Temperature characteristic to predict progressive sensor decline

Abstract

Described is a system for detecting the onset of Progressive Sensor Decline (PSD) by receiving a first analyte signal associated with a raw analyte measurement of a host from an analyte sensor within a first time period; receiving a first temperature signal associated with a first temperature measurement of the host from a temperature sensor within the first time period; determining a first relationship between the first analyte signal and the first temperature signal; receiving a second analyte signal associated with a raw analyte measurement of the host from the analyte sensor within a second time period; receiving a second temperature signal associated with a second temperature measurement of the host from the temperature sensor within the second time period; determining a second relationship between the second analyte signal and the second temperature signal; and determining an onset of sensor sensitivity degradation of the analyte sensor based on the first and second relationships.

Description

Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01TEMPERATURE CHARACTERISTIC TO PREDICT PROGRESSIVESENSOR DECLINECLAIM OF PRIORITY

[0001] This application claims the benefit of priority to U.S. Provisional Application Serial No. 63 / 691,647, filed on September 6. 2024, which is incorporated herein by reference in its entirety.BACKGROUND

[0002] Diabetes 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 food is processed by the digestive system, which produces 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), 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 presence of blood glucose and ketones, which are produced when the body cannot use glucose. The state of having lower than normal blood1Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 glucose levels is called “hypoglycemia.’' Severe hypoglycemia can lead to acute crises that can result in seizures or death.

[0005] A diabetes patient 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. A glucose sensor can provide an estimated glucose concentration level, which can be used as guidance by a patient or caregiver.

[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 to 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] Blood sugar concentration levels may be monitored with an analyte sensor, such as a continuous glucose monitor. A continuous glucose monitor is used by a host (e.g.. patient) to provide information, such as an estimated blood glucose value or a trend of estimated blood glucose levels.SUMMARY

[0008] This present application discloses, among other things, systems, devices, and methods related to analyte sensor, including, for example, deploy testing in analyte sensors.

[0009] Example 1 is a temperature-compensated analyte sensor system comprising: an analyte sensor configured to generate a first analyte signal associated with a raw analyte measurement of a host within a first time period; a temperature sensor configured to generate a first temperature signal associated with a first temperature measurement of the host within the first time period; and sensor electronics configured to perform operations, the operations comprising: determining a first relationship between the first2Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 analyte signal and the first temperature signal; receiving a second analyte signal associated with a raw analyte measurement of the host from the analyte sensor within a second time period; receiving a second temperature signal associated with a second temperature measurement of the host from the temperature sensor within the second time period; determining a second relationship between the second analyte signal and the second temperature signal; and determining an onset of sensor sensitivity degradation of the analyte sensor based on the first and second relationships.

[0010] In Example 2. the subject matter of Example 1 optionally includes, wherein the first time period includes an initial activation time period of the sensor system when the analyte sensor is inserted under the skin of the host.

[0011] In Example 3. the subject matter of any one or more of Examples 1-2 optionally includes, wherein the first time period includes a certain time threshold subsequent to an initial activation time period of the sensor system when the analyte sensor is disposed on a body of the host.

[0012] In Example 4. the subject matter of any one or more of Examples 1-3 optionally includes, wherein the analyte sensor is embedded within the skin of the host.

[0013] In Example 5. the subject matter of any one or more of Examples 2-4 optionally includes, wherein the temperature sensor is positioned on top of the skin of the host for direct surface temperature readings of the host's skin.

[0014] In Example 6. the subject matter of any one or more of Examples 1-5 optionally includes, wherein the temperature sensor is positioned on the same area of a body of the host as the analyte sensor.

[0015] In Example 7. the subject matter of any one or more of Examples 1-6 optionally includes, wherein determining the first relationship comprises accounting for an offset of time between the first analyte signal and the temperature signal.

[0016] In Example 8, the subject matter of Example 7 optionally includes, wherein the offset is based on a physiological delay between changes in blood analyte levels and their detection in interstitial fluid.3Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01

[0017] In Example 9, the subject matter of any one or more of Examples 7- 8 optionally includes, wherein the offset is based on a temporal offset responsive to an environmental or physiological change.

[0018] In Example 10, the subject matter of any one or more of Examples 1-9 optionally includes, wherein the first time period includes a threshold time subsequent to the sensor being positioned on the body.

[0019] In Example 11, the subject matter of any one or more of Examples 1-10 optionally includes, wherein the first time period includes a threshold time subsequent to an initial capture of sensor data.

[0020] In Example 12, the subject matter of any one or more of Examples 1-11 optionally includes, wherein the first time period includes a threshold time subsequent to initial stabilization of the sensor.

[0021] In Example 13, the subject matter of any one or more of Examples 1-12 optionally includes, wherein the operations further comprise: detecting a stabilization of the host's physiological state; and after detecting the stabilization of the host’s physiological state, triggering receiving of the first analyte signal.

[0022] In Example 14, the subject matter of any one or more of Examples 1-13 optionally includes, wherein the operations further comprise detecting whether the host has eaten a meal, and initiating the receiving of the first analyte signal and the first temperature signal at the first time period subsequent to the detection of the host eating the meal.

[0023] In Example 15, the subject matter of any one or more of Examples 1-14 optionally includes, wherein the analyte sensor and the temperature sensor are integrated into the same temperature-compensated analyte sensor system.

[0024] In Example 16, the subject matter of any one or more of Examples 1-15 optionally includes, wherein the first relationship includes identifying a difference in the first analyte signal and the first temperature signal at one or more time stamps within the first time period.

[0025] In Example 17. the subject matter of any one or more of Examples 1-1 optionally includes, wherein determining the first relationship includes correlating the first analyte signal and the first temperature signal using a4Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 machine learning model, the machine learning model trained to infer relationships between analyte signals and temperature signals using historical analyte signal data, historical temperature signal data, and historical relationship data.

[0026] In Example 18, the subject matter of Example 17 optionally includes, wherein correlating the first analyte signal and the first temperature signal comprises executing the machine learning model to generate a threshold difference value between the first analyte signal and the first temperature signal, and determining the onset of the sensor sensitivity degradation includes determining that a difference between the second analyte signal and the second temperature signal is above the threshold value.

[0027] In Example 19, the subject matter of any one or more of Examples 17-18 optionally includes, wherein correlating the first analyte signal and the first temperature signal comprises executing the machine learning model to generate an indication of the onset of sensor sensitivity degradation, the machine learning model receiving as input a continuous stream of analyte signals and temperature signals, the machine learning model determining a plurality of relationships between pairs of the analyte signals and the temperature signals from the continuous stream of analyte signals and temperature signals.

[0028] In Example 20, the subject matter of any one or more of Examples 1-19 optionally includes, wherein determining the onset of sensor sensitivity degradation comprises executing a machine learning model using continuous relationships between analyte signals and temperature signals to generate an indication of the onset, the machine learning model being trained to identify onsets of sensor sensitivity degradation from relationships between analyte signals and temperature signals.

[0029] In Example 21, the subject matter of any one or more of Examples 1-20 optionally includes, wherein the raw analyte measurement includes a raw glucose measurement.

[0030] Example 22 is a method comprising: receiving a first analyte signal associated with a raw analyte measurement of a host from an analyte sensor5Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 within a first time period; receiving a first temperature signal associated with a first temperature measurement of the host from a temperature sensor within the first time period; determining a first relationship between the first analyte signal and the first temperature signal; receiving a second analyte signal associated with a raw analyte measurement of the host from the analyte sensor within a second time period; receiving a second temperature signal associated with a second temperature measurement of the host from the temperature sensor within the second time period; determining a second relationship between the second analyte signal and the second temperature signal; and determining an onset of sensor sensitivity degradation of the analyte sensor based on the first and second relationships.

[0031] Example 23 is at least one machine-readable medium optionally including instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations to implement any one or more of Examples 1-22.

[0032] Example 24 is an apparatus comprising means to implement any one or more of Examples 1-22.

[0033] Example 25 is a system to implement any one or more of Examples 1-22.

[0034] Example 26 is a method to implement any one or more of Examples 1-22.

[0035] This summary is intended to provide an overview of subject matter of the present patent application. It is not intended to provide an exclusive or exhaustive explanation of the disclosure. The detailed description is included to provide further information about the present patent application. Other aspects of the disclosure will be apparent to persons skilled in the art upon reading and understanding the following detailed description and viewing the drawings that form a part thereof, each of which are not to be taken in a limiting sense.6Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THEDRAWINGS

[0036] In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. Like numerals having different letter suffixes may represent different instances of similar components. The drawings illustrate generally, by way of example, but not by way of limitation, various embodiments described in the present document.

[0037] FIG. 1 illustrates an aspect of the subject matter in accordance with one example.

[0038] FIG. 2 is a schematic illustration of an example analyte sensor system, which may for example, be the system shown in FIG. 1.

[0039] FIG. 3 is a diagram showing one example of a medical device system including the analyte sensor system of FIG. 1.

[0040] FIG. 4 is a side view of an example analyte sensor that may be implanted into a host.

[0041] FIG. 5 is a side view of another example analyte sensor in an arrangement including a mounting unit and an electronics unit.

[0042] FIG. 6 is an enlarged view of a distal portion of an analyte sensor, according to some examples.

[0043] FIG. 7 is a cross-sectional view through the sensor of FIG. 6 on plane 2-2 illustrating a membrane system, according to some examples.

[0044] FIG. 8 is a schematic illustration of a circuit that represents the behavior of an example analyte sensor, such as the analyte sensor shown in FIGS. 6-7, according to some examples.

[0045] FIG. 9 illustrates an example method for predicting progressive sensor decline using a temperature characteristic, according to some examples.

[0046] FIG. 10 illustrates a sensor system with a glucose sensor under the skin and a temperature sensor on top of the skin, according to some examples.7Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01

[0047] FIG. 11 illustrates an example of the identification of a baseline relationship and Progressive Sensor Decline (PSD) detection, according to some examples.

[0048] FIG. 12 illustrates a machine-learning pipeline, according to some examples.

[0049] FIG. 13 illustrates training and use of a machine-learning program, according to some examples.

[0050] FIG. 14 is a block diagram illustrating a computing device hardware architecture, within which a set or sequence of instructions can be executed to cause a machine to perform examples of any one of the methodologies discussed herein.DETAILED DESCRIPTION

[0051] Various examples described herein are directed to analyte sensor systems and methods for using analyte sensor systems. An analyte sensor system includes an analyte sensor that is positioned in contact with a bodily fluid of a host to measure a concentration of an analyte, such as glucose, in the bodily fluid. In some examples, the analyte sensor is inserted into the host to contact the bodily fluid in vivo. In some examples, the analyte sensor is inserted subcutaneously to contact interstitial fluid below the host’s skin.

[0052] When the analyte sensor is exposed to analyte in the host’s bodily fluid, an electrochemical reaction between the analyte sensor and the analyte causes the analyte sensor to generate a raw sensor signal that is indicative of the analyte concentration in the bodily fluid. For example, the analyte sensor may include two or more electrodes. An analyte sensor system sensor electronics apply a bias condition to the electrodes. The bias condition may be, for example, a potential difference applied between a working electrode of the analyte sensor and a reference electrode of the analyte sensor. The bias condition promotes the electrochemical reaction between the analyte and the analyte sensor, resulting in a current between the working electrode and at least one other analyte sensor electrode. The raw sensor signal may be and / or may not be based on the current.8Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01

[0053] The sensor electronics use the raw sensor signal to determine an estimated analyte concentration. In some examples, the sensor electronics are also programmed to output result data, which may include the estimated analyte concentration or other data. In some examples, the sensor electronics communicate result data to one or more other external devices.

[0054] In continuous glucose monitoring (CGM) systems, accurate and reliable sensor readings are essential for effective diabetes management. Analyte sensor systems typically involve sensors that measure glucose levels in the interstitial fluid and transmit data to a receiver or smartphone. These systems are designed to provide real-time glucose readings, trends, and alerts, enabling users to make informed decisions about their diabetes care. Over time, various factors can impact the performance of these sensors, necessitating ongoing improvements and innovations to maintain and enhance the accuracy and reliability of glucose monitoring.

[0055] Progressive Sensor Decline (PSD) is a recognized challenge in analyte sensor systems, impacting the accuracy of glucose readings over time. PSD typically occurs as a result of factors such as tissue encapsulation around the sensor, which can cause a gradual decline in the sensor's sensitivity to glucose levels. To mitigate the effects of PSD, existing technologies employ various filters and algorithms designed to detect and compensate for this degradation. These methods usually rely on analyzing the raw sensor signal, attempting to discern patterns and anomalies that indicate a decline in sensor performance. However, this approach can be inherently limited due to the dependence on glucose fluctuations within the interstitial space, which can be influenced by numerous physiological and environmental factors.

