Patent 5243655

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 Document

Title: System and Method for Encoding, Storing, and Decoding Data in High-Density, Environmentally-Adaptive, and Intelligent Machine-Readable Graphic Forms
Publication Date: April 30, 2026
Keywords: 2D Barcode, PDF417, Data Matrix, QR Code, Machine Vision, Optical Scanning, Error Correction, Data Encoding, AI, IoT, Blockchain, Microfluidics, Thermochromic, Phosphorescent, G-code, JWT, OpenCV.

Abstract

This publication discloses a series of enhancements, alternative embodiments, and novel applications for systems and methods related to the encoding and decoding of data in two-dimensional graphical forms, building upon the principles found in U.S. Patent 5,243,655. The disclosed variations are intended to enter the public domain to serve as prior art for future inventions. These disclosures cover: (1) The use of unconventional materials and components for data carriers and scanning systems, such as phosphorescent substrates and microfluidic nanoparticle assemblies. (2) The application of the technology in extreme operational parameters, including microscopic and high-temperature environments. (3) The application of the core encoding/decoding system to novel domains, including aerospace composite manufacturing, precision agriculture, and dynamic configuration of consumer electronics. (4) The integration of the core technology with emerging technologies, such as AI-driven error correction, IoT-enabled dynamic data carriers, and blockchain-based chain of custody verification. (5) The implementation of "inverse" or fail-safe modes, including gracefully degrading symbols for emergency services and self-redacting symbols for privacy.

Derivations Based on System for Encoding and Decoding (Ref: Claim 1)

1. Material & Component Substitution

  • Derivative 1.1: Phosphorescent Substrate & UV Laser Scanning
    • Enabling Description: The data carrier (16) is constructed from a strontium aluminate-doped polymer substrate capable of high-persistence phosphorescence. The encoding means (12) utilizes a high-precision ultraviolet (UV) laser etching system (e.g., 266 nm Nd:YAG) to selectively "deactivate" phosphorescent particles in the substrate, creating the dark elements of the PDF417 symbol. The recognition means (14) is a specialized scanner (28) that first excites the entire symbol area with a wide-beam UV flash lamp. The converting means is a time-gated CCD or CMOS sensor that captures the afterglow image after the excitation flash is extinguished. The contrast between the glowing and non-glowing (etched) areas provides a high signal-to-noise ratio, making it readable in low-light conditions or through semi-opaque-to-visible-light materials. The decoding logic (30) remains largely the same but must account for potential blooming effects in the phosphorescent image, using edge-detection algorithms like Canny or Sobel to refine the bar-space boundaries before applying the t-sequence analysis.
    • graph TD
    subgraph Encoding Means (12)
        A[Data Input] --> B{Processor & PDF417 Encoder};
        B --> C[266nm Nd:YAG Laser Controller];
        C --> D[Laser Etching Head];
    end
    subgraph Data Carrier (16)
        E[Strontium Aluminate Substrate]
    end
    D --Etches Symbol--> E;
    subgraph Recognition Means (14)
        F[UV Flash Lamp Excites Substrate] --> E;
        E --Emits Afterglow--> G[Time-Gated CCD Sensor];
        G --> H{Image Processor - Edge Detection};
        H --> I[Low-Level Decoder (30)];
        I --> J[Decoded Data Output (32)];
    end

2. Operational Parameter Expansion

  • Derivative 1.2: Microfluidic Nanoparticle Assembly
    • Enabling Description: This system operates at the micro/nanoscale for tagging biological samples. The "carrier" is a microfluidic channel. The "indicia" are not printed but are self-assembled from metallic nanoparticles (e.g., gold nanoparticles) and inert polymer spacers. The encoding means is a micro-electro-mechanical system (MEMS) that controls voltages at a series of micro-electrodes along the channel. Data from a computer file is converted into a sequence of voltage applications that trap the metallic nanoparticles at specific locations to form the rows of a micro-PDF417 symbol, while the spacers form the white space. The recognition means is a high-magnification microscope coupled with a high-speed camera. The decoding means uses image processing to identify the nanoparticle clusters and their relative spacing, converting the image into a digital representation of the symbol for decoding. This allows for embedding vast amounts of data directly onto a lab-on-a-chip device for tracking single-cell experiments.
    • sequenceDiagram
    participant Encoder as Encoding Control (MEMS)
    participant MChannel as Microfluidic Channel
    participant Decoder as Microscope/Camera System
    Encoder->>MChannel: Inject Nanoparticles & Spacers
    Encoder->>MChannel: Apply Voltage Sequence to Electrodes
    MChannel->>MChannel: Nanoparticles Assemble into PDF417
    Decoder->>MChannel: Capture High-Res Image of Assembled Symbol
    Decoder->>Decoder: Process Image to Extract Bar/Space Widths
    Decoder->>Decoder: Execute PDF417 Decoding Algorithm
    Decoder->>User: Output Decoded Data

