Derivative works

Defensive Disclosure and Prior Art Generation for US 10,623,095

Publication Date: May 6, 2026
Disclosing Entity: Project Grey Matter, Defensive Research Division
Subject Matter: Derivative and combinatory inventions based on the core optical channel monitoring methods for flexible grid networks disclosed in US Patent 10,623,095. This document is intended to enter the public domain to serve as prior art against future patent applications for incremental improvements in this field.

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Derivative Set 1: Material & Component Substitution

1.1. MEMS-Based Digital Transform Spectrometer OCM

• Enabling Description: This variation replaces the variable wavelength filter and single photodiode (PD) with a solid-state Micro-Electro-Mechanical System (MEMS) based Digital Transform Spectrometer (DTS). A stationary diffraction grating disperses the incoming multiplexed optical signal onto a MEMS Digital Micromirror Device (DMD). The controller, instead of tuning a filter, applies a series of binary patterns to the DMD array. Each pattern corresponds to a specific spectral basis function (e.g., Hadamard or Fourier basis vectors). Light from the "on" micromirrors is directed to a single, high-speed, broadband photodetector. By measuring the total power for each pattern and applying a fast inverse transform (e.g., Fast Hadamard Transform), the controller computes the full spectrum of the multiplexed signal simultaneously. It then applies the logic of identifying sampling points and corresponding intensity values from this digitally reconstructed spectrum to isolate the power of channels with different frequency widths. This method offers higher speed and resolution without any moving parts in the optical path besides the micromirrors.

• Mermaid Diagram:

  graph TD
      subgraph MEMS-DTS OCM
          A[Multiplexed Signal In] --> B(Collimating Lens);
          B --> C{Diffraction Grating};
          C --> D[MEMS DMD Array];
          D -- Reflected Light --> E(Focusing Lens);
          E --> F[Broadband Photodetector];
      end
  
      subgraph Controller
          G[FPGA/ASIC Controller] -- Controls --> D;
          F -- Electrical Signal --> G;
          G -- Computes --> H(Full Spectrum Data);
          H -- Applies Logic --> I(Identified Channel Powers);
      end
  
      style Controller fill:#f9f,stroke:#333,stroke-width:2px
  

1.2. Quantum Dot Array Spectrometer

• Enabling Description: This derivative utilizes a Quantum Dot (QD) array as the primary spectroscopic component. The multiplexed signal illuminates a substrate impregnated with a gradient of quantum dots, where the dot size varies systematically across the physical area. Due to the quantum confinement effect, QDs of different sizes absorb light at different, very specific wavelengths. The substrate is bonded directly to a CMOS image sensor. The controller analyzes the resulting 2D image from the sensor; the location of an illuminated pixel (or group of pixels) directly corresponds to a specific wavelength, and the pixel's brightness corresponds to the optical intensity. This creates a direct wavelength-to-position mapping. The controller processes this image data, treating pixel rows/columns as discrete sampling points, and identifies the intensity values for signals of varying frequency widths based on the spatial extent of their illumination signatures on the sensor. This approach is entirely solid-state and can be manufactured at low cost using semiconductor fabrication techniques.

• Mermaid Diagram:

  sequenceDiagram
      participant Signal as Multiplexed Signal
      participant QD as Quantum Dot Array
      participant CMOS as CMOS Image Sensor
      participant Controller
  
      Signal->>QD: Illuminates array
      QD->>CMOS: Absorbs light at specific locations based on wavelength
      CMOS->>Controller: Captures 2D intensity map (Image)
      Controller->>Controller: Processes image data
      Note over Controller: Pixel location maps to frequency,<br/>Pixel intensity maps to power
      Controller-->>User: Outputs identified<br/>channel powers
  

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Derivative Set 2: Operational Parameter Expansion

2.1. Cryogenic Quantum Communication Channel Monitor

• Enabling Description: The disclosed method is adapted for monitoring channels in a Quantum Key Distribution (QKD) network operating at cryogenic temperatures (below 77K). The "optical signals" are extremely low-power quantum channels, often at the single-photon level, with frequency widths defined by the pump laser's coherence and modulation scheme. The OCM itself is housed in a cryostat. The photodetector is a Superconducting Nanowire Single-Photon Detector (SNSPD) array, providing picosecond timing resolution and near-unity detection efficiency. The controller's logic is modified to work with photon counts instead of analog intensity values. It identifies "first and second intensity values" by time-gating the SNSPD array and correlating photon arrival events with the expected transmission windows for different QKD channels (e.g., BB84 decoy states vs. signal states), which constitute the different "frequency widths". The sampling interval is on the order of picoseconds. The alarm threshold is a statistical deviation from an expected photon count rate, indicating an eavesdropping attempt (intercept-resend attack) or system misalignment.