[0056] The reliance on raw' sensor signals for detecting PSD introduces potential inaccuracies, as these signals are not always reliable indicators of sensor degradation. Glucose fluctuations can be affected by factors such as hydration levels, physical activity, and individual metabolic responses, making it challenging to pinpoint the exact onset of PSD solely based on these signals. As a result, there can be a delay in identifying when a sensor begins to degrade, which may compromise the overall effectiveness of9Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 analyte sensor in providing accurate and timely glucose readings. Therefore, there is a need for more robust and reliable methods to detect PSD that can enhance the precision and reliability of glucose monitoring over extended periods.

[0057] Some examples described herein include a sensor that mitigates or eliminates the deficiencies of traditional approaches. Some examples use temperature as an additional source of information to detect a drop in sensor sensitivity' and, thereby, pick up a more accurate point at which sensitivity begins to decline. The sensor's local sensitivity at the working electrode may be correlated with temperature. By comparing changes in raw signals with temperature during the early sensor time period and the changes in the raw sensor signal with temperature that occur later on the sensor time period, the sensor improves the detection of the PSD.

[0058] Some examples provide a significant technological improvement to the performance of analyte sensor systems by specifically identifying and potentially addressing the adverse effects of PSD. By incorporating temperature information based on the similar morphology of temperature with glucose, the analyte sensor system effectively identifies the onset of PSD. This enhancement may improve the sensor's accuracy and reliability7by integrating temperature data as described herein rather than relying solely on glucose signal analysis. The application of temperature as a supplementary metric allows for a more precise determination of PSD. directly addressing the limitations of current analyte sensor systems. This technical advancement is grounded in the specific and practical implementation of comparing signal changes across different sensor time periods, thereby- providing a clear and innovative approach to improving analyte sensor system reliability7.

[0059] Although examples herein describe features to mitigate or eliminate PSD, it is appreciated that other types of sensor degradation can be mitigated or eliminated, such as sensor sensitivity degradation.

[0060] When the effects in this disclosure are considered in aggregate, one or more of the methodologies described herein may improve analyte sensor systems, providing additional functionality7(such as, but not limited to, the10Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 functionality mentioned above), making analyte sensor systems easier, faster, more accurate, and / or more intuitive to operate, and / or obviating a need for certain efforts or resources that otherwise would be involved in a PSD detection process. Computing resources used by one or more machines, databases, or networks may thus be more efficiently utilized or even reduced.

[0061] FIG. 1 is a diagram showing one example of an environment 100 including an analyte sensor system 102. The analyte sensor system 102 is coupled to a host 101, which may be a human patient. In some examples, the host is subject to a temporary or permanent diabetes condition or other health condition that makes analyte monitoring useful.

[0062] The analyte sensor system 102 includes an analyte sensor 104. In some examples, the analyte sensor 104 is or includes a glucose sensor configured to measure a glucose concentration in the host 101. The analyte sensor 104 can be exposed to analyte at the host 101 in any suitable way. In some examples, the analyte sensor 104 is fully implantable under the skin of the host 101. In other examples, the analyte sensor 104 is wearable on the body of the host 101 (e.g., on the body but not under the skin). Also, in some examples, the analyte sensor 104 is a transcutaneous device (e.g., with a sensor residing at least partially under or in the skin of a host). It should be understood that the devices and methods described herein can be applied to any device capable of detecting a concentration of an analyte, such as glucose, and providing an output signal that represents the concentration of the analyte.

[0063] In the example of FIG. 1, the analyte sensor system 102 also includes sensor electronics 106. In some examples, the sensor electronics 106 and analyte sensor 104 are provided in a single integrated enclosure (See FIG. 5). In other examples, the analyte sensor 104 and sensor electronics 106 are provided as separate components or modules (See FIG. 6). For example, the analyte sensor system 102 may include a disposable (e.g., single-use) sensor mounting unit that may include the analyte sensor 104, a component for attaching the sensor analyte sensor 104 to a host (e.g., an adhesive pad), and / or a mounting structure configured to receive a sensor electronics unit11Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 including some or all of the sensor electronics 106 shown in FIGS. 2 and 3. The sensor electronics unit 106 may be reusable.

[0064] The analyte sensor 104 may use any known method, including invasive, minimally-invasive, or non-invasive sensing techniques (e.g., optically excited fluorescence, microneedle, transdermal monitoring of glucose), to provide a raw sensor signal indicative of the concentration of the analyte in the host 101. The raw sensor signal may be converted into calibrated and / or filtered analyte concentration data used to provide a useful value of the analyte concentration (e.g., estimated blood glucose concentration level) to a user, such as the host or a caretaker (e.g., a parent, a relative, a guardian, a teacher, a doctor, a nurse, or any other individual that has an interest in the wellbeing of the host 101).

[0065] In some examples, the analyte sensor 104 is or includes a continuous glucose sensor. A continuous glucose sensor can be or include a subcutaneous, transdermal (e.g., transcutaneous), and / or intravascular device. In some examples, such a sensor or device may recurrently (e.g., periodically, or intermittently) analyze sensor data. The glucose sensor may use any method of glucose measurement, including enzymatic, chemical, physical, electrochemical, spectrophotometric, polarimetric, calorimetric, iontophoretic, radiometric, immunochemical, and the like. In various examples, the analyte sensor system 102 may be or include a continuous glucose monitor sensor available from DexCom™, (e.g., the DexCom G5™ sensor, Dexcom G6™ sensor, the DexCom G7™ sensor, or any variation thereof), from Abbott™ (e.g., the Libre™ sensor), or from Medtronic™ (e g., the Enlite™ sensor).

[0066] In some examples, analyte sensor 104 includes an implantable glucose sensor, such as described with reference to U.S. Patent 6,001,067 and U.S. Patent Publication No. US-2005-0027463-A1, which are incorporated by reference. In some examples, analyte sensor 104 includes a transcutaneous glucose sensor, such as described with reference to U.S. Patent Publication No. US-1306-0020187-A1, which is incorporated by reference. In some examples, analyte sensor 104 may be configured to be implanted in a host vessel or extracorporeally. such as is described in U.S.12Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Patent Publication No. US-2007-0027385-A1, co-pending U.S. Patent Publication No. US-1308-0119703-Al filed October 4, 1306. U.S. Patent Publication No. US-1308-0108942-A1 filed on March 26, 2007, and U.S. Patent Application No. US-2007-0197890-A1 filed on February 14, 2007, all of which are incorporated by reference. In some examples, the continuous glucose sensor may include a transcutaneous sensor such as described in U.S. Patent 6,565,509 to Say et al., which is incorporated by reference. In some examples, analyte sensor 104 may include a continuous glucose sensor that includes a subcutaneous sensor such as described with reference to U.S. Patent 6,579,690 to Bonnecaze et al. or U.S. Patent 6,484,046 to Say et al., which are incorporated by reference. In some examples, the continuous glucose sensor may include a refillable subcutaneous sensor such as described with reference to U.S. Patent 6,512,939 to Colvin et al., which is incorporated by reference. The continuous glucose sensor may include an intravascular sensor such as described with reference to U.S. Patent 6.477,395 to Schulman et al., which is incorporated by reference. The continuous glucose sensor may include an intravascular sensor such as described with reference to U.S. Patent 6,424,847 to Mastrototaro et al., which is incorporated by reference.

[0067] The environment 100 may also include various other external devices including, for example, a medical device 108. The medical device 108 may be or include a drug delivery device such as an insulin pump or an insulin pen. In some examples, the medical device 108 includes one or more sensors, such as another analyte sensor, a heart rate sensor, a respiration sensor, a motion sensor (e.g., accelerometer), posture sensor (e.g., 3-axis accelerometer), acoustic sensor (e.g., to capture ambient sound or sounds inside the body). The medical device 108 may be wearable, e.g., on a watch, glasses, contact lens, patch, wristband, ankle band, or another wearable item, or may be incorporated into a handheld device (e.g., a smartphone). In some examples, the medical device 108 includes a multi-sensor patch that may, for example, detect one or more of an analyte levels (e.g., glucose, lactate, insulin, or other substance), heart rate, respiration (e.g.. using impedance), activity (e g., using an accelerometer), posture (e g., using an accelerometer),13Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 galvanic skin response, tissue fluid levels (e.g., using impedance or pressure).

[0068] In some examples, the analyte sensor system 102 and the medical device 108 communicate with one another. Communication between the analyte sensor system 102 and medical device 108 may occur over any suitable wired connection and / or via a wireless communication signal 110. For example, the analyte sensor system 102 (e.g., the sensor electronics 106 thereof) may be configured to establish a communication connection with the medical device 108 using a suitable short-range communications medium such as. for example, a radio frequency medium (e.g.. Bluetooth. Medical Implant Communication System (MICS), Wi-Fi, near field communication (NFC), radio frequency identification (RFID), Zigbee, Z-Wave or other communication protocols), an optical medium (e.g., infrared), a sonic medium (e.g., ultrasonic), a cellular protocol-based medium (e.g., Code Division Multiple Access (CDMA) or Global System for Mobiles (GSM)), and / or the like.

[0069] In some examples, the environment 100 also includes other external devices such as, for example, a wearable sensor 130. The wearable sensor 130 can include a sensor circuit (e.g., a sensor circuit configured to detect a glucose concentration or other analyte concentration) and a communication circuit, which may, for example, be an NFC circuit. In some examples, information from the wearable sensor 130 may be retrieved from the wearable sensor 130 using a user computing device 132, such as a smart phone, that is configured to communicate with the wearable sensor 130 via the wearable sensor's communication circuit, for example, when the user device 132 is positioned near the wearable sensor 130. For example, swiping the user device 132 over the sensor 130 may retrieve sensor data from the wearable sensor 130 using NFC or other suitable wireless communication.

[0070] The use of NFC communication may reduce power consumption by the wearable sensor 130, which may reduce the size of a power source (e.g., battery or capacitor) in the wearable sensor 130 or extend the usable life of the power source. In some examples, the wearable sensor 130 may be wearable on an upper arm as shown. In some examples, a wearable sensor14Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01130 may additionally or alternatively be on the upper torso of the patient (e.g., over the heart or over a lung), which may, for example, facilitate detecting heart rate, respiration, or posture. A wearable sensor 136 may also be on the lower body (e.g., on a leg) or other part of the body (e.g., on the abdomen).

[0071] In some examples, an array or network of sensors may be associated with the patient. For example, one or more of the analyte sensor system 102, and / or external devices, such as the medical device 108, wearable device 120 such as a watch, an additional wearable sensor 130 and / or the like, may communicate with one another via a short-range communication medium (e.g., Bluetooth, MICS, NFC, or any of the other options described above,). The additional wearable sensor 130 may be any of the examples described above with respect to medical device 108. The analyte sensor system 102, medical device 108, and additional sensor 130 on the host 101 are provided for illustration and description and are not necessarily drawn to scale.

[0072] The environment 100 may also include one or more other external devices such as a hand-held smart device (e.g., smart phone) 112, tablet 114, smart pen 116 (e.g., insulin delivery pen with processing and communication capability), computer 118, a wearable device 120 such as a watch, or peripheral medical device 122 (which may be a proprietary device such as a proprietary user device available from DexCom™), any of which may communicate with the analyte sensor system 102 via a short-range communication medium, such as indicated by wireless communication signal 110, and may also communicate over a network 124 with a server system (e g., remote data center) or with a remote terminal 128 to facilitate communication with a remote user (not shown) such as a technical support staff member or a clinician.

[0073] The wearable device 120 may include an activity sensor, a heart rate monitor (e.g., light-based sensor or electrode-based sensor), a respiration sensor (e.g., acoustic- or electrode-based), a location sensor (e.g., GPS), or other sensors.

[0074] In some examples, the environment 100 includes a server system 126. The server system 126 can include one or more computing devices,15Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 such as one or more server computing devices. In some examples, the server system 126 is used to collect analyte data from the analyte sensor system 102 and / or analyte or other data from the plurality of other devices, and to perform analytics on collected data, generate, or apply universal or individualized models for glucose levels, and communicate such analytics, models, or information based thereon back to one or more of the devices in the environment 100. In some examples, the server system 126 gathers interhost and / or intra-host break-in data to generate one or more break-in characteristics, as described herein.