3. Cross-Domain Application

  • Derivative 1.3: Aerospace - Self-Verifying Composite Ply Layup

    • Enabling Description: In the manufacture of aerospace composites, each ply of carbon fiber pre-preg is marked with a PDF417 symbol by the encoding means (12), which is a high-speed, non-contact inkjet printer using a resin-soluble, non-contaminating ink. The symbol encodes the ply number, orientation, material batch number, and expiration date. The recognition means (14) is integrated into the Automated Fiber Placement (AFP) head. A compact CCD imager scans each ply immediately after it is placed on the mold. The decoded data is compared in real-time against the master CAD layup schedule stored in the AFP controller. Any discrepancy (wrong ply, wrong orientation) triggers an immediate machine halt and alerts the operator, preventing catastrophic layup errors. The system ensures full traceability for each ply in the final component.
    • graph TD
    A[CAD Layup Schedule] --> B{AFP Control System};
    B --> C[Inkjet Encoder on Cutting Table];
    C --Prints PDF417--> D(Carbon Fiber Ply);
    E[AFP Head] --Places Ply--> F(Mold);
    D --> E;
    subgraph Recognition System
        G[CCD Imager on AFP Head] --Scans Ply on Mold--> F;
        G --> H{Decoder};
        H --Decoded Ply Data--> B;
    end
    B --Compares Data--> A;
    B --> I{Decision: Continue or Halt?};

  • Derivative 1.4: AgTech - In-Field Seed Packet Generation

    • Enabling Description: A mobile agricultural system (e.g., mounted on a tractor or drone) includes an encoding means for creating customized seed packets on-the-fly. Based on real-time soil sensor data (moisture, pH, nutrients), a processing means (24) determines the optimal seed varietal, coating, and quantity for a specific micro-plot. This information is encoded into a PDF417 symbol and printed onto a biodegradable packet by a ruggedized thermal printer (26). The same packet is then filled with the prescribed seeds. The recognition means is used later in the season by a scouting drone equipped with a scanner. The drone reads the symbol on the now-degrading packet remnants to correlate the original seed data with observed plant growth and health metrics, creating a detailed feedback loop for precision agriculture algorithms.
    • graph TD
    A[Soil Sensor Data] --> B{On-Board Processor};
    B --Determines Seed Mix--> C(Seed Hopper/Coater);
    B --Generates PDF417 Data--> D(Thermal Printer);
    D --Prints Symbol--> E[Biodegradable Packet];
    C --Fills Packet--> E;
    E --Is Planted--> F(Field);
    G(Scouting Drone) --Scans Field--> F;
    H[Drone's Scanner] --Reads Symbol--> I{Drone's Decoder};
    I --Correlates with Growth Data--> J[Precision Ag Database];

  • Derivative 1.5: Consumer Electronics - Dynamic Appliance Configuration

    • Enabling Description: A smart appliance (e.g., a microwave oven, as mentioned in the patent) features a small, front-facing camera as its recognition means (14). Food packaging for compatible products includes a PDF417 symbol printed on it (the carrier, 16). The symbol doesn't just contain a simple product ID; it encodes a complex set of instructions, essentially a script (e.g., in JSON or XML format). When the user presents the package to the appliance's camera, it decodes the script. For a microwaveable meal, the script could define a multi-stage cooking process: {"stage1": {"power": "70%", "time": "90s"}, "stage2": {"power": "50%", "time": "120s", "instruction": "Stir now"}, "stage3": {"power": "100%", "time": "60s"}}. The appliance's processor executes this script, providing a perfectly tailored cooking cycle without requiring user input beyond the initial scan. The encoding means (12) is used by the food manufacturer at the time of packaging.
    • sequenceDiagram
    participant Manufacturer as Manufacturer's System
    participant Product as Product Package
    participant Microwave as Smart Appliance
    participant User as User

