System and methods for analyte sensor system calibration

Abstract

Certain aspects of the present disclosure relate to methods and systems for calibrating a continuous analyte sensor system while considering the effects or influence of manufacturing conditions experienced by a sensor. In certain aspects, the system determines a sensitivity value for the analyte sensor system and determines, based on the sensitivity value and a manufacturing parameter representing a condition experienced by the analyte sensor system during manufacturing of the analyte sensor system, a calibration parameter for the analyte sensor system. The system also configures the analyte sensor system to convert an electrical current generated by the analyte sensor system into an analyte concentration using the calibration parameter.

Description

Docket No.: 0922-PCT01SYSTEM AND METHODS FOR ANALYTE SENSOR SYSTEM CALIBRATIONCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to and the benefit of U.S. Provisional Patent Application No. 63 / 690,269, filed September 3, 2024, which is hereby incorporated by reference herein in its entirety.BACKGROUND

[0002] Diabetes mellitus is a metabolic condition relating to the production or use of insulin by the body. Insulin is a hormone that allows the body to use glucose for energy, or store glucose as fat.

[0003] When a person eats a meal that contains carbohydrates, the digestive system absorbs nutrients, ultimately depositing glucose in the person's blood. Blood glucose can be used for energy or stored as fat. The body normally maintains blood glucose levels in a range that provides sufficient energy to support bodily functions and avoids problems that can arise when glucose levels are too high, or too low. Regulation of blood glucose levels depends on the production and use of insulin, which regulates the movement of blood glucose into cells.

[0004] When the body does not produce enough insulin, or when the body is unable to effectively use insulin that is present, blood sugar levels can elevate beyond normal ranges. The state of having a higher than normal blood sugar level is called “hyperglycemia.” Chronic hyperglycemia can lead to a number of health problems, such as cardiovascular disease, cataract and other eye problems, nerve damage (neuropathy), skin ulcers, and kidney damage. Hyperglycemia can also lead to acute problems, such as diabetic ketoacidosis — a state in which the body becomes excessively acidic due to the production of excess ketones, or body acids. The state of having lower than normal blood glucose levels is called “hypoglycemia.” Severe hypoglycemia can lead to damage of the heart muscle, neurocognitive dysfunction, and in certain cases, acute crises that can result in seizures or even death.

[0005] A patient living with diabetes can receive insulin to manage blood glucose levels. Insulin can be received, for example, through a manual injection with a needle. Wearable insulin pumps are also available. Diet and exercise also affect blood glucose levels.

[0006] Diabetes conditions are sometimes referred to as “Type 1” and “Type 2”. A Type 1 diabetes patient is typically able to use insulin when it is present, but the body is unable toDocket No.: 0922-PCT01 produce sufficient amounts of insulin, because of a problem with the insulin-producing beta cells of the pancreas. A Type 2 diabetes patient may produce some insulin, but the patient has become “insulin resistant” due to a reduced sensitivity to insulin. The result is that even though insulin is present in the body, the insulin is not sufficiently used by the patient's body to effectively regulate blood sugar levels.

[0007] Patients with diabetes can benefit from real-time diabetes management guidance, as determined based on a physiological state of the patient, in order to stay within a target glucose range and avoid physical complications. In certain cases, the physiological state of the patient is determined using monitoring systems that measure glucose levels, which inform the identification and / or prediction of adverse glycemic events, such as hyperglycemia and hypoglycemia, and the type of guidance provided to the patient.

[0008] For example, such monitoring systems may utilize a continuous glucose monitor (CGM) to measure a patient’s glucose levels over time. The measured glucose levels may then be processed by the monitoring system to identify and / or predict adverse glycemic events, and / or to provide guidance to the patient for treatment and or actions to abate or prevent the occurrence of such adverse glycemic events. For example, trends, statistics, or other metrics may be derived from the glucose levels and used to identify and / or predict adverse glycemic events. Or, in certain cases, the glucose levels themselves may be used to identify and / or predict adverse glycemic events.

[0009] Even with the systems described above, however, the management of diabetes presents many challenges for patients, clinicians, and caregivers, as a confluence of various factors can impact a patient's glucose levels, thus affecting the accuracy of glycemic event prediction and the guidance provided by diagnostics systems.BRIEF DESCRIPTION OF THE DRAWINGS

[0010] So that the manner in which the above recited features of the present disclosure can be understood in detail, a more particular description of the disclosure, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only exemplary embodiments and are therefore not to be considered limiting of its scope, may admit to other equally effective embodiments.Docket No.: 0922-PCT01

[0011] Figure 1A is a diagram conceptually illustrating an example continuous analyte monitoring system including example continuous analyte sensors with sensor electronics, in accordance with certain aspects of the present disclosure.

[0012] Figure IB illustrates components of the continuous analyte monitoring system of Figure 1A, in accordance with certain aspects of the present disclosure.

[0013] Figure 2 illustrates an example system for calibrating the continuous analyte monitoring system of Figure 1A, in accordance with certain aspects of the present disclosure.

[0014] Figure 3 illustrates an example operation for calibrating the continuous analyte monitoring system of Figure 1A, in accordance with certain aspects of the present disclosure.

[0015] Figure 4 illustrates an example operation for calibrating the continuous analyte monitoring system of Figure 1A, in accordance with certain aspects of the present disclosure.

[0016] Figure 5 is a flowchart of an example method for calibrating the continuous analyte monitoring system of Figure 1A, in accordance with certain aspects of the present disclosure.

[0017] Figure 6A illustrates an example operation for developing a calibration model for the continuous analyte monitoring system of Figure 1A, in accordance with certain aspects of the present disclosure.

[0018] Figure 6B illustrates an example operation for developing a calibration model for the continuous analyte monitoring system of Figure 1A, in accordance with certain aspects of the present disclosure.

[0019] Figure 7 is a flowchart of an example method for developing a calibration model for the continuous analyte monitoring system of Figure 1 A, in accordance with certain aspects of the present disclosure.

[0020] Figure 8 illustrates an example computer system, in accordance with certain aspects of the present disclosure.

[0021] To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures. It is contemplated that elements and features of one embodiment may be beneficially incorporated in other embodiments without further recitation.Docket No.: 0922-PCT01DETAILED DESCRIPTION

[0022] In a continuous analyte sensor system, a transcutaneous continuous analyte sensor that is inserted into the interstitial fluid is used to monitor a patient’s analyte levels, thereby, providing analyte concentration measurements reflective of the physiological state of the patient. An analyte may be understood as any substance of interest that is to be measured or is being measured. Examples of such analytes include glucose, ketones, lactate, insulin, electrolytes, creatinine, as well as a number of other biomarkers including proteins, metabolites, and nucleic acids. The continuous analyte sensor may interact with the desired analyte(s), e.g., through aptamers (single- stranded DNA or RNA molecules that bind to a specific analyte). The sensor produces an electric signal (e.g., an electric current or voltage) that a sensor electronics module converts into an analyte concentration.

[0023] The sensor electronics module of the analyte sensor system uses sensitivity values that are determined through a calibration process to convert the electric signal into the analyte concentration. Existing calibration processes involve dipping the sensor into analyte solutions with known concentrations and measuring the electric signal produced by the sensor. The sensitivity values are then determined using this information. The sensitivity values may be programmed into the sensor electronics module or stored separately in another manner for later use. When the sensor is subsequently inserted into a patient, the sensor electronics module uses the sensitivity values to convert the electric signal produced by the sensor into analyte concentration levels.

[0024] Calibration processes for analyte sensor systems, however, may still result in errors. For example, manufacturing conditions experienced by the sensor may affect the performance of the sensor over time. However, certain calibration processes do not account for these manufacturing conditions — monitoring a large quantity of data associated with the manufacturing conditions can be difficult and result in increased computing requirements, and processing a large quantity of data associated with the manufacturing conditions when determining the calibration information used in analyte concentration (e.g., estimated values of the analyte concentrations) determinations can generally be slow and inefficient, while requiring significant computing resources. Because certain calibration processes do not account for these manufacturing conditions, and may be inefficient in processing a large quantity of data associated with accounting for those manufacturing conditions, the sensitivity values produced using such calibration processes may result in analyte concentrations that areDocket No.: 0922-PCT01 erroneous or inaccurate. Additionally, it is challenging to determine which manufacturing conditions should be considered during calibration and how these manufacturing conditions should be incorporated into the calibration process.

[0025] The present disclosure provides technical solutions that solve the technical problems described above. For example, the present disclosure describes a computer system that calibrates a continuous analyte sensor system while considering the effects or influence of manufacturing conditions experienced by a sensor. For example, the described system may use artificial intelligence, which allows the computer system to consider the effects or influence of manufacturing conditions experienced by a sensor during calibration. Generally, the computer system described herein provides a machine learning model (e.g., a regression model or a neural network, etc.) that considers manufacturing parameters indicating the manufacturing conditions experienced by an analyte sensor, such as during manufacture of the analyte sensor. The computer system also provides the model with the sensitivity values for the analyte sensor. The model considers the manufacturing parameters and the sensitivity values to determine calibration parameters for the analyte sensor. These calibration parameters are then stored (e.g., in the sensor electronics module or a database), and the sensor electronics module subsequently uses the calibration parameters to determine analyte concentration values (e.g., estimated values of analyte concentration levels) based at least in part on an electric signal from the analyte sensor. Thus, the system according to the present disclosure produces analyte concentration values (and calibration parameters, sensitivity values, etc.) with increased accuracy. The system according to the present disclosure also determines the analyte concentration values (and calibration parameters, sensitivity values, etc.) with increased computing efficiency and speed. The system also reduces the required computing resources associated with monitoring and / or considering a large quantity of data associated with accounting for manufacturing conditions.

[0026] Additionally, the computer system may train the model using training data that includes manufacturing parameters and sensitivity values for many analyte sensors. Through training, the model may learn which manufacturing conditions affect sensor performance and by how much these manufacturing conditions affect sensor performance. The model may develop weights that indicate the impact of these manufacturing conditions on sensor performance. The model may then apply these weights to determine calibration parameters for different sensors (e.g., analyte sensors associated with different types of analytes, differentDocket No.: 0922-PCT01 analyte sensors associated with the same type of analyte, different analyte sensors in the same manufacturing lot, etc.).

