Derivative works — US Patent 10792416

As a Senior Patent Strategist and Research Engineer, I have analyzed US patent 10,792,416. The following defensive disclosure document details derivative works and novel combinations designed to establish prior art against future incremental inventions in the field of plasmapheresis and fluid separation. This disclosure is intended to be enabling for a person skilled in the art.

Defensive Disclosure & Prior Art Derivations for US 10,792,416

Part 1: Derivatives of Core Methodologies (based on Claims 1, 18)

Axis 1: Material & Component Substitution

Derivative 1.1: Tangential Flow Filtration (TFF) System for Centrifuge-Free Plasma Separation

Enabling Description: This method replaces the centrifugal blood component separation device (214) with a disposable, hollow-fiber tangential flow filtration (TFF) cartridge. Whole blood, mixed with anticoagulant, is pumped parallel to the surface of the TFF membrane. A transmembrane pressure gradient drives plasma through the membrane pores (e.g., 0.2 µm pores) into a collection line, while the retentate, containing red blood cells and other cellular components, is recirculated and returned to the donor. A controller calculates pure plasma volume by monitoring the weight of the collected filtrate (permeate) and subtracting the calculated volume of anticoagulant, which is determined by tracking the mass flow rate from the anticoagulant source via a Coriolis flow meter. The donor's hematocrit is determined non-invasively using a multi-wavelength near-infrared spectroscopy (NIRS) sensor on the draw line, which correlates light absorption at specific wavelengths (e.g., 760nm, 850nm) to hemoglobin concentration.

Mermaid Diagram:

flowchart TD
    subgraph Extracorporeal Circuit
        A(Venous Access) -->|Draw Line| B{Anticoagulant Injection};
        B --> C[NIRS Hematocrit Sensor];
        C --> D[Positive Displacement Pump];
        D --> E(TFF Hollow-Fiber Cartridge);
        E -->|Permeate (Plasma + AC)| F[Coriolis Flow Meter] --> G(Plasma Collection Bag);
        E -->|Retentate (RBCs)| H[Recirculation Loop] --> I{Return to Donor};
    end
    subgraph Control System
        J(Controller) -->|Control Pump| D;
        G -- Weight --> J;
        F -- Mass Flow --> J;
        C -- Hct Data --> J;
        J -- Calculate Pure Plasma --> K[Display: Pure Plasma Volume];
    end

Derivative 1.2: Non-Hemolytic Magnetic Levitation Pumping System

Enabling Description: The system's blood draw pump (232) is replaced with a magnetic levitation centrifugal pump featuring a disposable, single-use pump head. The impeller inside the pump head is levitated and rotated by a magnetic field, eliminating mechanical contact, seals, and bearings. This significantly reduces shear stress on red blood cells, minimizing hemolysis compared to traditional peristaltic pumps. The controller calculates the volume of whole blood drawn by integrating the precise flow rate data provided by the mag-lev pump's internal controller, which is more accurate than relying on the assumed volume-per-rotation of a peristaltic pump. This improved accuracy in measuring whole blood volume leads to a more precise real-time calculation of the donor's hematocrit when combined with data from the optical sensor (213) in the separation bowl.

Mermaid Diagram:

sequenceDiagram
    participant Donor;
    participant MagLevPump;
    participant Separator;
    participant Controller;

    Donor->>MagLevPump: Whole Blood;
    MagLevPump->>Controller: Report Flow Rate (L/min);
    MagLevPump->>Separator: Pump Blood;
    Separator->>Controller: Report RBC Volume (from optical sensor);
    Controller->>Controller: Calculate Total WB Volume = ∫(Flow Rate) dt;
    Controller->>Controller: Calculate Hct = (RBC Volume) / (Total WB Volume);
    Controller->>MagLevPump: Adjust Speed;

Axis 2: Operational Parameter Expansion

Derivative 2.1: Hypothermic Plasmapheresis for Simultaneous Cryoprecipitate Collection

