Derivative works — US Patent 12168797

DEFENSIVE DISCLOSURE DOCUMENT

Title: Derivative Methods and Systems for Signal Encoding and Decoding in Multiplexed Biochemical Assays
Publication Date: 2026-05-13
Keywords: Multiplex assay, signal encoding, non-degenerate coding, prior art, defensive publication, quantitative PCR, fluorescence, quantum dots, SERS, electrochemiluminescence, AI, IoT, blockchain, cross-domain application.

Abstract

This document discloses a series of derivative works, improvements, and alternative embodiments for the methods of signal encoding and decoding in multiplexed biochemical assays as described in US Patent 12,168,797. The purpose of this disclosure is to place these variations into the public domain, thereby establishing them as prior art. These disclosures cover alternative materials and components, expanded operational parameters, novel cross-domain applications, integration with emerging technologies, and failure-mode or inverse operational designs.

1. Material & Component Substitution Derivatives

1.1. Quantum Dot (QD) Encoded Probes

Enabling Description: This variation replaces the organic fluorophores described in US 12,168,797 with semiconductor quantum dots (e.g., CdSe/ZnS core-shell QDs). Each analyte-specific oligonucleotide probe is conjugated to a QD of a specific size, which determines its emission wavelength. Due to the narrow, symmetric emission spectra of QDs, spectral overlap between channels is significantly reduced, allowing for a higher number of distinct "colors" (e.g., 10-12) to be used simultaneously. A single deep-UV excitation source (e.g., a 365 nm LED) is used to excite all QDs, simplifying the optical system. The intensity levels for encoding (e.g., 1x, 2x, 4x concentrations) are achieved by controlling the stoichiometry of QD-to-probe conjugation or by mixing probes with different QD conjugation ratios. Decoding follows the non-degenerate matrix method, but with a greatly expanded color dimension.

Mermaid Diagram:

graph TD
    subgraph Excitation
        A[UV LED @ 365nm]
    end
    subgraph Sample Chamber
        B{Analyte + QD Probes}
        B -- Emission --> C{Spectrometer}
    end
    subgraph Decoding
        C -- Raw Spectra --> D[Signal Processor]
        D -- Deconvolve QD Peaks --> E[Intensity Vector]
        E -- Apply Decoding Matrix --> F[Analyte Presence/Absence]
    end
    A --> B

1.2. Surface-Enhanced Raman Spectroscopy (SERS) Encoded Probes

Enabling Description: This derivative uses SERS as the detection modality. Gold or silver nanoparticles (50-100 nm) serve as the SERS substrate. Each analyte-specific probe is co-conjugated to the nanoparticles along with a unique Raman-active molecule (e.g., 4-mercaptobenzoic acid, malachite green isothiocyanate). The "color" dimension is the characteristic Raman shift (in cm⁻¹) of the reporter molecule, and the "intensity" is the peak height of the SERS signal at that shift. A laser (e.g., 785 nm) illuminates the sample, and a Raman spectrometer collects the scattered light. This method is immune to sample autofluorescence. A non-degenerate coding scheme is built using combinations of unique Raman shifts and signal intensities, achieved by varying the concentration of the tagged nanoparticles.

Mermaid Diagram:

sequenceDiagram
    participant Laser as 785nm Laser
    participant Sample as Sample (SERS Probes)
    participant Spectrometer as Raman Spectrometer
    participant Processor as Decoding Processor

    Laser->>Sample: Illuminate
    Sample->>Spectrometer: Raman Scattering
    Spectrometer->>Processor: Acquire Spectrum
    Processor->>Processor: Identify Raman Shift Peaks
    Processor->>Processor: Quantify Peak Intensities
    Processor->>Processor: Decode Intensity Vector
    Processor-->>User: Report Analytes

1.3. Electrochemiluminescence (ECL) Encoded Probes

Enabling Description: This embodiment employs ECL as the signal source. Analyte-specific probes are labeled with different ECL tags, primarily ruthenium(II) tris(bipyridine) ([Ru(bpy)₃]²⁺) and its derivatives, which have slightly different emission potentials or spectra. The assay is conducted in an electrochemical cell with a co-reactant like tripropylamine (TPA). When a voltage sweep is applied, each ECL tag emits light at a characteristic potential. The "intensity" is the photon count at that potential, and the "color" is the specific redox potential that triggers the emission. The cumulative signal is a light-vs-potential curve (voltammogram), which is deconvolved to determine the intensity in each potential "channel."