[0027] In certain embodiments, the computer system provides several technical advantages. For example, using the model to consider manufacturing conditions experienced by an analyte sensor during calibration may improve the accuracy and reliability of the analyte concentrations measured by the analyte sensor system relative to existing analyte sensor systems. As another example, the model may more accurately determine which manufacturing conditions to consider when calibrating an analyte sensor. The model may determine which manufacturing conditions are less relevant to sensor performance, which reduces the data set used by the computer system to calibrate the analyte sensor. Further, as described herein, the system determines the analyte concentration values (and calibration parameters, sensitivity values, etc.) with increased computing efficiency and speed. The system also reduces the required computing resources associated with monitoring and / or considering a large quantity of data associated with accounting for manufacturing conditions. As a result, the model and described processes may reduce computing resources used to calibrate the analyte sensor and generate analyte concentration values.Continuous Analyte Monitoring System

[0028] Figure 1A illustrates an example continuous analyte monitoring system 100. As seen in Figure 1A, the system 100 includes an analyte sensor system 110 (e.g., positioned on a user 102) and display devices 170. Generally, the analyte sensor system 110 measures levels of analytes (e.g., substances of interest) in the user 102 and communicates those measured levels to the display devices 170. In this manner, the continuous analyte monitoring system 100 assists the user 102 with decision support for managing and / or treating a disease, e.g., diabetes, kidney disease, liver disease, or other types of diseases.

[0029] The analyte sensor system 110 includes one or more continuous analyte sensors 120 (individually referred to herein as continuous analyte sensor 120 or analyte sensor 120 and collectively referred to herein as continuous analyte sensors 120 or analyte sensors 120) and a sensor electronics module 130. The sensor electronics module 130 may be in wired or wireless communication (e.g., directly or indirectly) with one or more of the display devices 170. While the analyte sensor system 110 may generally be described herein as a glucose sensor system including a continuous glucose sensor or a glucose sensor, etc., the analyte sensor system 110Docket No.: 0922-PCT01 may be a lactate sensor system, ketone sensor system, or other analyte sensor system for measuring the analytes described herein.

[0030] A continuous analyte sensor 120 may include one or more sensors for measuring analyte concentration levels (e.g., glucose concentration levels). A continuous analyte sensor 120 may include a multi-analyte sensor that continuously measures two or more analytes (e.g., glucose, lactate, potassium, ketone, etc.), and / or a single analyte sensor that continuously measures a single analyte. The continuous analyte sensor 120 may be a non-invasive device, a subcutaneous device, a transcutaneous device, a transdermal device, or an intravascular device. The continuous analyte sensor 120 may continuously measure analyte levels of a user using one or more techniques, such as enzymatic techniques, chemical techniques, physical techniques, electrochemical techniques, spectrophotometric techniques, polarimetric techniques, calorimetric techniques, iontophoretic techniques, radiometric techniques, immunochemical techniques, and the like. The continuous analyte sensor 120 may provide a signal stream indicative of the concentration of one or more analytes in the user over time.

[0031] An analyte may be a substance or chemical constituent in a biological fluid (for example, blood, interstitial fluid, cerebral spinal fluid, lymph fluid, sweat, or urine) that can be analyzed. Analytes can include naturally occurring substances, artificial substances, metabolites, or reaction products. Analytes for measurement by the devices and methods may include, but may not be limited to, glucose, acarboxyprothrombin; acylcamitine; adenine phosphoribosyl transferase; adenosine deaminase; albumin; alpha-fetoprotein; amino acid profiles (arginine (Krebs cycle), histidine / urocanic acid, homocysteine, phenylalanine / tyrosine, tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers; arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactive protein; carnitine; camosinase; CD4; ceruloplasmin; chenodeoxycholic acid; chloroquine; cholesterol; cholinesterase; conjugated 1-P hydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MM isoenzyme; cyclosporin A; d-penicillamine; de-ethylchloroquine; dehydroepiandrosterone sulfate; DNA (acetylator polymorphism, alcohol dehydrogenase, alpha 1 -antitrypsin, cystic fibrosis, Duchenne / Becker muscular dystrophy, glucose-6- phosphate dehydrogenase, hemoglobin A, hemoglobin S, hemoglobin C, hemoglobin D, hemoglobin E, hemoglobin F, D-Punjab, beta-thalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1, Leber hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax, sexual differentiation, 21-deoxycortisol); desbutylhalofantrine; dihydropteridine reductase;Docket No.: 0922-PCT01 diptheria / tetanus antitoxin; erythrocyte arginase; erythrocyte protoporphyrin; esterase D; fatty acids / acylglycines; free P-human chorionic gonadotropin; free erythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine (FT3); fumarylacetoacetase; galactose / gal-1- phosphate; galactose- 1 -phosphate uridyltransferase; gentamicin; glucose-6-phosphate dehydrogenase; glutathione; glutathione perioxidase; glycocholic acid; glycerol; glycosylated hemoglobin; halofantrine; hemoglobin variants; hexosaminidase A; human erythrocyte carbonic anhydrase I; 17-alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase; immunoreactive trypsin; lactate; lead; lipoproteins ((a), B / A-l, P); lysozyme; mefloquine; netilmicin; phenobarbitone; phenytoin; phytanic / pristanic acid; progesterone; prolactin; prolidase; purine nucleoside phosphorylase; potassium, quinine; reverse triiodothyronine (rT3); selenium; serum pancreatic lipase; sissomicin; somatomedin C; specific antibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody, arbovirus, Aujeszky's disease virus, dengue virus, Dracunculus medinensis, Echinococcus granulosus, Entamoeba histolytica, enterovirus, Giardia duodenalisa, Helicobacter pylori, hepatitis B virus, herpes virus, HIV-1, IgE (atopic disease), influenza virus, Leishmania donovani, leptospira, measles / mumps / rubella, Mycobacterium leprae, Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus, parainfluenza virus, Plasmodium falciparum, poliovirus, Pseudomonas aeruginosa, respiratory syncytial virus, rickettsia (scrub typhus), Schistosoma mansoni, Toxoplasma gondii, Trepenoma pallidium, Trypanosoma cruzi / rangeli, vesicular stomatis virus, Wuchereria bancrofti, yellow fever virus); specific antigens (hepatitis B virus, HIV-1); succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine (T4); thyroxine- binding globulin; trace elements; transferrin; UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A; white blood cells; and zinc protoporphyrin.

[0032] Salts, sugar, protein, fat, vitamins, and hormones naturally occurring in blood or interstitial fluids can also constitute analytes in certain implementations. The analyte can be naturally present in the biological fluid, for example, a metabolic product, a hormone, an antigen, an antibody, and the like. Alternatively, the analyte can be introduced into the body or exogenous, for example, a contrast agent for imaging, a radioisotope, a chemical agent, a fluorocarbon-based synthetic blood, or a drug or pharmaceutical composition, including but not limited to insulin; glucagon, ethanol; cannabis (marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide, amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine (crack cocaine); stimulants (amphetamines, methamphetamines, Ritalin, Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine); depressants (barbiturates, methaqualone,Docket No.: 0922-PCT01 tranquilizers such as Valium, Librium, Miltown, Serax, Equanil, Tranxene); hallucinogens (phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics (heroin, codeine, morphine, opium, meperidine, Percocet, Percodan, Tussionex, Fentanyl, Darvon, Talwin, Lomotil); designer drugs (analogs of fentanyl, meperidine, amphetamines, methamphetamines, and phencyclidine, for example, Ecstasy); anabolic steroids; and nicotine. The metabolic products of drugs and pharmaceutical compositions are also contemplated analytes. Analytes such as neurochemicals and other chemicals generated within the body can also be analyzed, such as, for example, ascorbic acid, uric acid, dopamine, noradrenaline, 3 -methoxy tyramine (3MT), 3,4-Dihydroxyphenylacetic acid (DOPAC), Homovanillic acid (HVA), 5- Hydroxytryptamine (5HT), and 5-Hydroxyindoleacetic acid (FHIAA), and intermediaries in the Citric Acid Cycle.

[0033] The sensor electronics module 130 includes electronic circuitry for measuring, processing, and adjusting signal streams from the analyte sensors 120 (which may be referred to as sensor data). The sensor electronics module 130 can be physically connected to the analyte sensors 120 and can be integral with (non-releasably attached to) or releasably attachable to the analyte sensors 120. The sensor electronics module 130 may include hardware, firmware, or software that enable measurement of levels of analytes via the analyte sensors 120. For example, the sensor electronics module 130 can include a potentiostat, a power source for providing power to the sensor, other components useful for signal processing and data storage, and a telemetry module for transmitting data from the sensor electronics module to, e.g., one or more display devices. Electronics can be affixed to a printed circuit board (PCB), or the like, and can take a variety of forms. For example, the electronics can take the form of an integrated circuit (IC), such as an Application-Specific Integrated Circuit (ASIC), a microcontroller, or a processor.

[0034] The display devices 170 may display sensor data, including measured levels of analytes or adjusted levels of analytes, which may be transmitted by the sensor electronics module 130. The sensor electronics module 130 may transmit raw sensor data that is converted to displayable sensor data via one or more of the display devices 170. The sensor electronics module 130 may convert raw sensor data to display able sensor data and transmit the display able sensor data to one or more of the display devices 170. Each of the display devices 170 may include a display such as a touchscreen display 171 for displaying sensor data to a user or for receiving inputs from the user. For example, a graphical user interface (GUI) may be presentedDocket No.: 0922-PCT01 to the user for such purposes. The display devices 170 may include other types of user interfaces such as a voice user interface instead of, or in addition to, a touchscreen display for communicating sensor data to the user of the display device or for receiving user inputs. The display devices 170 may display or otherwise communicate the sensor data as it is communicated from the sensor electronics module 130 (e.g., in a customized data package that is transmitted to the display devices 170 based on their respective preferences).

[0035] The display device 172 may include a custom display device specially designed for displaying certain types of displayable sensor data for analyte data received from the sensor electronics module 130. The display device 174 may be a smartphone or a mobile phone using a commercially available operating system (OS) and may display a graphical representation of the continuous sensor data (e.g., including current and historic data). The display device 176 may include a tablet, and the display device 178 may include a smart watch. The display devices 170 may include a desktop or laptop computer (not shown).

[0036] Because different display devices 170 provide different user interfaces, content of the data packages (e.g., amount, format, or type of data to be displayed, alarms, and the like) can be customized (e.g., programmed differently by the manufacture or by an end user) for each particular display device 170. Accordingly, different display devices 170 can be in direct wireless communication with the sensor electronics module 130 (e.g., such as an on-skin sensor electronics module 130 that is physically connected to the analyte sensors 120) during a sensor session to enable a plurality of different types or levels of display or functionality for the displayable sensor information.

[0037] The analyte sensor system 110 and the display devices 170 may communicate wireless signals to each other using a variety of wireless communication technologies (e.g., Wi-Fi, Bluetooth, Near Field Communication (NFC), cellular, etc.). A wireless access point (WAP) may be used to communicatively couple the analyte sensor system 110 and the display devices 170 to one another. For example, the WAP may provide Wi-Fi, Bluetooth, or cellular connectivity among these devices. NFC may also be used among the devices.

