Field of invention:
The present invention relates to point-of-care (POC) diagnostic systems, and more particularly to
a smart biosensor platfom1 integrating electrochemical and/or optical transduction with
embedded electronics and a machine-learning (ML) inference engine for rapid, quantitative
detection of biological and chemical analytes in minimally processed samples.
Background of the invention:
Early, accurate, and decentralized diagnosis is essenrial lor ellective disease management and
outbreak control. Conventional laboratory assays such as ELISA and PCR oiTer high
sensitivity but require skilled personnel, sophisticated instrumentation, and centralized
facilities. resulting in long turnaround times. Existing POC tests (e.g., lateral now assays) arc
simple but often qualitative or semi-quantitative with limited multiplexing and poor
calibration drift handling. Moreover, sensor responses are affected by temperature,
hematocrit, intederents, and user technique. There is a need for a portable, robust, and
connected roc system that enhances analytical performance by learning from raw signals,
compensating for confounders, and providing traceable, reproducible results with secure data
handling and decision support.
Objects of the Invention:
o To provide a handheld POC biosensor capable of quantitative detection of one or more
analytes from small-volume samples.
• To integrate ML models that transform raw transducer signals into calibrated
concentrations while compensating for confounders and sensor aging.
• To enable modular, disposable sensor ca11ridges with self~calibration and on-board
identifiers.
• To provide real-time quality control, error detection, and uncertainty reporting.
• To support otlline/online operation with secure data stnragc, encryption. ancl
interoperable reporting.

Summary of the Invention:
The invention discloses a smart biosensor system compnstng: (i) a disposable micronuidic
cartridge bearing one or more biorecognition elements on electrochemical and/or optical
transducers; (ii) a reader device with analog front-end (AFE), temperature sensmg,
microcontrollcr (MCU), and wireless connectivity; and (iii) a machine-learning pipeline
executed on-device and/or on a paired mobile application or secure cloud. The cartridge includes
a sample inlet, capillary flow channels, a reaction zone, and a calibration zone with internal
standards. The reader acquires raw current/voltage/impedance or optical intensity/time-series,
performs preprocessing, and forwards features to an ML regressor/classifier trained on annotated
datasets to infer analytt: concentration and diagnostic status. The system e.xecutes quality checks
(hemolysis/air bubble detection, cartridge validity, temperature range) and returns a result wirh
confidence: bounds, trt:nd analytics, and optional clinical decision support. The solution is
suitable for glucose, cardiac biomarkers (e.g .. troponin 1/T), inHammatory markers (e.g., CRP),
infectious-disease antigens/nucleic acids (isothermal amplification readouts), and environmental
or food toxins.

Claims
We Claim,
Claim I: A point-ol~carc biosensor system cumpnstng: a disposable rnicrutluidic cartridge
h~ving ai least one transducer functionalizcd with a biorccognition element; a portable reader
including an analog or optical front-end, a processor, and a communication interface; and a
machine-karning inference engine configured to receive raw sensor data acquired from the
transducer, preprocess the raw sensor data, and output an analyte concentration or classification
with an associated confidence value.
Claim 2: The system of claim I, wherein the cartridge comprises a machine-readable identifier
storing lot information and calibration parameters used by the reader to authenticate the cartridge
and apply lot-specific compensation.
Claim 3:. Tlw system of claim I or 2, wherein th~ reader executes dectrochemical techniques
selected ti·om chronoamperometry, voltammetry, and impedance spectroscopy, and the inference
engine uses features derived therefrom.
Claim 4: The system of any preceding claim, wherein the cartridge includes an internal
calibration zone providing a reference response measured during each test to conect for drift and
environmental variation.
Claim 5: The system or any preceding claim, wherein the machine-learning inference engine
compnses a gradient-boosted decision tree, a convolutional neural network, or a
transformer-based model configured for embedded execution and traim;d on datase.ts including
interfen;nts and temperature variations.
Ch1im 6: The system of any preceding claim, wherein the reader or a paired mobile device
performs anomaly detection to identify out-of-distribution inputs and to flag results with low
confidence.
Claim 7: The system of any preceding claim, wherein the cartridge compnses multiple
transducers enabling multiplex detection and the inference engine perfonns multi-output
regression or classification.
Claim 8: The system of any preceding claim, wherein the reader encrypts and transmits test
results and metadHta to a mobile application or cloud service compliant with healrhcarc data
standards.
Claim 9: A method for point-of-care analyte detection, comprising: inserting a disposable
cartridge into a portable reader; applying a sample to the cartridge; acquiring raw sensor data;
preprocessing the raw sensor data; inputting features and/or raw data into a trained
machine-learning model; and presenting an analyte result with an uncertainty metric and
quality-control status.
Claim 10: The method of claim 9, further comprising updating the machine-learning model on
the reader or paired device using a cryptographically signed model package and recording model
version information with each test result.