Derivative works — US Patent 10980934

DEFENSIVE DISCLOSURE AND PRIOR ART GENERATION

RE: System and Method for Collecting Plasma, U.S. Patent 10,980,934
Publication Date: April 26, 2026
Author: Senior Patent Strategist and Research Engineer

This document serves as a defensive publication of technical disclosures intended to enter the public domain and establish prior art. The concepts herein are derived from the core teachings of U.S. Patent 10,980,934 ("the '934 patent") and expanded to render obvious or anticipate potential future patent claims on incremental improvements.

Derivative Set A: Variations on the Personalized Collection Method (Ref: Claim 1)

A1. Material & Component Substitution: Non-Contact Raman Spectroscopy Sensor

Enabling Description: The system substitutes the gravimetric scale (weight sensor 195) used for measuring collected plasma volume with a non-contact Raman spectroscopy sensor positioned along the plasma outlet line (222). The sensor continuously irradiates the fluid in the line with a 785 nm laser and analyzes the scattered light. A processor calculates the concentration of key analytes like albumin, immunoglobulins, and the citrate anticoagulant based on their unique Raman spectral signatures. By integrating the known flow rate (from the pump controller) with the real-time concentration measurements, the system calculates the cumulative mass of pure plasma and pure anticoagulant collected, eliminating the need for a physical scale and providing a more direct, real-time chemical measurement of the collected product. The controller terminates collection when the calculated mass of pure plasma reaches the target.

Mermaid Diagram:

sequenceDiagram
    participant Donor
    participant Pump
    participant Separator
    participant RamanSensor as Raman Sensor
    participant Controller

    Donor->>Pump: Whole Blood
    Pump->>Separator: Anticoagulated Blood
    Separator->>RamanSensor: Plasma + Anticoagulant
    RamanSensor->>Controller: Real-time Concentration Data
    Controller->>Controller: Calculate Pure Plasma Mass
    alt Pure Plasma Mass >= Target
        Controller->>Pump: Stop Collection
        Controller->>Pump: Initiate Return Cycle
    end

A2. Operational Parameter Expansion: Microfluidic Organ-on-a-Chip Perfusion

Enabling Description: The core logic is scaled down for use in laboratory "organ-on-a-chip" (OOC) systems. A microfluidic chip contains cultured human cells (e.g., liver organoids). A micro-pump perfuses the chip with a complex cell culture medium from a reservoir. The system's goal is to maintain a target concentration of a key metabolite (e.g., glucose) while collecting waste products. A micro-sensor (e.g., an electrochemical glucose sensor) in the effluent line measures the real-time metabolite concentration. A controller calculates the total volume of medium consumed and the total mass of metabolite consumed by the cells based on the flow rate and concentration delta. It introduces fresh medium from a second reservoir to maintain the target metabolite level within a tight band (e.g., 5 mM ± 0.1 mM), thereby personalizing the "collection" of waste products based on the real-time metabolic activity of the specific cell culture.

Mermaid Diagram:

flowchart TD
    A[Medium Reservoir] -->|Micro-pump| B(Organ-on-a-Chip);
    B --> C{Metabolite Sensor};
    C -->|Concentration Data| D[Controller];
    D -- Adjusts Pump Speed --> A;
    D --> E[Waste Collection];
    F[Fresh Medium] --> D;
    D -- Controls Dosing --> F;
    style D fill:#f9f,stroke:#333,stroke-width:2px

A3. Cross-Domain Application: Aerospace Lubricant Purity Management

Enabling Description: An autonomous system for managing hydraulic lubricant in a deep-space probe. The controller's goal is to extend the life of the lubricant. It calculates the "total lubricant volume" based on system specifications. It determines a "target purity level" (e.g., < 50 ppm of metallic particulates). During operation, a small amount of lubricant is continuously diverted from the main hydraulic loop and passed through an optical particle counter. The controller calculates the "volume of pure lubricant" versus the mass of contaminants. When the contaminant level exceeds a threshold, the controller diverts the lubricant through a filtration unit and then returns it to the main reservoir. This process continues until the calculated "pure lubricant volume" percentage meets the target, at which point the filtration cycle is paused. This is analogous to calculating pure plasma and managing the intravascular deficit.

