Patent 6032137

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: Derivative Embodiments and Obvious Variations of a Three-Tiered Remote Data Capture System.

Publication Date: April 26, 2026
Reference Patent: US 6,032,137
Abstract: This document discloses a series of derivative inventions, technical variations, and cross-domain applications of a networked system comprising remote data capture, intermediate data collection, and central data processing. The purpose of this disclosure is to place these variations into the public domain to serve as prior art against future patent applications claiming these or substantially similar concepts.

I. Derivative Variations based on Core Claim 1

1. Material & Component Substitution

Derivative 1.1: Mobile-First, Optically-Driven Remote Data Access Subsystem

  • Enabling Description: The remote data access subsystem is embodied not as a dedicated hardware terminal (NC), but as a software application running on a commercial off-the-shelf (COTS) mobile device, such as a smartphone or ruggedized tablet. The data capture function, previously performed by a mechanical scanner, is replaced by the device's integrated high-resolution CMOS camera. The application uses an embedded computer vision library (e.g., OpenCV, Tesseract OCR) to perform real-time, on-device document scanning, feature extraction (including MICR line data from checks), and perspective correction. Biometric data (e.g., a handwritten signature) is captured via the device's capacitive touchscreen. The DAT modem component is replaced by a multi-modal wireless transceiver utilizing 5G NR (New Radio) for high-bandwidth, low-latency communication, with failover to a Low-Power Wide-Area Network (LPWAN) protocol such as LoRaWAN for essential data when out of cellular range.

  • Diagram:

    graph TD
    subgraph Mobile Remote Subsystem (DAT)
        A[COTS Mobile Device] --> B{Software Application};
        B --> C[CMOS Camera];
        B --> D[Capacitive Touchscreen];
        B --> E[Multi-Modal 5G/LoRaWAN Transceiver];
        C -- Raw Image Frame --> F[On-Device CV/OCR Engine];
        D -- Stylus/Finger Input --> G[Biometric Signature Vectorization];
        F -- Extracted Text & Metadata --> H[Payload Encryption Module];
        G -- Vector Data --> H;
        H -- Encrypted Packet --> E;
    end
    E --> I((Data Collecting Subsystem));

Derivative 1.2: Solid-State, Thermoelectrically-Cooled Collector Subsystem

  • Enabling Description: The data collecting subsystem (DAC), designed for high-reliability edge computing environments, is constructed as a fanless, solid-state server. The DEC Alpha servers are replaced with passively-cooled ARM-based System-on-a-Chip (SoC) clusters. Data storage is implemented using NVMe (Non-Volatile Memory Express) solid-state drives in a RAID 10 configuration, eliminating mechanical failure points associated with spinning hard drives. To ensure stable operation in environments with high ambient temperatures (e.g., a factory floor or a sealed telecommunications enclosure), the entire chassis is hermetically sealed and thermal regulation is achieved via Peltier-effect thermoelectric cooling (TEC) modules coupled with external heat sinks. Power is supplied via Power over Ethernet (PoE++) and backed by a local supercapacitor bank instead of a traditional UPS, providing instantaneous failover for short-term power loss.

  • Diagram:

    graph LR
    subgraph Solid-State Collector (DAC)
        A[PoE++ Input] --> B[Supercapacitor Bank];
        A --> C[ARM SoC Cluster];
        D[NVMe RAID 10 Array] -- PCIe --> C;
        E[Thermoelectric Cooling Modules] -- Manages Temp --> C;
        E -- Manages Temp --> D;
        C -- Data I/O --> F[Network Interface];
    end
    G((Remote Subsystems)) -- Data --> F;
    F -- Data --> H((Central Processor));