    Manufacturer->>Product: Encodes & Prints PDF417 Cooking Script
    User->>Microwave: Places product inside
    User->>Microwave: Presents package w/ PDF417 to camera
    Microwave->>Microwave: Scans & Decodes Symbol
    Microwave->>Microwave: Parses Cooking Script (e.g., JSON)
    Microwave->>Microwave: Executes Stage 1 (Power: 70%, Time: 90s)
    Microwave->>User: Beeps, Displays "Stir now"
    User->>Microwave: Stirs food
    Microwave->>Microwave: Executes Stage 3 (Power: 100%, Time: 60s)
    Microwave->>User: Beeps, "Cooking Complete"

4. Integration with Emerging Tech

  • Derivative 1.6: AI-Enhanced Error Correction & Predictive Decoding

    • Enabling Description: The decoding means (30) is augmented with a convolutional neural network (CNN) trained on millions of examples of pristine, damaged, and distorted PDF417 symbols. When the optical scanner (28) provides a raw image signal of a damaged or poorly printed symbol, the traditional decoding algorithm (steps 154-164) is attempted first. If it fails to decode a sufficient number of codewords to use the standard error correction, the raw image data is passed to the CNN. The AI model, rather than just reading bars and spaces, identifies characteristic features of entire codewords and their clusters. It can "in-paint" missing or unreadable portions of the symbol image based on its training, generating a corrected virtual image. This corrected image is then passed back to the standard low-level decoder, allowing it to successfully reconstruct the data matrix. The system can learn and improve its correction capabilities over time.
    • graph TD
    A[Scanner Captures Damaged Symbol] --> B{Low-Level Decoder};
    B --Fails to Decode--> C{AI Correction Module (CNN)};
    C --Processes Raw Image--> D[Corrected Virtual Image];
    D --> B;
    B --Successfully Decodes--> E[High-Level Decoder];
    E --> F[Output Data];

  • Derivative 1.7: IoT-Enabled Dynamic Data Carrier

    • Enabling Description: The system is used for tracking sensitive cold-chain shipments. The carrier (16) is a shipping container equipped with an IoT sensor package (temperature, humidity, shock) and a low-power e-paper display. The encoding means (12) initially prints a PDF417 symbol on a label containing the manifest and handling instructions. During transit, the IoT sensors monitor the container's environment. If a temperature excursion occurs, the onboard processor automatically re-encodes the PDF417 symbol, adding a new data block with a timestamp and the temperature data. This new symbol is then rendered on the e-paper display. The recognition means (14) at the receiving dock scans the current symbol on the e-paper display. The decoded data provides not only the original manifest but also a verifiable, tamper-evident log of any environmental deviations during the journey.
    • stateDiagram-v2
    [*] --> Initialized
    Initialized --> InTransit: Dispatch
    InTransit: E-Paper displays original PDF417
    InTransit --> InTransit: IoT Sensor records data
    InTransit --> AnomalyDetected: Temperature > Threshold
    AnomalyDetected --> InTransit: Processor updates PDF417 with anomaly data & renders on E-Paper
    InTransit --> Delivered: Arrives at Destination
    Delivered --> [*]: Scanned by Recognition Means

  • Derivative 1.8: Blockchain-Verified Chain of Custody

    • Enabling Description: The system is integrated into a supply chain for high-value goods (e.g., pharmaceuticals, luxury items). At each handoff point, an operator uses an encoding device (12) to generate a PDF417 symbol. This symbol contains a hash of the previous transaction block, the current timestamp, GPS location, and the recipient's digital signature. The symbol is printed on a tamper-evident label affixed to the item. A recipient uses a recognition device (14) to scan the symbol. The decoding means (30) extracts the data, verifies the digital signature, and calculates the hash. This data is then submitted as a new transaction to a distributed ledger (blockchain). This creates an immutable, physically-linked chain of custody. Any attempt to alter a label or create a fraudulent one would result in a hash mismatch that is instantly detectable by the next recipient in the chain.
    • sequenceDiagram
    participant PointA as Shipper
    participant Item as Physical Item
    participant PointB as Receiver
    participant Blockchain as Distributed Ledger

