Derivative works — US Patent 11275092

Defensive Disclosure: Derivatives of US Patent 11275092

Current Date: 2026-05-15

This document outlines derivative variations of US Patent 11275092, titled "Methods of determining a treatment protocol for and/or a prognosis of a patient's recovery from a brain injury." These disclosures are intended to serve as prior art, rendering future incremental improvements by competitors obvious or non-novel, thereby strengthening our defensive publishing strategy.

Derivations based on Independent Claim 1:

A method of determining a treatment protocol for and/or a prognosis of a patient's recovery from a brain injury, comprising performing an assay on a blood sample from the patient and/or plasma and/or serum derived from the blood sample to determine a measure of the concentration of tau protein in the sample, wherein the assay has a limit of detection of tau protein of less than about 0.2 pg/mL, and the measured concentration of tau protein is less than about 5 pg/mL; and determining a prognosis of the patient's recovery from the brain injury and/or a method of treatment based at least in part on the measured concentration of tau protein present in the sample.

1.1. Material & Component Substitution: Plasmon-Enhanced Nanoparticle Immunoassay

Enabling Description: A method wherein the tau protein assay utilizes surface plasmon resonance (SPR) enhanced gold nanoparticles as the detection label, replacing enzyme conjugates. Monoclonal anti-tau antibodies are conjugated to 50 nm gold nanoparticles. Capture antibodies are immobilized on a functionalized SPR sensor surface. Upon binding of tau protein from a patient's serum sample and subsequent binding of the gold nanoparticle-conjugated detection antibodies, the localized surface plasmon resonance (LSPR) shift or intensity change is measured directly by a spectrograph. This optical signal provides a highly sensitive, label-free (after initial conjugation) or plasmon-enhanced detection, achieving a limit of detection below 0.1 pg/mL for tau protein, enabling detection of concentrations less than 5 pg/mL in plasma. The sensor surface is regenerated after each measurement.

graph TD
    A[Patient Blood Sample] --> B{Plasma/Serum Separation};
    B --> C[SPR Sensor Surface with Capture Ab];
    C --> D{Tau Protein Binding};
    D --> E[Gold Nanoparticle-conjugated Detection Ab Binding];
    E --> F[LSPR Signal Measurement via Spectrograph];
    F --> G[Data Analysis for Tau Concentration];
    G --> H{Prognosis / Treatment Determination};

1.2. Operational Parameter Expansion: Ultra-High Throughput Microfluidic Analysis at Elevated Temperatures

Enabling Description: A method employing a microfluidic chip array designed for ultra-high-throughput processing of hundreds of patient samples simultaneously. Each microfluidic channel integrates bead-based capture and fluorescence detection. The assay is performed at an elevated temperature, specifically 37°C ± 0.5°C, maintained by integrated resistive heaters and temperature sensors within the microfluidic chip. This elevated temperature accelerates the kinetics of antibody-antigen binding and enzyme-substrate reactions, reducing incubation times by 50% compared to room temperature assays, thus increasing throughput. Sample volumes are reduced to 5 µL per assay, and automated robotic handling ensures sample loading and reagent addition. Detection occurs within 10 minutes per sample.

graph TD
    A[Patient Blood Samples (Batch)] --> B{Automated Sample Preparation};
    B --> C[Microfluidic Chip Loading (5 µL/sample)];
    C --> D{Incubation at 37°C};
    D --> E[Accelerated Tau-Antibody Binding];
    E --> F[Fluorescence Detection Array];
    F --> G[High-Throughput Data Acquisition];
    G --> H{Prognosis / Treatment Determination};