Enabling Description: The plasmapheresis method is performed under hypothermic conditions. The entire disposable tubing set and separation device are housed within a refrigerated chamber maintained between 1-6°C. As whole blood is drawn, it passes through a thermoelectric cooler (Peltier device) to rapidly lower its temperature before entering the separation device. The collected plasma, now chilled, is directed through a secondary filtration unit containing a cryoprecipitate-adhering mesh. The reduced temperature causes cryoprecipitable proteins (e.g., Factor VIII, fibrinogen) to precipitate out of the plasma and adhere to the mesh. The remaining cryo-poor plasma is collected in the final container. The controller calculates the volume of pure, cryo-poor plasma and separately estimates the mass of the collected cryoprecipitate based on differential pressure readings across the secondary filter.

Mermaid Diagram:

graph TD
    A(Start: Donor Blood at 37°C) --> B[Thermoelectric Cooler];
    B --> C(Blood Separation at 4°C);
    C -- RBCs --> D(Return to Donor);
    C -- Chilled Plasma --> E[Cryoprecipitate Filter Mesh];
    E -- Cryo-Poor Plasma --> F(Collection Bag);
    E -- Precipitate --> G(Cryoprecipitate Collected);
    H(Controller) -- Monitors Temp --> B;
    H -- Monitors ΔP --> E;
    H -- Monitors Weight --> F;

Derivative 2.2: Microgravity Fluid Separation System

Enabling Description: The method is adapted for a microgravity environment, such as the International Space Station. The gravity-dependent centrifugal separator is replaced with an acoustic separator. Anticoagulated whole blood flows through a resonant chamber where ultrasonic standing waves are generated by piezoelectric transducers. These waves create pressure nodes and antinodes, forcing the denser red blood cells to aggregate at the pressure nodes along the chamber's centerline. The less dense plasma is displaced to the peripheries. Separate ports at the chamber's outlet draw off the concentrated red cell stream and the cell-free plasma. All fluid movement is managed by syringe pumps to provide precise, positive-displacement volume control unaffected by the lack of gravity. Pure plasma volume is calculated by subtracting the known volume of anticoagulant dispensed by a dedicated syringe pump from the volume of plasma collected in a receiving syringe.

Mermaid Diagram:

classDiagram
  class MicrogravitySeparator {
    +transducerArray: Piezoelectric[]
    +resonantChamber: Chamber
    +inletPort: Port
    +rbcOutletPort: Port
    +plasmaOutletPort: Port
    +generateStandingWave()
  }
  class SyringePump {
    +volume_uL: double
    +flowRate_uL_min: double
    +dispense()
    +withdraw()
  }
  class Controller {
    +targetPurePlasmaVol: double
    +calculatePurePlasma()
  }
  Controller "1" -- "3" SyringePump : controls
  Controller "1" -- "1" MicrogravitySeparator : controls

Axis 3: Cross-Domain Application

Derivative 3.1: AgTech - Automated Bovine Colostrum Fractionation

Enabling Description: This method is applied to the processing of bovine colostrum. The system first determines the "donor" cow's weight and measures the colostrum's key parameters (analogous to hematocrit), such as immunoglobulin G (IgG) concentration and total solids, using an in-line refractometer. A target collection volume of "pure IgG fraction" is calculated based on a desired percentage of the total available IgG. The raw colostrum is mixed with a buffer solution (analogous to anticoagulant) and separated using a TFF system. The permeate, containing whey and lactose, is diverted, while the retentate, rich in IgG, is collected. The controller calculates the volume of pure IgG concentrate by accounting for the added buffer and stops the process when the target is reached. The remaining low-IgG colostrum is returned for other processing.

