Derivative works

Defensive Disclosure Document for US 7,557,788

Publication Date: May 14, 2026
Subject: Derivative Methods and Implementations for In-Situ Calibration of Emitter and Modulator Arrays.
Scope: This document discloses a series of derivative inventions and technical variations based on the core principles outlined in US Patent 7,557,788. The purpose is to place these variations into the public domain, thereby establishing them as prior art. The core principle involves a closed-loop system where an external sensor and control algorithm are used to program and permanently store optimized operational parameters in non-volatile memory integral to an electronic device, compensating for manufacturing variations.

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Derivatives Based on Claims 1 & 5: Method of Calibrating an Emitter/Modulator Array

Axis 1: Material & Component Substitution

• Derivative 1.1: Quantum Dot Color Filter Calibration

  • Enabling Description: The calibration method is applied to displays employing Quantum Dot (QD) color filters. The external optical sensor is a spectrometer calibrated to the narrow-band emission characteristics of the specific QDs used (e.g., CdSe/ZnS core-shell structures). The optimization algorithm adjusts gamma reference voltages to correct for minor center-wavelength shifts and full-width at half-maximum (FWHM) variations inherent in the QD synthesis and deposition process. This ensures the display's primaries precisely match a target color space, such as Rec. 2020, by compensating for per-panel material variations. The resulting color-corrected gamma curve is stored in the device's non-volatile memory.
  • Mermaid Diagram:

    graph TD
        A[Display with QD Filters] -- Emitted Light --> B{External Spectrometer};
        B -- Spectral Data --> C[Control Computer w/ Colorimetric Algorithm];
        C -- Control Signals --> D{External Voltage Control Circuit};
        D -- Vary Gamma Voltages --> A;
        C -- Store Final Curve --> E[On-Chip Non-Volatile Memory];
        E -- Provides Operating Voltages --> A;
    

• Derivative 1.2: Memristor-Based Analog Voltage Storage

  • Enabling Description: The non-volatile gamma control capability is implemented using an array of analog-programmable memristors (e.g., TiO₂-based). Instead of storing a digital value, the resistance of each memristor is tuned to an analog level that, through an op-amp buffer, produces the desired gamma voltage. The external control circuit uses a sequence of precise voltage/current pulses to incrementally adjust the memristor's resistance. The closed-loop feedback from the optical sensor guides this tuning process until the desired light output is achieved, at which point the resistance value is permanently stored. This allows for a higher-resolution, lower-power implementation of the non-volatile storage.
  • Mermaid Diagram:

    sequenceDiagram
        participant Optical Sensor
        participant Control Algorithm
        participant Gamma IC (Memristor Array)
        Optical Sensor->>Control Algorithm: Luminance Data
        Control Algorithm->>Gamma IC (Memristor Array): Select Memristor (Gamma Point)
        Control Algorithm->>Gamma IC (Memristor Array): Apply Tuning Pulse
        Gamma IC (Memristor Array)-->>Control Algorithm: Feedback (A_OUT or Resistance)
        Control Algorithm->>Control Algorithm: Compare to Target
        Control Algorithm->>Gamma IC (Memristor Array): Store Final Resistance State
    

Axis 2: Operational Parameter Expansion

• Derivative 2.1: Cryogenic Display Calibration

  • Enabling Description: The calibration method is performed on displays designed for cryogenic operation (e.g., 77 Kelvin), such as those used in quantum computing control interfaces or deep-space instrumentation. The display panel, optical sensor, and control circuitry are placed within a cryogenic test chamber. The optimization algorithm is parameterized to account for the altered electro-optical response of the liquid crystal or OLED emitters at cryogenic temperatures. Multiple gamma curves, each corresponding to a specific temperature range (e.g., 77K, 100K), are generated and stored in separate "banks" in the non-volatile memory, as described in the original patent. An on-board temperature sensor selects the appropriate curve during operation.
  • Mermaid Diagram:

    graph TD
        subgraph Cryogenic Chamber (77K)
            A[Display Panel]
            B[Cooled Optical Sensor]
        end
        A -- Light Output --> B;
        B -- Sensor Data --> C{External Control Computer};
        C -- Control Signals --> D{Voltage Control Circuit};
        D -- Drives Panel --> A;
        C -- Store Temp-Specific Curves --> E[On-Chip NVM];
    

