypted and sent to a central data center. To authenticate, a field scanner applies a subset of the challenges to the device's PUF and verifies that the responses match the ones registered at enrollment. For mechanical parts, a laser speckle pattern on the surface can be used as a PUF, with the "meter" being an optical scanner that records the unique pattern.

Mermaid.js Diagram:

sequenceDiagram
    participant M as Manufacturing Meter (Enrollment)
    participant A as Article (with PUF)
    participant DC as Data Center
    participant S as Scanner (Authentication)

    M->>A: Apply Challenge Set C1...Cn
    A-->>M: Return Response Set R1...Rn
    M->>M: Combine {C,R} pairs + Operator Data
    M->>DC: Store Encrypted({C,R}, OpData)
    Note over A, S: Article enters supply chain
    S->>A: Apply Challenge C(i)
    A-->>S: Return Response R(i)
    S->>DC: Request R(i) for Challenge C(i)
    DC-->>S: Provide stored R(i)
    S->>S: Compare responses
    alt Responses Match
        S-->>User: Authenticated
    else Responses Differ
        S-->>User: Counterfeit
    end

1.3. Operational Parameter Expansion: Nanoscale Authentication Meter

Enabling Description: The system is scaled down for authenticating individual semiconductor dies on a 300mm silicon wafer. The "manufacturing meter" is integrated into the automated test equipment (ATE) probe station. After a die passes electrical tests, the ATE encrypts the die's unique test results, its coordinates on the wafer (X, Y), the operator ID, and a timestamp. This encrypted payload is translated into a 2D datamatrix code measuring less than 10x10 micrometers. The "printer" is an electron-beam lithography or focused ion beam (FIB) system that etches this nano-barcode directly into a passivation layer of the die. Authentication is performed later in the supply chain using a scanning electron microscope (SEM) equipped with optical character recognition (OCR) software to read the nano-barcode and compare its contents against the foundry's secure production database.

Mermaid.js Diagram:

flowchart LR
    subgraph Wafer Fab
        A[Wafer Probe Station] --> B{Die Electrical Test};
        B -- Pass --> C[Encrypt Die-Specific Data];
        C --> D[Generate Nano-Datamatrix];
        D --> E[Focused Ion Beam Etcher];
        E --> F((Die on Wafer));
    end
    subgraph Downstream Verification
        G[Scanning Electron Microscope] --> H{Image Nano-Barcode};
        H --> I[Decode Datamatrix];
        J[Foundry Production Database] --> K{Compare Data};
        I --> K;
        K --> L{Authentication Status};
    end

1.4. Cross-Domain Application: Aerospace Component Lifecycle Meter

Enabling Description: The system is applied to track and authenticate flight-critical aerospace components. The "manufacturing meter" is a hardened computing module embedded within a 5-axis CNC milling machine. As it mills a component (e.g., a turbine blade), it securely records machine parameters, material batch numbers (read from the raw stock), operator ID, and real-time vibration analysis data, creating a unique "birth certificate." This data is encrypted and then micro-etched by a laser integrated into the machine tool head onto a non-critical surface of the component. The authentication "scanner" is a handheld device used by maintenance crews, which reads the micro-etching and compares it with the component's digital record in the airline's maintenance, repair, and overhaul (MRO) database.

Mermaid.js Diagram:

graph TD
    subgraph Manufacturing
        A[Material Batch ID] --> B{CNC Meter Module};
        C[Operator Authentication] --> B;
        D[Live CNC Machine Telemetry] --> B;
        B --> E[Encrypt "Birth Certificate"];
        E --> F[Integrated Laser Etcher];
        F --> G((Turbine Blade));
    end

    subgraph MRO Operations
        H[Handheld Scanner] --> I{Read Etched Data};
        I --> J[Decrypt Data];
        J --> K{Compare with MRO Database};
        L[MRO Database] --> K;
        K --> M[Authenticate & Log Service];
    end

1.5. Cross-Domain Application: Agricultural "Field-to-Fork" Meter

Enabling Description: The system tracks the provenance of organic agricultural products. The "manufacturing meter" is a ruggedized tablet or handheld device with GPS, a camera, and a secure enclave. In the field, a harvest worker logs in, and the device captures their ID, the current timestamp, and GPS coordinates of the specific field plot. The device also receives real-time data via LoRaWAN from in-ground IoT sensors measuring soil moisture and temperature. This data is encrypted and printed as a QR code on a biodegradable PLA (polylactic acid) label by a portable thermal printer. This label is affixed to the crate of produce. Authentication is performed by distributors or consumers scanning the QR code, which displays the provenance data and cross-references it against the farm's public harvest ledger.