[0038] FIG. IB illustrates components of the continuous analyte monitoring system 100 of Figures 1A, which includes the analyte sensor system 110 and the display device 170. As seen in Figure IB, the analyte sensor system 110 includes the analyte sensor 120 and the sensor electronics module 130.Docket No.: 0922-PCT01

[0039] Generally, the analyte sensor 120 may include one or more single- analyte sensors, one or more multi-analyte sensors, and / or a combination of single- analyte sensors and multianalyte sensors. Generally, each single-analyte sensor generates an analog electrical signal that is proportional to the concentration level of a particular analyte. Similarly, each multi-analyte sensor generates multiple analog electrical signals, and generally, each analog electrical signal is proportional to the concentration level of a particular analyte. As an illustrative example, analyte sensor 120 may include a single-analyte sensor that measures glucose concentration levels, and another single-analyte sensor that measures lactate concentration levels of the user. As another illustrative example, analyte sensor 120 may include a single-analyte sensor that measures glucose concentration levels, and one or more multi-analyte sensors that measure lactate concentration levels, potassium concentration levels, troponin concentration levels, and / or creatinine concentration levels. As yet another illustrative example, analyte sensor 120 may include a multi-analyte sensor that measures glucose concentration levels, lactate concentration levels, potassium concentration levels, troponin concentration levels, and / or creatinine concentration levels.

[0040] Accordingly, the analyte sensor 120 generates at least one analog electrical signal that is proportional to the concentration level of a particular analyte, and sensor electronics module 130 converts the analog electrical signal (e.g., data representing the analog electrical signal) into an analyte sensor count value, generates measured analyte concentration levels, and transmits the measured analyte concentration level data to a display device 170 via wireless connection 106. For example, the sensor electronics module 130 may sample the analog electrical signal at a particular sampling period (or rate), such as every 1 second (1 Hz), 5 seconds, 10 seconds, 30 seconds, 1 minute, 3 minutes, 5 minutes, etc., and the sensor electronics module 130 may transmit the measured analyte data to display device 170 at a particular transmission period (or rate), which may be the same as (or longer than) the sampling period, such as every 1 minute (0.016 Hz), 5 minutes, 10 minutes, 30 minutes, at the conclusion of the wear period, etc. Depending on the sampling and transmission periods, the measured analyte data transmitted to display device 170 includes at least one measured analyte concentration level having an associated time tag, sequence number, etc.

[0041] The analyte sensor 120 may be a non-invasive device, a subcutaneous device, a transcutaneous device, a transdermal device, a dermal device, an intradermal device, a subdermal device, and / or an intravascular device. In certain embodiments, the analyte sensorDocket No.: 0922-PCT01120 continuously measures analyte concentration levels using one or more measurement techniques, such as enzymatic, immunometric, aptameric, amperometric, voltametric, potentiometric, impedimetric, conductimetric, chemical, physical, electrochemical, spectrophotometric, polarimetric, calorimetric, iontophoretic, radiometric, immunochemical, optical, ion-selective, and / or the like.

[0042] In certain embodiments, the analyte sensor 120 is a single-analyte sensor with a percutaneous wire (or other substrate configuration) that has a proximal portion coupled to the sensor electronics module 130 and a distal portion with several electrodes, such as a measurement electrode and a reference electrode. The measurement (or working) electrode may be coated, covered, treated, embedded, or the like, with one or more chemical molecules that react with a particular analyte, and the reference electrode may provide a reference electrical voltage. The measurement electrode may generate the analog electrical signal, which is conveyed along a conductor that extends from the measurement electrode to the proximal portion of the percutaneous wire that is coupled to the sensor electronics module 130. After the analyte sensor system 110 has been applied to a user, the analyte sensor 120 penetrates the epidermis of the user and the distal portion extends into the dermis and / or subcutaneous tissue. Other configurations of the analyte sensor 120 may also be used, such as a multi-analyte sensor that includes multiple measurement electrodes, each generating an analog electrical signal that represents the concentration levels of a particular analyte. Although described herein and illustrated in the context of being configured as a “wire,” in some embodiments, the analyte sensor 120 includes a component that performs the functionality of a wire in a form factor other than a wire (e.g., planar sensors and so forth).

[0043] In certain embodiments, the analyte sensor 120 incorporates a thermocouple within, or alongside, the percutaneous wire to provide an analog temperature signal to the sensor electronics module 130, which may be used to correct the analog electrical signal or the measured analyte data for temperature. In other embodiments, the thermocouple may be incorporated into the sensor electronics module 130 above an adhesive pad, or, alternatively, the thermocouple may contact the epidermis of user through openings in the adhesive pad.

[0044] The sensor electronics module 130 includes a processor 132, a storage element or memory 134, a wireless transmitter / receiver (transceiver) 136, one or more antennas coupled to the wireless transceiver 136, analog electrical signal processing circuitry, analog-to-digitalDocket No.: 0922-PCT01(A / D) signal processing circuitry, digital signal processing circuitry, a power source for the analyte sensor 120 (such as a potentiostat), etc.

[0045] The processor 132 is any electronic circuitry, including, but not limited to one or a combination of microprocessors, microcontrollers, application specific integrated circuits (ASIC), application specific instruction set processor (ASIP), and / or state machines, that communicatively couples to the memory 134 and controls the operation of the analyte sensor system 110. The processor 132 may be 8-bit, 16-bit, 32-bit, 64-bit or of any other suitable architecture. The processor 132 may include an arithmetic logic unit (ALU) for performing arithmetic and logic operations, processor registers that supply operands to the ALU and store the results of ALU operations, and a control unit that fetches instructions from memory and executes them by directing the coordinated operations of the ALU, registers and other components. The processor 132 may include other hardware that operates software to control and process information. The processor 132 executes software stored on the memory 134 to perform any of the functions described herein. The processor 132 controls the operation and administration of the analyte sensor system 110 by processing information (e.g., information received from the analyte sensor 120, memory 134, and display device 170). The processor 132 is not limited to a single processing device and may encompass multiple processing devices contained in the same device or computer or distributed across multiple devices or computers. The processor 132 is considered to perform a set of functions or actions if the multiple processing devices collectively perform the set of functions or actions, even if different processing devices perform different functions or actions in the set.

[0046] Generally, the processor 132 may sample the analog electrical signal at regular intervals (such as the sampling period) to generate analyte sensor count values based on the analog electrical signals produced by analyte sensor 120, generate measured analyte data from the analyte sensor count values, and generate sensor data packages that include the measured analyte concentration level data. The processor 132 may store the measured analyte concentration level data in the memory 134, and generate the sensor data packages at regular intervals (such as the transmission period) for transmission by the wireless transceiver 136 to the display device 170. The processor 132 may also add additional data to the sensor data packages, such as supplemental sensor information that includes a sensor identifier, a sensor status, temperatures that correspond to the measured analyte data, etc.Docket No.: 0922-PCT01

[0047] The memory 134 may store, either permanently or temporarily, data, operational software, or other information for the processor 132. The memory 134 may include any one or a combination of volatile or non-volatile local or remote devices suitable for storing information. For example, the memory 134 may include random access memory (RAM), read only memory (ROM), magnetic storage devices, optical storage devices, or any other suitable information storage device or a combination of these devices. The software represents any suitable set of instructions, logic, or code embodied in a computer-readable storage medium. For example, the software may be embodied in the memory 134, a disk, a CD, or a flash drive. In particular embodiments, the software may include an application executable by the processor 132 to perform one or more of the functions described herein. The memory 134 is not limited to a single memory and may encompass multiple memories contained in the same device or computer or distributed across multiple devices or computers. The memory 134 is considered to store a set of data, operational software, or information if the multiple memories collectively store the set of data, operational software, or information, even if different memories store different portions of the data, operational software, or information in the set.

[0048] The memory 134 may store one or more analyte sensor system applications 140, modules, instruction sets, etc. for execution by the processor 132, such as instructions to generate measured analyte data from the analyte sensor count values, etc. The memory 134 may also store certain data that support this functionality such as calibration data, initial sensitivity mo 142, final sensitivity rm 144, calibration parameter(s), manufacturing parameter(s), etc. A calibration baseline 147 may also be stored in the memory 134.

[0049] During use, the analyte sensor system 110 determines the measured (e.g., estimated) analyte concentration levels based on the analyte sensor count values that are generated from the analog electrical signals produced by the analyte sensor 120, the initial sensitivity mo 142, the final sensitivity rm 144, the calibration baseline 147, and / or the time ti at which an analyte sensor count value is measured. Generally, the time ti is measured with respect to the wear session. Thus, the time ti represents the time since the beginning of the wear session. The time to is the beginning of the wear session, when the analyte sensor system 110 begins measuring analyte sensor count values. The time tf is the end of the wear session, such as when the analyte sensor system 110 is removed from the user or otherwise becomes deactivated. The time ti is a time during the wear session. The initial sensitivity mo 142 is the sensitivity at time to, while the final sensitivity rm 144 is the sensitivity at time tf.Docket No.: 0922-PCT01

[0050] A sensitivity function M(t) may be used to determine the sensitivity values (e.g., initial sensitivity mo 142, final sensitivity mf 144, baseline, etc.). The sensitivity function M(t) may be expressed in several different ways. In one example, the sensitivity function M(t) is a correction factor (CF) that is not dependent on time t. In this example, the correction factor (CF) is an average of the values of the initial sensitivity mo 142 and the final sensitivity mf 144. The correction factor (CF) may also be a weighted average that emphasizes mf, etc. Alternatively, the correction factor (CF) may be the value of the final sensitivity mf 144. Other types of corrections are also supported.

[0051] In another example, the sensitivity function M(t) provides a linear relationship between sensitivity and time ti. In this example, the linear relationship is determined based on the difference of the values of the initial sensitivity mo 142 and the final sensitivity mf 144 over a predetermined time period (tf - to), or tf when to is zero. The calibration baseline 147 may also be included in the sensitivity function M(t) as an intercept or constant offset.

[0052] In a further example, the sensitivity function M(t) provides an exponential relationship between sensitivity and time ti. In this example, the exponential relationship is determined based on the difference of the values of the initial sensitivity mo 142, the final sensitivity mf 144, and a rate of exponential drift (MR) that may be determined based on testing (e.g., long term drift testing), clinical testing, etc.

[0053] Generally, the sensor electronics module 130 may input the time ti of an analyte sensor count value into the sensitivity function M(t). The function M(t) operates on the time ti using the initial sensitivity mo 142, the final sensitivity mf 144, and the calibration baseline 147 to produce a sensitivity value. The sensor electronics module 130 then applies the sensitivity value to the analyte sensor count value (e.g., multiplies the analyte sensor count value by the sensitivity value) to produce an analyte concentration level. The sensor electronics module 130 then communications the analyte concentration level to the display device 170. As a result, the initial sensitivity mo 142, the final sensitivity mf 144, and / or the sensitivity function M(t) may indicate the slope of a curve used to convert electrical signals from an analyte sensor into analyte concentration levels.

