Derivative works — US Patent 12171916

As a Senior Patent Strategist and Research Engineer, I have analyzed U.S. Patent 12,171,916. The following document constitutes a defensive disclosure of derivative inventions and variations intended to enter the public domain as prior art. This disclosure is based on the core inventive concepts of the '916 patent and is designed to render obvious or non-novel any subsequent, incremental improvements by third parties.

Reference Patent: US 12,171,916
Title: System and method for collecting plasma
Core Concepts Disclosed:

Dynamic adjustment of target plasma volume based on real-time or iterative re-calculation of donor hematocrit during the procedure.
Pre-calculation of a target plasma product volume based on initial donor parameters (weight, hematocrit) and system parameters (anticoagulant ratio).
Electronic reception of donor parameters from an external control system to initialize the collection process.

Defensive Disclosure of Derivative Embodiments

Derivatives Based on Core Concept 1: Dynamic Hematocrit Recalculation

1. Material & Component Substitution

Derivative 1.1: Magnetorheological (MR) Fluid Pumps
- Enabling Description: The peristaltic pumps (first pump 232, second pump 234) are replaced with valveless pumps employing magnetorheological fluids. The controller (226) applies a variable magnetic field to control the viscosity of the MR fluid, thereby precisely modulating the flow of both whole blood and anticoagulant. This eliminates the mechanical wear of peristaltic tubing and allows for pulsation-free flow, which reduces shear stress on red blood cells. The controller adjusts the magnetic field strength in response to real-time hematocrit estimations, allowing for micro-liter per minute adjustments to the anticoagulant-to-blood ratio.
- Mermaid.js Diagram:
```
sequenceDiagram
    participant C as Controller (226)
    participant H as Hematocrit Sensor
    participant MRP_B as MR Pump (Blood)
    participant MRP_AC as MR Pump (Anticoagulant)
    participant D as Donor

    H->>C: Transmit Hematocrit Value (Hct_t1)
    C->>MRP_B: Set Magnetic Field B1 (for Blood Flow Rate Q_B1)
    C->>MRP_AC: Set Magnetic Field A1 (for AC Flow Rate Q_AC1)
    MRP_B-->>D: Draw Whole Blood
    MRP_AC-->>MRP_B: Meter Anticoagulant
    loop During Draw Cycle
        H->>C: Transmit New Hematocrit Value (Hct_t2)
        C->>C: Recalculate Target Volume & AC Ratio
        C->>MRP_AC: Adjust Magnetic Field to A2 (for New Rate Q_AC2)
    end
```
Derivative 1.2: Spectroscopic In-Line Hematocrit Sensor
- Enabling Description: Instead of relying on optical sensors (213) that measure the volume of separated red blood cells in the centrifuge, this embodiment integrates a multi-wavelength near-infrared (NIR) spectroscopic sensor directly into the donor line (218). The sensor measures the differential absorption of light by hemoglobin at multiple isosbestic and non-isosbestic points to calculate hematocrit directly from whole blood before it enters the separator. This provides a true real-time hematocrit value, allowing the controller to predict and adjust for changes caused by hydration shifts or fluid administration during the procedure, rather than reacting to measurements post-separation.
- Mermaid.js Diagram:
```
flowchart TD
    A[Donor's Arm] -->|Whole Blood| B(Donor Line 218)
    subgraph In-Line Sensing Module
        B --> C{NIR Spectrometer}
    end
    C -->|Real-time Hct data| D(Controller 226)
    D --> E(Anticoagulant Pump 234 Control)
    B --> F(Blood Pump 232) --> G(Blood Separator 214)
```