Mermaid Diagram:

graph TD
    subgraph Hydraulic System
        A[Reservoir]
        B[Actuators]
    end
    A --> B;
    B --> A;
    B --> C(Particle Counter);
    C --> D{Controller};
    D -- Contaminant Level > Threshold --> E[Activate Filter];
    C -->|Diverted Flow| F(Filtration Unit);
    F --> A;
    E --> F;

A4. Integration with Emerging Tech: AI-Predicted Hydration and Real-Time Target Adjustment

Enabling Description: The system integrates data from a donor's wearable device (e.g., smartwatch) providing real-time heart rate, heart rate variability (HRV), and skin impedance. A pre-trained machine learning model, running on the controller, uses this data along with the donor's initial height, weight, and hematocrit to create a dynamic model of the donor's hydration state. During the procedure, if the model detects signs of dehydration (e.g., rising heart rate, falling HRV), the controller automatically recalculates the donor's total plasma volume in real-time and adjusts the target plasma collection volume downward to a more conservative percentage. This AI-driven feedback loop ensures the collection target is continuously optimized for donor safety based on their real-time physiological response.

Mermaid Diagram:

flowchart LR
    subgraph Donor
        A(Wearable Sensor)
    end
    subgraph Apheresis Machine
        B(Controller)
        C(ML Model)
    end
    A -- Real-time Vitals --> B;
    B -- Feeds Vitals to --> C;
    C -- Dynamic Hydration State --> B;
    B -- Adjusts --> D[Target Plasma Volume];
    style C fill:#ccf,stroke:#333,stroke-width:2px

A5. The "Inverse" or Failure Mode: Safe Intravascular Deficit Mode

Enabling Description: A "Safe Deficit" mode is designed for situations with sensor unreliability or for particularly sensitive donors. In this mode, the system does not target a specific plasma volume. Instead, it targets a maximum intravascular deficit, set to a conservative value like 300 mL. The controller monitors the volume of whole blood drawn and the volume of red blood cells and other components returned. It calculates the intravascular deficit in real-time as (Volume_Drawn - Volume_Returned). Plasma is collected continuously, but the moment this calculated deficit reaches the 300 mL limit, the controller immediately stops the draw pump and initiates the saline return cycle, regardless of how much plasma has been collected. This method inverts the primary goal from yield to a guaranteed low-impact donor experience.

Mermaid Diagram:

stateDiagram-v2
    [*] --> Drawing: Procedure Start
    Drawing: enter / Calculate Deficit = V_drawn - V_returned
    Drawing --> Safe_Return: [Deficit >= 300mL]
    Drawing --> Drawing: [Deficit < 300mL] Collect Plasma
    Safe_Return: Stop Draw, Return Saline
    Safe_Return --> [*]: End Procedure

Derivative Set B: Variations on the System and Controller (Ref: Claim 9)

B1. Material & Component Substitution: Modular Disposable Cassette with Non-Contact Magnetic Drive

Enabling Description: The entire extracorporeal circuit (inlet/outlet lines, pumps, pressure sensors, separation chamber) is integrated into a single, disposable cassette molded from medical-grade polycarbonate and sealed with a flexible thermoplastic elastomer membrane. The separation chamber is a centrifugal bowl that contains a magnetically-coupled impeller at its base. The non-disposable base unit contains no peristaltic pump rollers; instead, it uses a series of pneumatic valves to press on the cassette's membrane to drive fluid flow. The centrifuge motor is replaced by a non-contact magnetic drive that spins the impeller inside the sealed bowl without any physical contact, reducing heat generation and eliminating the need for a rotary seal, thus lowering the risk of contamination and hemolysis.

Mermaid Diagram:

classDiagram
    class DisposableCassette {
      +polycarbonate_body
      +TPE_membrane
      +integrated_tubing
      +magnetic_impeller_bowl
    }
    class BaseUnit {
      +pneumatic_controller
      +non_contact_magnetic_drive
      +system_controller
      -actuatePneumaticValves()
      -spinMagneticDrive()
    }
    BaseUnit "1" -- "1" DisposableCassette : engages

B2. Cross-Domain Application: AgTech Automated Nutrient Dosing for Hydroponics

Enabling Description: A system for large-scale hydroponic farms. A central controller calculates the "total nutrient volume" required for a crop cycle based on plant type, age, and environmental sensors (light, temp, CO2). Each day, it draws nutrient solution that has been circulated through the plant beds into a separation and analysis module. This module uses ion-selective electrodes to measure the concentration of key nutrients (N, P, K). The controller calculates the "pure nutrient" uptake by the plants. It then calculates a target replenishment volume, introduces precise doses of concentrated N, P, and K stock solutions into the main reservoir, and adds water to compensate for evaporation, thereby achieving a "target nutrient deficit" of zero.