1.3. Cross-Domain Application: Industrial Robotics Predictive Maintenance

Enabling Description: A method applied to industrial robotic systems to predict structural integrity compromise. Instead of tau protein, the biomarker is a specific wear particulate or micro-fracture byproduct protein (e.g., a specific alloy degradation protein, ADP-1) released into the robot's hydraulic fluid or lubricant. Samples of the hydraulic fluid are periodically drawn and assayed using a single-molecule digital immunoassay adapted for ADP-1, with a limit of detection less than 0.2 ng/mL. A measured ADP-1 concentration above 5 ng/mL indicates a high likelihood of impending structural failure (analogous to poor prognosis for brain injury), triggering predictive maintenance protocols or shutdown. The system provides data to inform a maintenance schedule for robotic components.

graph TD
    A[Industrial Robot] --> B{Hydraulic Fluid Sample Collection};
    B --> C[Ultra-Sensitive ADP-1 Immunoassay];
    C --> D{ADP-1 Concentration Data (ng/mL)};
    D --> E{Predictive Maintenance Protocol Trigger};
    E --> F{Robot Shutdown / Component Replacement};

1.4. Integration with Emerging Tech: AI-Driven Personalized Treatment Recommendation

Enabling Description: A method where the ultra-sensitive tau protein concentration data (LOD < 0.2 pg/mL, measured conc < 5 pg/mL) from a patient's blood sample is fed into an AI-driven clinical decision support system. This system, trained on vast datasets of patient outcomes, treatment efficacies, genetic profiles, and concurrent biomarker levels, employs a deep neural network to generate a personalized treatment protocol. The AI considers the specific tau concentration, patient demographics, co-morbidities, and previous treatment responses, providing a probabilistically weighted recommendation for therapeutic agent dosage or intervention strategy, rather than a fixed prognosis. IoT sensors embedded in the assay instrument transmit real-time performance metrics to the AI for continuous calibration and drift correction.

graph TD
    A[Patient Blood Sample] --> B[Ultra-Sensitive Tau Assay (LOD < 0.2 pg/mL)];
    B --> C[IoT Sensor Data (Assay Performance)];
    C --> D[Secure Data Gateway];
    D --> E{AI Clinical Decision Support System};
    E --> F[Deep Neural Network Analysis];
    F --> G{Personalized Treatment Recommendation};
    G --> H[Healthcare Provider];

1.5. The "Inverse" or Failure Mode: Low-Power, Qualitative Triage Assay

Enabling Description: A method for rapid, qualitative triage of brain injury in remote or resource-limited settings. The assay is designed to operate in a low-power, "limited-functionality" mode. Instead of precise pg/mL quantification, the system provides a binary or semi-quantitative output: "Tau Elevated" (indicating concentration > 1 pg/mL) or "Tau Not Elevated" (concentration ≤ 1 pg/mL), operating with a limit of detection of approximately 1 pg/mL. The assay utilizes a simplified lateral flow immunoassay strip with a highly sensitive luminescent reporter. Power is supplied by a compact battery pack. If the full quantitative detection module is unavailable or power is critically low, the system defaults to this rapid, less sensitive, but highly robust qualitative mode to provide immediate actionable information for emergency medical personnel. The "prognosis" is simplified to "Requires Further Assessment" or "Low Risk, Monitor."

graph TD
    A[Patient Blood Sample] --> B[Simplified Lateral Flow Immunoassay];
    B --> C{Luminescent Reporter Activation};
    C --> D{Optical Reader (Low-Power)};
    D --> E{Qualitative Output (Elevated/Not Elevated)};
    E --> F{Triage Recommendation (e.g., "Requires Further Assessment")};
    F -- Low-Power Mode --> G[Battery Status Check];

Derivations based on Independent Claim 6:

A method for performing an assay and providing data for determining a treatment protocol for and/or a prognosis of a patient's recovery from a brain injury, comprising performing an assay on a blood sample from the patient and/or plasma and/or serum derived from the blood sample to determine a measure of the concentration of tau protein in the sample, wherein the assay has a limit of detection of tau protein of less than about 0.2 pg/mL; and providing data from the assay to enable determining a prognosis of the patient's recovery from the brain injury and/or a method of treatment based at least in part on the measured concentration of tau protein present in the sample.