2. Operational Parameter Expansion

Derivative 1.3: Microfluidic Organ-on-a-Chip Plasmapheresis
- Enabling Description: The entire plasmapheresis system is scaled down to a microfluidic chip. Whole blood is drawn at microliters-per-minute flow rates. Separation is achieved not by centrifugation but via deterministic lateral displacement (DLD) or acoustic micro-vortices within the chip. The "controller" is a microprocessor that analyzes data from integrated micro-sensors (e.g., electrical impedance sensors) to determine the red blood cell concentration (micro-hematocrit). It then actuates integrated micropumps (e.g., piezoelectric) to meter picoliter volumes of anticoagulant. This system is designed for neonatal applications or for continuous plasma sampling in critical care settings.
- Mermaid.js Diagram:
```
graph LR
    subgraph Microfluidic Chip
        A(Blood Inlet) --> B{DLD Array};
        C(Anticoagulant Inlet) --> D{Piezoelectric Micropump};
        D -- Anticoagulant --> A;
        B -- Plasma --> E(Plasma Outlet);
        B -- RBCs --> F(RBC Outlet);
        B -- Impedance Sensor --> G(On-chip Microprocessor);
    end
    G -- Control Signal --> D;
```
Derivative 1.4: High-G Force Veterinary Plasmapheresis for Large Animals
- Enabling Description: The system is scaled for veterinary use on large animals (e.g., equine, bovine) with significantly larger blood volumes and different hematocrit ranges. The blood separator (214) is a reinforced, high-capacity bowl designed to operate at centrifugal forces exceeding 5000 G to rapidly separate larger volumes of blood. The controller's algorithm is programmed with species-specific allometric scaling laws for blood volume and typical hematocrit ranges. It dynamically adjusts the collection target based on real-time measurements from a robust, large-bore in-line sensor, accounting for the more rapid physiological changes seen in animals under sedation.
- Mermaid.js Diagram:
```
stateDiagram-v2
    [*] --> Initializing
    Initializing --> Draw_Cycle_1 : Start Procedure (Equine Mode)
    Draw_Cycle_1 --> Hematocrit_Check_1 : 2L blood processed
    Hematocrit_Check_1 --> Draw_Cycle_2 : Hct stable, adjust target
    Draw_Cycle_2 --> Hematocrit_Check_2 : 4L blood processed
    Hematocrit_Check_2 --> Return_Cycle : Hct dropped >5%, trigger early return
    Return_Cycle --> Finalize : Target volume not met, flag for review
    Finalize --> [*]
```

3. Cross-Domain Application

Derivative 1.5: Aerospace - In-Flight Hydrazine Fuel Purification
- Enabling Description: A system adapted for spacecraft maneuvering thrusters. It continuously circulates hydrazine fuel from a storage tank through a compact, high-speed centrifugal separator to remove particulate contaminants and catalyst fines that accumulate over time. An in-line laser scattering sensor continuously measures the particulate concentration (analogous to hematocrit). A controller dynamically adjusts the flow rate and the metering of a trace chemical scavenger (analogous to anticoagulant) to optimize the removal of impurities without significantly altering fuel chemistry, ensuring thruster reliability during long-duration missions.
- Mermaid.js Diagram:
```
flowchart LR
    A[Fuel Tank] -- Contaminated Fuel --> B(Pump);
    B --> C(Separator);
    C -- Purified Fuel --> A;
    C -- Contaminant Slurry --> D(Waste Collector);
    subgraph ControlLoop
        B -- Fuel --> E(Laser Scattering Sensor);
        E -- Particulate Data --> F(Controller);
        F -- Pump Speed --> B;
        F -- Scavenger Rate --> G(Scavenger Injection Pump);
    end
```
Derivative 1.6: AgTech - Dynamic Separation of Milk Fat Globules
- Enabling Description: A dairy processing system that dynamically adjusts a cream separator to produce cream with a precise, consistent butterfat percentage, despite natural variations in the raw milk supply. Raw milk is pumped into a centrifugal separator. An in-line NIR sensor measures the fat globule concentration (analogous to hematocrit) of the incoming milk. The controller uses this data to dynamically adjust the rotational speed of the separator and the back-pressure on the cream outlet valve. This allows for real-time control to achieve a target butterfat percentage (e.g., 40.0% +/- 0.1%), compensating for variations between cows, breeds, or time of day.
- Mermaid.js Diagram:
```
sequenceDiagram
    participant MilkSilo as Raw Milk Silo
    participant NIR as NIR Sensor
    participant Controller as Process Controller
    participant Separator as Centrifugal Separator
    participant CreamTank as Cream Storage

    MilkSilo->>NIR: Pump Raw Milk
    NIR->>Controller: Transmit Fat %
    Controller->>Separator: Set RPM & Back-pressure
    Separator->>CreamTank: Output Standardized Cream
    Separator->>MilkSilo: Return Skim Milk
```