[0075] The environment 100 may also include a wireless access point (WAP) 138 used to communicatively couple one or more of analyte sensor system 102, network 124, server system 126, medical device 108 or any of the peripheral devices described above. For example, WAP 138 may provide Wi-Fi and / or cellular connectivity within environment 100. Other communication protocols, such as NFC or Bluetooth, may also be used among devices of the environment 100.

[0076] FIG. 2 is a schematic illustration of an example analyte sensor system 200, which may for example, be the system 102 shown in FIG. 1. The analyte sensor system may include an analyte sensor 202. The analyte sensor 202 may be configured to measure glucose or another suitable analyte. The analyte sensor system 200 may also comprise one or more temperature sensors 204, a processor 210. and a memory 206. The processor 210 may receive a signal indicative of an analyte concentration level from the analyte sensor 202 and receive a temperature signal indicative of a temperature parameter (e.g. absolute or relative temperature, or a temperature gradient) from the temperature sensor 204. The signal indicative of the analyte concentration may be a raw sensor signal or a processed sensor signal. The sensor system 200 may also include one or more additional sensors 208, which may include, for example, a heart rate sensor, activity sensor (e g. accelerometer), or a pressure gauge (e.g. to measure compression of the sensor against a host).

[0077] The processor 210 may determine a temperature-compensated analyte concentration level based on the temperature sensor signal (or16Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 multiple temperature sensor signals) and optionally also based on one or more signals from additional sensor(s) 208. The processor 210 may determine a temperature correction value, a specific temperature- compensated sensitivity value (e.g., analyte sensor sensitivity' value based on the temperature), or may determine a compensated estimated glucose value. The signal from the temperature sensor 204 may be used as an approximation of a temperature at an analyte sensor, or the signal from the temperature sensor 204 may be processed (e g., using methods described in detail below) to determine an estimated analyte temperature sensor based on the signal from the temperature sensor 204.

[0078] In some examples, the processor 210 may retrieve instructions or information from a memory' 206 to determine temperature-compensated analyte concentration level. For example, the processor may access a look-up table, or apply an algorithm based on the signal indicative of analyte concentration and temperature sensor signal or apply the signal indicative of analyte concentration and temperature signal to a model (e.g., use a state model or neural network).

[0079] In some examples, the processor may retrieve executable instructions from the memory' 206 (or a separate memory that may be operatively coupled to or integrated into the processor.) In some examples, the processor may include, or be part of, an application-specific integrated circuit (ASIC) that may be configured to determine a temperature- compensated glucose concentration level. In various examples, any one or more of the methods described herein may be executed by the processor 210 or temperature-compensated glucose sensor, either alone, or in combination with other processors or devices.

[0080] FIG. 3 is a diagram showing one example of a medical device system 300 including the analyte sensor system 102 of FIG. 1. In the example of FIG. 3. the analyte sensor system 102 includes sensor electronics 106 and an example sensor mounting unit 390, although in some examples, it will be appreciated that the analyte sensor 104 and sensor electronics 106 may be included in a common enclosure. While a specific example of division of components between the sensor mounting unit 390 and sensor17Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 electronics 106 is shown, it is understood that some examples may include additional components in the sensor mounting unit 390 or in the sensor electronics 106, and that some of the components (e.g., a battery or supercapacitor) that are shown in the sensor electronics 106 may be alternatively or additionally (e.g., redundantly) provided in the sensor mounting unit 390.

[0081] In the example shown in FIG. 3, the sensor mounting unit 390 includes the analyte sensor 104 and a battery 392. In some examples, the sensor mounting unit 390 may be replaceable, and the sensor electronics 106 may include a debouncing circuit (e.g.. gate with hysteresis or delay) to avoid, for example, recurrent execution of a power-up or power down process when a battery is repeatedly connected and disconnected or avoid processing of noise signal associated with removal or replacement of a battery.

[0082] The sensor electronics 106 may include electronics components that are configured to process sensor information, such as raw sensor signals, and generate corresponding analyte concentration values. The sensor electronics 106 may, for example, include electronic circuitry associated with measuring, processing, storing, or communicating continuous analyte sensor data, including prospective algorithms associated with processing and calibration of the raw sensor signal. The sensor electronics 106 may include hardware, firmware, and / or software that enables measurement of levels of the analyte via a glucose sensor. Electronic components may be affixed to a printed circuit board (PCB), or the like, and can take a variety of forms. For example, the electronic components may take the form of an integrated circuit (IC), such as an Application-Specific Integrated Circuit (ASIC), a microcontroller, and / or a processor.

[0083] In the example of FIG. 3, the sensor electronics 106 include a measurement circuit 302 (e.g., potentiostat) coupled to the analyte sensor 104 and configured to recurrently obtain analyte sensor readings using the analyte sensor 104. For example, the measurement circuit 302 may continuously or recurrently measure a raw sensor signal indicating a current flow at the analyte sensor 104 between a working electrode and a reference18Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 electrode. The sensor electronics 106 may include a gate circuit 394, which may be used to gate the connection between the measurement circuit 302 and the analyte sensor 104. For example, the analyte sensor 104 may accumulate charge over an accumulation period. After the accumulation period, the gate circuit 394 is opened so that the measurement circuit 302 can measure the accumulated charge. Gating the analyte sensor 104 may improve the performance of the sensor system 102 by creating a larger signal to noise or interference ratio (e.g., because charge accumulates from an analyte reaction, but sources of interference, such as the presence of acetaminophen near a glucose sensor, do not accumulate, or accumulate less than the charge from the analyte reaction).

[0084] The sensor electronics 106 may also include a processor 304. The processor 304 is configured to retrieve instructions 306 from memory 308 and execute the instructions 306 to control various operations in the analyte sensor system 102. For example, the processor 304 may be programmed to control application of bias potentials to the analyte sensor 104 via a potentiostat at the measurement circuit 302, interpret raw sensor signals from the analyte sensor 104, and / or compensate for environmental factors.

[0085] The processor 304 may also save information in data storage memory' 310 or retrieve information from data storage memory 310. In various examples, data storage memory’ 310 may be integrated with memory’ 308, or may be a separate memory circuit, such as a non-volatile memory circuit (e.g., flash RAM). Examples of systems and methods for processing sensor analyte data are described in more detail herein and in U.S. Patent Nos. 7,310.544 and 6,931.327.

[0086] The sensor electronics 106 may also include one or more sensors, such as the sensor 312, which may be coupled to the processor 304. The sensor 312 may be a temperature sensor, accelerometer, or another suitable sensor. The sensor electronics 106 may also include a power source such as a capacitor or battery 314, which may be integrated into the sensor electronics 106, or may be removable, or part of a separate electronics unit. The battery’ 314 (or other power storage component, e.g., capacitor) may optionally be rechargeable via a wired or wireless (e.g., inductive or ultrasound)19Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 recharging system recharging system 316. The recharging system recharging system 316 may harvest energy or may receive energy’ from an external source or on-board source. In various examples, the recharge circuit may include a triboelectric charging circuit, a piezoelectric charging circuit, an RF charging circuit, a light charging circuit, an ultrasonic charging circuit, a heat charging circuit, a heat harvesting circuit, or a circuit that harvests energy from the communication circuit. In some examples, the recharging circuit may recharge the rechargeable battery using power supplied from a replaceable battery' (e.g., a battery' supplied with a base component).

[0087] The sensor electronics 106 may also include one or more supercapacitors in the sensor electronics unit (as shown), or in the sensor mounting unit 390. For example, the supercapacitor may allow energy to be drawn from the battery' 314 in a highly consistent manner to extend the life of the battery 314. The battery 314 may recharge the supercapacitor after the supercapacitor delivers energy to the communication circuit or to the processor 304, so that the supercapacitor is prepared for delivery' of energy during a subsequent high-load period. In some examples, the supercapacitor may be configured in parallel with the battery 314. A device may be configured to preferentially draw energy from the supercapacitor, as opposed to the battery' 314. In some examples, a supercapacitor may' be configured to receive energy from a rechargeable battery' for short-term storage and transfer energy to the rechargeable battery for long-term storage.

[0088] The supercapacitor may extend an operational life of the battery' 314 by reducing the strain on the battery' 314 during the high-load period. In some examples, a supercapacitor removes at least 10% of the strain off the battery during high-load events. In some examples, a supercapacitor removes at least 30% of the strain off the battery during high-load events. In some examples, a supercapacitor removes at least 30% of the strain off the battery' during high-load events. In some examples, a supercapacitor removes at least 50% of the strain off the battery during high-load events.

[0089] The sensor electronics 106 may also include a wireless communication circuit 318, which may for example include a wireless transceiver operatively coupled to an antenna. The wireless communication20Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 circuit 318 may be operatively coupled to the processor 304 and may be configured to wirelessly communicate with one or more peripheral devices or other medical devices, such as an insulin pump or smart insulin pen.

[0090] In the example of FIG. 3, the medical device system 300 also includes optional external devices including, for example, a peripheral device 350. The peripheral device 350 may be any suitable user computing device such as, for example, a wearable device (e.g., activity monitor), such as a wearable device 120. In other examples, the peripheral device 350 maybe a hand-held smart device (e.g., smartphone or other device such as a proprietary handheld device available from Dexcom). a tablet 114. a smart pen 116, or special-purpose computer 118 shown in FIG. 1.

[0091] The peripheral device 350 may include a UI 352. a memory circuit 354, a processor 356, a wireless communication circuit 358, a sensor 360. or any combination thereof. The peripheral device 350 may not necessarily include all the components shown in FIG. 3. The peripheral device 350 may also include a power source, such as a battery.

[0092] The UI 352 may, for example, be provided using any suitable input / output device or devices of the peripheral device 350 such as, for example, a touch-screen interface, a microphone (e.g., to receive voice commands), or a speaker, a vibration circuit, or any combination thereof. The UI 352 may receive information from the host or another user (e.g., instructions, glucose values). The UI 352 may also deliver information to the host or other user, for example, by displaying UI elements at the UI 352. For example, UI elements can indicate glucose or other analyte concentration values, glucose or other analyte trends, glucose, or other analyte alerts, etc. Trends can be indicated by UI elements such as arrows, graphs, charts, etc.

[0093] The processor 356 may be configured to present information to a user, or receive input from a user, via the UI 352. The processor 356 may also be configured to store and retrieve information, such as communication information (e.g., pairing information or data center access information), user information, sensor data or trends, or other information in the memory circuit 354. The wireless communication circuit 358 may include a transceiver and antenna configured to communicate via a wireless protocol,21Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 such as any of the wireless protocols described herein. The sensor 360 may, for example, include an accelerometer, a temperature sensor, a location sensor, biometric sensor, or blood glucose sensor, blood pressure sensor, heart rate sensor, respiration sensor, or another physiologic sensor.

[0094] The peripheral device 350 may be configured to receive and displaysensor information that may be transmitted by sensor electronics 106 (e.g., in a customized data package that is transmitted to the display devices based on their respective preferences). Sensor information (e.g., blood glucose concentration level) or an alert or notification (e.g., “high glucose level”, “low glucose level” or “fall rate alert” may be communicated via the UI 352 (e g., via visual display, sound, or vibration). In some examples, the peripheral device 350 may be configured to display or otherwise communicate the sensor information as it is communicated from the sensor electronics 106 (e.g., in a data package that is transmitted to respective display devices). For example, the peripheral device 350 may transmit data that has been processed (e.g., an estimated analyte concentration level that may be determined by processing raw sensor data), so that a device that receives the data may not be required to further process the data to determine usable information (such as the estimated analyte concentration level). In other examples, the peripheral device 350 may process or interpret the received information (e.g., to declare an alert based on glucose values or a glucose trend). In various examples, the peripheral device 350 may receive information directly from sensor electronics 106, or over a network (e.g., via a cellular or Wi-Fi network that receives information from the sensor electronics 106 or from a device that is communicatively coupled to the sensor electronics 106).

[0095] In the example of FIG. 3, the medical device system 300 includes an optional medical device 370. For example, the medical device 370 may be an external device used in addition to or instead of the peripheral device 350. The medical device 370 may be or include any suitable type of medical or other computing device including, for example, the medical device 108, peripheral medical device 122, wearable device 120, wearable sensor 130, or wearable sensor 136 shown in FIG. 1. The medical device 370 may include a22Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01UI 372, a memory circuit 374, a processor 376, a wireless communication circuit 378. a sensor 380, a therapy circuit 382, or any combination thereof.