[0054] As discussed previously, the sensor electronics module 130 (or another database) is programmed with the initial sensitivity mo 142, the final sensitivity mf 144, and the calibration baseline 147 during a calibration process for the analyte sensor 120. In some existing systems, the calibration process may involve dipping the analyte sensor 120 into different analyteDocket No.: 0922-PCT01 solutions with known analyte concentrations to produce different sensor signals. The initial sensitivity mo 142, the final sensitivity mf 144, and the calibration baseline 147 that would convert the sensor signals to their respective known analyte concentrations are determined and stored in the sensor electronics module 130. This calibration process, however, may not consider the manufacturing conditions experienced by the analyte sensor 120 that affect the performance of the analyte sensor 120. As a result, using the initial sensitivity mo 142, the final sensitivity mf 144, and the calibration baseline 147 may produce inaccurate or unreliable analyte concentration levels.Calibration Process

[0055] Figure 2 illustrates an example system 200 for calibrating the continuous analyte monitoring system 100 of Figure 1A, in accordance with certain aspects of the present disclosure. As seen in Figure 2, the system 200 includes a computer system 202 that calibrates one or more analyte sensor systems 110 and a database 203. Generally, the computer system 202 uses artificial intelligence (e.g., one or more machine learning models, such as neural networks, etc.) to consider manufacturing conditions experienced by the analyte sensor systems 110 when calibrating the analyte sensor systems 110.

[0056] The computer system 202 may perform the calibration process shortly after the analyte sensor systems 110 have been manufactured. During manufacture, analyte sensors (e.g., the analyte sensors 120 shown in Figure 1A) of the analyte sensor systems 110 may undergo various processes. For example, portions (e.g., wires, planar sensors, etc.) of the analyte sensors may be dipped (e.g., as a batch) in various solutions to form layers of material on the wires during a dipping process. These layers of material may detect one or more analytes when the sensors are positioned on a user. The characteristics of the solutions (e.g., chemical composition, temperature, viscosity, etc.) and the characteristics of the calibration (e.g., dipping, deposition, coating, etc.) process (e.g., the time dipped in the solutions, the temperature in the dipping chamber, the pressure in the dipping chamber, the humidity in the dipping chamber, stir times, the position of the analyte sensors in the batch during the dipping process, viscosity targets or desired viscosity, humidity targets or desired humidity, etc.) may affect the structure of the analyte sensors, which impacts how the analyte sensors should be calibrated. While a dipping process is described herein, other coating or membrane deposition processes may be similarly implemented. In some instances, analyte sensor systems 110 may be in the form of a wire or a planar sensor. Depending on the type of analyte sensor system, theDocket No.: 0922-PCT01 sensor may undergo one or more coating deposition processes to apply a membrane, enzyme, etc. The characteristics of the deposition process may be tracked and used to determine one or more calibration parameters in accordance with examples of this disclosure.

[0057] As another example, a material (e.g., layers of material) may be applied to a substrate during analyte sensor formation. The characteristics of the materials and / or the application process (e.g., the time material is applied, the temperature in the material application chamber, the pressure in the material application chamber, the humidity in the material application chamber, the position of the analyte sensors during the material application process, viscosity targets or desired viscosity of the material, humidity targets or desired humidity, etc.) may similarly affect the structure of the analyte sensors, which impacts how the analyte sensors should be calibrated.

[0058] As another example, the analyte sensors may be exposed to heat in an oven during a curing process. The characteristics of the curing process (e.g., temperature of the oven, the duration of the curing process, humidity in the oven, humidity targets or desired humidity, the position of the analyte sensors in the batch during the curing process, etc.) may affect the structure of the analyte sensors, which impacts how the analyte sensors should be calibrated. As another example, the analyte sensors may undergo a plasma or other treatment to clean the analyte sensors. The characteristics of the plasma treatment (e.g., the type of plasma used, the duration of the plasma treatment, the position of the analyte sensors in the batch during the plasma treatment, etc.) may affect the structure of the analyte sensors, which impacts how the analyte sensors should be calibrated.

[0059] As another example, the analyte sensors may be dipped into analyte solutions with various concentrations during an initial step in the calibration process. The characteristics of this calibration (e.g., dipping, deposition, etc.) process (e.g., the analytes in the solutions, the concentrations of the analytes, the temperature of the solutions, the viscosity of the solutions, the duration of the dipping or deposition process, etc.) may also impact how the analyte sensors should be calibrated.

[0060] Monitoring such a large quantity of data associated with the manufacturing conditions (e.g., dipping process, material application process, curing process, plasma or other treatment process, calibration process, etc.) that may impact how analyte sensors should be calibrated can require increased computing requirements. Further, processing such a large quantity of data when determining the calibration information used in analyte concentrationDocket No.: 0922-PCT01(e.g., estimated values of the analyte concentrations) determinations can generally be slow and inefficient, while requiring significant computing resources.

[0061] The computer system 202, the analyte sensor system 110, etc. according to the present disclosure produces analyte concentration values (and calibration parameters, sensitivity values, etc.) with increased accuracy. The computer system 202, the analyte sensor system 110, etc. according to the present disclosure also determines the analyte concentration values (and calibration parameters, sensitivity values, etc.) with increased computing efficiency and speed. The system also reduces the required computing resources associated with monitoring and / or considering a large quantity of data associated with accounting for manufacturing conditions.

[0062] The computer system 202 uses artificial intelligence to analyze these manufacturing conditions experienced by the analyte sensors when calibrating the analyte sensors. For example, the computer system 202 may apply a machine learning model (e.g., a neural network, etc.) to the manufacturing conditions experienced by an analyte sensor and / or the sensitivity values for the analyte sensor (e.g., the initial sensitivity mo, the final sensitivity mf, the calibration baseline, and / or the sensitivity function M(t)) to determine calibration parameters for the analyte sensor. In some examples, the computer system 202 corrects a sensitivity value (e.g., the initial sensitivity mo, the final sensitivity mf, the calibration baseline, and / or the sensitivity function M(t)) for the analyte sensor using the manufacturing conditions experienced by the analyte sensor, such as via the artificial intelligence.

[0063] While the computer system 202 is described herein as using the artificial intelligence (e.g., machine learning model, such as a neural network, etc.) to determine calibration parameters for an analyte sensor based on manufacturing conditions experienced by the analyte sensor during manufacturing, the computer system 202 may consider manufacturing conditions experienced by a lot of analyte sensors by obtaining sensor lot values (e.g., by averaging the values associated with the manufacturing conditions or applying other statistical analyses to the values associated with the manufacturing conditions corresponding to the analyte sensors of the lot of analyte sensors). In this example, the computer system 202 may then apply the sensor lot values to the individual analyte sensors of the lot of analyte sensors, such as by using the artificial intelligence described herein.

[0064] The computer system 202 then configures the analyte sensor system 110 to use the calibration parameters to convert signals from the analyte sensor into analyte concentrationDocket No.: 0922-PCT01 levels. For example, the computer system 202 may store the calibration parameters in the analyte sensor system 110 or in a database 203 that the analyte sensor system 110 may access.

[0065] While the computer system 202 is described herein as determining calibration parameters for a single analyte sensor by, for example, using artificial intelligence (e.g., a machine learning model, such as a neural network, etc.) to account for manufacturing conditions experienced by an analyte sensor, the computer system 202 can be used to determine calibration parameters for a plurality of analyte sensors. For example, the calibration parameters determined by the computer system 202 for an analyte sensor system 110 may be considered representative of the calibration parameters of the analyte sensor systems in a lot of analyte sensor systems, and thus stored for the other analyte sensor systems in the lot of analyte sensor systems. In another example, the calibration parameters determined by the computer system 202 for a subset or all of the analyte sensor systems in the lot of analyte sensor systems may be averaged and applied to the subset of the analyte sensor systems and / or remainder of the analyte sensor systems in the lot of analyte sensor systems. In yet another example, the computer system 202 may apply statistical analysis, a correction factor, etc. to the calibration parameters determined by the computer system 202 for a subset or all of the analyte sensor systems in the lot of analyte sensor systems to determine calibration parameters that should be applied to another subset or all of the analyte sensor systems in the lot of analyte sensor systems.

[0066] The database 203 may store parameters indicating the manufacturing conditions experienced by the analyte sensors of the analyte sensor systems 110. For example, as the analyte sensor systems 110 undergo the various manufacturing processes (e.g., calibration processes, dipping processes, deposition processes, coating processes, material application processes, curing processes, plasma treatment, etc.), the computer system 202 may receive and / or generate parameters indicating the conditions experienced by the analyte sensors during the manufacturing processes. The computer system 202 may then store these parameters (e.g., within the computer system 202 and / or within the database 203). After the manufacturing processes are complete, the computer system 202 may use these parameters to calibrate the analyte sensor systems 110. The computer system 202 may also store the determined calibration parameters for the analyte sensor systems 110 in the database 203.

[0067] Figure 3 illustrates an example operation 300 for calibrating the continuous analyte monitoring system 100 of Figure 1A, in accordance with certain aspects of the presentDocket No.: 0922-PCT01 disclosure. A computer system (e.g., the computer system 202 shown in Figure 2) performs the operation 300. By performing the operation 300, the computer system uses artificial intelligence to consider the manufacturing conditions experienced by an analyte sensor of the analyte monitoring system when calibrating the analyte monitoring system. By calibrating the analyte monitoring system in this manner, the computer system improves the accuracy of the analyte monitoring system, which may improve patient health, more accurate dosage (e.g., of insulin) recommendations, etc. Calibrating the analyte monitoring system in this manner further reduces required computing and storage resources and improves calibration processing speeds.

[0068] The computer system begins by analyzing data produced by the analyte monitoring system (e.g., the analyte sensor 120 and / or sensor electronics module 130 shown in Figures 1A and IB) during an existing calibration process. As explained previously, the analyte sensor may be dipped into solutions with different analyte concentrations. The analyte sensor produces electrical signals 302 (e.g., electrical current or electrical voltage) when the analyte sensor is dipped into the solutions. These electrical signals 302 may have a magnitude that changes based on the analyte concentrations in the solutions. The computer system receives indications of the electrical signals 302 and indications of the analyte concentrations 304 of the solutions. The computer system analyzes the electrical signals 302 and the analyte concentrations 304 to determine sensitivity values 306 for the analyte monitoring system. The sensitivity values 306 may include the initial sensitivity mo, the final sensitivity rm, the calibration baseline, and / or the sensitivity function M(t).

[0069] The computer system also receives manufacturing parameters 308 indicating the manufacturing conditions experienced by the analyte sensor during manufacturing. The manufacturing parameters 308 may be associated with a particular analyte sensor, subset of analyte sensors, lot of analyte sensors, etc. For example, manufacturing parameters 308 may be associated with a particular analyte sensor, subset of analyte sensors, lot of analyte sensors, etc. using a code, such as a bar code, a QR code, a numeric code, an alphanumeric code, an alphabetic code, or other linker.