Mermaid.js Diagram:

classDiagram
class FieldMeter {
  +operatorID: string
  +timestamp: datetime
  +gpsCoordinates: string
  +soilMoisture: float
  +temperature: float
  +encryptProvenanceData()
}
class IoT_SoilSensor {
  +sensorID: string
  +moisture: float
  +temperature: float
  +transmitData()
}
class PortablePrinter {
  +printLabel(encryptedData)
}
class ProductCrate {
  +crateID: string
  +provenanceLabel: QR_Code
}
IoT_SoilSensor --|> FieldMeter : sends data to
FieldMeter --|> PortablePrinter : commands
PortablePrinter --|> ProductCrate : affixes label to

1.7. Integration with Emerging Tech: AI-Enhanced Process Fingerprinting

Enabling Description: This system integrates AI to create a "process fingerprint" for authentication. The manufacturing meter is an IoT gateway connected to high-frequency sensors on the production machine (e.g., acoustic sensors, spectral analyzers, power consumption monitors). During production, the meter streams this raw sensor data to a cloud-based AI model (e.g., a convolutional neural network or an autoencoder). The AI computes a compressed representation vector (the "fingerprint") that uniquely characterizes that specific production run. This fingerprint, along with operator ID and serial number, is encrypted and affixed to the article as a 2D barcode. Authentication requires a scanner connected to the same AI service. The scanner reads the barcode, retrieves the fingerprint, and the AI service confirms if that fingerprint corresponds to a known, genuine production event stored in its database, preventing counterfeiting even if the code is copied, as the underlying process cannot be easily replicated.

Mermaid.js Diagram:

sequenceDiagram
    participant SM as Sensor-Rich Machine
    participant MM as IoT Meter
    participant AI as Cloud AI Model
    participant P as Printer
    participant S as Authenticator Scanner

    loop Production Run
        SM->>MM: Stream high-frequency sensor data
    end
    MM->>AI: Send raw sensor data bundle
    AI->>AI: Compute Process Fingerprint (Vector)
    AI-->>MM: Return Fingerprint + genuine_flag
    MM->>MM: Encrypt (SN, OperatorID, Fingerprint)
    MM->>P: Print encrypted barcode
    Note over S: Downstream Authentication
    S->>S: Scan barcode, decrypt payload
    S->>AI: Verify(SN, Fingerprint)
    AI-->>S: Authentication Status (Match/No Match)

1.8. Integration with Emerging Tech: Blockchain Oracle Meter

Enabling Description: The manufacturing meter functions as a trusted hardware oracle for a supply chain blockchain (e.g., a permissioned Hyperledger Fabric or public Polygon chain). Upon an article's creation, the meter gathers manufacturing data, encrypts it, and generates a Keccak-256 hash of the encrypted payload. The meter then uses its unique cryptographic identity to call a smart contract on the blockchain, writing a new record that includes the article's serial number, the data hash, and its own digital signature. The physical article is marked with a QR code containing the transaction hash from the blockchain. Authentication is decentralized: anyone can scan the QR code, look up the transaction on a public block explorer, and verify that the hash in the transaction matches the hash they generate by scanning the article's full encrypted data. This provides immutable, auditable proof of origin.