Mermaid Diagram:

sequenceDiagram
    participant PlantBeds
    participant AnalysisModule
    participant Controller
    participant DosingPumps

    PlantBeds->>AnalysisModule: Circulated Solution
    AnalysisModule->>Controller: Nutrient Concentrations
    Controller->>Controller: Calculate Nutrient Uptake
    Controller->>DosingPumps: Command Replenishment Doses
    DosingPumps->>PlantBeds: Add Concentrated Nutrients & Water

B3. Integration with Emerging Tech: Blockchain-Verified Cold Chain for Plasma

Enabling Description: The system controller is enhanced with an IoT module that includes a temperature sensor, GPS, and a cryptographic co-processor. Upon procedure completion, the controller generates a unique digital token (e.g., an ERC-721 token on an Ethereum-compatible blockchain) representing the collected unit of plasma. This token immutably stores the donor ID hash, the volume of pure plasma collected, the machine ID, and the timestamp. As the plasma unit is transported and stored, the IoT module continuously monitors its temperature and location, appending these readings to the token's metadata on the blockchain. This creates a verifiable, unbroken cold chain record from collection to fractionation, ensuring the integrity and provenance of the final product.

Mermaid Diagram:

flowchart TD
    Start((Collection Complete)) --> A{Generate Plasma NFT};
    A --> B[Write Collection Data to Blockchain];
    B --> C{Transport & Storage};
    C --> D[IoT Sensor Monitoring];
    D --> |Temp & GPS Data| E{Append Data to NFT Metadata};
    C -- Loop --> D;
    E --> F((End of Supply Chain));
    style A fill:#bbf,stroke:#333,stroke-width:2px

B4. The "Inverse" or Failure Mode: Failsafe Gravity-Feed Return System

Enabling Description: The system includes a failsafe return mechanism that operates without electrical power. The disposable set is designed such that the blood processing components (bowl, pumps) are physically elevated above the donor's access site. In the event of a catastrophic power failure, a normally-closed solenoid valve, held shut by power, automatically opens. This action bypasses the pumps entirely and opens a direct, wide-bore tubing path from the bottom of the separation bowl back to the donor. The entire volume of the extracorporeal circuit is then returned to the donor purely by gravity. The controller's only role in this failure mode is to de-energize the valve. This ensures the donor's blood volume is safely restored even if the controller and all pumps fail simultaneously.

Mermaid Diagram:

stateDiagram-v2
    state "Normal Operation" as ON
    state "Power Failure" as OFF

    [*] --> ON
    ON --> OFF: Power Loss
    OFF --> [*]: Power Restored

    state OFF {
        direction LR
        [*] --> OpenValve
        OpenValve --> GravityReturn: Solenoid Valve De-energized
        GravityReturn --> DonorSafe
    }

Combination Prior Art Scenarios

Combination with MQTT Protocol: The apheresis system's controller (as in Claim 9) is configured as an MQTT client. It publishes real-time operational data (e.g., draw pressure, return pressure, anticoagulant flow rate, current collected volume, calculated pure plasma volume, machine status, and error codes) to a central MQTT broker within the donation center's network. The data is published to structured topics (e.g., devices/apheresis/bay04/pressure/draw). This allows any authorized system, such as a central monitoring dashboard or the facility's Laboratory Information System (LIS), to subscribe to these topics and receive real-time updates without custom integration, using the open-source and widely adopted MQTT standard for M2M communication.
Combination with TensorFlow Lite Framework: The method for calculating the volume of pure plasma (as in Claim 1) is enhanced by an on-device machine learning model built with TensorFlow Lite. The model is trained to detect early signs of hemolysis by analyzing high-frequency data from the optical line sensor (185). Instead of just detecting fluid density changes (plasma vs. platelets), the model analyzes the full spectral data from the sensor. It can identify the subtle spectral signature of free hemoglobin in the plasma line, indicating red blood cell damage. If hemolysis is detected, the controller can flag the collected product and alert the operator, improving product quality and donor safety, all without requiring a network connection for the AI inference.
Combination with DICOM Standard: At the conclusion of a plasma collection procedure, the system generates a DICOM (Digital Imaging and Communications in Medicine) Structured Report. This is not an image but a standardized data object. The report contains all key information from the procedure: Patient Information (name, ID), Procedure Information (date, time, machine ID, operator ID), Input Parameters (height, weight, initial hematocrit), Calculated Values (total plasma volume, target collection volume), and Final Results (volume of pure plasma collected, volume of anticoagulant used, volume of saline returned, final intravascular deficit). This DICOM object can be sent directly to the facility's PACS (Picture Archiving and Communication System) or VNA (Vendor Neutral Archive), where it becomes a permanent part of the donor's medical record, viewable and accessible alongside MRIs, CT scans, and other medical reports.