4. Integration with Emerging Tech

Derivative 1.7: AI-Driven Predictive Hematocrit Modeling
- Enabling Description: The system controller (226) integrates a pre-trained machine learning model (e.g., a recurrent neural network - RNN). The model is trained on a massive dataset of past donations. It uses the donor's initial parameters (weight, height, age, initial hematocrit, donation history) and real-time data from IoT sensors (e.g., a wearable measuring hydration via skin impedance, heart rate variability) to predict the donor's hematocrit trajectory throughout the procedure. The controller then proactively adjusts pump speeds and anticoagulant ratios based on this prediction, aiming to complete the donation in the shortest possible time while maximizing yield and ensuring donor safety.
- Mermaid.js Diagram:
```
flowchart TD
    A[Initial Donor Data] --> B{AI/RNN Model};
    C[Real-time IoT Sensor Data] --> B;
    B -- Predicted Hct Curve --> D(Controller 226);
    D -- Proactive Control Signals --> E[Pumps & Valves];
    F[In-line Hct Sensor] --> D;
    D -- Model Correction --> B;
    E --> G(Donation Process);
```
Derivative 1.8: Blockchain-Verified "Plasma-Chain-of-Custody"
- Enabling Description: Each plasma collection system functions as a node on a private blockchain. When a procedure begins, a new block is initiated. The controller (226) writes immutable transaction records to the block, including the donor's anonymized ID, initial parameters, real-time sensor readings (hematocrit, volumes), anticoagulant lot number, and final collected pure plasma volume. The final plasma bag is labeled with a QR code corresponding to the block's hash. This creates an auditable, tamper-proof chain of custody from vein to fractionator, ensuring compliance with regulatory standards (e.g., FDA) and verifying product integrity.
- Mermaid.js Diagram:
```
erDiagram
    DONOR ||--o{ DONATION_SESSION : has
    DONATION_SESSION {
        string sessionID PK
        string anonDonorID FK
        datetime startTime
        string blockHash
    }
    DONATION_SESSION ||--|{ DATA_TRANSACTION : records
    DATA_TRANSACTION {
        string txID PK
        string sessionID FK
        datetime timestamp
        string dataType
        string dataValue
    }
    PLASMA_UNIT ||--|| DONATION_SESSION : results from
    PLASMA_UNIT {
        string unitID PK
        string sessionID FK
        float purePlasmaVolume
        string qrCode
    }
```

5. The "Inverse" or Failure Mode

Derivative 1.9: Fail-Safe Default to Volumetric Collection Mode
- Enabling Description: The system is designed with a redundant control architecture. The primary mode of operation uses dynamic hematocrit calculation to target a pure plasma volume. However, if the hematocrit sensor (in-line or separator-based) provides erratic readings, fails its self-test, or disconnects, the controller (226) automatically and seamlessly reverts to a "Safe Volumetric Mode." In this mode, it disregards the hematocrit calculation and instead collects a pre-calculated, conservative total volume of anticoagulated plasma based only on the donor's weight category, as is common in prior art systems. This ensures the procedure can be completed safely without complex calculations relying on a failed sensor, preventing under- or over-collection. An alarm notifies the operator of the mode switch.
- Mermaid.js Diagram:
```
stateDiagram-v2
    state "Dynamic Pure Plasma Mode" as Dynamic {
        state "Monitoring Hct" as Mon
        state "Adjusting Target" as Adj
        Mon --> Adj : Hct Data OK
    }
    state "Safe Volumetric Mode" as Safe

    [*] --> Dynamic
    Dynamic --> Safe : Sensor Fault Detected
    Safe --> Procedure_Complete : Target Volume Reached
    Dynamic --> Procedure_Complete : Target Pure Plasma Reached
    Procedure_Complete --> [*]
```

Combination Prior Art Scenarios

Combination 1: Integration with HL7 FHIR for Health Record Interoperability
- Description: The system's controller (226) is configured with an HL7 FHIR (Fast Healthcare Interoperability Resources) compliant API endpoint. Upon completion of a donation, the controller generates a "DiagnosticReport" FHIR resource containing the final calculated pure plasma volume, initial and final hematocrit values, and total procedure time. It also generates an "Observation" resource for the hematocrit readings taken during the procedure. These resources are then transmitted securely over HTTPS to the plasma center's Electronic Health Record (EHR) system, directly populating the donor's record without manual data entry. This leverages the open FHIR standard (published by Health Level Seven International) to combine the patented collection method with standard, open interoperability protocols.
Combination 2: Using MQTT Protocol for Centralized Real-Time Monitoring
- Description: Each plasma collection device in a center acts as an MQTT client. The controller (226) publishes real-time operational data (current draw/return phase, flow rates, instantaneous hematocrit, collected volume, active alarms) to specific topics on a central MQTT broker (e.g., Mosquitto, an open-source broker). A central dashboard subscribes to these topics to provide supervisors with a real-time overview of all active donations. This utilizes the lightweight, open-source publish/subscribe messaging protocol MQTT (ISO/IEC 20922) to add a layer of distributed, real-time monitoring and fleet management to the patented system.
Combination 3: Implementation on an Open-Source Real-Time Operating System (RTOS)
- Description: The control logic described in the patent, including the dynamic recalculation of hematocrit and target volumes, is implemented as a set of tasks on a single-board computer (e.g., BeagleBone or Raspberry Pi) running a Linux kernel with the PREEMPT_RT patch or a dedicated RTOS like FreeRTOS. The software reads data from sensors via standard interfaces (I2C, SPI) and controls the pumps via GPIO pins connected to motor drivers. This disclosure demonstrates that the patented method can be implemented entirely using open-source software (Linux, FreeRTOS) and commodity, open-spec hardware, thereby placing the specific software implementation of the control logic into the public domain.