Mermaid Diagram:

flowchart LR
    A[Raw Colostrum] --> B(Refractometer for IgG/Solids);
    B --> C{Mix with Buffer};
    C --> D[TFF Separation];
    D -- Permeate (Waste) --> E;
    D -- Retentate (IgG Concentrate) --> F(Collection Tank);
    G(Controller) <-- Data --- B;
    G <-- Weight --- F;
    G -- Calculate Pure IgG --> H[Display];
    G -- Control Pumps --> C;

Derivative 3.2: Aerospace - In-Flight Bioreactor Product Harvesting

Enabling Description: The system is integrated into a bioreactor on a long-duration spacecraft for producing therapeutic proteins. The controller calculates the total protein volume within the bioreactor based on cell density (measured by an optical sensor) and a known protein expression rate (analogous to donor plasma volume). A target percentage of this protein is set for harvesting. The system draws cell culture media from the bioreactor, adds a stabilizing agent (anticoagulant analog), and separates the target protein from the cells and media using an affinity chromatography column. The controller calculates the "pure protein" yield based on the eluate volume and concentration (measured by a UV-Vis spectrophotometer), accounting for the elution buffer volume. The cells and unused media are returned to the bioreactor to maintain the culture, managing its "intravascular deficit."

Mermaid Diagram:

stateDiagram-v2
    [*] --> Running
    Running --> Harvesting: Target Protein Level Reached
    Harvesting: Draw media
    Harvesting: Separate Protein
    Harvesting: Calculate Pure Yield
    Harvesting --> Running: Return cells & media
    state Harvesting {
        [*] --> Draw
        Draw --> Separate
        Separate --> Calculate
        Calculate --> Return
        Return --> [*]
    }

Axis 4: Integration with Emerging Tech

Derivative 4.1: AI-Optimized Procedure with Vasovagal Prediction

Enabling Description: An AI model, specifically a Long Short-Term Memory (LSTM) network, runs on the system's controller. It receives a continuous stream of real-time data from IoT sensors: donor heart rate and blood pressure from a wireless cuff, draw line pressure, fluid temperatures, and current hematocrit from an NIRS sensor. The LSTM is pre-trained on a massive dataset of past donations and is able to predict the probability of an adverse event (e.g., vasovagal reaction, vein collapse) within the next 60 seconds. Based on this predictive output, the AI dynamically modulates the blood draw pump speed and the anticoagulant-to-whole-blood ratio, slowing down the procedure if risk increases, to maximize pure plasma yield while keeping the donor's predicted risk score below a safety threshold.

Mermaid Diagram:

sequenceDiagram
    participant IoT_Sensors;
    participant Controller_AI;
    participant Pumps;
    participant Donor;

    loop Real-time Loop
        IoT_Sensors->>Controller_AI: Stream Vitals, Pressure, Hct;
        Controller_AI->>Controller_AI: Predict Adverse Event Probability;
        alt Risk > Threshold
            Controller_AI->>Pumps: Decrease Draw Speed;
        else Risk <= Threshold
            Controller_AI->>Pumps: Maintain/Increase Draw Speed;
        end
    end

Derivative 4.2: Blockchain-Verified Chain of Custody for Plasma Units

Enabling Description: The plasma collection system functions as a node on a private, permissioned blockchain (e.g., Hyperledger Fabric). At the conclusion of a successful donation, the controller executes a smart contract to mint a non-fungible token (NFT) representing the plasma unit. This token immutably records a hash of the donation data: anonymized donor key, final pure plasma volume, collection timestamp, machine serial number, phlebotomist ID, and a summary of QC data (e.g., hemolysis index). As the physical plasma unit moves through the supply chain (storage, testing, fractionation), each transaction is recorded on the blockchain, linking back to the original NFT. This creates a fully auditable, tamper-proof "digital passport" for the plasma unit, ensuring provenance and safety from vein to vial.