• Derivative 2.2: High-Frequency Micro-LED Per-Pixel Calibration

  • Enabling Description: The method is adapted for Micro-LED (µLED) displays operating at refresh rates above 1 kHz. The "columns" are individual µLED pixels, and the "gamma reference voltage" is the analog driving voltage or PWM duty cycle for each pixel. The optical sensor is a high-speed photodiode array synchronized with the pixel driving sequence. The algorithm creates a per-pixel correction map to normalize light output, compensating for variations in epitaxial growth that cause non-uniformity in quantum efficiency. This map of micro-corrections is stored in the non-volatile memory and accessed by the display driver IC to ensure uniformity at extreme frame rates.
  • Mermaid Diagram:

    flowchart LR
        subgraph Calibration System
            controller[Control PC / Algorithm]
            sensor[High-Speed Photodiode Array]
            driver[External µLED Driver]
        end
        subgraph Display Assembly
            uLED_Panel[Micro-LED Panel]
            NVM[On-Chip NVM]
        end
        controller -- Optimize Drive Signal --> driver;
        driver -- Drives Individual µLEDs --> uLED_Panel;
        uLED_Panel -- Emitted Light --> sensor;
        sensor -- Per-Pixel Luminance Data --> controller;
        controller -- Store Per-Pixel Correction Map --> NVM;
        NVM -- Provides Correction Data to internal driver --> uLED_Panel;
    

Axis 3: Cross-Domain Application

• Derivative 3.1: Aerospace - Electrochromic Window Uniformity Calibration

  • Enabling Description: The method is used to calibrate large, segmented electrochromic "smart windows" for aircraft cabins. Each addressable segment of the window is a "column". An array of external photometers measures the optical transmission of each segment. The control circuit varies the voltage applied to the electrochromic layer of each segment. The optimization algorithm generates and stores a voltage correction map in non-volatile memory to ensure all segments tint uniformly and that all windows across the aircraft exhibit identical tinting characteristics, compensating for manufacturing variations in the electrochromic material.
  • Mermaid Diagram:

    stateDiagram-v2
        [*] --> Calibrating
        Calibrating: For each window segment...
        Calibrating --> Optimizing: Segment optical transmission acquired
        Optimizing: Algorithm calculates ideal voltage for target opacity
        Optimizing --> Storing: Optimal voltage found
        Storing: Write voltage to NVM for that segment
        Storing --> Calibrating: Move to next segment
        Calibrating --> Calibrated: All segments complete
        Calibrated --> [*]
    

• Derivative 3.2: AgTech - Spectral Tuning of LED Grow Lights

  • Enabling Description: The method calibrates multi-channel, programmable LED grow light arrays. Each "column" is a string of LEDs of a specific color (e.g., deep red, far-red, blue). The sensor is a spectrometer that measures the spectral power distribution (SPD) of the combined output. Based on a target plant growth recipe (e.g., maximizing the photosynthetically active radiation), the optimization algorithm iteratively adjusts the current supplied to each color channel. It stores the final current settings in the driver's non-volatile memory to produce the exact target spectrum, compensating for variations in LED binning, thermal droop, and lens optics.
  • Mermaid Diagram:

    graph TD
        A[Multi-Channel LED Array] -- Light Output --> B{Spectrometer};
        B -- Spectral Data --> C[Control PC w/ Plant Growth Algorithm];
        C -- Adjust Channel Currents --> D{Multi-Channel LED Driver};
        D -- Drives LEDs --> A;
        C -- Store Optimized Current Settings --> E[Driver's NVM];
        E -- Provides Drive Currents --> D;
    

• Derivative 3.3: Medical - Phased-Array Ultrasound Transducer Homogenization

  • Enabling Description: The method calibrates the individual piezoelectric elements of a medical phased-array ultrasound transducer. Each element is a "column". A calibrated hydrophone, acting as the sensor, measures the acoustic pressure and phase of the output from each element. The control circuit varies the amplitude and phase of the driving voltage. The optimization algorithm adjusts these parameters for each element to normalize the acoustic output across the array, compensating for manufacturing variations in the piezoelectric material. The final table of per-element amplitude and phase corrections is stored in non-volatile memory on the transducer probe.
  • Mermaid Diagram:

    sequenceDiagram
        participant Hydrophone
        participant Controller
        participant Transducer Element Array
        Controller->>Transducer Element Array: Excite element N with voltage V, phase P
        Transducer Element Array->>Hydrophone: Acoustic Pulse
        Hydrophone->>Controller: Measured Acoustic Pressure/Phase
        Controller->>Controller: Compare to desired uniform response
        Controller->>Transducer Element Array: Store optimized (V', P') for element N in NVM
    

Axis 4: Integration with Emerging Tech

• Derivative 4.1: AI-Driven Perceptual Quality Optimization

  • Enabling Description: The "predetermined algorithm" is replaced by a pre-trained Convolutional Neural Network (CNN) that models human visual perception. The optical sensor (a high-resolution camera) captures test patterns displayed on the screen. The CNN analyzes these images for artifacts that are difficult to quantify with simple metrics, such as mura, backlight bleed, and color banding. The network's output guides the control circuit in adjusting gamma levels to minimize these perceived flaws. The resulting gamma correction map, optimized for human perception rather than pure colorimetric accuracy, is stored in the non-volatile memory.
  • Mermaid Diagram:

    flowchart TD
        A[Display Panel] -- Shows Test Pattern --> B(High-Resolution Camera);
        B -- Captured Image --> C(AI Inference Engine - Perceptual CNN);
        C -- Generates Perceptual Score & Correction Vector --> D(Optimization Loop);
        D -- New Gamma Values --> E(External Control Circuit);
        E -- Applies Voltages --> A;
        D -- Final Perceptually-Tuned Gamma Map --> F(On-Chip NVM);
    