2.1. Material & Component Substitution: Quantum Dot-Labeled Electrochemiluminescence (ECL) Assay

Enabling Description: A method employing an electrochemiluminescence (ECL) assay format using cadmium-free quantum dots (QDs) as reporters, replacing traditional enzymatic labels. Capture antibodies are immobilized on carbon electrode arrays. After tau protein binding from the sample (plasma/serum) and subsequent binding of QD-conjugated detection antibodies, an electrical potential is applied to the electrode. This triggers a light emission from the QDs, directly proportional to the bound tau concentration. The emitted light is detected by a photomultiplier tube (PMT), and the digital signal is converted into tau concentration data. This QD-ECL system offers superior photostability and multiplexing capabilities, achieving a limit of detection below 0.1 pg/mL. The data output is streamed via a secure digital interface.

graph TD
    A[Patient Blood Sample] --> B{Plasma/Serum Separation};
    B --> C[Carbon Electrode Array with Capture Ab];
    C --> D{Tau Protein Binding};
    D --> E[QD-conjugated Detection Ab Binding];
    E --> F[Electrical Potential Application];
    F --> G[ECL Light Emission Detection (PMT)];
    G --> H[Digital Data Conversion & Transmission];
    H --> I{External Prognosis/Treatment System};

2.2. Operational Parameter Expansion: Sub-Microliter Sample Analysis with Cryogenic Storage

Enabling Description: A method specifically designed for analyzing sub-microliter patient samples (e.g., 200 nL) sourced from capillary blood draws. The assay instrument incorporates micro-pipetting robotics capable of handling these minute volumes. After sample acquisition and preparation, the samples, along with their associated metadata, are stored in a cryogenic storage unit at -80°C until batched for processing. The assay itself maintains its ultra-sensitive detection (LOD < 0.2 pg/mL) characteristics. The collected data includes not only tau concentration but also sample collection time, storage duration, and temperature history, all provided to enable precise prognosis determination, particularly for samples subject to variable pre-analytical conditions.

graph TD
    A[Capillary Blood Sample (Patient)] --> B[200 nL Sample Extraction];
    B --> C[Cryogenic Sample Storage (-80°C)];
    C --> D[Automated Thaw & Micro-Pipetting];
    D --> E[Ultra-Sensitive Tau Assay (LOD < 0.2 pg/mL)];
    E --> F[Tau Concentration Data + Metadata];
    F --> G{Data Provision for Prognosis/Treatment};

2.3. Cross-Domain Application: Environmental Toxin Monitoring in Aquatic Systems

Enabling Description: A method adapted for environmental monitoring, specifically detecting neurotoxic compounds (e.g., certain algal toxins or heavy metal complexes that induce stress responses in aquatic organisms) in water samples. The "tau protein" analog here is a specific stress-response protein released by sentinel aquatic organisms (e.g., a specific fish or invertebrate species). Water samples are processed to concentrate the protein, and an ultra-sensitive immunoassay (LOD < 0.2 ng/L) is performed. The detected concentration data for the stress protein is provided to enable determining a "prognosis" for the aquatic ecosystem's health (e.g., risk of fish kill) and/or "treatment" (e.g., initiating remediation efforts, issuing water use advisories).

graph TD
    A[Aquatic System] --> B[Water Sample Collection];
    B --> C[Concentration of Sentinel Organism Stress Protein];
    C --> D[Ultra-Sensitive Immunoassay (LOD < 0.2 ng/L)];
    D --> E[Stress Protein Concentration Data];
    E --> F{Environmental Health Prognosis / Remediation Strategy};

2.4. Integration with Emerging Tech: IoT-Enabled Real-time Data Reporting and Anonymization

Enabling Description: A method where the assay system, an IoT-enabled device, automatically performs the ultra-sensitive tau protein assay (LOD < 0.2 pg/mL) and securely transmits the raw and processed tau concentration data to a cloud-based analytics platform. Before transmission, patient-specific identifiers are anonymized using a privacy-preserving hashing algorithm. The data package includes assay run parameters, quality control metrics, and the measured tau concentration. This anonymized, real-time data is then made available to authorized clinicians or research databases for determining prognoses and treatment protocols, adhering to strict data governance policies enforced by blockchain-based access controls.