[0096] Similar to the UI 352, the UI 372 may be provided using any suitable input / output device or devices of the medical device 370 such as, for example, a touch-screen interface, a microphone, or a speaker, a vibration circuit, or any combination thereof. The UI 372 may receive information from the host or another user (e.g., glucose values, alert preferences, calibration coding). The UI 372 may also deliver information to the host or other user, for example, by displaying UI elements at the UI 352. For example, UI elements can indicate glucose or other analyte concentration values, glucose or other analyte trends, glucose, or other analyte alerts, etc. Trends can be indicated by UI elements such as arrows, graphs, charts, etc.

[0097] The processor 376 may be configured to present information to a user, or receive input from a user, via the UI 372. The processor 376 may also be configured to store and retrieve information, such as communication information (e.g., pairing information or data center access information), user information, sensor data or trends, or other information in the memory circuit 374. The wireless communication circuit 378 may include a transceiver and antenna configured communicate via a wireless protocol, such as any of the wireless protocols described herein.

[0098] The sensor 380 may, for example, include an accelerometer, a temperature sensor, a location sensor, biometric sensor, or blood glucose sensor, blood pressure sensor, heart rate sensor, respiration sensor, or another physiologic sensor. The medical device 370 may include two or more sensors (or memories or other components), even though only one sensor 380 is shown in the example in FIG. 3. In various examples, the medical device 370 may be a smart handheld glucose sensor (e.g., blood glucose meter), drug pump (e.g., insulin pump), or other physiologic sensor device, therapy device, or combination thereof.

[0099] In examples where medical device 370 is or includes an insulin pump, the pump and analyte sensor system 102 may be in two-way communication (e.g., so the pump can request a change to an analyte transmission protocol, e g., request a data point or request data on a more23Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 frequent schedule), or the pump and analyte sensor system 102 may communicate using one-way communication (e.g., the pump may receive analyte concentration level information from the analyte sensor system). In one-way communication, a glucose value may be incorporated in an advertisement message, which may be encrypted with a previously shared key. In a two-way communication, a pump may request a value, which the analyte sensor system 102 may share, or obtain and share, in response to the request from the pump, and any or all of these communications may be encrypted using one or more previously shared keys. An insulin pump may receive and track analyte (e.g., glucose) values transmitted from analyte sensor system 102 using one-way communication to the pump for one or more of a variety of reasons. For example, an insulin pump may suspend or activate insulin administration based on a glucose value being below or above a threshold value.

[0100] In some examples, the medical device system 300 includes two or more peripheral devices and / or medical devices that each receive information directly or indirectly from the analyte sensor system 102. Because different display devices provide many different user interfaces, the content of the data packages (e.g., amount, format, and / or type of data to be displayed, alarms, and the like) may be customized (e.g., programmed differently by the manufacturer and / or by an end user) for each device. For example, referring now to the example of FIG. 1, a plurality of different peripheral devices may be in direct wireless communication with sensor electronics 106 (e.g., such as an on-skin sensor electronics 106 that are physically connected to the continuous analyte sensor 104) during a sensor time period to enable a plurality of different types and / or levels of display and / or functionality associated with the displayable sensor information, or, to save battery power in the sensor system 102, one or more specified devices may communicate with the analyte sensor system 102 and relay (i.e., share) information to other devices directly or through a server system (e.g., a network-connected data center) 126.

[0101] FIG. 4 is a side view of an example analyte sensor 434 that may be implanted into a host. An enclosure 402 may be adhered to the host's skin24Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 using an adhesive pad 408. The adhesive pad 408 may be formed from an extensible material, which may be removably attached to the skin using an adhesive. Sensor electronics may be positioned within the enclosure 402. The sensor 434 may extend from the enclosure 402 and under the skin of a host, as shown.

[0102] FIG. 5 is a side view of another example analyte sensor 534 in an arrangement including a mounting unit 514 and an electronics unit 518. The mounting unit 514 may be adhered to the host's skin using an adhesive pad 508, which may be like the adhesive pad 408 described herein. The electronics unit 518 comprises an enclosure 502 that may have sensor electronics positioned thereon. In some examples, the electronics unit 518 and mounting unit 514 are arranged in a manner like the sensor electronics 106 and sensor mounting unit 390 shown in FIGS. 1 and 4. For example, the sensor 534 may extend from the enclosure 502 via the mounting unit 514.

[0103] FIG. 6 is an enlarged view of a distal portion of an analyte sensor 634. The analyte sensor 634 illustrates one example arrangement that may be used to implement the analyte sensors described herein, such as, for example, the analyte sensors 104, 434, 534. The analyte sensor 634 may be adapted for insertion under the host's skin and may be mechanically coupled to an enclosure, such as the enclosures 502, and / or to a mounting unit 514, such as the mounting unit 514. The analyte sensor 634 may be electrically coupled to sensor electronics, which may be positioned within the enclosure 402, 502.

[0104] The example analyte sensor 634 shown in FIG. 6 includes an elongated conductive body 641. The elongated conductive body 641 can include a core with various layers positioned thereon. A first layer 638 that at least partially surrounds the core and includes a working electrode, for example located in window 639). In some examples, the core and the first layer 638 are made of a single material (such as. for example, platinum). In some examples, the elongated conductive body 641 is a composite of two conductive materials, or a composite of at least one conductive material and at least one non-conductive material. A membrane system 632 is located25Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 over the working electrode and may cover other layers and / or electrodes of the sensor 634. as described herein.

[0105] The first layer 638 may be formed of a conductive material. The working electrode (at window 639) is an exposed portion of the surface of the first layer 638. Accordingly, the first layer 638 is formed of a material configured to provide a suitable electroactive surface for the working electrode. Examples of suitable materials include, but are not limited to, platinum, platinum-iridium, gold, palladium, iridium, graphite, carbon, a conductive polymer, an alloy, and / or the like.

[0106] A second layer 640 surrounds at least a portion of the first layer 638, thereby defining boundaries of the working electrode. In some examples, the second layer 640 serves as an insulator and is formed of an insulating material, such as polyimide, polyurethane, parylene, or any other suitable insulating materials or materials. In some examples, the second layer 640 is configured such that the working electrode (of the layer 638) is exposed via the window 639.

[0107] In some examples, the sensor 634 further includes a third layer 643 comprising a conductive material. The third layer 643 may comprise a reference electrode. In some examples, the third layer 643, including the reference electrode, is formed of a silver-containing material that is applied onto the second layer 640 (e.g., an insulator). The silver-containing material may include various materials and be in various forms such as, for example, Ag / AgCl-polymer pasts, paints, polymer-based conducting mixtures, inks, etc.

[0108] The analyte sensor 634 may include two (or more) electrodes, e.g., a working electrode at the layer 638 and exposed at window 639 and at least one additional electrode, such as a reference electrode of the layer 643. In the example arrangement of FIGS. 6-7, the reference electrode also functions as a counter electrode, although other arrangements can include a separate counter electrode. While the analyte sensor 634 may be used with a mounting unit in some examples, in other examples, the analyte sensor 634 may be used with other types of sensor systems. For example, the analyte sensor 634 may be part of a system that includes a battery and sensor in a26Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 single package, and may optionally include, for example, a near-field communication (NFC) circuit.

[0109] FIG. 7 is a cross-sectional view through the sensor 634 of FIG. 6 on plane 2-2 illustrating a membrane system 632. The membrane system 632 may include a number of domains (e.g., layers). In an example, the membrane system 632 may include an enzyme domain 642, a diffusion resistance domain 644, and a bioprotective domain 646 located around the working electrode. In some examples, a unitary7diffusion resistance domain and bioprotective domain may be included in the membrane system 632 (e.g., wherein the functionality of both the diffusion resistance domain and bioprotective domain are incorporated into one domain).

[0110] The membrane system 632, in some examples, also includes an electrode layer 647. The electrode layer 647 may be arranged to provide an environment between the surfaces of the working electrode and the reference electrode that facilitates the electrochemical reaction between the electrodes. For example, the electrode layer 647 may include a coating that maintains a layer of water at the electrochemically reactive surfaces of the sensor 634.

[0111] In some examples, the sensor 634 may be configured for short-term implantation (e.g., from about 1 to 30 days). However, it is understood that the membrane system 632 can be modified for use in other devices, for example, by including only one or more of the domains, or additional domains. For example, a membrane system 632 may include a plurality of resistance layers, or a plurality7of enzyme layers. In some examples, the resistance domain 644 may include a plurality of resistance layers, or the enzyme domain 642 may include a plurality of enzyme layers.

[0112] The diffusion resistance domain 644 may include a semipermeable membrane that controls the flux of oxygen and glucose to the underlying enzyme domain 642. As a result, the upper limit of linearity of glucose measurement is extended to a much higher value than that which is achieved without the diffusion resistance domain 644.

[0113] In some examples, the membrane system 632 may include a bioprotective domain 646, also referred to as a domain or biointerface domain, comprising a base polymer. However, the membrane system 632 of27Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 some examples can also include a plurality of domains or layers including, for example, an electrode domain, an interference domain, or a cell disruptive domain, such as described in more detail elsewhere herein and in U.S. Patent Nos. 7,494,465, 8,682,608, and 9,044,199, which are incorporated herein by reference in their entirety.

[0114] It is to be understood that sensing membranes modified for other sensors, for example, may include fewer or additional layers. For example, in some examples, the membrane system 632 may comprise one electrode layer, one enzyme layer, and two bioprotective layers, but in other examples, the membrane system 632 may comprise one electrode layer, two enzyme layers, and one bioprotective layer. In some examples, the bioprotective layer may be configured to function as the diffusion resistance domain 644 and control the flux of the analyte (e.g., glucose) to the underlying membrane layers.

[0115] In some examples, one or more domains of the sensing membranes may be formed from materials such as silicone, polytetrafluoroethylene, polyethylene-co-tetrafluoroethylene, polyolefin, polyester, polycarbonate, biostable polytetrafluoroethylene, homopolymers, copolymers, terpolymers of polyurethanes, polypropylene (PP), polyvinylchloride (PVC), polyvinylidene fluoride (PVDF), polybutylene terephthalate (PBT), polymethylmethacrylate (PMMA), polyether ether ketone (PEEK), polyurethanes, cellulosic polymers. poly(ethylene oxide), polypropylene oxide) and copolymers and blends thereof, polysulfones and block copolymers thereof including, for example, di-block, tri-block, alternating, random and graft copolymers.

[0116] In some examples, the sensing membrane can be deposited on the electroactive surfaces of the electrode material using known thin or thick film techniques (for example, spraying, electro-depositing, dipping, or the like). The sensing membrane located over the working electrode does not have to have the same structure as the sensing membrane located over the reference electrode; for example, the enzyme domain 642 deposited over the working electrode does not necessarily need to be deposited over the reference or counter electrodes.28Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01

[0117] Although the examples illustrated in FIGS. 6-7 involve circumferentially extending sensors and membrane systems, the membranes described herein may be applied to any planar or non-planar surface (e.g., planar sensors instead of wire sensors), for example, the substrate-based sensor structure of U.S. Pat. No. 6,565,509 to Say et al., which is incorporated by reference.

[0118] In an example in which the analyte sensor 634 is a glucose sensor, glucose analyte can be detected utilizing glucose oxidase. Glucose oxidase reacts with glucose to produce hydrogen peroxide (H2O2). The hydrogen peroxide reacts with the surface of the working electrode, producing two protons (2H+), two electrons (2e ) and one molecule of oxygen (O2). This produces an electronic current that may be detected by the sensor electronics 106. The amount of current is a function of the glucose concentration level. A calibration curve may be used to provide an estimated glucose concentration level based on a measured current. The amount of current is also a function of the diffusivity of glucose through the sensor membrane. The glucose diffusivity' may change over time, which may cause the sensor glucose sensitivity to change over time, or “drift.”

[0119] FIG. 8 is a schematic illustration of a circuit 800 that represents the behavior of an example analyte sensor, such as the analyte sensor 634 shown in FIGS. 6-7. As described above, the interaction of hydrogen peroxide (generated from the interaction between glucose analyte and glucose oxidase) and working electrode (WE) 804 produces a voltage differential between the working electrode (WE) 804 and reference electrode (RE) 806 which drives a current. The current may make up all or part of a raw sensor signal that is measured by sensor electronics, such as the sensor electronics 106 of FIGS. 1 -2, and used to estimate an analyte concentration (e g., glucose concentration).