[0070] The manufacturing parameters 308 may indicate the characteristics of the solutions (e.g., chemical composition, temperature, viscosity, etc.) and calibration (e.g., dipping, coating, deposition, etc.) process (e.g., the time dipped in the solutions, the amount of time a solution is deposited on the sensor, the temperature in the dipping chamber, the temperature at which aDocket No.: 0922-PCT01 coating is deposited on the sensor, the pressure in the dipping chamber, the humidity in the dipping chamber or during the deposition process, stir times, the position of the analyte sensors in the batch during the dipping or deposition process, viscosity targets or desired viscosity, etc.) used to form layers of material of the analyte sensor. Additionally, and / or alternatively, the manufacturing parameters 308 may indicate characteristics of a material that may be applied to a substrate during analyte sensor formation and / or characteristics of the material application process (e.g., the time material is applied, the temperature in the material application chamber, the pressure in the material application chamber, the humidity in the material application chamber, the position of the analyte sensors during the material application process, viscosity targets or desired viscosity of the material, humidity targets or desired humidity, etc.) that are used to form the material (e.g., layers of material) of the analyte sensor. While a dipping process is described herein, other coating or membrane deposition processes may be similarly implemented. In some instances, analyte sensor systems 110 may be in the form of a wire or a planar sensor. Depending on the type of analyte sensor system, the sensor may undergo one or more coating deposition processes to apply a membrane, enzyme, etc. The characteristics of the deposition process may be tracked and used to determine one or more calibration parameters in accordance with examples of this disclosure.

[0071] Additionally, and / or alternatively, the manufacturing parameters 308 may indicate the characteristics of the curing process (e.g., temperature of the oven, the duration of the curing process, humidity in the oven, humidity targets or desired humidity, the position of the analyte sensors in the batch during the curing process, etc.) and the plasma or other treatment (e.g., the type of plasma used, the duration of the plasma treatment, the position of the analyte sensors in the batch during the plasma treatment, etc.) used during manufacturing. Additionally, and / or alternatively, the manufacturing parameters 308 may indicate the characteristics of the calibration process, such as the dipping process (e.g., the analytes in the solutions, the concentrations of the analytes, the temperature of the solutions, the viscosity of the solutions, the duration of the dipping process, etc.) used during calibration.

[0072] The computer system inputs the sensitivity values 306 and / or the manufacturing parameters 308 into a machine learning model 310 (e.g., a regression model or a neural network) to produce calibration parameters 312. In another example, the computer system inputs calibration information used to generate the sensitivity values 306 (e.g., without firstDocket No.: 0922-PCT01 determining the sensitivity values 306) with the manufacturing parameters 308 (or underlying data) into the machine learning model 310 to produce calibration parameters 312.

[0073] Generally, the machine learning model 310 determines how to adjust the sensitivity values 306 based on the manufacturing parameters 308. The machine learning model 310 may be trained to determine which manufacturing conditions impact how the analyte monitoring system should be calibrated and the effect or influence each manufacturing condition has on the calibration. The machine learning model 310 then determines the manufacturing parameters 308 corresponding to these manufacturing conditions (and in some examples, the weights associated with the manufacturing parameters 308, a selection of the manufacturing parameters 308, etc.), and adjusts the sensitivity values 306 based on these manufacturing parameters 308. The calibration parameters 312 may be adjusted versions of the initial sensitivity mo, the final sensitivity mf, the calibration baseline, and / or the sensitivity function M(t). As a result, these calibration parameters may be expressed as functions of the manufacturing conditions experienced by the analyte sensor.

[0074] Figure 4 illustrates an example operation 400 for calibrating the continuous analyte monitoring system 100 of Figure 1A, in accordance with certain aspects of the present disclosure. A computer system (e.g., the computer system 202 shown in Figure 2) performs the operation 400. The computer system begins with the sensitivity values 306 (e.g., the initial sensitivity mo, the final sensitivity mf, the calibration baseline, and / or the sensitivity function M(t)) and the manufacturing parameters 308 that indicate the manufacturing conditions experienced by the analyte sensor. Using a machine learning model (e.g., the machine learning model 310 shown in Figure 3), the computer system determines the calibration parameters 312.

[0075] As seen in Figure 4, the calibration parameters 312 include adjusted versions of the initial sensitivity mo, the final sensitivity mf, and / or the calibration baseline). Originally, the initial sensitivity mo, the final sensitivity mf, and the calibration baseline may be values that were determined independently of some or all of the manufacturing conditions experienced by the analyte sensor system. The machine learning model (e.g., the machine learning model 310) adjusts the initial sensitivity mo, the final sensitivity mf, and the calibration baseline to produce the calibration parameters 312. As a result, each of the calibration parameters 312 (e.g., the adjusted initial sensitivity mo, the adjusted final sensitivity mf, and the adjusted calibration baseline of the calibration parameters 312) depend on the manufacturing parameters 308.Docket No.: 0922-PCT01Additionally, some of the calibration parameters may depend on one or more of the sensitivity values 306.

[0076] After determining the calibration parameters 312, the computer system stores the calibration parameters 312 so that the analyte monitoring system may use the calibration parameters 312 to determine analyte concentration levels based at least on the electrical signals from the analyte sensor. For example, the computer system may store the calibration parameters 312 into the sensor electronics module of the analyte monitoring system. As another example, the computer system may store the calibration parameters 312 into a database, and the sensor electronics module may retrieve the calibration parameters 312 when the analyte monitoring system is in use.

[0077] Figure 5 is a flowchart of an example method 500 for calibrating the continuous analyte monitoring system 100 of Figure 1A, in accordance with certain aspects of the present disclosure. In particular embodiments, a computer system (e.g., the computer system 202 shown in Figure 2) performs the method 500. By performing the method 500, the computer system uses artificial intelligence to improve the calibration of an analyte monitoring system. Thus, the method 500 improves the accuracy of the analyte monitoring system, which may improve patient health, more accurate dosage (e.g., of insulin) recommendations, etc., reducing required computing and storage resources and improving calibration processing speeds.

[0078] At block 502, the computer system determines a sensitivity value for an analyte sensor system of the analyte monitoring system. For example, an analyte sensor of the analyte sensor system may be dipped in analyte solutions with various analyte concentrations. The analyte sensor system produces an electrical signal with a magnitude that changes depending on the analyte concentrations of the analyte solutions. The computer system analyzes these magnitudes along with the analyte concentrations to determine the sensitivity value. For example, the computer system may determine the initial sensitivity mo, the final sensitivity mf, the calibration baseline, and / or the sensitivity function M(t) that would convert the magnitude of the electrical signal to the analyte concentrations of the solutions.

[0079] At block 504, the computer system receives manufacturing parameters for the analyte sensor system. The manufacturing parameters 308 may be associated with a particular analyte sensor, subset of analyte sensors, lot of analyte sensors, etc. For example, manufacturing parameters 308 may be associated with a particular analyte sensor, subset ofDocket No.: 0922-PCT01 analyte sensors, lot of analyte sensors, etc. using a code, such as a bar code, a QR code, a numeric code, an alphanumeric code, an alphabetic code, or other linker.

[0080] The manufacturing parameters may indicate the manufacturing conditions experienced by the analyte sensor of the analyte sensor system. For example, the manufacturing parameters may indicate the characteristics of the solutions (e.g., chemical composition, temperature, viscosity, etc.) and calibration (e.g., dipping) process (e.g., the time dipped in the solutions, the temperature in the dipping chamber, the pressure in the dipping chamber, the humidity in the dipping chamber, stir times, the position of the analyte sensors in the batch during the dipping process, viscosity targets or desired viscosity, etc.) used to manufacture the analyte sensor. Additionally, and / or alternatively, the manufacturing parameters 308 may indicate characteristics of a material that may be applied to a substrate during analyte sensor formation and / or characteristics of the material application process (e.g., the time material is applied, the temperature in the material application chamber, the pressure in the material application chamber, the humidity in the material application chamber, the position of the analyte sensors during the material application process, viscosity targets or desired viscosity of the material, humidity targets or desired humidity, etc.) that are used to form the material (e.g., layers of material) of the analyte sensor.

[0081] Additionally, and / or alternatively, the manufacturing parameters may indicate the characteristics of the curing process (e.g., temperature of the oven, the duration of the curing process, humidity in the oven, humidity targets or desired humidity, the position of the analyte sensors in the batch during the curing process, etc.) and the plasma treatment (e.g., the type of plasma used, the duration of the plasma treatment, the position of the analyte sensors in the batch during the plasma treatment, etc.) used during manufacturing. Additionally, and / or alternatively, the manufacturing parameters may indicate the characteristics of the calibration (e.g., dipping) process (e.g., the analytes in the solutions, the concentrations of the analytes, the temperature of the solutions, the viscosity of the solutions, the duration of the dipping process, etc.) used during calibration.

[0082] At block 506, the computer system determines a calibration parameter for the analyte sensor system based on the sensitivity value and the manufacturing parameters. Generally, the computer system uses artificial intelligence to determine the calibration parameter. For example, the computer system may input the sensitivity value and the manufacturing parameters into a machine learning model. The machine learning model mayDocket No.: 0922-PCT01 have been trained to determine the manufacturing conditions that impact how the analyte sensor system should be calibrated and the effect or influence each manufacturing condition has on the calibration. The machine learning model then determines the manufacturing parameters corresponding to these manufacturing conditions and adjusts the sensitivity value based on these manufacturing parameters. Adjusting the sensitivity value produces the calibration parameter (e.g., an adjusted sensitivity value).

[0083] At block 508, the computer system stores the calibration parameter for use by the analyte sensor system. For example, the computer system may store the calibration parameter in the sensor electronics module of the analyte sensor system. As another example, the computer system may store the calibration parameter in a database, and the analyte sensor system may retrieve the calibration parameter from the database when the analyte sensor system is in use. The analyte sensor system may use the calibration parameter to convert electrical signals produced by the analyte sensor into analyte concentration levels.

[0084] In certain embodiments, by using artificial intelligence to consider the manufacturing conditions experienced by the analyte sensor when calibration the analyte sensor system, the computer system provides the technical advantage of improving the accuracy of the analyte sensor system. By producing analyte concentration levels that are closer to the actual analyte concentration levels in a user, the analyte sensor system improves the health of the user and reduces computing resources wasted on generating inaccurate analyte concentration levels.Calibration Model Development

[0085] A computer system may develop or train the machine learning model used to calibrate analyte sensor systems. Generally, the model is trained to learn which manufacturing conditions experienced by an analyte sensor impact how an analyte sensor system should be calibrated and to learn what impact the manufacturing conditions have on calibration.