graph TD
    A[Patient Blood Sample] --> B[IoT-Enabled Assay Device];
    B --> C[Ultra-Sensitive Tau Assay (LOD < 0.2 pg/mL)];
    C --> D[Tau Concentration Data Generation];
    D --> E[Data Anonymization (Hashing)];
    E --> F[Secure IoT Transmission];
    F --> G[Cloud Analytics Platform];
    G --> H{Authorized Clinicians/Research DBs};

2.5. The "Inverse" or Failure Mode: Diagnostic Mode with Reduced Precision for Rapid Output

Enabling Description: A method where the assay system includes a "diagnostic mode" for accelerated data provision when full precision is not immediately critical, or when instrument conditions are suboptimal (e.g., partial reagent degradation). In this mode, the assay utilizes a reduced number of imaging cycles and a simplified signal processing algorithm, yielding tau concentration data with a precision of ± 20% rather than the standard ± 5%. While the limit of detection remains below 0.2 pg/mL to confirm presence, the reported concentration is an estimate, provided much faster (e.g., within 30 minutes instead of 2 hours). This allows for rapid preliminary prognoses or treatment decisions in time-sensitive situations, with a clear flag indicating "Reduced Precision Data" to the receiving system.

graph TD
    A[Patient Blood Sample] --> B[Assay System];
    B --> C{System State: Optimal?};
    C -- Yes --> D[Standard High-Precision Assay];
    C -- No / Rapid Output Needed --> E[Diagnostic Mode (Reduced Precision)];
    D --> F[High-Precision Tau Data];
    E --> G[Rapid, Reduced-Precision Tau Data];
    F --> H{Data Provision};
    G --> H{Data Provision (Flagged)};

Derivations based on Independent Claim 13:

A method of determining a treatment protocol for and/or a prognosis of a patient's recovery from a brain injury, comprising determining a measure of the concentration of tau protein in each of a plurality of samples obtained from the patient following the brain injury, wherein the measure of the concentration of tau protein is determined by performing an assay on each sample, wherein the assay has a limit of detection of tau protein of less than about 0.2 pg/mL, and the measured concentration of tau protein is less than about 5 pg/mL; and determining a prognostic of the patient's recovery from the brain injury and/or a method of treatment based at least in part on the measured concentration tau protein present in the sample.

3.1. Material & Component Substitution: Multiplexed Raman Spectroscopy with Functionalized Nanowires

Enabling Description: A method utilizing multiplexed surface-enhanced Raman spectroscopy (SERS) for simultaneous detection of tau protein and other neurological biomarkers (e.g., GFAP, NFL) in a plurality of blood samples. Gold nanowires functionalized with specific capture antibodies for each biomarker are integrated into a microfluidic array. Upon binding of biomarkers from the patient samples (LOD < 0.1 pg/mL for tau, < 0.5 pg/mL for others), a Raman-active reporter molecule is introduced, which generates a unique Raman signature for each biomarker. A tunable laser and spectrometer simultaneously acquire multiplexed Raman spectra from each nanowire array, providing kinetic profiles for multiple biomarkers. The raw spectral data is processed by chemometric algorithms to yield individual protein concentrations, forming the basis for a more comprehensive prognostic indicator and refined treatment strategy.

graph TD
    A[Patient (Serial Samples)] --> B[Blood Sample Collection (T1...Tn)];
    B --> C[Plasma/Serum Preparation];
    C --> D[Microfluidic Array with Functionalized Nanowires];
    D --> E{Multiplexed Biomarker Binding (Tau, GFAP, NFL)};
    E --> F[Raman-Active Reporter Introduction];
    F --> G[Tunable Laser & Spectrometer (SERS)];
    G --> H[Multiplexed Raman Spectra Acquisition];
    H --> I[Chemometric Data Processing];
    I --> J[Kinetic Profiles of Biomarker Concentrations];
    J --> K{Comprehensive Prognosis / Treatment};