    PointA->>Item: Generates & Attaches PDF417 Label 1 (Block N)
    PointB->>Item: Scans PDF417 Label 1
    PointB->>Blockchain: Submits Transaction N (Data from Label 1)
    Blockchain-->>PointB: Transaction N Confirmed
    PointB->>Item: Generates & Attaches PDF417 Label 2 (Block N+1, hashes Block N)
    PointB->>Blockchain: Submits Transaction N+1 (Data from Label 2)

5. The "Inverse" or Failure Mode

  • Derivative 1.9: Graceful Degradation Mode for Emergency Services

    • Enabling Description: The system is used for patient identification wristbands in a mass casualty incident. The PDF417 symbol is structured with a "critical data block" and multiple "secondary data blocks." The critical block, always located in the first three rows of the symbol, contains only the patient's unique ID, blood type, and critical allergies, and it uses the highest level of error correction (e.g., Level 8). Secondary blocks contain less critical information like home address or primary physician, with lower error correction. The recognition means (14), when switched to "Triage Mode," performs a low-resolution, high-speed scan. Its decoding algorithm (30) is programmed to only search for and decode the first three rows containing the critical data block. This allows first responders to rapidly identify dozens of patients in seconds, ignoring the secondary data. In "Full-Record Mode," the scanner performs a more detailed scan to capture and decode the entire symbol. This provides a fail-safe where essential information is prioritized and readable even if the symbol is partially obscured or damaged.
    • graph TD
    subgraph PDF417 Symbol
        A[Rows 0-2: Critical Data Block - High ECC]
        B[Rows 3-N: Secondary Data - Low ECC]
    end
    subgraph Scanner
        C{Mode Selection};
        C -- Triage Mode --> D[High-Speed, Low-Res Scan];
        C -- Full-Record Mode --> E[Standard Scan];
        D --> F{Decode Critical Block Only};
        E --> G{Decode Full Symbol};
        F --> H[Output: ID, Blood Type, Allergies];
        G --> I[Output: Full Patient Record];
    end

  • Derivative 1.10: Self-Redacting Privacy Symbol

    • Enabling Description: The system is used for temporary identification or access control. The data carrier (16) is a "smart paper" substrate a thermochromic ink layer over the printed PDF417 symbol (18). The symbol is printed using standard ink. A transparent, resistive heating element is laminated over the symbol. The recognition means (14) is a specialized reader that, in addition to the optical scanner, contains a power-driver circuit. After a successful scan and decode, the decoding means (30) sends a signal to the power-driver circuit. This circuit applies a brief, low-voltage pulse to the heating element, raising the temperature of the thermochromic ink just enough to turn it opaque (e.g., from clear to black), permanently redacting the underlying PDF417 symbol and preventing any further reads. This provides a single-use, physically secure method for data transfer where the data carrier self-destructs its information after use.
    • flowchart TD
    Start --> A[Present Carrier to Reader];
    A --> B{Scan and Decode PDF417 Symbol};
    B -- Success --> C{Authenticate Data/Grant Access};
    C --> D{Send 'Redact' Signal};
    D --> E[Apply Voltage to Heating Element];
    E --> F[Thermochromic Ink Turns Opaque];
    F --> G[Symbol is Now Unreadable];
    G --> End;
    B -- Failure --> H[Deny Access / No Redaction];
    H --> End;

Combination Prior Art Scenarios

1. Combination with OpenCV (Open Source Computer Vision Library)

  • Scenario: A robust, open-source decoding system for mobile platforms.
  • Enabling Description: A software library is created that leverages the core algorithms of the '655 patent but is implemented using functions from the open-source OpenCV library. The cv::VideoCapture function is used to acquire a continuous video stream from a smartphone camera. Each frame is converted to grayscale. The cv::Canny edge detection and cv::findContours functions are used to rapidly locate potential barcode regions within the frame, replacing the patent's described method of searching for start/stop patterns in a linear scan. Once a candidate region is identified, its perspective is corrected using cv::getPerspectiveTransform and cv::warpPerspective to produce a flat, rectangular image of the symbol. This normalized image is then binarized, and the core decoding logic from the '655 patent—identifying codewords, calculating cluster numbers, assigning row numbers based on row indicators, and populating the codeword matrix—is applied to decode the data. This combination makes the robust "stitching" decoding of PDF417 available in a royalty-free, high-performance library accessible to any developer.
  • sequenceDiagram
    participant App as User Application
    participant Camera as Device Camera
    participant OpenCV as OpenCV Library
    participant PDF417Decoder as '655 Logic Core