Mermaid Diagram:

erDiagram
    PLASMA_UNIT_NFT {
        string tokenId PK
        string donationHash
        datetime timestamp
        int purePlasmaVolume
    }
    DONATION_RECORD {
        string donationHash PK
        string donorKey
        string machineId
        string qcSummary
    }
    SUPPLY_CHAIN_EVENT {
        string eventId PK
        string tokenId FK
        string eventType
        datetime eventTimestamp
        string location
    }
    PLASMA_UNIT_NFT ||--o{ DONATION_RECORD : has
    PLASMA_UNIT_NFT ||--|{ SUPPLY_CHAIN_EVENT : tracks

Axis 5: The "Inverse" or Failure Mode

Derivative 5.1: Graceful Degradation & Early Return Protocol

Enabling Description: A method for safely terminating a procedure upon detection of a non-critical but unrecoverable error (e.g., a clogged filter, repeated high-pressure alarms). Instead of a hard stop, the system enters a "Graceful Return" mode. The controller calculates the current extracorporeal volume of red blood cells. It stops the draw and anticoagulant pumps but continues to operate the separation device to harvest any remaining plasma in the bowl. It then uses the return pump to slowly return the concentrated red blood cells, diluted with a volume of saline calculated to achieve a target hematocrit for the return fluid, ensuring a safe and comfortable return for the donor. The system logs the final pure plasma volume collected, even if it is short of the original target.

Mermaid Diagram:

stateDiagram-v2
    state "Normal Operation" as Normal
    state "Graceful Return" as Return
    [*] --> Normal
    Normal --> Return: Unrecoverable Error Detected
    Return: Stop Draw & AC
    Return: Harvest Residual Plasma
    Return: Calculate RBC volume to return
    Return: Dilute RBCs with Saline
    Return: Slowly pump back to donor
    Return --> [*]: Procedure End

Part 2: Combination Prior Art with Open-Source Standards

Scenario 2.1: DICOM (Digital Imaging and Communications in Medicine) Integration

Enabling Description: The plasma collection system is configured as a DICOM modality. Upon procedure completion, it generates a DICOM Structured Report object. The report contains standardized data elements (tags) for patient information (e.g., (0010,0020) Patient ID, (0010,1020) Patient Size, (0010,1030) Patient Weight), procedure parameters (e.g., Start/End Time), and results. Custom, private tags are used to store plasmapheresis-specific data, such as (XXXX,0010) Initial Hematocrit, (XXXX,0011) Anticoagulant Volume, and (XXXX,0012) Calculated Pure Plasma Volume. The system then uses the DICOM C-STORE protocol to transmit this report over a TCP/IP network to a central archive, such as a vendor-neutral archive (VNA) or a donor center's electronic health record (EHR) system, ensuring interoperability.

Scenario 2.2: HL7 FHIR (Fast Healthcare Interoperability Resources) Integration

Enabling Description: The system uses the open-source HL7 FHIR standard for real-time data exchange with a Donor Management System (DMS). When a procedure begins, the system creates a Procedure resource linked to the Patient resource. During the donation, it continuously POSTs Observation resources for key measurements like blood_pressure, hematocrit, and flow_rate. The final calculated pure plasma volume is recorded in a final Observation resource with a LOINC code (e.g., 30511-3 "Volume of Plasma collected"). The DMS can subscribe to these resources, allowing for a live dashboard view of all ongoing donations across a facility.

Scenario 2.3: MQTT (Message Queuing Telemetry Transport) for Fleet Management

Enabling Description: Each plasma collection system in a fleet is configured as an MQTT client. The device publishes real-time operational data and status alerts to a central MQTT broker on a secure network. Data is organized by topics, for example: fleet/center_101/device_SN123/status (publishes "running", "alarm", "idle"), fleet/center_101/device_SN123/data/pure_plasma_ml (publishes the current calculated pure plasma volume), and fleet/center_101/device_SN123/alerts/pressure_high. A cloud-based fleet management application subscribes to these topics, allowing for centralized monitoring, predictive maintenance scheduling (e.g., by tracking pump motor hours), and remote troubleshooting, leveraging the lightweight and efficient nature of the open-source MQTT protocol.