Mermaid Diagram:

graph TD
    A[Potentiostat] -- Applies Voltage Sweep --> B{ECL Cell with Probes};
    B -- ECL Emission --> C[Photomultiplier Tube];
    C -- Photon Count --> D[Data Acquisition];
    A -- Voltage Data --> D;
    D -- Synchronized Data (Light vs. V) --> E[Decoder];
    E -- Deconvolve Peaks --> F[Intensity per Potential Channel];
    F -- Apply Coding Matrix --> G[Analyte Results];

2. Operational Parameter Expansion Derivatives

2.1. High-Pressure Real-Time PCR (HP-qPCR) Implementation

Enabling Description: The entire multiplexed assay, including PCR amplification and signal detection, is performed within a high-pressure vessel (e.g., 50-200 MPa) fitted with optical windows. The instrumentation includes a pressure pump and a thermocycler capable of operating under such conditions. High pressure alters DNA hybridization kinetics and melting temperatures (Tm), which can be exploited to increase specificity and reduce amplification of non-target sequences. The encoding scheme uses pressure-stable fluorophores. The high pressure reduces bubble formation at high temperatures, enabling superheated denaturation cycles (e.g., >100°C) for faster cycling protocols.

Mermaid Diagram:

graph LR
    subgraph Control
        A[PC] --> B[Pressure Controller]
        A --> C[Thermocycler Controller]
        A --> D[Optical Detector]
    end
    subgraph System
        B --> E[High-Pressure Pump]
        E --> F{Pressure Vessel with Optical Windows}
        C --> G[Peltier Elements]
        G --> F
        F -- Optical Signal --> D
    end

2.2. Nanoscale Digital Droplet Implementation

Enabling Description: The single-volume assay is partitioned into millions of picoliter-sized droplets using a droplet microfluidic generator. Each droplet contains the necessary reagents for the multiplexed assay. After thermal cycling, each droplet is individually read by a micro-flow cytometer. The instrument measures the cumulative fluorescence intensity in multiple color channels for each droplet. The results are binarized: droplets with a signal above a threshold are "positive." The encoding scheme determines the unique color/intensity signature of a positive droplet. For example, in a digital assay for 3 analytes, some droplets will be positive for Analyte 1 (Code A), some for Analyte 2 (Code B), and some for Analytes 1 & 2 (cumulative signal of Code A + Code B). The system counts the number of droplets corresponding to each unique cumulative signal, allowing for absolute quantification of analyte combinations.

Mermaid Diagram:

flowchart TD
    A[Reagent Mix] --> B(Droplet Generator);
    B --> C{Droplets in Oil};
    C --> D(PCR Thermocycling);
    D --> E(Droplet Reader);
    E -- Read Fluorescence per Droplet --> F[Data Plot];
    F -- Cluster Identification --> G{Counting Droplets per Cluster};
    G -- Apply Decoding Matrix to Cluster Signals --> H[Absolute Analyte Quantification];

3. Cross-Domain Application Derivatives

3.1. Aerospace: Embedded Composite Material Health Monitoring

Enabling Description: Microcapsules containing different combinations of fluorescent reporters are embedded within the epoxy matrix of a carbon fiber composite material during manufacturing. Each type of microcapsule is engineered with a shell that fractures at a specific mechanical strain threshold. The "analyte" is the experience of a specific strain level. For example, Analyte 1 (1000 microstrain) is encoded as "Blue", Analyte 2 (2000 microstrain) is "Green", and Analyte 3 (3000 microstrain) is "Blue + Green". An embedded fiber optic network or an external scanner excites the material and reads the cumulative fluorescence spectrum. A reading of "Blue only" means the material has experienced at least 1000 microstrain but less than 2000. A cumulative signal of "2x Blue + 1x Green" indicates that Analyte 1 and Analyte 3 are present (fractured), revealing a complex strain history.

Mermaid Diagram:

stateDiagram-v2
    [*] --> Intact: Material is pristine
    Intact --> Strain_Level_1: Strain > 1000με
    Strain_Level_1: Emits Blue (1,0,0)
    Strain_Level_1 --> Strain_Level_2: Strain > 2000με
    Strain_Level_2: Emits Blue + Green (1,1,0)
    Strain_Level_2 --> Strain_Level_3: Strain > 3000με
    Strain_Level_3: Emits 2*Blue + Green (2,1,0)

3.2. AgTech: In-Situ Soil Nutrient Sensing

Enabling Description: This derivative uses engineered bacteria as biosensors. Different strains of bacteria are created, each designed to respond to a specific soil analyte (e.g., nitrate, phosphate, potassium). Upon detecting its target analyte, each bacterial strain synthesizes a unique combination of fluorescent proteins (e.g., GFP, YFP, RFP) according to a pre-defined non-degenerate code. A soil probe contains a mixture of these lyophilized bacterial strains in a hydrogel. When inserted into moist soil, the bacteria are rehydrated and begin sensing. After a set incubation period, an optical reader in the probe measures the cumulative fluorescence. The resulting signal (e.g., 2 units Yellow, 1 unit Red) is decoded to provide a quantitative profile of soil nutrients.