3.2. Operational Parameter Expansion: Continuous In-Vivo Monitoring via Implantable Biosensor

Enabling Description: A method involving continuous, real-time measurement of tau protein concentration via an implantable subcutaneous biosensor. The biosensor consists of a microdialysis probe coupled to a miniature, flow-through ultra-sensitive immunoassay module (LOD < 0.1 pg/mL). The module draws interstitial fluid, performs the tau assay using localized enzymatic detection, and transmits concentration data wirelessly at 1-minute intervals. This provides a high-resolution kinetic profile of tau release and clearance. The continuous data stream, covering periods of weeks to months, allows for dynamic adjustments to treatment protocols based on immediate changes in tau levels, far exceeding the temporal resolution of discrete sampling.

graph TD
    A[Patient] --> B[Implantable Subcutaneous Biosensor];
    B --> C[Microdialysis (Interstitial Fluid)];
    C --> D[Miniature Flow-Through Immunoassay (LOD < 0.1 pg/mL)];
    D --> E[Real-time Tau Concentration Data];
    E --> F[Wireless Data Transmission (1-min intervals)];
    F --> G[Data Aggregation & Analysis Platform];
    G --> H{Dynamic Prognosis / Adaptive Treatment Protocol};

3.3. Cross-Domain Application: Structural Health Monitoring of Advanced Composite Materials

Enabling Description: A method for monitoring the structural health of advanced composite materials (e.g., in aerospace or civil engineering applications). Instead of brain injury biomarkers, the method detects specific degradation products (e.g., polymer cleavage fragments, composite delamination markers) released into a circulating tracer fluid embedded within the composite matrix. A plurality of fluid samples is collected over time from different locations within the structure. Each sample is analyzed using an ultra-sensitive immunoassay (LOD < 0.2 pg/mL) adapted to detect these degradation markers. Kinetic profiles of marker concentrations are used to determine the "prognosis" of the composite's structural integrity (e.g., risk of catastrophic failure) and inform "treatment" (e.g., repair scheduling, load reduction, or replacement).

graph TD
    A[Composite Structure] --> B[Embedded Tracer Fluid Circulation];
    B --> C[Serial Fluid Sample Collection (T1...Tn)];
    C --> D[Ultra-Sensitive Degradation Marker Assay (LOD < 0.2 pg/mL)];
    D --> E[Degradation Marker Concentration Data];
    E --> F[Kinetic Profile Analysis];
    F --> G{Structural Integrity Prognosis / Maintenance Schedule};

3.4. Integration with Emerging Tech: AI-Driven Adaptive Sampling and Prognosis Refinement

Enabling Description: A method where a machine learning algorithm dynamically optimizes the timing and frequency of sample collection for tau protein measurement. Initial tau measurements (LOD < 0.2 pg/mL, conc < 5 pg/mL) are fed into an AI model. The AI, considering the patient's initial clinical status and early tau kinetics, predicts optimal future sampling points to maximize prognostic accuracy while minimizing invasiveness. This adaptive sampling strategy (e.g., increasing frequency during expected peak times, reducing during stable phases) ensures efficient data collection. The complete time-series tau data is then processed by a recurrent neural network to continuously refine the patient's prognosis and treatment protocol as new data becomes available, with all data inputs and AI recommendations securely logged on a distributed ledger.

graph TD
    A[Patient Brain Injury] --> B[Initial Blood Sample & Tau Assay];
    B --> C[AI Adaptive Sampling Module];
    C -- Recommends --> D[Next Sample Collection (T1, T2...)];
    D --> E[Ultra-Sensitive Tau Assay];
    E --> F[Tau Concentration Data];
    F --> G[Recurrent Neural Network for Prognosis];
    G --> H[Prognosis & Treatment Refinement];
    G & F --> I[Secure Distributed Ledger (Blockchain)];