2. Operational Parameter Expansion

Derivative 2.1: Microfluidic Transaction System for Lab-on-a-Chip Analytics

  • Enabling Description: The invention is scaled down to operate at the microfluidic level for automated laboratory diagnostics. The "remote data access subsystem" is a microfluidic chip with integrated biosensors. A "transaction" is defined as the analysis of a single biological sample (e.g., a drop of blood). The chip captures data by measuring electrical impedance, fluorescence, or colorimetric changes. This analog sensor data is digitized on-chip by a micro-controller unit (MCU). The "data collecting subsystem" is a benchtop analysis instrument that houses multiple microfluidic chips, polling each one for its results and aggregating the data. The "central data processing subsystem" is a Laboratory Information Management System (LIMS) server that receives the aggregated batch data, archives it against patient records, and performs trend analysis across thousands of samples. The communication network between the chip and the instrument is a Serial Peripheral Interface (SPI) bus, while the instrument communicates with the LIMS via a standard TCP/IP network.

  • Diagram:

    sequenceDiagram
    participant R as Remote (Microfluidic Chip)
    participant C as Collector (Benchtop Instrument)
    participant P as Central Processor (LIMS)
    R->>R: Analyzes biological sample
    R->>C: Sends digitized sensor data via SPI bus
    C->>C: Aggregates data from multiple chips
    C->>P: Transmits encrypted batch results via TCP/IP
    P->>P: Stores data and performs analysis
    P-->>C: Sends new analysis parameters

Derivative 2.2: Global Logistics System Operating Over High-Latency Satellite Networks

  • Enabling Description: The system is adapted for managing logistics data from intermodal shipping containers in remote or transoceanic locations. The remote subsystem is a device affixed to each container, equipped with GPS, and sensors for shock, temperature, and humidity. It communicates via a low-throughput, high-latency satellite communication link (e.g., Iridium or Starlink IoT). Due to the high cost and latency of satellite data, the remote subsystem performs significant on-board data compression and aggregation, only transmitting a "heartbeat" summary packet at predefined intervals (e.g., once every 6 hours). The "data collecting subsystem" is a cloud-based gateway service provided by the satellite network operator, which receives these packets from thousands of containers, buffers them, and exposes them to the central processor via a message queue API. The "central data processing subsystem" is the logistics company's global tracking and analytics platform, which consumes the data to provide real-time visibility and predictive ETAs. The system is designed to tolerate network outages of up to 72 hours, with the remote subsystem caching all sensor readings in local flash memory.

  • Diagram:

    graph TD
    A[Container Sensor Unit] -- Satellite Uplink --> B(Satellite Network);
    B -- Ground Station --> C[Cloud Gateway API];
    subgraph Remote Subsystem
        A
    end
    subgraph Collector Subsystem
        C
    end
    C -- Message Queue --> D[Global Logistics Platform];
    subgraph Central Processor
        D
    end
    D -- Analytics --> E[Customer Dashboard];

3. Cross-Domain Application

Derivative 3.1: Aerospace - Aircraft Component Lifecycle Management

  • Enabling Description: The system is applied to track the service history of critical aircraft components.

    • Remote Subsystem: A handheld device used by an aircraft maintenance engineer on the tarmac. It uses a combination of a barcode/QR code scanner and NFC reader to identify a component (e.g., a turbine blade). The engineer inputs maintenance actions, and the device captures a digital signature and biometric thumbprint for attribution.
    • Collector Subsystem: The airport's Maintenance, Repair, and Overhaul (MRO) server. It collects data from all maintenance devices on-site via a secure Wi-Fi network and synchronizes it with the central system.
    • Central Processor: The aircraft manufacturer's (e.g., Airbus A350) global digital twin database. It processes the incoming data to update the lifecycle record for that specific serialized component, schedules future inspections, and performs fleet-wide predictive failure analysis.

  • Diagram:

    erDiagram
    AIRCRAFT_COMPONENT {
        string ComponentID PK
        string Type
        int FlightHours
    }
    MAINTENANCE_RECORD {
        string RecordID PK
        string ComponentID FK
        string MRO_Server_ID FK
        datetime Timestamp
        string ActionTaken
        blob EngineerSignature
    }
    MRO_SERVER {
        string MRO_Server_ID PK
        string AirportCode
    }
    AIRCRAFT_COMPONENT ||--o{ MAINTENANCE_RECORD : "has"
    MRO_SERVER ||--o{ MAINTENANCE_RECORD : "collects"

Derivative 3.2: AgTech - Precision Agriculture Data Aggregation

  • Enabling Description: The system is used for managing data in a smart farming operation.