• Mermaid Diagram:

  graph LR
      subgraph Cryostat (77K)
          A[Quantum Channels In] --> B(SNSPD Array);
          B -- Photon Events --> C[Time-Gating & Correlation Logic];
      end
      subgraph Control System
          C -- Correlated Counts --> D[Controller];
          D --> E{Statistical Analysis};
          E -- Anomaly --> F[Alarm: Eavesdropper/Fault];
          E -- Normal --> G[Nominal Channel State];
      end
      style Cryostat fill:#cde,stroke:#333,stroke-width:2px
  

2.2. Terahertz Band Multiplexed Signal Monitor

• Enabling Description: The apparatus operates in the Terahertz (THz) frequency band (0.1-10 THz) for next-generation wireless communication (6G). The "optical signals" are THz carriers multiplexed in a free-space link. The "frequency widths" correspond to different data channels, which can be dynamically allocated bandwidth from hundreds of MHz to several GHz. The OCM uses a Schottky diode-based heterodyne receiver. A tunable local oscillator (LO), controlled by the controller, scans across the THz band, analogous to the variable wavelength filter. The LO output is mixed with the incoming multiplexed THz signal, and the down-converted intermediate frequency (IF) signal's power is measured. The controller's logic remains the same: it acquires the center frequencies and frequency widths of the active THz channels, identifies the measured IF power at the correct LO frequencies (sampling points), and compares them to a threshold to detect channel degradation or interference.

• Mermaid Diagram:

  flowchart TD
      A[Multiplexed THz Signal] --> B{Mixer};
      C[Tunable THz LO] --> B;
      D[Controller] -- LO Frequency --> C;
      B -- IF Signal --> E[IF Amplifier & Filter];
      E --> F[Power Detector];
      F -- Measured Power --> D;
      D -- Processes Data --> G((Output Channel Status));
  

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Derivative Set 3: Cross-Domain Application

3.1. Aerospace: Hypersonic Plume Spectroscopy

• Enabling Description: The method is applied to real-time analysis of a hypersonic vehicle's exhaust plume for engine diagnostics and trajectory analysis. The "multiplexed signal" is the full-spectrum electromagnetic emission from the hot gases in the plume. The "first and second optical signals" are the distinct spectral emission/absorption lines from different chemical species (e.g., H2O, CO2, NOX), each with a unique "frequency width" determined by temperature, pressure, and Doppler shifting. The OCM is an airborne or satellite-based hyperspectral imager. The controller ingests the hyperspectral data cube, identifies the intensity values corresponding to the known spectral signatures of key chemical species, and calculates their relative power. An "alarm" is triggered if the power of a specific species' signature deviates from a predicted model, indicating an off-nominal engine condition (e.g., incomplete combustion, engine damage).

• Mermaid Diagram:

  stateDiagram-v2
      [*] --> Analyzing
      Analyzing --> Nominal: Plume signature matches flight model
      Nominal --> Analyzing: Continuous monitoring
      Analyzing --> AnomalyDetected: NOx intensity > threshold
      AnomalyDetected: Issue alarm: 'Off-Nominal Combustion'
      AnomalyDetected --> Analyzing: Acknowledge and continue
      Analyzing --> AnomalyDetected: H2O intensity < threshold
  

3.2. AgTech: In-Situ Soil Nutrient Mapping

• Enabling Description: An agricultural drone or ground robot is equipped with a compact version of the apparatus for soil analysis. A broadband light source (the "multiplexed signal") illuminates a patch of soil. The reflected light is captured by the OCM. The "first and second optical signals" are the absorption bands within the reflected spectrum corresponding to different soil components like nitrogen, phosphorus, potassium, and water content. Each component has a characteristic absorption "frequency width". The controller analyzes the reflected spectrum, identifies the depth (intensity) of these absorption bands, and correlates them to the concentration of the respective nutrients. The data is geotagged and used to generate a high-resolution field map for precision fertilization, triggering an "alarm" (or indication) for areas with critically low nutrient levels.

