Patent 10614477

Derivative works

Defensive disclosure: derivative variations of each claim designed to render future incremental improvements obvious or non-novel.

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Derivative works

Defensive disclosure: derivative variations of each claim designed to render future incremental improvements obvious or non-novel.

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Defensive Disclosure and Prior Art Generation

RE: U.S. Patent 10,614,477 - Subscription bill service, systems and methods
Publication Date: May 9, 2026
Author: Senior Patent Strategist and Research Engineer

This document discloses a series of technical implementations, derivative works, and combinations with existing technologies intended to enter the public domain. The purpose of this disclosure is to establish prior art against future patent applications claiming incremental or obvious improvements upon the concepts described in U.S. Patent 10,614,477 (hereafter 'the base patent').

Part 1: Derivative Variations on Core Claims

Derivative 1.1: Hyperspectral Imaging for Agricultural Subsidy Reconciliation

  • Core Claim Basis: Claim 1 (Method), Claim 10 (Apparatus)

  • Axis: Material & Component Substitution / Cross-Domain Application (AgTech)

  • Enabling Description: The transaction apparatus is an agricultural drone equipped with a hyperspectral imaging sensor instead of a standard RGB camera. The recognition module is configured to analyze the hyperspectral data cube (e.g., 400-2500 nm range) of a crop field. The derived object attributes are not visual features but rather biophysical parameters such as the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and soil organic carbon content. When the system detects attributes indicating adherence to a specific sustainable farming practice (e.g., low nitrogen runoff potential), it generates a "virtual coupon" which is a digital voucher for a government environmental subsidy. The transaction interface then reconciles a three-way transaction: (1) crediting the farmer's account with the subsidy value, (2) debiting the governmental agency's (e.g., USDA) disbursement account, and (3) crediting a carbon credit marketplace account based on the calculated carbon sequestration of the field.

  • Mermaid Diagram:

    graph TD
    A[Drone with Hyperspectral Sensor] -- Captures Data Cube --> B(Recognition Module);
    B -- Analyzes NDVI, LAI, etc. --> C{Derived Attributes: Sustainable Practice Confirmed};
    C -- Yes --> D[Virtual Coupon Generator];
    D -- Creates Subsidy Voucher --> E[Transaction Interface];
    E -- Reconciles Multi-Party Transaction --> F[Farmer Account: +Subsidy];
    E --> G[Government Agency Account: -Disbursement];
    E --> H[Carbon Marketplace Account: +Credits];
    C -- No --> I[Log Data for Review];

Derivative 1.2: Neuromorphic Processing for High-Frequency Trading Reconciliation

  • Core Claim Basis: Claim 1 (Method), Claim 10 (Apparatus)

  • Axis: Component Substitution / Operational Parameter Expansion

  • Enabling Description: The recognition module is implemented on a dedicated neuromorphic processing unit (NPU) that processes market data as a series of asynchronous spikes (a "digital representation") rather than traditional time-series data. The "real-world object" is a specific arbitrage opportunity pattern detected within the spike train in sub-millisecond timeframes. The derived attributes are the pattern's characteristics: latency, involved securities, and predicted alpha. Upon recognition, a "virtual coupon" is generated, representing a pre-authorized risk limit for this specific trade. The system initiates the trades and reconciles the transaction in near-real-time across multiple accounts: (1) the firm's main trading account, (2) the trader's individual profit-and-loss (P&L) account, and (3) a clearing house's collateral account. The entire process from recognition to reconciliation occurs within a 10-millisecond latency budget.

  • Mermaid Diagram:

    sequenceDiagram
    participant MarketData as Market Data Feed
    participant NPU as Neuromorphic Processor
    participant VCG as Virtual Coupon Generator
    participant TI as Transaction Interface
    participant ClearingHouse as Clearing House

    MarketData-->>NPU: Asynchronous Spike Train
    NPU->>NPU: Recognize Arbitrage Pattern (<1ms)
    NPU->>VCG: Send Pattern Attributes
    VCG->>TI: Generate Pre-Authorized Risk Limit
    TI->>MarketData: Execute Trades
    TI->>ClearingHouse: Reconcile P&L and Collateral
    ClearingHouse-->>TI: Confirmation

Derivative 1.3: Quantum Key Distribution for Secure Aerospace Maintenance Reconciliation

  • Core Claim Basis: Claim 10 (Apparatus), Claim 18 (Fraud Mitigation)

  • Axis: Component Substitution / Cross-Domain Application (Aerospace)

  • Enabling Description: The transaction apparatus is a ruggedized tablet used for aircraft maintenance. The sensor is a high-resolution optical scanner. It recognizes a specific part (e.g., a turbine blade) by its laser-etched serial number matrix code. The transaction interface is substituted with a quantum key distribution (QKD) module for communication with the airline's and FAA's servers. This ensures the transaction data is immune to eavesdropping and future decryption by quantum computers. Upon authenticating the transaction (verifying part-to-plane pairing), the system credits a "target account" selected by the technician. The options include (1) a standard parts inventory account, (2) a specialized "experimental parts" tracking account for R&D, or (3) a third-party leasing company's account. This selection dictates the reconciliation logic.