[0120] The circuit 800 also includes a double-layer capacitance (Cdl) 808. which occurs at an interface between the working electrode (WE) 804 and the adjacent membrane (not shown in FIG. 8, see, e.g., FIGS. 6-7 above). The double-layer capacitance (Cdl) may occur at an interface between the working electrode 804 and the adjacent membrane due to the presence of29Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 two layers of ions with opposing polarity, as may occur during application of an applied voltage between the working electrode 804 and reference electrode. The equivalent circuit 800 may also include a polarization resistance (Rpol) 810, which may be relatively large, and may be modeled, for example, as a static value (e.g., 100 mega-Ohms), or as a variable quantity that varies as a function of glucose concentration level.

[0121] An estimated analyte concentration may be determined from a raw sensor signal based upon a measured current (or charge flow) through the analyte sensor membrane 812 when a bias potential is applied to the sensor circuit 800. For example, sensor electronics or another suitable computing device can use the raw sensor signal and a sensitivity of the sensor, which correlates a detected current flow to a glucose concentration level, to generate the estimated analyte concentration. In alternative sensor embodiments, detection of voltage at or across various electrodes may be used for calculating an estimated analyte concentration level instead of, or in addition to, current flow.

[0122] With reference to the equivalent circuit 800, when a voltage is applied across the working and reference electrodes 804 and 806, a current may be considered to flow (forward or backward depending on polarity) through the internal electronics of transmitter (represented by R_Tx_internal) 822; through the reference electrode (RE) 806 and working electrode (WE) 804. which may be designed to have a relatively low resistance; and through the sensor membrane 812 (Rmembr, which is relatively small). Depending on the state of the circuit, current may also flow through, or into, the relatively large polarization resistance 810 (which is indicated as a fixed resistance but may also be a variable resistance that varies with the body’s glucose level, where a higher glucose level provides a smaller polarization resistance), or into the double-layer capacitance 808 (i.e., to charge the double-layer membrane capacitor formed at the working electrode 804). or both.

[0123] The impedance (or conductance) of the membrane (Rmembr) 812 is related to electrolyte mobility in the membrane, which is in turn related to glucose diffusivity in the membrane. As the impedance goes down (i.e.,30Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 conductance goes up, as electrolyte mobility in the membrane 812 goes up), the glucose sensitivity goes up (i.e., a higher glucose sensitivity means that a particular glucose concentration will produce a larger signal in the form of more current or charge flow). Impedance, glucose diffusivity, and glucose sensitivity are further described in U.S. Patent Publication No. US2012 / 0262298, which is incorporated by reference in its entirety.

[0124] FIG. 9 illustrates an example method 900 for predicting PSD using a temperature characteristic, according to some examples. Although the example method 900 depicts a particular sequence of operations, the sequence may be altered without departing from the scope of the present disclosure. For example, some of the operations depicted may be performed in parallel or in a different sequence that does not materially affect the function of the method 900. In other examples, different components of an example device or system that implements the method 900 may perform functions at substantially the same time or in a specific sequence. In differing embodiments, the various steps shown in FIG. 9 may be rearranged and / or modified (e.g., addition of new steps and / or elimination of steps shown).

[0125] At block 902, the analyte sensor system (such as the temperature- compensated glucose sensor system) may receive a first glucose signal associated with a raw glucose measurement of a user from a glucose sensor within a first time period. The sensor acquires the initial glucose measurement data (such as the raw sensor signal) from the glucose sensor at a certain time period, such as at the start of the monitoring process. This data serves as the foundation for subsequent analyses and comparisons that will detect PSD over time.

[0126] Values of the raw sensor signal over the first time period may be stored. The first time period may be, for example, at or near the beginning of a sensor time period. Values of the raw sensor signal captured during the first time period may be used for subsequent analyses and comparisons to detect sensor sensitivity degradation over time.

[0127] Although examples described herein include accessing, generating, or receiving certain signals over a time period, it is appreciated that the31Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 system can access, generate, or receive a signal at a particular time, and vice versa.

[0128] Although examples described herein describe examples to a temperature-compensation glucose sensor system, it is appreciated that the features can be applied to other systems, such as an analyte sensor system.

[0129] FIG. 10 illustrates a sensor system with a glucose sensor 1010 under a skin 1008 and a temperature sensor 1006 outside of the skin 1008 (e.g., on top of the skin 1008), according to some examples. A glucose sensor 1010 of the sensor system 1000 is embedded under the skin 1008 of a patient that continuously measures the glucose concentration in the interstitial fluid. Sensor electronics 1004 (e.g., may include one or more processors and / or memon') within a housing 1002 collects and processes data from the glucose sensor 1010.

[0130] In some cases, the first time period where initial raw glucose measurements are taken can be an initial timeframe immediately after the glucose sensor 1010 starts operating. This period may be immediately after initial use, or after a certain time period upon initial use, such as a few minutes or hours of sensor use, when the sensor's performance is expected to be optimal.

[0131] The glucose sensor 1010 measures the glucose concentration in the interstitial fluid. The raw glucose measurement is the direct, unprocessed output from the glucose sensor 1010, which may not yet have been subjected to any filtering or calibration. In some cases, the glucose sensor 1010 performs one or more processing to convert the raw sensor signal to an estimated analyte concentration.

[0132] The raw sensor signal includes an electrical signal that corresponds to the glucose concentration in the interstitial fluid. The raw sensor signal is represented in units of electrical current or voltage, which are proportional to the glucose levels.

[0133] This raw sensor signal is transmitted from the glucose sensor 1010 to the sensor electronics 1004. The sensor electronics 1004 captures the raw sensor signal data and logs this data along with a timestamp to mark the specific time within the first time period when the measurement was taken.32Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01The received data is stored at a memory' of the sensor electronics and / or sent to a connected device (such as a smartphone app) for further processing and analysis.

[0134] For example, upon insertion, the glucose sensor 1010 may begin generating the raw sensor signal. The glucose sensor 1010 takes its initial measurement of glucose concentration in the interstitial fluid and converts the glucose concentration into an electrical signal (raw sensor signal). The raw sensor signal is transmitted to the sensor electronics 1004, and the sensor electronics 1004 logs the raw sensor signal along with the timestamp in memory.

[0135] Returning to the flow diagram of FIG. 9, at block 904, the temperature-compensated glucose sensor system may receive a first temperature signal associated with a first temperature measurement of the user from a temperature sensor within the first time period. The temperature sensor acquires an initial temperature measurement data from the temperature sensor during the same time period as the first glucose measurement. This temperature data can be used in conjunction with the glucose data to improve the accuracy and reliability of detecting PSD.

[0136] In some cases, the analyte sensor system acquires the temperature measurement data at the same time as the raw sensor signal. In some cases, the analyte sensor system acquires the temperature measurement data and the raw sensor signal at multiple times within the same time period and matches the readings between the two signals. In some cases, the temperature measurement data is measured at a certain time period and the raw sensor signal is measured at a time period that is offset from the time period for the temperature measurement data. In some cases, the temperature measurement data is measured at a certain time and the raw sensor signal is measured at a time that is offset from the time for the temperature measurement data (as further described herein). Although examples herein describe one type of timing, it is appreciated that other types of measurement timings can be applied.

[0137] The sensor electronics that collects and processes data from the glucose sensor can also receive data from the temperature sensor. In other33Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 cases, the glucose sensor and the temperature sensor data are processed by different sensor electronics, such as a smart watch and a glucose monitor.

[0138] When the analyte sensor system is activated, the temperature sensor (or multiple temperature sensors) can also be initialized. The temperature sensor measures the temperature at the site of the sensor (e.g., ambient temperature near the body of the patient, skin temperature, body temperature, etc.). For example, this can be done through direct contact with the skin or by measuring the ambient temperature in the immediate vicinity of the glucose sensor.

[0139] As shown in FIG. 10, the system includes a temperature sensor 1006 that is disposed at, on, or near a surface of the host’s skin 1008. The sensor electronics 1004 capture the raw temperature signal data at a certain time period, such as the first time period. The sensor electronics 1004 log this data along with a timestamp to mark the specific moment within the first time period when the measurement was taken.

[0140] In some cases, the analyte sensor system measures the temperature and the glucose at the same time, such as a first time period. In some cases, the analyte sensor system delays measurement of either the temperature or glucose by an offset.

[0141] The analyte sensor system may need to account for slight temporal offsets between glucose and temperature measurements to ensure accurate data correlation and interpretation. For example, there may be some physiological delay between changes in blood glucose levels and their detection in interstitial fluid, where analyte system sensor typically measure glucose. This delay, known as the interstitial fluid lag, can vary, such as from a few milliseconds to a few seconds. To align the glucose readings more closely with real-time physiological changes, temperature measurements, which can reflect immediate metabolic activity, may need to be slightly offset in time.

[0142] Another reason for temporal offsets is the sensor's response time to environmental or physiological changes. For instance, after physical activity or a meal, glucose levels and body temperature can both change, but not necessarily at the same rate or moment. Temperature sensors might detect34Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 increases in skin temperature almost immediately, while glucose sensors might take a few minutes to register corresponding changes in glucose levels due to the time required for glucose to equilibrate between blood and interstitial fluid. By allowing for a controlled time offset, the analyte sensor system can more accurately synchronize these readings, improving the reliability of detecting sensor performance degradation (PSD).

[0143] Moreover, technological and environmental factors can introduce delays in signal processing and data transmission. For example, wireless data transfer from the sensor to a receiver or smartphone may experience brief lags, and the sensor itself may have a slight processing delay when converting raw signals into readable data. By implementing a time offset between glucose and temperature measurements, the system can compensate for these minor discrepancies, ensuring that the data used for PSD detection reflects a more synchronized and accurate representation of the user’s physiological state.

[0144] In some cases, the first time period includes an initial activation period. The initial activation period can include a certain time subsequent to the sensor being applied to the body. The initial activation period can include a certain time subsequent to the capture of sensor data .

[0145] The initial activation period can include a certain time subsequent to initial stabilization of the sensor. The sensor can determine an initial stabilization of the sensor based on one or more factors of the sensor system. The initial stabilization of a continuous glucose monitoring (CGM) sensor can include a period where the sensor adjusts to the user's body and begins to provide accurate readings.

[0146] For example, when first inserted, the sensor may need to become hydrated by the interstitial fluid to function correctly. This hydration process allows the sensor's enzyme (e.g., glucose oxidase) to start reacting with glucose. The system can monitor the initial fluctuations in sensor readings and detect when they stabilize, indicating proper hydration.

[0147] The body’s tissue surrounding the sensor may need time to adjust to the foreign object. This includes reducing initial inflammation and tissue reactions that can interfere with accurate glucose measurements. The system35Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 can detect stabilization by monitoring for a decrease in noise and variability in the signal, which often corresponds with reduced tissue response.

[0148] The sensor’s electrodes may need to reach a stable electrochemical state. Initial readings might be unstable until this is achieved. The sensor system can track the consistency of electrochemical signals and detect when they reach a steady state.

[0149] The sensor may need to reach thermal equilibrium with the body's temperature. The system can use temperature readings to determine when the sensor’s temperature matches the body's temperature, ensuring accurate glucose measurements.

[0150] The enzyme coating on the sensor may need to reach optimal activity levels. The system can detect consistent enzyme activity by monitoring for stable glucose readings over time.

[0151] The user's overall physiological condition, such as hydration status, recent physical activity, and metabolic rate, can affect initial sensor readings. The system can assess stabilization by detecting consistent readings that account for these physiological factors. The system can access sensor data of other types of data, such as hydration or physical activity, to wait for a certain physiological state to measure the baseline data of glucose and temperature.

[0152] In some cases, the first time period includes a daily cycle, such as in the morning after waking up when the body is at rest, midday during typical active periods, or evenings before going to bed when the body is winding down.

[0153] In some cases, the first time period includes activity-based periods, such as pre-exercise before starting physical activities, post-exercise immediately after completing physical activities, during exercise, resting during periods of minimal physical activity, such as sitting or lying down, or post-meal after consuming food, such as after main meals like breakfast, lunch, or dinner.

[0154] In some cases, the first time period includes physiological cycles, such as fasting periods of times when the user has not eaten for several36Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 hours, postprandial periods shortly after eating when glucose levels typically rise, or sleep periods during nighttime sleep cycles.

[0155] In some cases, the first time period includes environmental conditions, such as stable temperature conditions when the user is in a controlled environment with consistent temperature (e.g.. indoors), or variable temperature conditions when the user is exposed to changing environmental temperatures (e.g., moving between indoor and outdoor settings).