[0086] Figure 6A illustrates an example operation 600 for developing a machine learning model for calibrating the continuous analyte monitoring system 100 of Figure 1A, in accordance with certain aspects of the present disclosure. A computer system (e.g., the computer system 202 shown in Figure 2) performs the operation 600. By performing the operation 600, the computer system adjusts weights of the machine learning model using an optimization process.Docket No.: 0922-PCT01

[0087] The computer system begins by receiving or retrieving data 602. For example, the computer system may retrieve the data 602 from a database. Generally, the data 602 may include information about multiple analyte sensor systems. In the example of Figure 6A, the data 602 includes manufacturing parameters 604 indicating manufacturing conditions experienced by analyte sensors of the analyte sensor systems. The data 602 also includes sensitivity values 606 for these analyte sensor systems. The sensitivity values 606 may have been determined via a calibration process (e.g., by dipping the analyte sensors into analyte solutions with different analyte concentration). The data 602 also includes accuracy values 608 for the analyte sensor systems. Generally, the accuracy values 608 indicate a baseline level of accuracy that the analyte sensor systems have by using the sensitivity values 606 (e.g., without considering the manufacturing conditions experienced by the analyte sensors).

[0088] The computer system trains the model using the data 602 or a subset of the data 602. For example, the computer system may separate the data 602 into a training set and a validation set. The computer system then trains the model 610 using the training set of the data 602. The model 610 analyzes the manufacturing parameters 604, the sensitivity values 606, and / or the accuracy values 608 in the training set to determine the manufacturing conditions that impact how an analyte sensor system should be calibrated and to determine the impact of the manufacturing conditions on calibration. The computer system may set weights 612 of the model 610 as part of the training process. The weights 612 may correspond to different manufacturing conditions. The values of the weights 612 may indicate the effect or influence of certain manufacturing conditions on the calibration process. For example, the higher the value for a weight 612, the more impact the manufacturing condition corresponding to that weight 612 has on the calibration process.

[0089] After setting the weights 612 of the model 610, the computer system hones the model 610. For example, the computer system may use the validation set of the data 602 to hone the model 610. The computer system may use a cost function 614 to produce an error 616 for the model 610 based on the validation set. For example, the computer system may apply the model 610 to the manufacturing parameters 604 and the sensitivity values 606 in the validation set to predict calibration parameters (e.g., the initial sensitivity mo, the final sensitivity rm, the calibration baseline, and / or the sensitivity function M(t)). The computer system may then use the cost function 614 to determine an accuracy of analyte sensor systemsDocket No.: 0922-PCT01 using these calibration parameters. The error 616 may indicate the difference between the determined accuracy and the accuracy values 608 for the analyte sensor systems.

[0090] The computer system determines an adjustment 618 to the weights 612 of the model 610 based on the error 616. For example, the adjustment 618 may change the values of the weights 612, which may improve the error 616. As a result, the adjustment 618 may improve the accuracy of the model 610.

[0091] The computer system may perform multiple iterations of this training and honing process to improve the model 610. When the computer system reaches a particular threshold (e.g., a threshold number of iterations or the error 616 is not improved beyond a threshold), the computer system may consider the model 610 trained. The computer system may then deploy the model to calibrate analyte sensor systems. In this manner, the computer system trains the model 610 to learn which manufacturing parameters 604 and / or manufacturing conditions affect the calibration of analyte sensors and to learn the impact that the manufacturing parameters 604 and / or manufacturing conditions have on calibration, which may improve the accuracy of the model 610.

[0092] In some embodiments, the model 610 is a linear model that expresses the initial sensitivity mo, the final sensitivity rm, and / or the calibration baseline as linear functions of manufacturing conditions and sensitivity values. For example, the linear model may express a calibration parameter as:where {a, bi, b2, ..., bn, c] are the weights of the model 610 that are optimized.

[0093] Figure 6B illustrates an example operation 650 for developing a calibration model for the continuous analyte monitoring system 100 of Figure 1A, in accordance with certain aspects of the present disclosure. A computer system (e.g., the computer system 202 shown in Figure 2) performs the operation 650. By performing the operation 650, the computer system trains a machine learning model.

[0094] As with the operation 600, the computer system begins by receiving or retrieving data 602. Generally, the data 602 may include information about multiple analyte sensor systems. In the example of Figure 6A, the data 602 includes manufacturing parameters 604 indicating manufacturing conditions experienced by analyte sensors of the analyte sensorDocket No.: 0922-PCT01 systems. The data 602 also includes sensitivity values 606 for these analyte sensor systems. The sensitivity values 606 may have been determined via a calibration process (e.g., by dipping the analyte sensors into analyte solutions with different analyte concentration). The data 602 also includes accuracy values 608 for the analyte sensor systems. Generally, the accuracy values 608 indicate a baseline level of accuracy that the analyte sensor systems have by using the sensitivity values 606 (e.g., without considering the manufacturing conditions experienced by the analyte sensors).

[0095] The computer system uses a neural network 652 (or other machine learning model) to analyze the data 602. The neural network 652 may analyze the manufacturing parameters 604, the sensitivity values 606, and / or the accuracy values 608 to determine the effects or influence of the manufacturing parameters 604 on calibration. The neural network 652 may determine or adjust the weights 656 of a model 654 to indicate the determined effects of the manufacturing parameters 604. For example, the value of each weight 656 may indicate the effect or influence that a manufacturing parameter 604 has on calibration.

[0096] In some embodiments, the computer system may apply constraints 658 determined through studies (e.g., bench studies) to the weights 656. For example, the computer system may add the constraint 658 to an output of the model 654 to produce the calibration parameter. Furthermore, in certain embodiments, as a result of using the neural network 652 to train the model 654, the model 654 may produce calibration parameters, which may express the initial sensitivity mo, the final sensitivity mf, the calibration baseline, and / or the sensitivity function M(t) as non-linear functions of the manufacturing parameters and the sensitivity values.

[0097] Figure 7 is a flowchart of an example method 700 for developing a calibration model for the continuous analyte monitoring system 100 of Figure 1A, in accordance with certain aspects of the present disclosure. A computer system (e.g., the computer system 202 shown in Figure 2) performs the method 700. By performing the method 700, the computer system trains a machine learning model for calibrating analyte sensor systems.

[0098] At block 702, the computer system receives manufacturing parameters for analyte sensor systems. The manufacturing parameters may indicate manufacturing conditions experienced by the analyte sensor systems during manufacture. For example, the manufacturing parameters may indicate the characteristics of the solutions (e.g., chemical composition, temperature, viscosity, etc.) and calibration (e.g., dipping) process (e.g., the time dipped in the solutions, the temperature in the dipping chamber, the pressure in the dippingDocket No.: 0922-PCT01 chamber, the humidity in the dipping chamber, stir times, the position of the analyte sensors in the batch during the dipping process, viscosity targets or desired viscosity, etc.) used to manufacture the analyte sensor. Additionally, and / or alternatively, the manufacturing parameters may indicate characteristics of a material that may be applied to a substrate during analyte sensor formation and / or characteristics of the material application process (e.g., the time material is applied, the temperature in the material application chamber, the pressure in the material application chamber, the humidity in the material application chamber, the position of the analyte sensors during the material application process, viscosity targets or desired viscosity of the material, humidity targets or desired humidity, etc.) that are used to form the material (e.g., layers of material) of the analyte sensor.

[0099] Additionally, and / or alternatively, the manufacturing parameters may indicate the characteristics of the curing process (e.g., temperature of the oven, the duration of the curing process, humidity in the oven, humidity targets or desired humidity, the position of the analyte sensors in the batch during the curing process, etc.) and the plasma or other treatment (e.g., the type of plasma used, the duration of the plasma treatment, the position of the analyte sensors in the batch during the plasma treatment, etc.) used during manufacturing. Additionally, and / or alternatively, the manufacturing parameters may indicate the characteristics of the calibration (e.g., dipping) process (e.g., the analytes in the solutions, the concentrations of the analytes, the temperature of the solutions, the viscosity of the solutions, the duration of the dipping process, etc.) used during calibration.

[0100] At block 704, the computer system receives accuracy values for the analyte sensor systems. The accuracy values may indicate the accuracy of the analyte sensor systems. The accuracy may indicate how close the analyte concentration levels determined by the analyte sensor systems are to the actual analyte concentration levels of various analyte solutions.

[0101] At block 706, the computer system determines a machine learning model for determining calibration parameters for the analyte sensor systems. In some embodiments, the computer system uses an optimization process to determine the machine learning model as a linear model for producing calibration parameters based on manufacturing conditions experienced by the analyte sensor systems. In certain embodiments, the computer system uses a neural network to analyze the manufacturing parameters and the accuracy values to determine the machine learning model as a non-linear model for producing calibration parameters based on manufacturing conditions experienced by the analyte sensor systems. The calibrationDocket No.: 0922-PCT01 parameters may be sensitivity values (e.g., the initial sensitivity mo, the final sensitivity rm, the calibration baseline, and / or the sensitivity function M(t)) that are adjusted based on the manufacturing parameters.

[0102] FIG. 8 is a block diagram depicting a computer system 800, which may be the computer system 202 shown in Figure 2. Although depicted as a single physical device, in embodiments, the computer system 800 may be implemented using virtual device(s), and / or across a number of devices, such as in a cloud environment and / or via separate modules of portable or cloud devices. As illustrated, the computer system 800 includes a processor 805, a memory 810, a storage 815, a network interface 825, and one or more VO interfaces 820. In the illustrated embodiment, the processor 805 retrieves and executes programming instructions stored in the memory 810, as well as stores and retrieves application data residing in the storage 815. The processor 805 is generally representative of a single CPU and / or GPU, multiple CPUs and / or GPUs, a single CPU and / or GPU having multiple processing cores, and the like.

[0103] The processor 805 is any electronic circuitry, including, but not limited to one or a combination of microprocessors, microcontrollers, application specific integrated circuits (ASIC), application specific instruction set processor (ASIP), and / or state machines, that communicatively couples to the memory 810 and controls the operation of the computer system 800. The processor 805 may be 8-bit, 16-bit, 32-bit, 64-bit or of any other suitable architecture. The processor 805 may include an arithmetic logic unit (ALU) for performing arithmetic and logic operations, processor registers that supply operands to the ALU and store the results of ALU operations, and a control unit that fetches instructions from memory and executes them by directing the coordinated operations of the ALU, registers and other components. The processor 805 may include other hardware that operates software to control and process information. The processor 805 executes software stored on the memory 810 to perform any of the functions described herein. The processor 805 controls the operation and administration of the computer system 800 by processing information (e.g., information received from the memory 810). The processor 805 is not limited to a single processing device and may encompass multiple processing devices contained in the same device or computer or distributed across multiple devices or computers. The processor 805 is considered to perform a set of functions or actions if the multiple processing devices collectively perform the set of functions or actions, even if different processing devices perform different functions or actions in the set.Docket No.: 0922-PCT01

[0104] The memory 810 is generally included to be representative of a random access memory (RAM). The storage 815 may be any combination of disk drives, flash-based storage devices, and the like, and may include fixed and / or removable storage devices, such as fixed disk drives, removable memory cards, caches, optical storage, network attached storage (NAS), or storage area networks (SAN). The memory 810 may store, either permanently or temporarily, data, operational software, or other information for the processor 805. The memory 810 may include any one or a combination of volatile or non-volatile local or remote devices suitable for storing information. For example, the memory 810 may include random access memory (RAM), read only memory (ROM), magnetic storage devices, optical storage devices, or any other suitable information storage device or a combination of these devices. The software represents any suitable set of instructions, logic, or code embodied in a computer- readable storage medium. For example, the software may be embodied in the memory 810, a disk, a CD, or a flash drive. In particular embodiments, the software may include an application executable by the processor 805 to perform one or more of the functions described herein. The memory 810 is not limited to a single memory and may encompass multiple memories contained in the same device or computer or distributed across multiple devices or computers. The memory 810 is considered to store a set of data, operational software, or information if the multiple memories collectively store the set of data, operational software, or information, even if different memories store different portions of the data, operational software, or information in the set.

