Derivative works — US Patent 10980926

Defensive Disclosure and Prior Art Publication

Publication Date: May 13, 2026
Subject Matter: Derivative Methods, Systems, and Applications for Optimized Fluid Component Separation, based on the principles disclosed in US Patent 10,980,926.
Purpose: This document is published for defensive purposes to establish prior art, thereby rendering obvious or non-novel any future patent claims on the described variations, expansions, and applications. The following disclosures are dedicated to the public domain.

Part 1: Derivative Embodiments of the Core Method and System

The following disclosures describe variations on the core method of calculating a target volume of a pure fluid component (e.g., plasma) by accounting for the volume of a secondary fluid (e.g., anticoagulant) based on initial properties of the mixed fluid (e.g., hematocrit).

Axis 1: Material & Component Substitution

Derivative 1.1: System with Non-Invasive, Real-Time Hematocrit Sensing

Enabling Description: The method of claim 1 is modified to eliminate step (b), the pre-determination of hematocrit. Instead, the blood draw line (218) is fitted with a non-contact, near-infrared (NIR) spectroscopic sensor module. As blood is drawn, the sensor continuously measures the absorption spectrum, from which hematocrit and total protein levels are derived in real-time using a partial least squares regression model stored in the controller's (226) memory. The controller dynamically updates the hematocrit value every 500 milliseconds, recalculating the projected final anticoagulant volume and adjusting the target collection volume (step e) throughout the procedure. This provides a more accurate, time-averaged hematocrit value compared to a single pre-donation sample.

Diagram:

flowchart TD
    subgraph Real-Time Sensing Loop
        A[Withdraw Whole Blood] --> B{NIR Sensor Module};
        B --Spectral Data--> C[Controller];
        C --Calculates Hct--> C;
        C --Recalculates Target Volume--> C;
    end
    C --> D{Pump Control};
    D --Stop Signal--> E[End Collection];
    F[Donor] --> A;
    A --> G[Separation Device];
    G --> H[Plasma Collection];
    G --> F;

Derivative 1.2: System Utilizing Magnetohydrodynamic (MHD) Pumps

Enabling Description: The system of claim 8 is improved by replacing the peristaltic blood draw pump (232) and anticoagulant pump (234) with pulseless, non-occlusive magnetohydrodynamic (MHD) pumps. The disposable tubing set for the blood and anticoagulant lines incorporates a channel section flanked by permanent neodymium magnets and two electrodes. The controller (226) applies a precise, variable DC voltage across the electrodes. The resulting Lorentz force propels the conductive fluid (blood and anticoagulant saline) at a flow rate directly proportional to the applied current. This eliminates mechanical friction and shear stress on red blood cells, reducing hemolysis. The controller calculates the delivered anticoagulant volume by integrating the measured current over time, providing a more precise input for the pure plasma calculation than counting mechanical pump rotations.

Diagram:

sequenceDiagram
    participant C as Controller
    participant MHD_Pump as MHD Pump
    participant Sensor as Flow Sensor
    C->>MHD_Pump: Set Current (I)
    MHD_Pump->>Sensor: Propels Fluid
    Sensor-->>C: Report Flow Rate (Q)
    loop Volume Calculation
        C->>C: V_ac = ∫ I(t) * K dt
    end
    C->>C: Calculate Pure Plasma Target

Derivative 1.3: System with Bio-Integrated Thromboresistant Fluid Path

Enabling Description: The method of claim 1 is modified by utilizing a disposable tubing set (200) where all blood-contacting surfaces are manufactured from a thermoplastic polyurethane covalently bonded with heparin. This functionalized surface mimics the endothelium and actively inhibits thrombin formation. The result is a significant reduction in the required anticoagulant-to-whole-blood ratio (step g), from a typical 1:16 down to 1:30 or less. The controller's (226) programming is updated with this new, lower ratio for its calculation of the anticoagulant volume to be collected (step c), thereby increasing the percentage of pure plasma in the final collected product and raising the overall efficiency of the procedure.

Diagram:

graph TD
    A[Standard System] --> B{AC Ratio: 1:16};
    B --> C[AC Volume in Product: ~11%];
    D[Heparin-Grafted System] --> E{AC Ratio: 1:30};
    E --> F[AC Volume in Product: ~6%];
    C --> G[Lower Pure Plasma Yield];
    F --> H[Higher Pure Plasma Yield];

Axis 2: Operational Parameter Expansion

Derivative 2.1: Microfluidic Diagnostic Apheresis System

Enabling Description: The system is miniaturized onto a disposable lab-on-a-chip (LOC) cartridge for separating 50-200 microliters of whole blood for diagnostic analysis. Instead of centrifugation, separation is achieved via bulk acoustic wave (BAW) transducers bonded to the chip. When activated by the controller, the transducers create ultrasonic standing waves that exert differential acoustic radiation forces on the blood cells, focusing them to a central stream while the cell-free plasma is collected from side channels. The controller uses an integrated microscopic imaging sensor and a simple image processing algorithm to determine the hematocrit. It then calculates the target pure plasma volume (typically 20-50 microliters) and the expected volume of pre-loaded anticoagulant in the collection well, stopping the acoustic separation once the target collected volume is reached.