    App->>Camera: Start Capture
    loop Frame Processing
        Camera->>OpenCV: Provides Video Frame
        OpenCV->>OpenCV: Grayscale, Canny Edge, findContours
        OpenCV-->>App: Bounding Box of Symbol
        App->>OpenCV: Warp Perspective on Bounding Box
        OpenCV-->>App: Normalized Symbol Image
        App->>PDF417Decoder: Pass Normalized Image
        PDF417Decoder->>PDF417Decoder: Decode using 'Stitching' Logic
        PDF417Decoder-->>App: Decoded Data or Failure
    end

2. Combination with IETF RFC 7519 (JSON Web Tokens - JWT)

  • Scenario: A system for physically verifiable, offline digital identity or authorization tokens.
  • Enabling Description: A server-side application generates a standard JSON Web Token (JWT) containing user claims (e.g., user ID, roles, expiration time) and signs it with a private key. Instead of transmitting this JWT electronically, the entire Base64URL-encoded string (header.payload.signature) is passed to the encoding means (12) of the '655 patent system. The data is encoded into a high-density PDF417 symbol and printed onto a physical ID card or a single-use ticket. A client-side recognition means (14), such as a secure kiosk or mobile device, scans the PDF417 symbol. The decoding means (30) extracts the JWT string. The client then performs standard JWT validation: it checks the signature using the corresponding public key, verifies the expiration timestamp (exp), and parses the payload to grant access or confirm identity. This allows for a stateless, secure, offline authentication mechanism where the physical token is the "single source of truth."
  • graph LR
    subgraph ServerSide
        A[User Data] --> B{JWT Creation};
        B -- "header.payload.signature" --> C{PDF417 Encoder};
        C --> D[Printer];
    end
    D -- Prints --> E(Physical ID Card with PDF417);
    subgraph ClientSide
        F[Scanner/Camera] -- Scans --> E;
        F --> G{PDF417 Decoder};
        G -- "JWT String" --> H{JWT Validator};
        H -- Uses Public Key --> I[Signature & Claim Verification];
        I --> J[Access Granted/Denied];
    end

3. Combination with G-code (RS-274 Standard)

  • Scenario: A "paper-based programming" system for CNC machines and 3D printers.
  • Enabling Description: A Computer-Aided Manufacturing (CAM) software package is modified to include an output option for "Paper G-code." When selected, the CAM software generates the G-code toolpath instructions for a machining operation. This text-based G-code file is then fed into the encoding means (12) of the '655 patent system and encoded into a large PDF417 symbol. The symbol is printed (26) on a durable, oil-resistant label (the carrier, 16). An operator affixes this label directly to the raw material stock. The CNC machine is retrofitted with a recognition means (14) consisting of a simple fixed-mount camera and a microcontroller (e.g., a Raspberry Pi). Before starting the job, the operator positions the material and initiates a scan. The recognition means scans the PDF417 symbol, decodes it back into the original G-code text file, and loads it directly into the CNC controller's memory for execution. This eliminates the need for network connections, USB drives, or manual data entry on the factory floor, reducing the risk of loading the wrong file for a given piece of stock.
  • flowchart TD
    subgraph Office
        A[CAD Model] --> B[CAM Software];
        B -- Generates G-code --> C{PDF417 Encoder};
        C -- Encoded Data --> D[Printer];
        D --> E[Print G-code PDF417 Label];
    end
    subgraph Factory Floor
        F[Operator] -- Affixes Label --> G(Raw Material Stock);
        G -- Placed in Machine --> H[CNC Machine];
        I[Camera on CNC] -- Scans Label --> J{PDF417 Decoder};
        J -- Decoded G-code --> K[CNC Controller];
        K -- Executes Program --> L[Machined Part];
    end

Generated 4/30/2026, 2:23:05 AM