Mermaid Diagram:

graph TD
    subgraph Soil Environment
        A(Nitrate)
        B(Phosphate)
    end
    subgraph Biosensor Probe
        C[Strain 1: Senses Nitrate] -- Encoded Response --> D(Produces 1x GFP)
        E[Strain 2: Senses Phosphate] -- Encoded Response --> F(Produces 1x GFP + 1x RFP)
    end
    subgraph Readout
        G{Cumulative Signal} -- (2x GFP, 1x RFP) --> H(Decoding Logic)
        H -- Decodes to --> I(Nitrate: Present, Phosphate: Present)
    end
    A --> C
    B --> E
    D --> G
    F --> G

3.3. Consumer Electronics: Liquid Damage Indication

Enabling Description: A multi-layered indicator strip is placed inside an electronic device. Each layer contains dehydrated reagents corresponding to a specific type of liquid (e.g., freshwater, saltwater, coffee, alcohol). The reagents are analyte-specific probes that generate an encoded fluorescent signal. For example, freshwater (low ion) might trigger a "Blue" signal. Saltwater (high chloride) triggers a "Green" signal. Coffee (presence of specific organic acids) triggers "Blue + Green". If the device is exposed to liquid, the strip wicks the fluid, activating the corresponding layer(s). A technician can then illuminate the strip with a UV light and measure the cumulative signal with a simple detector, providing a detailed report on the nature of the liquid damage for warranty claim validation.

Mermaid Diagram:

flowchart LR
    subgraph Liquid Exposure
        A{Liquid Ingress}
    end
    subgraph Indicator Strip
        B(Layer 1: Freshwater) -- Triggers --> C(Code: 1,0,0)
        D(Layer 2: Saltwater) -- Triggers --> E(Code: 0,1,0)
        F(Layer 3: Coffee) -- Triggers --> G(Code: 1,1,0)
    end
    subgraph Analysis
        H(Cumulative Signal) -- Decode --> I(Damage Type Report)
    end
    A --> B; A --> D; A --> F
    C --> H; E --> H; G --> H

4. Integration with Emerging Technology Derivatives

4.1. AI-Powered Adaptive Decoding

Enabling Description: The decoding process is handled by a trained neural network (NN) instead of a static lookup matrix. The NN is trained on thousands of experimental runs with known analyte combinations, learning to map complex raw spectral data to analyte presence. This allows the system to compensate for instrument drift, non-linear signal accumulation at high concentrations, and spectral bleed-through. Furthermore, the AI can operate in an active learning mode. If it encounters an ambiguous or "illegitimate" signal, it flags the result for confirmation by a secondary method and uses the new, validated data point to retrain and improve itself, making the assay more robust over time.

Mermaid Diagram:

sequenceDiagram
    participant Instrument
    participant AI_Decoder as AI Decoder (NN)
    participant Database
    participant User

    Instrument->>AI_Decoder: Submit Raw Spectral Data
    AI_Decoder->>AI_Decoder: Process Data & Predict Analytes
    alt Confidence > 95%
        AI_Decoder->>User: Report Results
    else Ambiguous Signal
        AI_Decoder->>User: Flag for Confirmation
        User->>Database: Input Validated Result
        Database->>AI_Decoder: Trigger Model Retraining
    end

4.2. IoT Network for Pathogen Outbreak Monitoring

Enabling Description: The multiplexed assay is embedded into a low-cost, disposable cartridge used in a network of IoT-enabled point-of-care devices. These devices are deployed in clinics, airports, and public transit hubs. Each device runs a panel for common respiratory viruses (e.g., Influenza A/B, RSV, SARS-CoV-2 variants). After a test, the device reads the cumulative optical signal, decodes the result locally, and transmits the anonymized, geolocated result (e.g., "SARS-CoV-2 Omicron BA.5 detected at Lat/Lon") to a central cloud server via a cellular or Wi-Fi connection. A real-time dashboard visualizes the data, allowing public health officials to monitor and predict viral outbreaks as they emerge.

Mermaid Diagram:

graph TD
    A[Patient Sample] --> B{IoT Diagnostic Device};
    B -- Runs Multiplex Assay --> C[Decoded Result];
    C -- (GPS + Result + Timestamp) --> D((Cloud Platform));
    D --> E[Real-time Outbreak Map];
    D --> F[Predictive Analytics Engine];
    E --> G(Public Health Officials);
    F --> G;

5. Inverse or Failure Mode Derivatives

5.1. Fail-Safe Critical Threat Assay

Enabling Description: In this derivative for biodefense, the system is designed to provide an unambiguous signal for a single, high-priority threat while intentionally obscuring other data. The probe for the critical analyte (e.g., Anthrax) is labeled with a FRET donor (Fluorophore D) and a quencher. All other probes for lower-priority analytes are labeled with a FRET acceptor (Fluorophore A) that has a large spectral overlap with the donor. In the presence of the critical analyte, the donor is unquenched and fluoresces strongly. This fluorescence excites, via FRET, any acceptor fluorophores that have been released due to the presence of other analytes. The energy transfer is designed to be >95% efficient, effectively silencing the signals from the lower-priority analytes and causing only the acceptor to emit light. The device reports a simple "CRITICAL THREAT" if any acceptor light is seen, ignoring all other signals.