[0156] In some cases, the first time period includes times during a sensor time period, such as early sensor time periods including an initial phase immediately following application of the sensor, such as the first day or first few days, mid sensor time period including the middle of the sensor's expected lifespan, or late sensor time period including a final phase before the sensor is due for replacement.

[0157] In some cases, the first time period includes steady state periods when glucose levels are relatively stable without significant fluctuations, glucose spikes when glucose levels experience rapid increases or decreases, or recovery' periods following a glucose spike, when levels are returning to baseline.

[0158] In some cases, the first time period includes post-insulin administration, such as immediate post-administration shortly after insulin injection or pump activation or delayed post-administration several minutes to an hour after insulin administration.

[0159] In some cases, the first time period includes a time period based on the hydration state of the user, a stress level of the user, hormonal cycle of the user, or environmental changes such as an altitude change.

[0160] As shown in FIG. 10, the temperature sensor 1006 measures the temperature at or near the site of the glucose sensor. For example, the temperature sensor may be positioned on the same area of the body of the user as the glucose sensor. In some cases, the temperature sensor is positioned on the same appendage of the body of the user as the glucose sensor.37Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01

[0161] In some cases, the glucose and temperature sensors are integrated into a single device. The glucose sensor penetrates the skin to measure interstitial glucose levels, while the temperature sensor is on the device's surface, measuring skin temperature. In other cases, both glucose and temperature sensors are embedded beneath the skin in a single device, providing real-time data from the same anatomical location, or both measured from the skin of the user.

[0162] In some cases, both sensors are mounted on a common adhesive patch. The glucose sensor penetrates the skin, while the temperature sensor remains on the surface, ensuring coordinated measurements.

[0163] In some cases, the glucose sensor is inserted into the skin, while the temperature sensor is part of a wearable device such as a wearable device 120 or a fitness tracker. The wearable device and the glucose sensor communicate wirelessly with each other or a third computing device (such as a hand-held smart device 112 of FIG. 1) to provide synchronized data.

[0164] In some cases, the glucose sensor is positioned on one part of the body (e.g., the abdomen), and the temperature sensor is located on a different part (e.g., the arm or wrist). The system integrates data from both locations to assess sensor performance.

[0165] Returning to FIG. 9, at block 906, the temperature-compensated glucose sensor system may determine a first relationship between the first glucose signal and the first temperature signal. The sensor analyzes the data collected in block 902 and block 904 to identify how the glucose measurements and temperature measurements correlate with each other during the initial time period.

[0166] In some cases, the system identifies a difference or pattern between the glucose and temperature data sets to identify a relationship between the two data sets. The system can identify a difference at one or more points between the glucose measurement and the temperature measurement, such as in the first time frame.

[0167] FIG. 11 illustrates an example of the identification of a baseline relationship and PSD detection, according to some examples. The sensor system can identify a difference in one or more time stamps, such as at38Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 steady state (e.g., baseline 1 1102) at the start of the wearing of the monitor by the user or at a peak event (e.g., baseline 2 1104) such as a first spike when there is no PSD.

[0168] In some cases, the system performs a correlation analysis between the glucose and temperature signals. This analysis involves statistical techniques to assess the strength and nature of the relationship between the two signals. For example, the sensor system can measures the linear correlation between the glucose and temperature signals, indicating how closely the changes in one signal correspond to changes in the other. In some cases, the sensor system establishes a mathematical model that describes the relationship between glucose and temperature. For instance, the sensor system can derive a linear equation where glucose levels are predicted based on temperature readings.

[0169] In some cases, the sensor system applies a machine learning model to identify a relationship between the glucose and temperature signals. For example, the machine learning model can take, as training data, historical glucose and temperature data to generate a certain relationship or baseline in order to identify PSD in future current readings.

[0170] The system examines the morphological patterns of the glucose and temperature signals by the shapes of the signal curves correspond over time, considering aspects such as peaks, troughs, and overall trends. The system may apply rate of change analysis by evaluating how the rate of change in glucose levels corresponds to the rate of change in temperature. The system may apply frequency analysis by identifying common frequency components in both signals using methods like Fast Fourier Transform (FFT) to see if they share similar periodic behaviors.

[0171] The result of the correlation and morphology analysis forms a baseline relationship between the glucose and temperature signals during the initial time period. This baseline represents the expected normal interaction between glucose levels and temperature under optimal sensor conditions.

[0172] The baseline relationship can be represented through various metrics such as a difference value, correlation coefficients, regression equations, or similarity indices derived from the morphological comparison. The system39Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 stores the established relationship parameters, which serve as a reference for future comparisons. This relationship can include the statistical and mathematical representations of the relationship, any observed patterns, and the contextual conditions under which the data was collected (e.g., user activity levels, environmental conditions).

[0173] At block 908, the temperature-compensated glucose sensor system may receive a second glucose signal associated with a raw glucose measurement of the user from the glucose sensor within a second time period. The sensor system collects glucose data during a later phase of the sensor's lifespan, beyond the initial time period used for establishing the baseline. The second glucose signal reflects the sensor's current performance and is used for ongoing monitoring and comparison against the baseline data to detect potential degradation.

[0174] The second time period can be selected based on various criteria, such as a predetermined interval after the initial period, a specific duration of continuous wear, or triggered by certain events (e.g., user activity changes or environmental conditions).

[0175] The collected glucose measurements during this second period can include raw, unprocessed data from the glucose sensor, representing the realtime glucose levels in the user's interstitial fluid.

[0176] At block 910, the temperature-compensated glucose sensor system may receive a second temperature signal associated with a second temperature measurement of the user from the temperature sensor within the second time period. This step may mirror the process of block 908 but focuses on gathering temperature data during the same later phase of the sensor's usage. The second temperature signal reflects the thermal conditions that the glucose sensor is operating under during the second time period.

[0177] By collecting temperature data with glucose measurements concurrently within the second time period, the system ensures that both sets of data are aligned temporally, facilitating accurate correlation and analysis in subsequent steps.

[0178] At block 912, the temperature-compensated glucose sensor system may determine a second relationship between the second glucose40Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 signal and the second temperature signal. The sensor system can analyze the newly collected data to identify how the glucose and temperature signals correlate during the second time period. The process can include one or more features as described in block 906 for the initial period, involving statistical and morphological analysis to establish the nature and strength of the relationship between glucose and temperature under the current sensor conditions or the application of machine learning models.

[0179] The sensor system may calculate correlation coefficients, perform regression analysis, and compare the morphological patterns of the signals. The resulting second relationship reflects the sensor's performance and how the sensor interacts with temperature at this later stage.

[0180] Returning to FIG. 9, at block 914, the temperature-compensated glucose sensor system may determine an onset of PSD of the glucose sensor based on the first and second relationships. By comparing this second relationship to the baseline established earlier, the system can detect any deviations that might indicate PSD. thereby ensuring timely interventions and maintaining accurate glucose monitoring.

[0181] Based on the baseline relationship, the system can set thresholds for detecting deviations in future measurements. If subsequent glucose and temperature signals significantly deviate from this baseline, the sensor system may indicate that the onset of PSD has occurred.

[0182] Returning to FIG. 11, the system compares a relationship during a time frame of normal readings (e.g., when the user is in steady state) when there has been an onset of PSD. As shown, the system determines that there is a disparity over a certain threshold between the relationship found in baseline 1 1102 and the relationship 1106 during normal readings and determines that there has been an onset of PSD. In some cases, the system determines that there is a disparity over a certain threshold between the relationship found in baseline 2 1104 and the relationship 1108 during the second spike and determines that there has been an onset of PSD.

[0183] In some cases, the system compares the two relationships between glucose and temperature signals by analyzing how the correlations and patterns have changed over time. For example, the sensor system can41 Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 perform correlation coefficient comparison by measuring the linear correlation between the glucose and temperature signals in both periods and comparing the coefficients to see if there is a significant change in the strength of the relationship. In other cases, the system assesses the monotonic relationship between glucose and temperature signals. And compares changes in rank correlation between the two periods.

[0184] In some cases, the system performs regression analysis and compares the linear regression equations derived from the glucose and temperature data in both periods by analyzing shifts in the slope or intercept of the regression lines. In some cases, the system evaluate changes in higher-order polynomial fits to understand if the relationship becomes nonlinear over time.

[0185] In some cases, the system calculates the absolute difference between the glucose and temperature signal pairs from the two periods, which helps in identifying changes in the relationship strength. In some cases, the system analyzes the relative percentage change between the two sets of glucose and temperature relationships.

[0186] In some cases, the system compares the overall shape and patterns of the glucose and temperature signal curves, and looks for shifts or distortions in the patterns that indicate potential issues. In some cases, the system examines changes in the locations and magnitudes of peaks and troughs in both signal sets.

[0187] In some cases, the system compares the rate of change in glucose levels relative to the rate of change in temperature and identifies differences in how quickly glucose and temperature fluctuate over time. In some cases, the system assess the gradients of glucose and temperature signals and compare how these gradients differ between the two periods.

[0188] In some cases, the system performs an FFT on both sets of signals to compare their frequency components to analyze differences in dominant frequencies or spectral power. In some cases, the system looks at changes in the frequency components and their magnitudes to determine if the periodic behavior has shifted.42Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01

[0189] In some cases, the system compares the means and standard deviations of glucose and temperature signals, where significant changes in these statistics can indicate shifts in the relationship. In some cases, the system analyze changes in the variance of the glucose and temperature signals to understand if there is increased fluctuation in later periods.

[0190] In some cases, the system uses moving averages to smooth out the signals and compare how the smoothed trends change over time. In some cases, the system applies different smoothing techniques and compare the results to understand variations in the relationships.

[0191] In some cases, the system trains machine learning models to predict glucose levels from temperature data and compare model performance across the two periods. The system can evaluate changes in prediction accuracy or error metrics between the two periods. In some cases, the machine learning model outputs a threshold difference that is applied to new glucose and temperature signals, such as in particular circumstances (e.g., steady state, peaks, etc). In other cases, the machine learning model receives as input continuous temperature data and glucose data, and outputs an indication of an onset of PSD based on current temperature and glucose data.

[0192] Detection of PSD can be effectively achieved by comparing the relationships between glucose and temperature signals at different time frames. By analyzing the correlation and patterns of these signals during both steady-state periods and peak events, the sensor system can identify significant deviations that indicate sensor degradation.

[0193] For instance, if the relationship between glucose and temperature signals remains consistent during steady-state conditions but shows noticeable changes during peak events or fluctuations, this can signal an onset of PSD. Such deviations suggest that the sensor's ability to maintain accurate glucose readings relative to temperature changes is compromised.

[0194] The comparison involves assessing how well the glucose signal correlates with temperature during various states of sensor operation. For example, during a steady-state period, the system expects a stable relationship between glucose and temperature. Significant deviations from this expected relationship, such as increased noise or a breakdown in the43Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 correlation, may indicate that the sensor's sensitivity is declining. However, the system may also assess relationships during peak events where glucose and temperature are expected to exhibit sharp, correlated fluctuations, and any unexpected changes in their relationship can further confirm the presence of PSD. By examining these relationships across different operational conditions, the system can more reliably detect the onset of PSD and ensure timely maintenance or calibration to maintain accurate glucose monitoring.

[0195] In the description herein, the sensor system applies a temperature signal to determine the onset of PSD due to its similar morphological behavior to glucose signals. Both glucose and temperature signals exhibit dynamic fluctuations that can be influenced by various physiological and environmental factors, leading to patterns that often correlate closely under certain conditions.

[0196] By utilizing temperature data, which tends to mirror glucose fluctuations in its trends and peaks, the sensor system gain an additional metric to improve the detection of PSD in glucose sensors. The correlation between glucose and temperature changes helps identify deviations from expected patterns, enhancing the ability to detect when sensor sensitivity is beginning to decline.

[0197] However, it is appreciated that other types of signals with a similar morphology to glucose can be used. For example, the system can apply the rate of change in temperature to the rate of change in glucose levels to identify discrepancies that might indicate sensor degradation.

[0198] The system can apply temperature gradients and the mean or standard deviation of temperature readings to provide a basis for understanding how closely temperature tracks glucose levels and the gradients, means, and standard deviations thereof.

[0199] While temperature provides a valuable supplementary metric, other physiological signals can also have similar morphologic characteristics to glucose, and which can also be assessed instead of or in conjunction with temperature to identify the onset of PSD. For instance, the sensor signal can apply the features described herein to humidity7, spO2 oxygen saturation,44Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 skin impedance, heart rate, and hydration levels. Blood pressure, sweat characteristics, skin pH levels, electrodermal measurements, and metabolic rate. By integrating these diverse signals, the system can create alternate or more comprehensive systems for monitoring sensor performance, improving the reliability and accuracy of glucose monitoring over time.