    • Remote Subsystem: An IoT sensor package mounted on an autonomous tractor. It captures soil nutrient data (N, P, K), moisture levels, high-resolution imagery of crops for pest detection, and GPS location data.
    • Collector Subsystem: An on-farm edge computing server located in the barn. It receives data from tractors, drones, and stationary soil sensors via a local mesh network (e.g., Zigbee or Wi-Fi HaLow). It performs initial data filtering and compression.
    • Central Processor: A cloud-based agricultural analytics platform. It aggregates data from hundreds of farms, combines it with weather satellite data, and uses machine learning to generate variable-rate fertilizer application prescriptions, which are then sent back to the farm equipment.

  • Diagram:

    flowchart LR
    subgraph Remote
        A[Tractor Sensor Array]
    end
    subgraph Collector
        B[On-Farm Edge Server]
    end
    subgraph Central
        C[Cloud Analytics Platform]
    end
    A -- Zigbee/Wi-Fi --> B;
    B -- Internet --> C;
    C -- ML Model --> D[Fertilizer Prescription];
    D --> A;

4. Integration with Emerging Tech

Derivative 4.1: AI-Enhanced Real-Time Fraud Prevention

  • Enabling Description: The system is integrated with an AI/ML pipeline for fraud detection. The remote data access subsystem captures transaction data as before. The data collecting subsystem (DAC), instead of merely aggregating data, runs a lightweight, pre-trained anomaly detection model (e.g., an autoencoder) at the edge. This model flags transactions with anomalous characteristics (e.g., unusual time, location, or amount for that specific terminal) and assigns them a preliminary risk score. The full data packet, along with the risk score, is forwarded to the central data processing subsystem (DPC). The DPC uses a more complex, deep learning model (e.g., a Graph Neural Network analyzing the relationship between merchants, customers, and locations) to perform a final risk assessment. The system uses federated learning to continuously update the edge models on the DACs without exposing raw transaction data from different regions to each other.

  • Diagram:

    sequenceDiagram
    actor User
    participant DAT
    participant DAC
    participant DPC
    User->>DAT: Initiates Transaction
    DAT->>DAC: Send Transaction Data
    DAC->>DAC: Run Edge Anomaly Model
    DAC->>DPC: Forward Data + Edge Risk Score
    DPC->>DPC: Run Central GNN Fraud Model
    DPC-->>DAT: Return Approval/Denial/Challenge
    DPC->>DAC: Periodically Push Updated Edge Model

Derivative 4.2: Blockchain-Anchored Audit Trail for Supply Chain

  • Enabling Description: The system leverages a private, permissioned blockchain (e.g., Hyperledger Fabric) to provide an immutable audit trail. When a transaction (e.g., the scanning of a bill of lading for a pharmaceutical shipment) is captured by the remote subsystem, it is transmitted to the collector. The central processing subsystem validates the transaction against business rules (e.g., ensuring the shipment temperature remained within limits, using integrated IoT sensor data). Upon validation, the DPC does not store the full image data on-chain. Instead, it computes a cryptographic hash (SHA-256) of the Tagged Encrypted Compressed Bitmap Image (TECBI) and stores this hash, along with key metadata (timestamp, GPS location, custodian ID), as a transaction on the blockchain. The full TECBI is stored in an off-chain distributed file system (like IPFS), with its content-addressable link included in the on-chain record. This provides a tamper-proof, auditable record of the transaction without bloating the blockchain with large image files.