• Derivative 4.2: IoT-Enabled Real-Time Environmental Compensation

  • Enabling Description: The display unit is augmented with an IoT sensor module that measures ambient light color temperature, intensity, and display operating temperature. During manufacturing, the calibration process is repeated under various simulated environmental conditions, and a unique gamma curve for each condition is stored in a separate bank within the non-volatile memory. In the field, the IoT module reports the current environment to an onboard microcontroller, which then dynamically selects the appropriate pre-calibrated gamma curve from memory, ensuring optimal image quality regardless of viewing conditions.
  • Mermaid Diagram:

    classDiagram
        Display {
            +Panel
            +GammaControlIC
            +selectGammaCurve(environment)
        }
        GammaControlIC {
            -NonVolatileMemory
            -storedCurves[]
            +retrieveCurve(index)
        }
        IoTSensorModule {
            +ambientLightSensor
            +temperatureSensor
            +getCurrentEnvironment()
        }
        Display "1" *-- "1" GammaControlIC
        Display "1" *-- "1" IoTSensorModule
        IoTSensorModule ..> Display : reports environment
    

• Derivative 4.3: Blockchain-Verified Calibration Provenance

  • Enabling Description: After the calibration process is complete, a cryptographic hash of the final stored gamma voltage data is generated. This hash, along with the display's serial number, the calibration date, and the sensor equipment ID, is recorded as an immutable transaction on a manufacturing blockchain. This provides an auditable and tamper-proof record of calibration for each specific panel, which is essential for devices requiring certified performance, such as medical diagnostic displays or avionics controls.
  • Mermaid Diagram:

    sequenceDiagram
        participant Calibration Station
        participant Display
        participant Manufacturing Blockchain
        Calibration Station->>Display: Perform Calibration
        Display-->>Calibration Station: Final Gamma Data
        Calibration Station->>Calibration Station: Generate Hash(Gamma Data + Serial#)
        Calibration Station->>Manufacturing Blockchain: Create Provenance Transaction
        Blockchain-->>Calibration Station: Transaction Confirmed
    

Axis 5: "Inverse" or Failure Mode

• Derivative 5.1: Failsafe Default Gamma from ROM
  • Enabling Description: The gamma reference generator IC is designed with a hardware-masked, read-only memory (ROM) section that contains a generic, failsafe gamma curve. Upon device power-on, a checksum of the primary reprogrammable non-volatile memory is performed. If the checksum fails, indicating data corruption, the device's control logic automatically bypasses the corrupted data and loads the default gamma curve from the ROM. This ensures the display remains operational with a usable, albeit non-optimized, image, preventing a critical failure.
  • Mermaid Diagram:

    stateDiagram-v2
        [*] --> PowerOn
        PowerOn --> CheckingNVM: Boot Sequence
        CheckingNVM --> LoadingCustomCurve: NVM Checksum OK
        CheckingNVM --> LoadingDefaultCurve: NVM Checksum FAILED
        LoadingCustomCurve --> DisplayActive: Using Optimized Curve
        LoadingDefaultCurve --> DisplayActive: Using Failsafe Curve
        DisplayActive --> [*]: PowerOff
    

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Combination Prior Art Scenarios with Open Standards

1. Combination with VESA DisplayHDR Standard: The iterative method of Claim 3 is employed to ensure compliance with the open VESA DisplayHDR standard. The "optimization criteria" are the specific test parameters of a target HDR level (e.g., DisplayHDR 1000). The external sensor and control algorithm execute the official VESA tests, iteratively adjusting the gamma and tone-mapping reference voltages until the panel passes. The resulting compliant voltage curves are stored in the non-volatile memory, effectively using the patented method as the means to achieve an open-standard certification.

2. Combination with MIPI DSI-2 Protocol: The dedicated parallel programming interface (A0-A2, R/W) of the gamma generator is replaced by a command-based interface accessible via the open MIPI Display Serial Interface 2 (DSI-2) bus. The external calibrator sends standardized Display Command Set (DCS) commands to select and program the non-volatile gamma registers. This integrates the calibration function into the primary display communication bus, reducing pin count and cost, and standardizing the programming method according to an open protocol.

3. Combination with Open-Source Hardware (Raspberry Pi/Arduino): The external calibration system (sensor, control circuit, and algorithm execution) is implemented using widely available, low-cost, open-source hardware, such as a Raspberry Pi and an Arduino. The Pi runs an optimization script (e.g., in Python) that processes data from a compatible open-source color sensor and controls an Arduino board, which in turn generates the necessary analog voltages and digital control signals for programming the gamma IC. This demonstrates that the method is readily implemented using non-proprietary, open-source tools, establishing a low threshold for what a person skilled in the art could obviously achieve.