Mermaid Diagram:

stateDiagram-v2
    state "No Threat" as S1
    state "Low Threat" as S2
    state "Critical Threat" as S3
    state "Mixed Threat" as S4

    [*] --> S1
    S1 --> S2: Low-priority analyte present
    S2: Emits Acceptor Signal (e.g. Red)
    S1 --> S3: Critical analyte present
    S3: Emits Donor Signal (e.g. Green)
    S2 --> S4: Critical analyte added
    S3 --> S4: Low-priority analyte added
    S4: Donor signal is quenched by FRET to Acceptor. Only Acceptor emits.
    note right of S4 : System reports only "CRITICAL"

5.2. Error-Indicating Code Design

Enabling Description: The coding scheme is designed with intentional gaps. Specific "illegitimate" cumulative signals, which cannot be formed by the addition of valid analyte codes, are assigned to known failure modes. For example, a set of pan-bacterial primers is included with a probe that generates a unique signal (e.g., intensity 8 in the Violet channel). This code (8,0,0,0) is not assigned to any specific analyte. If widespread primer-dimer or non-specific amplification occurs, this probe will likely be cleaved, generating the error-specific signal. The decoder, upon seeing this signal, reports "Error: Non-Specific Amplification" instead of an incorrect analyte result, providing valuable diagnostic feedback.

Mermaid Diagram:

flowchart TD
    A[Cumulative Signal Measured] --> B{Decoder};
    B -- Is signal in valid code space? --> C[Yes];
    B -- No --> D{Is signal an error code?};
    C --> E[Report Analytes Present];
    D -- Yes --> F[Report Specific Error Type];
    D -- No --> G[Report 'Indeterminate Result'];

6. Combination Prior Art with Open-Source Standards

6.1. Integration with Micro-Manager for Automated Control

Enabling Description: This disclosure describes a system where the multiplexed assay is performed on an instrument controlled by the open-source Micro-Manager software platform. A custom device adapter is written in C++ to control a thermocycler block via its serial command interface. A Micro-Manager script, written in BeanShell or Python, orchestrates the entire workflow: (1) prompts the user for sample information, (2) executes the pre-programmed thermal cycling profile using the custom adapter, (3) at the final cycle, switches the microscope's filter wheel to each of the required emission channels (e.g., DAPI, FITC, TRITC, Cy5), (4) acquires an image for each channel using a connected scientific camera, (5) processes the images using the integrated OpenCV library to calculate the mean intensity for each channel, forming the cumulative signal vector, and (6) decodes the vector using a NumPy-based implementation of the decoding matrix to display the final results in the Micro-Manager log window. This integrates the patented method into a fully open and reproducible hardware control environment.

6.2. Integration with GMOD for Standardized Data Reporting

Enabling Description: This method standardizes the output of the multiplexed assay for interoperability with open-source bioinformatics databases. After decoding, the results are formatted into an XML file conforming to the CHADO schema, a standard relational database schema from the Generic Model Organism Database (GMOD) project. The analysis table stores metadata about the assay run (instrument, date, operator). The analysisfeature table links this run to the analytes tested. For each analyte, a featureprop entry is created with a type defined by a controlled vocabulary term (e.g., "presence/absence") and a value of "present" or "absent". Another featureprop entry stores the raw quantitative cumulative signal vector (e.g., "") as a text property, ensuring full data provenance. This structured output can be directly loaded by any CHADO-compatible LIMS or analysis tool.

6.3. Integration with Variant Call Format (VCF) for Genotyping

Enabling Description: The multiplexed assay is used for targeted SNP genotyping. Probes are designed to be specific to different alleles (e.g., wild-type vs. mutant) at several SNP loci. The resulting signal combination is a unique "barcode" for the subject's genotype across those loci. This disclosure describes a software tool that converts this optical barcode into the standard VCF (Variant Call Format) 4.2. The output VCF file has one line per SNP. The CHROM, POS, ID, REF, and ALT columns are standard. The GT (Genotype) sub-field in the FORMAT column is populated based on the decoding (e.g., 0/0, 0/1, 1/1). A custom FORMAT sub-field, OB (Optical Barcode), is defined in the VCF header and is used to store the raw cumulative signal vector (e.g., GT:OB 0/1:1,5,3,2), creating a direct, auditable link between the standard genotype call and the underlying proprietary assay signal.