  • Mermaid Diagram:

    stateDiagram-v2
    [*] --> ScanningPart
    ScanningPart: Technician scans turbine blade
    ScanningPart --> Authenticating: Serial number recognized
    Authenticating: Generate Quantum Key via QKD
    Authenticating --> SelectingAccount: Secure Channel Established
    SelectingAccount: Technician selects target account (Inventory, R&D, Leasing)
    SelectingAccount --> Reconciling: Account selected
    Reconciling: Reconcile transaction with Airline, FAA, and Target Account servers
    Reconciling --> [*]

Derivative 1.4: Blockchain and IoT Integration for Pharmaceutical Cold Chain Verification

  • Core Claim Basis: All Claims

  • Axis: Integration with Emerging Tech

  • Enabling Description: The method is integrated with an IoT and blockchain backend. A user (e.g., a pharmacist) uses a mobile device to scan a 2D barcode on a vaccine vial (the real-world object). The recognition module derives the batch number. This batch number is used to query a permissioned blockchain (e.g., Hyperledger Fabric) for the vial's immutable history, sourced from IoT sensors (temperature, shock) during its transit. The "derived attributes" are this complete provenance data. If the data confirms the cold chain was never broken, the virtual coupon generator creates a "co-pay reduction coupon" from the manufacturer. The transaction is reconciled via a smart contract that (1) credits the patient's insurance account, (2) credits the pharmacy's inventory account, and (3) debits the manufacturer's rebate account. The entire transaction history is appended to the vial's record on the blockchain.

  • Mermaid Diagram:

    flowchart LR
    subgraph MobileDevice
        A[Scan Vaccine Barcode] --> B{Recognition Module};
    end
    subgraph Backend
        D[IoT Sensor Data] --> E(Permissioned Blockchain);
        C[Query Blockchain] --> F{Verify Cold Chain};
        G[Virtual Coupon Generator] --> H(Smart Contract);
    end
    B --> C;
    F -- Provenance OK --> G;
    H -- Executes Reconciliation --> I[Pharmacy Account];
    H --> J[Insurance Account];
    H --> K[Manufacturer Account];
    F -- Provenance Fail --> L[Flag for Quarantine];

Derivative 1.5: Failsafe "Ghost Coupon" Mode for Fraud Mitigation

  • Core Claim Basis: Claim 18 (Fraud Mitigation)

  • Axis: The "Inverse" or Failure Mode

  • Enabling Description: A method for mitigating transaction fraud where the recognition module assesses a confidence score for the object recognition. If the score is below a predefined threshold (e.g., < 95%), or if sensor data suggests a spoofing attempt (e.g., recognizing a 2D image on a screen instead of a 3D object), the system enters a failsafe mode. Instead of a real virtual coupon, it generates a "ghost coupon"—a non-functional placeholder visually identical to a real coupon. The system mimics engagement with an electronic transaction, but all reconciliation calls are routed to a sandboxed honeypot server that logs the attacker's behavior. No actual accounts are touched. The user is given a generic error message ("Coupon could not be applied at this time"), while the fraudulent attempt is flagged for security analysis.

  • Mermaid Diagram:

    graph TD
    A[Recognize Object] --> B{Confidence Score > 95%?};
    B -- Yes --> C[Generate Real Virtual Coupon];
    C --> D[Reconcile Live Accounts];
    B -- No --> E[Enter Failsafe Mode];
    E --> F[Generate 'Ghost Coupon' UI];
    F --> G[Route Transaction to Honeypot Server];
    G --> H[Log Attacker Behavior];
    H --> I[Display Generic Error to User];

Part 2: Combination Prior Art Scenarios

Combination 2.1: Implementation with OpenCV and ISO/IEC 18004 (QR Codes)

  • Disclosure: A system where the recognition engine is explicitly implemented using the open-source OpenCV library to detect and decode QR codes (defined by the ISO/IEC 18004 standard). The "digital representation of a real-world object" is an image containing a QR code. The "derived attributes" are the data fields encoded within the QR code payload (e.g., a URL, product ID, and a vendor ID). Activating the virtual coupon involves the mobile device making an HTTP GET request to the URL from the QR code, which triggers the transaction and reconciliation process as described in the base patent. This combination renders the recognition and attribute derivation steps obvious to anyone skilled in the art of mobile application development.

Combination 2.2: Implementation with WebAuthn and W3C Payment Request API

  • Disclosure: A method where the transaction authentication (as in Claim 18) is performed using the open WebAuthn (W3C standard) protocol. The user's mobile device acts as a FIDO2 authenticator. After the user selects a target account, the browser triggers the W3C Payment Request API. The user authorizes the transaction using device biometrics (e.g., fingerprint), which generates a signed assertion via WebAuthn. This cryptographically signed assertion, containing the transaction details and target account ID, is sent to the transaction reconciliation engine for verification. This combination makes the authentication step an implementation of existing, open web standards for secure payments.

Combination 2.3: Implementation with Hyperledger Fabric for Loyalty Point Exchange

  • Disclosure: A system where the "multiple electronic accounts" are wallets on a Hyperledger Fabric permissioned blockchain network, and the "virtual coupon" represents a cross-chain atomic swap of loyalty points. A user scans a partner brand's product. The derived attributes identify the product and the partner. This triggers a chaincode (smart contract) that allows the user to select a target account (e.g., an airline miles program). The chaincode executes an atomic transaction, simultaneously debiting the partner brand's loyalty point pool and crediting the user's selected airline miles account, using a pre-defined exchange rate. This makes the multi-account reconciliation a direct application of open-source distributed ledger technology for asset exchange.

Generated 5/9/2026, 6:46:51 PM