• Mermaid Diagram:

  sequenceDiagram
      participant Drone as Drone System
      participant OCM as Soil OCM
      participant GPS as GPS Module
      participant Controller as Onboard Controller
  
      Drone->>OCM: Illuminate soil & capture reflected spectrum
      OCM->>Controller: Send spectral data
      GPS->>Controller: Send current coordinates
      Controller->>Controller: Identify nutrient absorption bands (N, P, K)
      Controller-->>Drone: Generate geotagged nutrient map
  

3.3. Consumer Electronics: Non-Invasive Food Allergen Detector

• Enabling Description: The technology is miniaturized into a handheld device for detecting food allergens. The device uses near-infrared (NIR) spectroscopy. The user points the device at a food item, which is illuminated by an NIR LED array. The reflected light is the "multiplexed signal". Specific allergens like gluten, peanuts, and dairy proteins have unique spectral fingerprints (absorption bands) in the NIR range, which serve as the "first and second optical signals" with different "frequency widths". The controller's firmware contains a library of these allergen signatures. It analyzes the captured spectrum, attempts to identify the intensity values matching any signatures in its library, and if a match is found with power above a confidence threshold, it outputs a clear "first indication signal" (e.g., "Peanut Allergen Detected" on an LCD screen).

• Mermaid Diagram:

  graph TD
      A(User scans food item) --> B[NIR LED illuminates sample];
      B --> C[Reflected NIR light captured];
      C --> D{OCM analyzes spectrum};
      D --> E{Controller};
      E -- Compares with --> F[(Allergen Signature Library)];
      E -- Match Found --> G[Display: 'Allergen Detected!'];
      E -- No Match --> H[Display: 'No Allergens Found'];
  

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Derivative Set 4: Integration with Emerging Tech

4.1. AI-Driven Predictive Channel Monitoring

• Enabling Description: The OCM controller is integrated with a machine learning (ML) model, specifically a Long Short-Term Memory (LSTM) network. The model is trained on historical network traffic data, including channel power, OSNR, and data rate information. Instead of relying on static configurations, the controller uses the ML model to predict future network states. It dynamically adjusts the OCM's sampling interval and measurement points to focus on channels predicted to have high traffic or an increased probability of failure. The model also learns the spectral shapes ("frequency widths") of new signal types as they are introduced to the network, enabling zero-day monitoring of novel transmission formats. The "alarm" function is enhanced to trigger pre-emptive alerts for channels that are predicted to fail within a future time window.

• Mermaid Diagram:

  flowchart LR
      A[Historical Network Data] --> B(LSTM Model Training);
      C[Real-Time OCM Data] --> D{ML Inference Engine};
      B -- Trained Model --> D;
      D -- Predicts --> E(Future Channel State);
      D -- Adjusts --> F[OCM Sampling Parameters];
      F -- Configures --> G(Physical OCM);
      G -- Measures --> C;
      E -- Failure Imminent --> H((Pre-emptive Alarm));
  

4.2. IoT-Enabled Distributed Optical Performance Monitoring

• Enabling Description: The OCM is designed as a low-power, lightweight IoT device. A large number of these IoT-OCMs are deployed throughout the optical network (e.g., at every amplifier and ROADM). Each device monitors its local multiplexed signal and publishes its findings (identified channel powers and alarm states) via a lightweight protocol like MQTT or CoAP to a central cloud-based analytics platform. The "controller" logic is split between the edge device (for real-time measurement and basic thresholding) and the cloud (for network-wide correlation and trend analysis). This creates a fine-grained, real-time map of network health, allowing operators to pinpoint the exact location and nature of signal degradation with unprecedented precision.

• Mermaid Diagram:

  classDiagram
      class IoT_OCM {
          +deviceID: string
          +location: string
          -mqttClient: MQTT_Client
          +measureAndPublish()
          +runLocalThresholdCheck()
      }
      class CloudAnalytics {
          +ingestData(topic, payload)
          +correlateNetworkEvents()
          +generateNetworkHealthMap()
      }
      IoT_OCM "N" -- "1" CloudAnalytics : Publishes data to
  

4.3. Blockchain-Verified SLA Compliance

• Enabling Description: The monitoring apparatus is integrated with a private blockchain for immutable logging of Service Level Agreement (SLA) compliance. When the controller identifies a channel's power and determines its status, it creates a data package containing the timestamp, channel ID, measured power, OSNR, and a pass/fail status relative to the SLA threshold. This data package is cryptographically signed by the device and submitted as a transaction to a permissioned blockchain ledger shared between the network operator and the customer. The "signal interruption alarm" from the patent is recorded as a "SLA Violation" transaction. This provides an undisputed, auditable, and tamper-proof record for billing, penalties, and network performance validation.