3.5. The "Inverse" or Failure Mode: Fail-Safe Trend Detection with Reduced Resolution

Enabling Description: A method where, in the event of partial sensor failure or power constraints during prolonged monitoring (plurality of samples), the system defaults to a fail-safe mode for trend detection. Instead of high-resolution quantitative data (pg/mL), it provides simplified trend indicators (e.g., "Tau Increasing," "Tau Stable," "Tau Decreasing") based on coarser concentration thresholds, or reports values as "High," "Medium," or "Low." This limited functionality ensures that even with reduced data fidelity, a critical kinetic trend (e.g., a rapid increase in tau indicating worsening injury) can still be identified and communicated for triage. The system continuously attempts to restore full functionality and logs the duration of reduced resolution operation.

graph TD
    A[Patient (Serial Samples)] --> B[Multi-Sample Tau Assay Module];
    B --> C{Sensor/Power Status Check};
    C -- Optimal --> D[High-Resolution Tau Data (pg/mL)];
    C -- Suboptimal --> E[Fail-Safe Trend Detection Mode];
    E --> F[Simplified Trend Indicators (High/Med/Low, Inc/Dec/Stable)];
    D --> G{Prognosis/Treatment Based on Full Data};
    F --> H{Triage/Emergency Action Based on Trends};

Derivations based on Independent Claim 17:

A method of determining a treatment protocol for and/or a prognosis of a patient's recovery from a brain injury, comprising (a) performing an assay on each of a plurality of samples obtained from the patient following the brain injury to determine the measured concentration of tau protein in each of the samples, wherein the plurality of samples are obtained from the patient over a period of time of at least about 48 hours, wherein the assay has a limit of detection of tau protein of less than about 0.2 pg/mL, and the measured concentration of tau protein is less than about 5 pg/mL; (b) determining the area under the curve of a graph of the tau protein concentration in the plurality of samples versus time, wherein the area is determined for the entire time period and/or for a second peak in the tau protein concentration; and (c) determining a prognosis of the patient's recovery from the brain injury and/or a method of treatment based at least in part on the area under the curve for the entire time period and/or the second peak in the tau protein concentration determined in step (b).

4.1. Material & Component Substitution: Micro-electromechanical Systems (MEMS) Based Assay with Piezoelectric Detection

Enabling Description: A method utilizing a MEMS-based microcantilever array for tau protein detection. Each cantilever is coated with capture antibodies. When tau protein from a patient sample (over 48 hours, LOD < 0.2 pg/mL, conc < 5 pg/mL) binds to the cantilever, it causes a mass-induced deflection. This deflection is precisely measured by integrated piezoelectric sensors. The array allows for parallel, label-free detection across multiple time points. The time-series deflection data is converted to mass, then to concentration, enabling calculation of the area under the curve (AUC) for both overall tau kinetics and specific peaks, particularly the "second peak," to inform prognosis and treatment. This system is highly miniaturized and robust.

graph TD
    A[Patient (Serial Samples over 48+ hrs)] --> B[MEMS Microcantilever Array];
    B --> C{Tau Protein Binding & Mass Loading};
    C --> D[Piezoelectric Deflection Measurement];
    D --> E[Time-Series Deflection Data];
    E --> F[Concentration Derivation];
    F --> G{AUC Calculation (Total and Second Peak)};
    G --> H{Prognosis / Treatment Determination};

4.2. Operational Parameter Expansion: Ultra-Dense Sampling with Fourier Transform Analysis

Enabling Description: A method involving ultra-dense sampling of tau protein concentrations (LOD < 0.2 pg/mL, conc < 5 pg/mL) obtained from a patient, with samples collected every 15 minutes over a period of at least 72 hours. This high temporal resolution generates a detailed kinetic curve. Instead of traditional AUC calculation, the data undergoes Fourier Transform analysis to decompose the complex tau protein concentration curve into its constituent frequency components. Specific frequency signatures and phase shifts, particularly those corresponding to the "second peak" observed after 24-48 hours, are correlated with prognostic outcomes. This allows for a more nuanced analysis of the kinetics, potentially identifying subtle changes indicative of recovery or deterioration, leading to highly optimized treatment protocols.