[0200] After the detecting of the onset of PSD, the sensor system can perform one or more actions. The system can send notifications or alerts to the user, informing them that the sensor is experiencing degradation. These alerts could include visual, auditory’, or haptic notifications to ensure that the user is promptly aware of the issue.

[0201] The analyte sensor system can end the sensor time period and / or may not enable the user to start a new time period until a new sensor is inserted. The host may be required to insert a new sensor to begin a new time period.

[0202] The system may prompt the user to recalibrate the sensor, such as by performing a manual calibration with a blood glucose meter to restore accuracy and adjust for any detected degradation. If the degradation is significant or persistent, the system could recommend or remind the user to replace the glucose sensor, ensuring that the user continues to receive accurate glucose readings.

[0203] The system can log the data associated with the PSD detection, including glucose and temperature signals, which can be later analyzed to provide insights into the cause of the degradation and to improve future sensor designs.

[0204] The system may initiate diagnostic checks to determine if the degradation is due to external factors, such as incorrect sensor placement or environmental conditions, which can help isolate the problem and provide solutions. The system may provide enhanced reporting features to track the performance trends of the sensor over time, helping users and healthcare providers to monitor and assess the sensor’s long-term reliability.

[0205] The system may be integrated with healthcare provider platforms, automatically sharing information about sensor degradation and allowing healthcare professionals to review the data and make informed45Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 recommendations. In some cases, the healthcare provider platforms can automatically schedule an appointment or order a replacement as a result of the onset of PSD.

[0206] The system may perform self-diagnostics and apply software updates to optimize sensor performance and address potential issues, ensuring that the monitoring system remains up-to-date and effective. The system may offer preventive measures, such as guidance on optimal sensor placement, avoiding certain activities that may accelerate degradation, or an indication of an unreliable glucose reading.

[0207] The system can adjust its monitoring algorithms to account for the detected degradation by modifying the way glucose readings are interpreted based on the current performance of the sensor. For instance, if performance degradation causes the sensor to produce consistently lower or erratic glucose readings, the system can apply correction factors or adjustments to normalize the data. The sensor system can use historical calibration data or real-time corrections based on observed deviations between glucose and temperature signals. The algorithms can be fine-tuned to filter out noise and minimize the impact of degradation, thereby enhancing the reliability of glucose measurements.

[0208] Additionally, the system can adapt its algorithms by incorporating adaptive filtering techniques or dynamic models that adjust to changing sensor conditions. For example, the system can use predictive modeling to estimate and correct for anticipated errors based on the current degradation pattern. By continuously updating these algorithms based on ongoing performance metrics, the system can provide more accurate glucose readings and maintain effective diabetes management, even as the sensor’s performance evolves over time.

[0209] In some cases, the sensor system can apply a threshold difference between the first relationship and the second relationship. The threshold can then be defined as a specific degree or percentage above or below the relationship characteristic. For instance, if the initial steady-state relationship indicates a difference in 2 °C, a threshold might be set at ±.2°C, meaning that any significant deviation beyond 1.8°C to 2.2°C would trigger46Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 an indication of PSD. Alternatively, the threshold could be a percentage of the initial relationship, such as 5%. which would translate to a range of 1.9°C to 2.1 °C for the same baseline relationship.

[0210] In some cases, the sensor system determines the threshold using statistical analysis of historical temperature data to identify typical fluctuations and set thresholds accordingly. For example, the sensor system can use standard deviations from the mean relationships during normal operation to define acceptable limits.

[0211] In some cases, the sensor system applies a machine learning algorithm to leam from patterns in the relationship data over time, dynamically adjusting thresholds based on detected trends and anomalies. Additionally, thresholds can be set by comparing the initial relationship with known environmental factors or operational parameters, ensuring that the sensor operates within safe and optimal conditions.

[0212] FIG. 13 is a flowchart depicting a machine-learning pipeline 1300, according to some examples. The machine-learning pipelines 1300 may be used to generate a trained model, for example the trained machine-learning program 1302 of FIG. 13, described herein to perform operations associated with searches and query responses.Machine learning may involve using computer algorithms to automatically leam patterns and relationships in data, potentially without the need for explicit programming to do so after the algorithm is trained. Examples of machine learning algorithms may include models based on supervised learning, models based on unsupervised learning, and models based on reinforcement learning models. Supervised learning involves training a model using labeled data to predict an output for new, unseen inputs. Examples of supervised learning algorithms include linear regression, decision trees, and neural networks. Unsupervised learning involves training a model on unlabeled data to find hidden patterns and relationships in the data. Examples of unsupervised learning algorithms include clustering, principal component analysis, and generative models like autoencoders. Reinforcement learning involves training a model to make decisions in a dynamic environment by receiving feedback in the form of rew ards or47Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 penalties. Examples of reinforcement learning algorithms include Q-leaming and policy gradient methods.

[0213] Examples of specific machine learning algorithms that may be deployed, according to some examples, include logistic regression, which is a type of supervised learning algorithm used for binary classification tasks. Logistic regression models the probability of a binary response variable based on one or more predictor variables. Another example type of machine learning algorithm is Naive Bayes, which is another supervised learning algorithm used for classification tasks. Naive Bayes is based on Bayes' theorem and assumes that the predictor variables are independent of each other. Random Forest is another type of supervised learning algorithm used for classification, regression, and other tasks. Random Forest builds a collection of decision trees and combines their outputs to make predictions. Further examples include neural networks which consist of interconnected layers of nodes (or neurons) that process information and make predictions based on the input data. Matrix factorization is another ty pe of machine learning algorithm used for recommender systems and other tasks. Matrix factorization decomposes a matrix into two or more matrices to uncover hidden patterns or relationships in the data. Support Vector Machines (SVM) are a type of supervised learning algorithm used for classification, regression, and other tasks. SVM finds a hyperplane that separates the different classes in the data. Other types of machine learning algorithms include decision trees, k-nearest neighbors, clustering algorithms, and deep learning algorithms such as convolutional neural networks (CNN), recurrent neural networks (RNN), and transformer models. The choice of algorithm depends on the nature of the data, the complexity of the problem, and the performance requirements of the application.

[0214] The performance of machine learning models is ty pically evaluated on a separate test set of data that was not used during training to ensure that the model can generalize to new, unseen data. Evaluating the model on a separate test set helps to mitigate the risk of overfitting, a common issue in machine learning where a model learns to perform exceptionally well on the training data but fails to maintain that performance on data it hasn't48Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 encountered before. By using a test set, the system obtains a more reliable estimate of the model's real-world performance and its potential effectiveness when deployed in practical applications.

[0215] Although several specific examples of machine learning algorithms are discussed herein, the principles discussed herein can be applied to other machine learning algorithms as well. Deep learning algorithms such as convolutional neural networks, recurrent neural networks, and transformers, as well as more traditional machine learning algorithms like decision trees, random forests, and gradient boosting may be used in various machine learning applications.

[0216] Two example ty pes of problems in machine learning are classification problems and regression problems. Classification problems, also referred to as categorization problems, aim at classifying items into one of several category7values (for example, is this object an apple or an orange?). Regression algorithms aim at quantifying some items (for example, by providing a value that is a real number).

[0217] Generating a trained machine-learning program 1302 may include multiple types of phases that form part of the machine-learning pipeline 1300, including for example the following phases 1200 illustrated in FIG. 12. Data collection and preprocessing 1202 may include acquiring and cleaning data to ensure that it is suitable for use in the machine learning model. Data can be gathered from user content creation and labeled using a machine learning algorithm trained to label data. Data can be generated by applying a machine learning algorithm to identify or generate similar data. This may also include removing duplicates, handling missing values, and converting data into a suitable format.

[0218] Feature engineering 1204 may include selecting and transforming the training data 1304 to create features that are useful for predicting the target variable. Feature engineering may include (1) receiving features 1306 (e.g., as structured or labeled data in supervised learning) and / or (2) identifying features 1306 (e g., unstructured or unlabeled data for unsupervised learning) in training data 1304.49Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01

[0219] Model selection and training 1206 may include specifying a particular problem or desired response from input data, selecting an appropriate machine learning algorithm, and training it on the preprocessed data. This may further involve splitting the data into training and testing sets, using cross-validation to evaluate the model, and tuning hyperparameters to improve performance. Model selection can be based on factors such as the type of data, problem complexity, computational resources, or desired performance.

[0220] Model evaluation 1208 may include evaluating the performance of a trained model (e.g., the trained machine-learning program 1302) on a separate testing dataset. This can help determine if the model is overfitting or underfitting and if it is suitable for deployment.

[0221] Prediction 1210 may involve using a trained model (e.g., trained machine-learning program 1302) to generate predictions on new, unseen data. Validation, refinement or retraining 1212 may include updating a model based on feedback generated from the prediction phase, such as new data or user feedback. Deployment 1214 may include integrating the trained model (e.g., the trained machine-learning program 1302) into a larger system or application, such as a web service, mobile app, medical device, or loT device. This can involve setting up APIs, building a user interface, and ensuring that the model is scalable and can handle large volumes of data.

[0222] FIG. 13 illustrates two example phases, namely a training phase 1308 (part of the model selection and trainings 1206) and a prediction phase 1310 (part of prediction 1210). Prior to the training phase 1308, feature engineering 1204 is used to identify features 1306. This may include identifying informative, discriminating, and independent features for the effective operation of the trained machine-learning program 1302 in pattern recognition, classification, and regression. In some examples, the training data 1304 includes labeled data, which is known data for pre-identified features 1306 and one or more outcomes.

[0223] Each of the features 1306 may be a variable or attribute, such as individual measurable property of a process, article, system, or phenomenon represented by a data set (e.g., the training data 1304). Features 1306 may50Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 also be of different types, such as numeric features, strings, vectors, matrices, encodings, and graphs, and may include one or more of content 1312, concepts 1314, attributes 1316, historical data 1318 and / or user data 1320, merely for example. Concept features can include abstract relationships or patterns in data, such as determining a topic of a document or discussion in a chat window between users. Content features include determining a context based on input information, such as determining a context of a user based on user interactions or surrounding environmental factors.

[0224] In training phases 1308, the machine-learning pipeline 1300 uses the training data 1304 to find correlations among the features 1306 that affect a predicted outcome or prediction / inference data 1322.

[0225] With the training data 1304 and the identified features 1306, the trained machine-learning program 1302 is trained during the training phase 1308 during machine-learning program training 1324. The machine-learning program training 1324 appraises values of the features 1306 as they correlate to the training data 1304. The result of the training is the trained machinelearning program 1302 (e.g., a trained or learned model).

[0226] Further, the training phase 1308 may involve machine learning, in which the training data 1304 is structured (e.g., labeled during preprocessing operations), and the trained machine-learning program 1302 implements a relatively simple neural network 1326 capable of performing, for example, classification and clustering operations. In other examples, the training phase 1308 may involve deep learning, in which the training data 1304 is unstructured, and the trained machine-learning program 1302 implements a deep neural network 1326 that is able to perform both feature extraction and classification / clustering operations.

[0227] A neural network 1326 may, in some examples, be generated during the training phase 1308, and implemented within the trained machinelearning program 1302. The neural network 1326 includes a hierarchical (e g., layered) organization of neurons, with each layer including multiple neurons or nodes. Neurons in the input layer receive the input data, while neurons in the output layer produce the final output of the network. Between51Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 the input and output layers, there may be one or more hidden layers, each including multiple neurons.

[0228] Each neuron in the neural network 1326 operationally computes a small function, such as an activation function that takes as input the weighted sum of the outputs of the neurons in the previous layer, as well as a bias term. The output of this function is then passed as input to the neurons in the next layer. If the output of the activation function exceeds a certain threshold, an output is communicated from that neuron (e.g., transmitting neuron) to a connected neuron (e.g., receiving neuron) in successive layers. The connections between neurons have associated weights, which define the influence of the input from a transmitting neuron to a receiving neuron. During the training phase, these weights are adjusted by the learning algorithm to optimize the performance of the network. Different types of neural networks may use different activation functions and learning algorithms, which can affect their performance on different tasks. Overall, the layered organization of neurons and the use of activation functions and weights enable neural networks to model complex relationships between inputs and outputs, and to generalize to new inputs that were not seen during training.