[0105] In some embodiments, the VO devices 835 (such as keyboards, monitors, etc.) can be connected via the VO interface(s) 820. Further, via the network interface 825, the computer system 800 can be communicatively coupled with one or more other devices and components. In certain embodiments, the computer system 800 is communicatively coupled with other devices via a network, which may include the Internet, local network(s), and the like. The network may include wired connections, wireless connections, or a combination of wired and wireless connections. As illustrated, the processor 805, memory 810, storage 815, network interface(s) 825, and the VO interface(s) 820 are communicatively coupled by one or more interconnects 830. In certain embodiments, the computer system 800 is representative of the display device associated with the user. In certain embodiments, as discussed above, the display device can include the user’s laptop, computer, smartphone, and the like. In another embodiment, the computer system 800 is a server executing in a cloud environment.Docket No.: 0922-PCT01

[0106] In this manner, the computer system determines which manufacturing parameters affect the calibration of the analyte sensor systems, and the computer system determines the impact each manufacturing parameter has on calibration.

[0107] The methods disclosed herein comprise one or more steps or actions for achieving the methods. The method steps and / or actions may be interchanged with one another without departing from the scope of the claims. In other words, unless a specific order of steps or actions is specified, the order and / or use of specific steps and / or actions may be modified without departing from the scope of the claims.

[0108] In view of the above-described implementations of subject matter this application discloses the following list of examples, wherein one feature of an example in isolation or more than one feature of said example taken in combination and, optionally, in combination with one or more features of one or more further examples are further examples also falling within the disclosure of this application:

[0109] Example 1 : A system for calibrating an analyte sensor system, the system comprising: a memory; a processor communicatively coupled to the memory, the processor configured to: determine a sensitivity value for the analyte sensor system; determine, based on the sensitivity value and a manufacturing parameter representing a condition experienced by the analyte sensor system during manufacturing of the analyte sensor system, a calibration parameter for the analyte sensor system; and configure the analyte sensor system to convert an electrical current generated by the analyte sensor system into an analyte concentration using the calibration parameter.

[0110] Example 2: The system of Example 1, further comprising the analyte sensor system, wherein the analyte sensor system is configured to: generate the electrical current; and determine the analyte concentration based on the electrical current using the calibration parameter.

[0111] Example 3 : The system of any one of Examples 1 to 2, wherein the condition comprises (i) a characteristic and a component of a solution into which a wire was dipped to form an analyte sensor of the analyte sensor system and (ii) a condition of an environment around the wire when the wire was dipped into the solution.Docket No.: 0922-PCT01

[0112] Example 4: The system of any one of Examples 1 to 3, wherein the condition comprises a characteristic and a component of an analyte solution into which an analyte sensor of the analyte sensor system was dipped to determine the sensitivity value.

[0113] Example 5: The system of any one of Examples 1 to 4, wherein the condition comprises at least one of (i) a characteristic of a curing process performed on an analyte sensor of the analyte sensor system or (ii) a characteristic of a plasma treatment used to clean the analyte sensor system.

[0114] Example 6 : The system of any one of Examples 1 to 5, wherein determining the calibration parameter comprises analyzing, using a machine learning model, the sensitivity value and the manufacturing parameter to determine the calibration parameter.

[0115] Example 7: The system of any one of Examples 1 to 6, wherein the calibration parameter comprises at least one of an initial sensitivity, a final sensitivity, or a calibration baseline.

[0116] Example 8: The system of any one of Examples 1 to 7, wherein the calibration parameter indicates a slope of a curve that indicates values of electrical current and corresponding values of analyte concentration.

[0117] Example 9 : A method for calibrating an analyte sensor system, the method comprising: determining a sensitivity value for the analyte sensor system; determining, based on the sensitivity value and a manufacturing parameter representing a condition experienced by the analyte sensor system during manufacturing of the analyte sensor system, a calibration parameter for the analyte sensor system; and configuring the analyte sensor system to convert an electrical current generated by the analyte sensor system into an analyte concentration using the calibration parameter.

[0118] Example 10: The method of Example 9, wherein the condition comprises (i) a characteristic and a component of a solution into which a wire was dipped to form an analyte sensor of the analyte sensor system and (ii) a condition of an environment around the wire when the wire was dipped into the solution.

[0119] Example 11: The method of any one of Examples 9 to 10, wherein the condition comprises a characteristic and a component of an analyte solution into which an analyte sensor of the analyte sensor system was dipped to determine the sensitivity value.Docket No.: 0922-PCT01

[0120] Example 12: The method of any one of Examples 9 to 11, wherein the condition comprises at least one of (i) a characteristic of a curing process performed on an analyte sensor of the analyte sensor system or (ii) a characteristic of a plasma treatment used to clean the analyte sensor system.

[0121] Example 13: The method of any one of Examples 9 to 12, wherein determining the calibration parameter comprises analyzing, using a trained machine learning model, the sensitivity value and the manufacturing parameter to determine the calibration parameter.

[0122] Example 14: The method of any one of Examples 9 to 13, wherein the calibration parameter comprises at least one of an initial sensitivity, a final sensitivity, or a calibration baseline.

[0123] Example 15: The method of any one of Examples 9 to 14, wherein the calibration parameter indicates a slope of a curve that indicates values of electrical current and corresponding values of analyte concentration.

[0124] Example 16: A system for generating a model, the system comprising: a memory; and a processor communicatively coupled to the memory, the processor configured to: receive a plurality of manufacturing parameters indicating conditions experienced by a plurality of analyte sensor systems during manufacturing of the plurality of analyte sensor systems, wherein the plurality of analyte sensor systems are each configured to generate electrical currents and convert the electrical currents into analyte concentrations; receive a plurality of values indicating an accuracy of the plurality of analyte sensor systems; and determine, based on the plurality of manufacturing parameters and the plurality of values, a model for determining calibration parameters for analyte sensor systems, wherein the model comprises weights indicating an influence of corresponding manufacturing conditions on the calibration parameters.

[0125] Example 17: The system of Example 16, wherein the conditions comprise (i) characteristics and components of solutions into which wires were dipped to form analyte sensors of the plurality analyte sensor systems and (ii) conditions of environments around the wires when the wires were dipped into the solutions.

[0126] Example 18: The system of any one of Examples 16 to 17, wherein the conditions comprise characteristics and components of analyte solutions into which analyte sensors of theDocket No.: 0922-PCT01 plurality of analyte sensor systems were dipped to determine sensitivity values for the plurality of analyte sensor systems.

[0127] Example 19: The system of any one of Examples 16 to 18, wherein the conditions comprise at least one of (i) characteristics of curing processes performed on analyte sensors of the plurality of analyte sensor systems or (ii) characteristics of plasma treatments used to clean the plurality of analyte sensor systems.

[0128] Example 20: The system of any one of Examples 16 to 19, wherein determining the model comprises: determining an error of the model using a cost function; and adjusting the weights based on the error.

[0129] Example 21: The system of any one of Examples 16 to 20, wherein determining the model comprises analyzing the plurality of manufacturing parameters and the plurality of values using at least one of a linear model or a neural network.

[0130] Example 22: The system of any one of Examples 16 to 21, wherein the processor is further configured to use the model to determine one or more calibration parameters for analyte sensor systems; and determine an estimated analyte value based on the one or more calibration parameters.

[0131] Example 23: An apparatus comprising: determining a sensitivity value for the analyte sensor system; determining, based on the sensitivity value and a manufacturing parameter representing a condition experienced by the analyte sensor system during manufacturing of the analyte sensor system, a calibration parameter for the analyte sensor system; and configuring the analyte sensor system to convert an electrical current generated by the analyte sensor system into an analyte concentration using the calibration parameter.

[0132] Example 24: The apparatus of Example 23, comprising: means for performing the method of any one of Examples 10 to 15.

[0133] Example 25: A non-transitory computer readable medium storing instructions, which when executed by at least one data processor, result in operations comprising: determining a sensitivity value for the analyte sensor system; determining, based on the sensitivity value and a manufacturing parameter representing a condition experienced by the analyte sensor system during manufacturing of the analyte sensor system, a calibration parameter for the analyte sensor system; and configuring the analyte sensor system to convert an electrical currentDocket No.: 0922-PCT01 generated by the analyte sensor system into an analyte concentration using the calibration parameter.

[0134] Example 26: The non-transitory computer readable medium of Example 25 wherein the operations further comprise the method of any one of Examples 10 to 15.

[0135] Example 27: An analyte sensor system comprising: an analyte sensor; and sensor electronics coupled to the analyte sensor, the sensor electronics comprising a memory and a processor configured to: generate a signal associated with an analyte concentration; and determine, based at least on the signal and a calibration parameter stored on the memory, an estimated value of the analyte concentration, wherein the calibration parameter is determined based at least on a sensitivity value for the analyte sensor and a manufacturing parameter representing a condition experienced by the analyte sensor during manufacturing of the analyte sensor.

[0136] Example 28: The system of Example 27, wherein the condition comprises (i) a characteristic and a component of a solution into which at least a portion of the analyte sensor was dipped to form the analyte sensor and (ii) a condition of an environment around the analyte sensor when the portion of the analyte sensor was dipped into the solution.

[0137] Example 29: The system of any one of Examples 27 to 28, wherein the condition comprises a characteristic and a component of an analyte solution into which the analyte sensor was dipped to determine the sensitivity value.

[0138] Example 30: The system of any one of Examples 27 to 29, wherein the condition comprises at least one of (i) a characteristic of a curing process performed on the analyte sensor or (ii) a characteristic of a treatment used to clean the analyte sensor.

[0139] Example 31: The system of any one of Examples 27 to 30, wherein the calibration parameter is determined by at least analyzing, using a machine learning model, the sensitivity value and the manufacturing parameter.