graph TD
    A[Patient (Brain Injury)] --> B[Automated Blood Sampling (15-min intervals, 72+ hrs)];
    B --> C[Ultra-Sensitive Tau Assay (LOD < 0.2 pg/mL)];
    C --> D[High-Resolution Time-Series Tau Data];
    D --> E[Fourier Transform Analysis];
    E --> F{Identification of Kinetic Signatures/Frequencies};
    F --> G{Prognosis / Treatment Optimization};

4.3. Cross-Domain Application: Precision Agriculture for Plant Stress Response

Enabling Description: A method applied in precision agriculture for determining the "prognosis" of plant health and optimizing "treatment" (e.g., nutrient delivery, pest control). The "brain injury" is analogous to environmental stress (e.g., drought, pathogen attack). The "tau protein" is a specific plant stress hormone or secondary metabolite (e.g., jasmonic acid, abscisic acid) released into the plant's sap. Sap samples are collected from a plurality of plants over a period of at least 7 days (analogous to 48+ hours). An ultra-sensitive assay (LOD < 0.2 pg/mL) determines the concentration of the stress marker. The AUC of the stress marker concentration versus time, particularly a "second peak" (e.g., indicating chronic stress response), is calculated. This AUC value determines the plant's recovery prognosis and dictates precise adjustments to environmental controls or targeted agrochemical application.

graph TD
    A[Crop Field (Stressed Plants)] --> B[Automated Sap Collection (Serial, 7+ days)];
    B --> C[Ultra-Sensitive Stress Marker Assay (LOD < 0.2 pg/mL)];
    C --> D[Time-Series Stress Marker Concentration];
    D --> E{AUC Calculation (Total and Secondary Peak)};
    E --> F{Plant Health Prognosis / Precision Ag Treatment};

4.4. Integration with Emerging Tech: Predictive AI for Multi-Factor Prognosis with Blockchain Verification

Enabling Description: A method where the calculated AUC of tau protein concentration (LOD < 0.2 pg/mL, conc < 5 pg/mL) from samples over at least 48 hours, including the "second peak" AUC, is combined with additional patient data (e.g., genomics, imaging, clinical scores, IoT vital sign data) and fed into a sophisticated predictive AI model. This AI, leveraging federated learning across multiple healthcare institutions, generates a highly accurate, multi-factor prognosis and a dynamic, adaptive treatment protocol. The entire process, from sample collection to AI output and physician override, is recorded on an immutable blockchain ledger, providing an auditable trail for regulatory compliance, insurance claims, and transparent clinical decision-making. Smart contracts on the blockchain could automate alerts for specific AUC thresholds.

graph TD
    A[Patient (Serial Samples over 48+ hrs)] --> B[Ultra-Sensitive Tau Assay];
    B --> C[Tau Concentration Time-Series];
    C --> D{AUC Calculation (Total/Second Peak)};
    D --> E[Additional Clinical Data (Genomics, Imaging, IoT Vitals)];
    E & D --> F[Predictive AI Model (Federated Learning)];
    F --> G[Multi-Factor Prognosis / Adaptive Treatment Plan];
    G --> H[Blockchain Ledger (Immutable Record)];
    H --> I{Healthcare Provider / Regulatory Body};

4.5. The "Inverse" or Failure Mode: Threshold-Based Early Warning System with AUC Estimation

Enabling Description: A method designed to prioritize early warning for potential poor outcomes, especially when full computational resources for precise AUC calculation are unavailable. For samples collected over at least 48 hours (LOD < 0.2 pg/mL), the system first establishes two simple thresholds for tau concentration: T1 (e.g., 2 pg/mL) and T2 (e.g., 4 pg/mL). If the tau concentration crosses T1 for a sustained period (e.g., >6 hours) or crosses T2 at any point, an "Early Warning" is triggered. The "second peak" is only qualitatively detected (e.g., "Secondary Elevation Present/Absent"). A simplified, computationally lightweight AUC estimation is performed using trapezoidal rule for only the first 24 hours of data, providing a rapid, albeit less precise, indicator for initial triage, deferring full AUC calculation to a high-power system when available.