Diagram:

classDiagram
  class LOCCartridge {
    +inletPort
    +outletPlasma
    +outletRBCs
    +BAWTransducer
    +imagingSensor
  }
  class Controller {
    +calculateMicroHct()
    +calculateTargetNanoVolume()
    +activateBAW()
    +stopBAW()
  }
  Controller -- LOCCartridge : controls

Derivative 2.2: Continuous Industrial Bioreactor Clarification System

Enabling Description: The method is scaled up for industrial purification of biologics (e.g., monoclonal antibodies) from a large-scale (10,000 L) mammalian cell culture bioreactor. The "blood" is the cell culture fluid, and the "plasma" is the cell-free supernatant containing the product. A continuous-flow centrifuge operating at over 5,000 Gs separates the cells. An inline turbidity sensor measures real-time cell density (the analog to "hematocrit"). A "clarifying agent" (the analog to "anticoagulant") is added to promote flocculation. The controller calculates the target volume of pure supernatant based on the total batch size ("donor weight") and dynamically adjusts the collection target based on real-time cell density, optimizing the clarification process over a multi-day run.

Diagram:

flowchart LR
    A[Bioreactor] --> B(Pump);
    B --> C{Inline Turbidity Sensor};
    C --Cell Density--> D[Controller];
    E[Clarifying Agent] --> B;
    D --Pump Control--> B;
    B --> F[High-G Centrifuge];
    F --> G[Cell Waste];
    F --> H[Pure Product Collection];
    D --Calculates Target Product Volume--> H;

Axis 3: Cross-Domain Application

Derivative 3.1: AgTech - Automated Milk Fractionation System

Enabling Description: A system for on-farm extraction of high-value whey protein from raw whole milk. The "donor weight" is the weight of the milk batch in a holding tank. The "hematocrit" is the butterfat percentage, measured by an inline optical scattering sensor. An acidulant ("anticoagulant") is introduced to precipitate casein. The controller calculates the volume of acidulant required based on the milk volume and butterfat content. It then calculates a target volume of pure whey protein concentrate to be collected. A centrifugal separator divides the acidified milk into casein curds, fat, and whey. The system collects the whey fraction until the target collection volume (whey + residual acidulant) is reached, maximizing whey yield without excessive acidification.

Diagram:

stateDiagram-v2
    [*] --> Input_Parameters
    Input_Parameters: Receive Milk Volume
    Input_Parameters: Receive Butterfat %
    Input_Parameters --> Calculate_Target: Done
    Calculate_Target: Calculate Acidulant Volume
    Calculate_Target: Calculate Pure Whey Target
    Calculate_Target: Calculate Total Collection Volume
    Calculate_Target --> Process_Milk: Begin
    Process_Milk: Add Acidulant, Centrifuge, Collect Whey
    Process_Milk --> Process_Milk: Volume < Target
    Process_Milk --> [*]: Volume = Target

Derivative 3.2: Aerospace - Onboard Fuel/Water Separation System

Enabling Description: A system integrated into an aircraft's fuel line to continuously remove water contamination. The "donor weight" is the total fuel mass, provided by the Fuel Quantity Indicating System (FQIS). The "hematocrit" is the parts-per-million (PPM) water content, measured by an inline capacitive sensor. No "anticoagulant" is added, but the controller calculates the volume expansion/contraction of the fuel due to temperature changes (a known variable analogous to anticoagulant volume). The controller's target is not a collection volume but a target purity level (e.g., < 10 PPM water). It actuates a centrifugal water separator and drains the collected water, stopping the separation process only when the target purity is achieved and maintained, thus optimizing engine efficiency and preventing flameouts.

Diagram:

sequenceDiagram
    participant FQIS
    participant H2O_Sensor
    participant Controller
    participant Separator

    loop Real-time Monitoring
        FQIS->>Controller: Report Fuel Mass
        H2O_Sensor->>Controller: Report PPM Water
        Controller->>Controller: Calculate Purity vs. Target
        alt Water PPM > Target
            Controller->>Separator: Activate
        else Water PPM <= Target
            Controller->>Separator: Deactivate
        end
    end

Axis 4: Integration with Emerging Tech

Derivative 4.1: AI-Driven Predictive Apheresis

Enabling Description: The system controller (226) is augmented with an edge AI inference chip running a pre-trained recurrent neural network (RNN). The model's inputs include the donor's static data (weight, pre-donation hematocrit) and real-time data streams from a wearable IoT patch on the donor (hydration level via skin impedance, heart rate, core temperature). The RNN predicts the donor's hematocrit drift and plasma refill rate during the procedure. Instead of a static target, the controller uses the model's output to continuously update the target collection volume, maximizing pure plasma yield while ensuring the procedure remains within the donor's physiological tolerance limits.