[0140] Example 32: The system of any one of Examples 27 to 31, wherein the calibration parameter comprises at least one of an initial sensitivity, a final sensitivity, or a calibration baseline.

[0141] Example 33: The system of any one of Examples 27 to 32, wherein the calibration parameter indicates a slope of a curve that indicates values of electrical current and corresponding values of analyte concentration.Docket No.: 0922-PCT01

[0142] As used herein, a phrase referring to “at least one of’ a list of items refers to any combination of those items, including single members. As an example, “at least one of: a, b, or c” is intended to cover a, b, c, a-b, a-c, b-c, and a-b-c, as well as any combination with multiples of the same element (e.g., a-a, a-a-a, a-a-b, a-a-c, a-b-b, a c c, b-b, b-b-b, b-b-c, c-c, and c-c-c or any other ordering of a, b, and c).

[0143] The term “continuous,” as used herein, is a broad term, and is used in its ordinary sense, and can mean continuous, semi-continuous, continual, periodic, intermittent, regular, etc.

[0144] The terms “continuous analyte sensor,” “continuous multi-analyte sensor,” “continuous glucose sensor,” and “continuous lactate sensor,” as used herein, are broad terms, and are used in their ordinary sense, and refer without limitation to a device that continuously measures a concentration of an analyte or calibrates the device (e.g., by continuously adjusting or determining the sensor’s sensitivity and background), for example, at time intervals ranging from fractions of a second up to, e.g., 1, 2, or 5 minutes, or longer.

[0145] The terms “sensitivity” or “sensor sensitivity,” as used herein, are broad terms, and are used in their ordinary sense, and refer without limitation to an amount of signal produced by a certain concentration of a measured analyte, or a measured species (e.g., H2O2) associated with a measured analyte. For example, a sensor may have a sensitivity of from about 1 to about 300 picoAmps of current for every 1 mg / dL of glucose analyte. In some examples, the measured analyte includes glucose, lactate, ketones, insulin, electrolytes, creatinine, etc.

[0146] The term “sensor data,” as used herein, is a broad term, and is used in its ordinary sense, and refers without limitation to any data associated with a sensor, such as a continuous analyte or continuous multi-analyte sensor. Sensor data includes a raw data stream, or simply data stream, of analog or digital signal directly related to a measured analyte from an analyte sensor (or other signal received from another sensor), as well as calibrated or filtered raw data. The terms “sensor data point” and “data point” refer generally to a digital representation of sensor data at a particular time. The terms broadly encompass a plurality of time spaced data points from a sensor, such as a continuous analyte sensor, which comprises individual measurements taken at time intervals ranging from fractions of a second up to, e.g., 1, 2, or 5 minutes or longer. In another example, the sensor data includes an integrated digital value representative of one or more data points averaged over a time period. Sensor data may includeDocket No.: 0922-PCT01 calibrated data, smoothed data, filtered data, transformed data, or any other data associated with a sensor.

[0147] The term “sensor electronics,” as used herein, is a broad term, and is used in its ordinary sense, and refers without limitation to components, e.g., hardware or software, of a device configured to process sensor data.

[0148] Although certain embodiments herein are described with reference to management of diabetes, diabetes management is only an example of one application for which the present systems and methods may be utilized. The systems and methods described herein can also be used for managing one or more other diseases or conditions, which may or may not include diabetes. For example, the systems and methods described herein can be utilized for managing kidney disease, liver disease, and other types of diseases or conditions.

[0149] The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects. Thus, the claims are not intended to be limited to the aspects shown herein, but is to be accorded the full scope consistent with the language of the claims, wherein reference to an element in the singular is not intended to mean “one and only one” unless specifically so stated, but rather “one or more.” Unless specifically stated otherwise, the term “some” refers to one or more. All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. No claim element is to be construed under the provisions of 35 U.S.C. § 112(f) unless the element is expressly recited using the phrase “means for” or, in the case of a method claim, the element is recited using the phrase “step for.”

[0150] While various examples have been described above, it should be understood that they have been presented by way of example only, and not by way of limitation. Likewise, the various diagrams may depict an example architectural or other configuration for the disclosure, which is done to aid in understanding the features and functionality that can be included in the disclosure. The disclosure is not restricted to the illustrated example architectures or configurations, but can be implemented using a variety of alternative architectures andDocket No.: 0922-PCT01 configurations. Additionally, although the disclosure is described above in terms of various example examples and aspects, it should be understood that the various features and functionality described in one or more of the individual examples are not limited in their applicability to the particular example with which they are described. They instead can be applied, alone or in some combination, to one or more of the other examples of the disclosure, whether or not such examples are described, and whether or not such features are presented as being a part of a described example. Thus, the breadth and scope of the present disclosure should not be limited by any of the above-described example examples.

[0151] All references cited herein are incorporated herein by reference in their entirety. To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and / or take precedence over any such contradictory material.

[0152] Unless otherwise defined, all terms (including technical and scientific terms) are to be given their ordinary and customary meaning to a person of ordinary skill in the art, and are not to be limited to a special or customized meaning unless expressly so defined herein.

[0153] Terms and phrases used in this application, and variations thereof, especially in the appended claims, unless otherwise expressly stated, should be construed as open ended as opposed to limiting. As examples of the foregoing, the term ‘including’ should be read to mean ‘including, without limitation,’ ‘including but not limited to,’ or the like; the term ‘comprising’ as used herein is synonymous with ‘including,’ ‘containing,’ or ‘characterized by,’ and is inclusive or open-ended and does not exclude additional, unrecited elements or method steps; the term ‘having’ should be interpreted as ‘having at least;’ the term ‘includes’ should be interpreted as ‘includes but is not limited to;’ the term ‘example’ is used to provide example instances of the item in discussion, not an exhaustive or limiting list thereof; adjectives such as ‘known’, ‘normal’, ‘standard’, and terms of similar meaning should not be construed as limiting the item described to a given time period or to an item available as of a given time, but instead should be read to encompass known, normal, or standard technologies that may be available or known now or at any time in the future; and use of terms like ‘preferably,’ ‘preferred,’ ‘desired,’ or ‘desirable,’ and words of similar meaning should not be understood as implying that certain features are critical, essential, or even important to the structure or function of the embodiments, but instead as merely intended to highlight alternative or additional features that may or may not be utilized in a particular example. Likewise, a groupDocket No.: 0922-PCT01 of items linked with the conjunction ‘and’ should not be read as requiring that each and every one of those items be present in the grouping, but rather should be read as ‘and / or’ unless expressly stated otherwise. Similarly, a group of items linked with the conjunction ‘or’ should not be read as requiring mutual exclusivity among that group, but rather should be read as ‘and / or’ unless expressly stated otherwise.

[0154] The term “comprising as used herein is synonymous with “including,” “containing,” or “characterized by” and is inclusive or open-ended and does not exclude additional, unrecited elements or method steps.

[0155] All numbers expressing quantities of ingredients, reaction conditions, and so forth used in the specification are to be understood as being modified in all instances by the term ‘about.’ Accordingly, unless indicated to the contrary, the numerical parameters set forth herein are approximations that may vary depending upon the desired properties sought to be obtained. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of any claims in any application claiming priority to the present application, each numerical parameter should be construed in light of the number of significant digits and ordinary rounding approaches.

[0156] Furthermore, although the foregoing has been described in some detail by way of illustrations and examples for purposes of clarity and understanding, it is apparent to those skilled in the art that certain changes and modifications may be practiced. Therefore, the description and examples should not be construed as limiting the scope of the disclosure to the specific examples and examples described herein, but rather to also cover all modification and alternatives coming with the true scope and spirit of the disclosure.

Claims

Docket No.: 0922-PCT01CLAIMSWhat is claimed is:

1. A system for calibrating an analyte sensor system, the system comprising: a memory; a processor communicatively coupled to the memory, the processor configured to: determine a sensitivity value for the analyte sensor system; determine, based on the sensitivity value and a manufacturing parameter representing a condition experienced by the analyte sensor system during manufacturing of the analyte sensor system, a calibration parameter for the analyte sensor system; and configure the analyte sensor system to convert an electrical current generated by the analyte sensor system into an analyte concentration using the calibration parameter.

2. The system of claim 1, further comprising the analyte sensor system, wherein the analyte sensor system is configured to: generate the electrical current; and determine the analyte concentration based on the electrical current using the calibration parameter.

3. The system of Claim 1, wherein the condition comprises (i) a characteristic and a component of a solution into which a wire was dipped to form an analyte sensor of the analyte sensor system and (ii) a condition of an environment around the wire when the wire was dipped into the solution.

4. The system of Claim 1, wherein the condition comprises a characteristic and a component of an analyte solution into which an analyte sensor of the analyte sensor system was dipped to determine the sensitivity value.

5. The system of Claim 1, wherein the condition comprises at least one of (i) a characteristic of a curing process performed on an analyte sensor of the analyte sensor system or (ii) a characteristic of a plasma treatment used to clean the analyte sensor system.Docket No.: 0922-PCT016. The system of Claim 1, wherein determining the calibration parameter comprises analyzing, using a machine learning model, the sensitivity value and the manufacturing parameter to determine the calibration parameter.

7. The system of Claim 1, wherein the calibration parameter comprises at least one of an initial sensitivity, a final sensitivity, or a calibration baseline.

8. The system of Claim 1, wherein the calibration parameter indicates a slope of a curve that indicates values of electrical current and corresponding values of analyte concentration.

9. A method for calibrating an analyte sensor system, the method comprising: determining a sensitivity value for the analyte sensor system; determining, based on the sensitivity value and a manufacturing parameter representing a condition experienced by the analyte sensor system during manufacturing of the analyte sensor system, a calibration parameter for the analyte sensor system; and configuring the analyte sensor system to convert an electrical current generated by the analyte sensor system into an analyte concentration using the calibration parameter.

10. The method of Claim 9, wherein the condition comprises (i) a characteristic and a component of a solution into which a wire was dipped to form an analyte sensor of the analyte sensor system and (ii) a condition of an environment around the wire when the wire was dipped into the solution.

11. The method of Claim 9, wherein the condition comprises a characteristic and a component of an analyte solution into which an analyte sensor of the analyte sensor system was dipped to determine the sensitivity value.

12. The method of Claim 9, wherein the condition comprises at least one of (i) a characteristic of a curing process performed on an analyte sensor of the analyte sensor system or (ii) a characteristic of a plasma treatment used to clean the analyte sensor system.

13. The method of Claim 9, wherein determining the calibration parameter comprises analyzing, using a trained machine learning model, the sensitivity value and the manufacturing parameter to determine the calibration parameter.Docket No.: 0922-PCT0114. The method of Claim 9, wherein the calibration parameter comprises at least one of an initial sensitivity, a final sensitivity, or a calibration baseline.

15. The method of Claim 9, wherein the calibration parameter indicates a slope of a curve that indicates values of electrical current and corresponding values of analyte concentration.

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