• Mermaid Diagram:

  sequenceDiagram
      participant OCM as Monitoring Apparatus
      participant Node as Operator's Blockchain Node
      participant Ledger as Shared Ledger
      participant Customer as Customer's Node
  
      OCM->>OCM: Measure channel power & compare to SLA
      OCM->>Node: Create & sign transaction (ChannelID, Power, Status)
      Node->>Ledger: Validate & commit transaction
      Ledger-->>Customer: Replicates transaction
      Customer->>Customer: Independently verify SLA compliance
  

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Derivative Set 5: The "Inverse" or Failure Mode

5.1. Failsafe Coarse WDM Monitoring Mode

• Enabling Description: This version is designed to fail into a limited-functionality, failsafe state. Under normal operation, the controller executes the full channel identification logic. If the controller experiences a critical error (e.g., memory corruption, loss of communication with the management system), a hardware watchdog timer forces a system reboot into "Coarse Mode." In this mode, a simplified firmware is loaded that bypasses the complex channel identification logic. The OCM is configured to perform a rapid, low-resolution scan, measuring power in only a few wide, pre-defined spectral bands (e.g., C-Band Low, C-Band High, L-Band). It only reports the total integrated power in these coarse bands. This ensures that even during a catastrophic failure of the primary monitoring intelligence, the system can still provide a basic "light/no-light" status for entire sections of the WDM spectrum, preventing a total loss of network visibility.

• Mermaid Diagram:

  stateDiagram-v2
      state "Full Monitoring" as Full {
          [*] --> Full
          Full --> Coarse: Watchdog Timeout / Critical Error
          Full --> Full: Normal Operation
      }
      state "Coarse Failsafe Mode" as Coarse {
          Coarse --> Full: Manual Reset / Command
          state "Scan Low C-Band" as ScanLowC
          state "Scan High C-Band" as ScanHighC
          state "Report Total Power" as Report
          [*] --> ScanLowC
          ScanLowC --> ScanHighC
          ScanHighC --> Report
          Report --> ScanLowC
      }
  

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Combination Prior Art Scenarios with Open-Source Standards

1. Integration with NETCONF/YANG (IETF RFC 6241 & 6020): The OCM's controller exposes its full configuration and state data through a YANG data model. An operator can use a standard NETCONF client to configure the list of expected optical signals, including their central frequencies and frequency widths. The operator can also subscribe to real-time notifications, receiving a NETCONF <notification> event whenever the controller identifies a channel power that falls below its configured threshold. This combines the patented method with established network management standards, making it a component in a larger, automated, multi-vendor network.

2. Integration with OpenMetrics/Prometheus: The OCM controller runs a Prometheus exporter service on an embedded web server. This service exposes a /metrics endpoint with data formatted according to the OpenMetrics standard. Key metrics include optical_channel_power_dbm{channel_id="...", center_freq_hz="..."} and optical_channel_alarms_total{channel_id="...", type="low_power"}. A central Prometheus server scrapes this endpoint at regular intervals, storing the data in its time-series database. This allows network operators to use the entire open-source Prometheus ecosystem, including Grafana for dashboarding and Alertmanager for sophisticated alerting rules, based on the data generated by the patented monitoring method.

3. Integration with the Telecom Infra Project (TIP) Open Optical & Packet Transport (OOPT) Architecture: The monitoring apparatus is presented as a "pluggable" monitoring module compliant with the standards defined by TIP's OOPT group. The device's physical form factor, power consumption, and data reporting APIs adhere to the open specifications for Disaggregated Optical Systems. Specifically, its output data stream, containing identified channels and their power levels, is formatted as a standardized JSON or Protobuf object sent over a gRPC connection to a higher-level network controller, as defined in the TransportPCE open-source project. This positions the patented invention as a modular component within a broader, open, and disaggregated network architecture.