Diagram:

graph TD
    subgraph Inputs
        A[Donor Weight]
        B[Initial Hct]
        C[IoT Patch: Hydration, HR]
    end
    subgraph Controller
        D[RNN Model]
        E[Target Volume Calculator]
    end
    subgraph Outputs
        F[Pump Speed]
        G[Stop Signal]
    end
    A & B & C --> D;
    D --Predicted Hct Drift--> E;
    E --Dynamic Target Volume--> F & G;

Derivative 4.3: Blockchain-Verified Plasma Supply Chain

Enabling Description: The system controller (226) includes a cryptographic module and a network interface. Upon completion of a collection, the controller generates a non-fungible token (NFT) on a permissioned blockchain (e.g., Hyperledger Fabric). The NFT's metadata contains an immutable, cryptographically signed record of the procedure: an anonymized donor hash, the initial hematocrit, the calculated target pure plasma volume, the final collected volume, timestamp, and device ID. This creates a "digital passport" for the plasma unit, allowing regulators and fractionation facilities to instantly verify its provenance and confirm that collection limits (e.g., FDA 880 mL total) were algorithmically enforced and not exceeded.

Diagram:

erDiagram
    PLASMA_UNIT ||--o{ BLOCKCHAIN_TRANSACTION : contains
    BLOCKCHAIN_TRANSACTION {
        string TransactionHash PK
        string AnonymizedDonorID
        float InitialHematocrit
        float TargetPurePlasmaVol
        float FinalCollectedVol
        datetime Timestamp
        string DeviceID
    }

Axis 5: The "Inverse" or Failure Mode

Derivative 5.1: Graceful Degradation Failsafe Mode

Enabling Description: The system controller's (226) software includes a failsafe state machine. If the plasma collection container's weight sensor (195) provides an erratic or out-of-range reading for more than 3 consecutive polling cycles, the controller declares a sensor failure. It immediately abandons the pure-plasma calculation method (steps c, d, e of claim 1). It then switches to a "Volumetric Failsafe Mode," where it continues the collection based solely on the integrated volume of whole blood drawn, as calculated by the blood pump's (232) rotations. It stops the procedure when the total whole blood processed reaches a conservative, pre-set limit (e.g., 2000 mL) that ensures donor safety, prioritizing a safe exit from the procedure over yield optimization.

Diagram:

stateDiagram-v2
    state "Normal Operation" as Normal
    state "Failsafe Mode" as Failsafe

    [*] --> Normal
    Normal --> Normal: Weight Sensor OK
    Normal --> Failsafe: Weight Sensor Failure
    Failsafe: Stop using pure plasma target.
    Failsafe: Use whole blood volume limit.
    Failsafe --> [*]: Procedure End

Part 2: Combination with Open-Source Standards

Combination 2.1: HL7 FHIR Integration for Automated Parameter Input

Enabling Description: The apheresis system's control software includes an HL7 FHIR (Fast Healthcare Interoperability Resources) client library. Prior to starting a procedure, the operator scans the donor's wristband. The system uses the donor ID to query the facility's FHIR-compliant Electronic Health Record (EHR) server. It makes a GET Patient/{id} request to retrieve the donor's weight and a GET Observation?code=20570-8 request to retrieve the most recent hematocrit lab result. These values automatically populate the parameters for steps (a) and (b) of claim 1, eliminating manual entry and transcription errors.

Combination 2.2: MQTT Protocol for Remote Fleet Monitoring

Enabling Description: The system controller (226) runs an MQTT client service. Throughout the plasma collection process, it publishes status messages to a central MQTT broker on the facility's network. Topics include devices/{deviceID}/status (e.g., "RUNNING", "IDLE", "ERROR"), devices/{deviceID}/progress (e.g., JSON payload {"collected_ml": 550, "target_ml": 880}), and devices/{deviceID}/alerts (e.g., {"code": "A102", "message": "Low Flow"}). A central dashboard application subscribes to devices/+/+ to display a real-time overview of all devices in the collection center, enabling efficient staff allocation and proactive maintenance.

Combination 2.3: OPC UA for Industrial Process Integration

Enabling Description: The apheresis system is exposed to the network as an OPC UA server, conforming to the open standard for industrial interoperability. The controller's variables—such as Donor Weight, Donor Hematocrit, Target Pure Plasma Volume, Target Collection Volume, Current Collected Volume, and Machine State—are mapped to nodes in the server's address space. A central SCADA (Supervisory Control and Data Acquisition) or MES (Manufacturing Execution System) can read these nodes to log process data for batch records and write to specific nodes (e.g., a "Start Procedure" node) to remotely control the device, fully integrating the plasma collection step into a larger, automated biomanufacturing workflow.