graph TD
    A[Patient (Serial Samples over 48+ hrs)] --> B[Ultra-Sensitive Tau Assay];
    B --> C[Tau Concentration Time-Series];
    C --> D{Tau Threshold Check (T1, T2)};
    D -- T1 or T2 Crossed --> E[Early Warning Triggered];
    C --> F{Qualitative Secondary Peak Detection};
    C --> G[Lightweight AUC Estimation (First 24 hrs)];
    E & F & G --> H{Rapid Triage Prognosis / Initial Treatment Recommendation};

Combination Prior Art Scenarios

Here are at least three scenarios combining US Patent 11275092 with existing open-source standards, making further incremental improvements obvious.

US11275092 + Open-Source Image Processing & Data Analysis Libraries (e.g., OpenCV, SciPy, Pandas):
The '092 patent describes performing ultra-sensitive assays, often involving imaging of microwell arrays (e.g., FIGS. 1a, 1b and Example 1). The processing of these images to detect positive wells and subsequent data analysis (e.g., AUC calculation as per Claim 17) can be directly implemented using widely available, open-source libraries. For instance, image acquisition from a CCD camera can be processed with OpenCV (Open Source Computer Vision Library) for bead detection, artifact discrimination, and signal growth analysis. The resulting digital signals and fluorescence intensities can be fed into SciPy and NumPy for numerical computations, including Poisson distribution adjustments and area under the curve calculations. Pandas can then be used for efficient management and statistical analysis of the time-series tau concentration data. The combination of these standard, open-source computational tools with the described assay methodology for tau protein analysis (LOD < 0.2 pg/mL) would be an obvious implementation for anyone skilled in the art of biomedical data processing.
US11275092 + Open-Source Microfluidics Design Platforms (e.g., KiCad for schematics, OpenSCAD for CAD, academic repositories):
The '092 patent describes assay methods utilizing microfluidic structures, specifically "femtoliter-sized reaction wells etched into bundles of optical fibers" (Example 1) or "a plurality of reaction vessels in an array format" (Description). The physical design and fabrication of such microfluidic platforms are increasingly reliant on open-source hardware design principles and software tools. For example, microfluidic layouts can be designed using open-source CAD software like OpenSCAD or Blender, and electronic control systems (e.g., for heaters, fluidic pumps) can be designed using KiCad. Numerous academic research groups openly publish their microfluidic designs (e.g., on platforms like ResearchGate, university repositories, or dedicated open hardware initiatives). Combining the assay principles of US11275092 (ultra-sensitive tau detection) with existing open-source microfluidic designs for reaction vessels, fluidic routing, and bead manipulation would represent an obvious engineering integration, particularly for achieving cost-effective and reproducible assay consumables.
US11275092 + Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) Standard for Data Exchange:
The '092 patent's core utility lies in determining a prognosis and/or treatment protocol based on measured tau protein concentrations. For this data to be actionable within a modern healthcare system, it must be effectively communicated and integrated with electronic health records (EHRs) and other clinical decision support systems. HL7 FHIR is a widely adopted, open-source standard for exchanging healthcare information electronically. Integrating the tau protein concentration data (including time-series, AUC, and derived prognoses) generated by the methods of US11275092 into a FHIR-compliant format (e.g., as Observation or DiagnosticReport resources) would be a standard and obvious practice for any healthcare IT developer. This combination enables the seamless flow of ultra-sensitive biomarker data from the lab to the patient's digital health record, facilitating automated clinical alerts, longitudinal analysis, and population health management, thereby making the clinical application and data interoperability of the patent's output obvious.