Mermaid.js Diagram:

flowchart TD
    A[Manufacturing Event] --> B[Meter Gathers Data];
    B --> C{Encrypt Payload};
    C --> D{Generate Hash (H1)};
    D --> E[Sign Transaction with Meter's Key];
    E --> F[Call Smart Contract];
    F --> G((Blockchain Ledger));
    C --> H[Print QR Code of Full Payload];
    G --> I[Get Transaction ID (TxID)];
    I --> J[Print QR Code of TxID];
    subgraph Authentication
        K[Scanner reads TxID] --> L{Query Blockchain};
        L --> M[Retrieve Stored Hash (H1)];
        N[Scanner reads full payload] --> O{Generate Hash (H2)};
        O --> P{Compare H1 and H2};
        M --> P;
        P --> Q{Verification Result};
    end

1.9. Inverse / Failure Mode: Graceful Degradation Offline Meter

Enabling Description: The manufacturing meter is designed for high-availability environments where network connectivity to the central data center may be intermittent. The meter is pre-loaded by the data center with a batch of single-use, numbered cryptographic keys. During normal operation, it communicates with the data center for each article. If connectivity is lost, it enters "offline mode." In this mode, it uses the next available single-use key from its secure storage to encrypt a minimal dataset (e.g., serial number, offline flag, and key number) and allows production to continue. The printed barcode is structured differently to indicate it was generated offline. When connectivity is restored, the meter syncs its offline transaction log with the data center. Downstream scanners that detect an offline-generated code can perform a "provisional" authentication and flag the item for full verification once the data has been synced.

Mermaid.js Diagram:

stateDiagram-v2
    [*] --> Online
    Online: Normal operation with Data Center
    Online --> Offline: Network Connection Lost
    Offline: Use pre-loaded single-use keys.
    Offline: Encrypt minimal data + 'offline_flag'.
    Offline --> Online: Network Connection Restored
    Online --> Online: Sync offline transaction log

    state Downstream_Scan {
        [*] --> Read_Code
        Read_Code --> Is_Offline_Flag_Present
        Is_Offline_Flag_Present -- Yes --> Provisional_Auth
        Is_Offline_Flag_Present -- No --> Full_Auth
        Provisional_Auth: Flag for later verification
        Full_Auth: Standard verification process
    }

Combination Prior Art Scenarios with Open-Source Standards

2.1. Combination with EPCIS 2.0 and GS1 Digital Link

Enabling Description: The system of US patent 5,768,384 is integrated into a GS1 standards-compliant supply chain. The "manufacturing meter" generates its unique encrypted payload as described in the patent. This payload is then embedded within the query parameters of a GS1 Digital Link URI. The URI is encoded into a QR code and affixed to the article. The URI points to a cloud-based authentication service. When a user scans the QR code, their device makes an HTTP request to the URI. The authentication service first parses the encrypted payload and performs the patented comparison against shipping manifest data. If authentication succeeds, the service then logs a standards-compliant EPCIS 2.0 ObjectEvent (action: ADD) to a distributed EPCIS repository, using the article's SGTIN as the identifier. This creates an immutable, verifiable, and interoperable record of the article's commissioning, securely initiated by the patented authentication process.

2.2. Combination with FIDO2/WebAuthn for Operator Authentication

Enabling Description: The authentication of the equipment operator, a key input to the "manufacturing meter," is enhanced using the FIDO2 open standard. The meter, or its associated terminal, acts as a FIDO2 Relying Party. Before starting a production run, the operator is prompted to authenticate not with a password or card, but with a FIDO authenticator (e.g., a YubiKey or a biometric sensor on their phone). The operator touches the key, which performs a cryptographic signature challenge-response with the meter. The public key credential of the operator is then included in the data encrypted by the meter and affixed to the article. This provides non-repudiable proof of the operator's presence and authorization for that specific article's production, replacing less secure methods like PINs or magnetic stripe cards.

2.3. Combination with W3C Verifiable Credentials Data Model

Enabling Description: The entire authentication artifact is re-imagined as a W3C Verifiable Credential (VC). The "manufacturing meter" assumes the role of an "Issuer," associated with a Decentralized Identifier (DID). Upon production, the meter generates a VC in JSON-LD format. The credentialSubject of the VC is the manufactured article, identified by its serial number. The VC contains claims such as manufacturer, productionDate, operatorID, and machineID. The meter signs this VC with the private key associated with its DID, producing a compact, encoded Verifiable Presentation (often a JSON Web Token - JWT). This JWT is then encoded into a QR code and affixed to the article. A "Verifier" (e.g., a customs agent's app) can scan the code, decode the JWT, use the Issuer's DID to retrieve their public key from a distributed ledger or DID Document, and cryptographically verify the signature's authenticity, thus authenticating the product and its claimed attributes in a fully decentralized and standards-compliant manner.