  • Diagram:

    graph TD
    A[Remote Subsystem captures Image] --> B{Central Processor};
    B -- 1. Validate Transaction --> B;
    B -- 2. Compute SHA-256 Hash --> C[Image Hash];
    B -- 3. Store Image Off-Chain --> D[(IPFS)];
    D -- Returns IPFS Link --> B;
    B -- 4. Create Blockchain Tx --> E[[Permissioned Blockchain]];
    subgraph On-Chain Transaction
        C
        F[Metadata]
        G[IPFS Link]
    end
    E -- Confirms Tx --> B;

5. The "Inverse" or Failure Mode

Derivative 5.1: Graceful Degradation with Store-and-Forward Protocol

  • Enabling Description: The system is designed for high availability in environments with intermittent network connectivity. The remote data access subsystem operates in different states based on network health.

    • State 1: Online Mode. A stable connection exists to the data collector. The remote subsystem transmits the full TECBI immediately after capture.
    • State 2: Degraded Mode (Store-and-Forward). The connection is lost. The remote subsystem transitions to a low-power state. It captures only essential transaction text data (e.g., from OCR) and a low-resolution grayscale thumbnail of the document. This minimal data is stored locally in an encrypted SQLite database. The system can continue to process transactions in this mode for a configurable period (e.g., 24 hours or 1,000 transactions).
    • State 3: Synchronization Mode. When the network connection is restored, the remote subsystem establishes a handshake with the collector, transmits its queue of stored transactions, and then purges its local cache upon successful receipt confirmation.

  • Diagram:

    stateDiagram-v2
    [*] --> Online
    Online --> Degraded: Network Loss
    Degraded --> Online: Network Restored
    Online: Transmit full TECBI in real-time
    Degraded: Store minimal data locally (Encrypted DB)
    Online: On entry / Initiate sync of queued data

II. Combination Prior Art with Open-Source Standards

Combination 1: System Architecture based on Apache Kafka

  • Enabling Description: The system's data transport and collection layer is implemented using Apache Kafka, an open-source distributed event streaming platform. Each remote data access subsystem (DAT) acts as a Kafka Producer. When a document is scanned and processed, the resulting TECBI is published as a message to a specific Kafka topic, partitioned by geographical region or merchant ID (e.g., topic: us-west-transactions). The data collecting subsystem (DAC) is realized as a cluster of Kafka Brokers, which provides fault tolerance, scalability, and data persistence. The central data processing subsystem (DPC) is a distributed application (e.g., using Kafka Streams or Apache Flink) that acts as a Kafka Consumer, subscribing to the topics, processing the incoming streams of transaction data in real-time, and writing the results to a database. This architecture decouples the data capture and processing stages, allowing each to scale independently.

Combination 2: Data Payload Standardization with ISO 20022

  • Enabling Description: The content of the data transmitted through the system is standardized using the ISO 20022 protocol for financial messaging. When a remote subsystem captures a paper document like a check or invoice, its on-board OCR and data extraction logic formats the extracted information into a structured XML-based ISO 20022 message. For example, a check scan would be converted into a pacs.008 (FI-to-FI Customer Credit Transfer) message. This standardized message, along with the original document image (TECBI) as an attachment, becomes the payload that is transmitted to the collector. The central processor can then natively process these messages using standard financial industry software, eliminating the need for proprietary data transformation logic.

Combination 3: Authentication and Authorization via OpenID Connect (OIDC)

  • Enabling Description: The security and access control for the entire system are managed using the OpenID Connect (OIDC) and OAuth 2.0 open standards. Every remote subsystem (DAT) and every operator is registered as a client in a central Identity Provider (IdP). To initiate a session, the operator authenticates with the IdP via an OIDC flow. Upon success, the DAT receives a cryptographically signed ID Token and an Access Token. The Access Token, which contains specific scopes (e.g., write:transactions, read:config), is presented as a Bearer token with every API call from the DAT to the DAC. The DAC validates the token signature against the IdP's public key, ensuring that the request is both authenticated and authorized before accepting any data. This provides a robust, standardized, and auditable security framework for the entire network.

Generated 5/11/2026, 12:13:51 AM