[0229] In some examples, the neural network 1326 may also be one of a number of different types of neural networks or a combination thereof, such as a single-layer feed-forward network, a Multilayer Perceptron (MLP), an Artificial Neural Network (ANN), a Recurrent Neural Network (RNN), a Long Short-Term Memory' Network (LSTM), a Bidirectional Neural Network, a symmetrically connected neural network, a Deep Belief Network (DBN). a Convolutional Neural Network (CNN), a Generative Adversarial Network (GAN), an Autoencoder Neural Network (AE), a Restricted Boltzmann Machine (RBM), a Hopfield Network, a Self-Organizing Map (SOM), a Radial Basis Function Network (RBFN), a Spiking Neural Network (SNN), a Liquid State Machine (LSM), an Echo State Network (ESN), a Neural Turing Machine (NTM), or a Transformer Network, merely for example.52Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01

[0230] In addition to the training phase 1308, a validation phase may be performed evaluated on a separate dataset known as the validation dataset. The validation dataset is used to tune the hyperparameters of a model, such as the learning rate and the regularization parameter. The hyperparameters are adjusted to improve the performance of the model on the validation dataset.

[0231] The neural network 1326 is iteratively trained by adjusting model parameters to minimize a specific loss function or maximize a certain objective. The system can continue to train the neural network 1326 by adjusting parameters based on the output of the validation, refinement, or retraining block 1212, and rerun the prediction 1210 on new or already run training data. The system can employ optimization techniques for these adjustments such as gradient descent algorithms, momentum algorithms, Nesterov Accelerated Gradient (NAG) algorithm, and / or the like. The system can continue to iteratively train the neural network 1326 even after deployment 1214 of the neural network 1326. The neural network 1326 can be continuously trained as new data emerges, such as based on user creation or system-generated training data.

[0232] Once a model is fully trained and validated, in a testing phase, the model may be tested on a new dataset that the model has not seen before. The testing dataset is used to evaluate the performance of the model and to ensure that the model has not overfit the training data.

[0233] In prediction phase 1310, the trained machine-learning program 1302 uses the features 1306 for analyzing query data 1328 to generate inferences, outcomes, or predictions, as examples of a prediction / inference data 1322. For example, during prediction phase 1310, the trained machinelearning program 1302 is used to generate an output. Query7data 1328 is provided as an input to the trained machine-learning program 1302, and the trained machine-learning program 1302 generates the prediction / inference data 1322 as output, responsive to receipt of the query' data 1328.

[0234] In some examples the trained machine-learning program 1302 may be a generative Al model. Generative Al is a term that may refer to any type of artificial intelligence that can create new content from training data 1304.53Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01For example, generative Al can produce text, images, video, audio, code or synthetic data that are similar to the original data but not identical.

[0235] Some of the example techniques that may be used in generative Al include:• Convolutional Neural Networks (CNNs): CNNs are commonly used for image recognition and computer vision tasks. They are designed to extract features from images by using filters or kernels that scan the input image and highlight important patterns.• Recurrent Neural Networks (RNNs): RNNs are designed for processing sequential data, such as speech, text, and time series data. They have feedback loops that allow them to capture temporal dependencies and remember past inputs.• Generative adversarial networks (GANs): These are models that consist of two neural networks: a generator and a discriminator. The generator tries to create realistic content that can fool the discriminator, while the discriminator tries to distinguish between real and fake content. The two networks compete with each other and improve over time.• Variational autoencoders (VAEs): These are models that encode input data into a latent space (a compressed representation) and then decode it back into output data. The latent space can be manipulated to generate new variations of the output data. They may use self-attention mechanisms to process input data, allowing them to handle long sequences of text and capture complex dependencies.• Transformer models: These are models that use attention mechanisms to leam the relationships between different parts of input data (such as words or pixels) and generate output data based on these relationships. Transformer models can handle sequential data such as text or speech as well as non-sequential data such as images or code.

[0236] In generative Al examples, the predict! on / inference data 1322 that is output include trend assessment and predictions, translations, summaries, image or video recognition and categorization, natural language processing, face recognition, user sentiment assessments, advertisement targeting and54Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 optimization, voice recognition, or media content generation, recommendation, and personalization.

[0237] FIG. 14 is a block diagram illustrating a computing device hardware architecture 1400, within which a set or sequence of instructions can be executed to cause a machine to perform examples of any one of the methodologies discussed herein. The hardware architecture 1400 can describe various computing devices including, for example, the sensor electronics 106, the peripheral medical device 122, the smart device 112, the tablet 114, etc.

[0238] The architecture 1400 may operate as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the architecture 1400 may operate in the capacity of either a server or a client machine in server-client network environments, or it may act as a peer machine in peer-to-peer (or distributed) network environments. The architecture 1400 can be implemented in a personal computer (PC), a tablet PC, a hybrid tablet, a set-top box (STB), a personal digital assistant (PDA), a mobile telephone, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing instructions (sequential or otherwise) that specify operations to be taken by that machine.

[0239] The example architecture 1400 includes a processor unit 1402 comprising at least one processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both, processor cores, compute nodes). The architecture 1400 may further comprise a main memory' 1404 and a static memory 1406, which communicate with each other via a link 1408 (e.g., bus). The architecture 1400 can further include a video display unit 1410, an input device 1412 (e.g., a keyboard), and a UI navigation device 1414 (e.g., a mouse). In some examples, the video display unit 1410, input device 1412, and UI navigation device 1414 are incorporated into a touchscreen display. The architecture 1400 may additionally include a storage device 1416 (e.g., a drive unit), a signal generation device 1418 (e.g., a speaker), a network interface device 1420, and one or more sensors (not shown), such as a Global Positioning System (GPS) sensor, compass, accelerometer, or another sensor.55Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01

[0240] In some examples, the processor unit 1402 or another suitable hardware component may support a hardware interrupt. In response to a hardware interrupt, the processor unit 1402 may pause its processing and execute an ISR, for example, as described herein.

[0241] The storage device 1416 includes a machine-readable medium 1422 on which is stored one or more sets of data structures and instructions 1424 (e.g., software) embodying or used by any one or more of the methodologies or functions described herein. The instructions 1424 can also reside, completely or at least partially, within the main memory’ 1404, within the static memory 1406. and / or within the processor unit 1402 during execution thereof by the architecture 1400, with the main memory71404, the static memory' 1406, and the processor unit 1402 also constituting machine- readable media.EXECUTABLE INSTRUCTIONS AND MACHINE-STORAGE MEDIUM

[0242] The various memories (i.e., 1404, 1406, and / or memory of the processor unit(s) 1402) and / or storage device 1416 may store one or more sets of instructions and data structures (e.g., instructions) 1424 embodying or used by any one or more of the methodologies or functions described herein. These instructions, when executed by processor unit(s) 1402 cause various operations to implement the disclosed examples.

[0243] As used herein, the terms “machine-storage medium,” “devicestorage medium,” “computer-storage medium” (referred to collectively as “machine-storage medium 1422”) mean the same thing and may be used interchangeably in this disclosure. The terms refer to a single or multiple storage devices and / or media (e.g., a centralized or distributed database, and / or associated caches and servers) that store executable instructions and / or data, as well as cloud-based storage systems or storage networks that include multiple storage apparatus or devices. The terms shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to processors. Specific examples of machine-storage media, computer-storage media, and / or device-storage media 1422 include non-volatile memory', including by 5Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 way of example semiconductor memory' devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), FPGA, and flash memory' devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms machine-storage media, computer-storage media, and device-storage media 1422 specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term ‘‘signal medium” discussed below.SIGNAL MEDIUM

[0244] The term “signal medium” or “transmission medium” shall be taken to include any form of modulated data signal, carrier wave, and so forth. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a matter as to encode information in the signal.COMPUTER-READABLE MEDIUM

[0245] The terms “machine-readable medium,” “computer-readable medium” and “device-readable medium” mean the same thing and may be used interchangeably in this disclosure. The terms are defined to include both machine-storage media and signal media. Thus, the terms include both storage devices / media and carrier waves / modulated data signals.

[0246] The instructions 1424 can further be transmitted or received over a communications network 1426 using a transmission medium via the network interface device 1420 using any one of a number of well-known transfer protocols (e.g., HTTP). Examples of communication netw orks include a LAN, a WAN, the Internet, mobile telephone networks, plain old telephone service (POTS) networks, and wireless data networks (e.g., Wi-Fi, 3G, 4G LTE / LTE-A, 5G or WiMAX networks). The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible media to facilitate communication of such software.57Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01

[0247] Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein.

[0248] Various components are described in the present disclosure as being configured in a particular way. A component may be configured in any suitable manner. For example, a component that is or that includes a computing device may be configured with suitable software instructions that program the computing device. A component may also be configured by virtue of its hardware arrangement or in any other suitable manner.

[0249] The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) can be used in combination with others. Other examples can be used, such as by one of ordinary skill in the art upon reviewing the above description. The Abstract is to allow the reader to quickly ascertain the nature of the technical disclosure, for example, to comply with 37 C.F.R.§1.72(b) in the United States of America. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.

[0250] Also, in the above Detailed Description, various features can be grouped together to streamline the disclosure. However, the claims cannot set forth every feature disclosed herein, as examples can feature a subset of said features. Further, examples can include fewer features than those disclosed in a particular example. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate example. The scope of the examples disclosed herein is to58Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

[0251] Each of these non-limiting examples in any portion of the above description may stand on its own or may be combined in various permutations or combinations with one or more of the other examples.

[0252] The above detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show, by way of illustration, specific embodiments in which the subject matter can be practiced. These embodiments are also referred to herein as “examples.” Such examples can include elements in addition to those shown or described. However, the present inventors also contemplate examples in which only those elements shown or described are provided. Moreover, the present inventors also contemplate examples using any combination or permutation of those elements shown or described (or one or more aspects thereof), either with respect to a particular example (or one or more aspects thereof), or with respect to other examples (or one or more aspects thereof) shown or described herein.

[0253] In the event of inconsistent usages between this document and any documents so incorporated by reference, the usage in this document controls.

[0254] In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or. such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. In this document, the terms “including” and “in which” are used as the plain- English equivalents of the respective terms “comprising” and “wherein.” Also, in the following claims, the terms “including” and “comprising” are open-ended, that is, a system, device, article, composition, formulation, or process that includes elements in addition to those listed after such a term in a claim are still deemed to fall within the scope of that claim. Moreover, in the following claims, the terms “first,” “second,” “third,” etc., are used merely as labels, and are not intended to impose numerical requirements on their objects.59Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01

[0255] Geometric terms, such as "parallel", "perpendicular", ‘‘round”, or “square” are not intended to require absolute mathematical precision, unless the context indicates otherwise. Instead, such geometric terms allow for variations due to manufacturing or equivalent functions. For example, if an element is described as “round” or “generally round”, a component that is not precisely circular (e.g., one that is slightly oblong or is a many-sided polygon) is still encompassed by this description.

[0256] Method examples described herein can be machine or computer- implemented at least in part. Some examples can include a computer- readable medium or machine-readable medium encoded with instructions operable to configure an electronic device to perform methods as described in the above examples. An implementation of such methods can include code, such as microcode, assembly language code, a higher-level language code, or the like. Such code can include computer readable instructions for performing various methods. The code may form portions of computer program products. Further, in an example, the code can be tangibly stored on one or more volatile, non -transitor , or non-volatile tangible computer- readable media, such as during execution or at other times. Examples of these tangible computer-readable media can include, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact disks and digital video disks), magnetic cassettes, memory7cards or sticks, random access memories (RAMs), read only memories (ROMs), and the like.

[0257] The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. Other embodiments can be used, such as by one of ordinary skill in the art upon reviewing the above description. The Abstract is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Also, in the above Detailed Description, various features may be grouped together to streamline the disclosure. This should not be interpreted as intending that an unclaimed disclosed feature is60Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description as examples or embodiments, with each claim standing on its own as a separate embodiment, and it is contemplated that such embodiments can be combined with each other in various combinations or permutations. The scope of the subject matter should be determined with reference to the claims, along with the full scope of equivalents to which such claims are entitled.

[0258] Unless the context clearly requires otherwise, throughout the description and the claims, the words “comprise,” “comprising,” and the like are to be construed in an inclusive sense, as opposed to an exclusive or exhaustive sense, i.e., in the sense of “including, but not limited to.” As used herein, the terms “connected,” “coupled,” or any variant thereof means any connection or coupling, either direct or indirect, between two or more elements; the coupling or connection between the elements can be physical, logical, or a combination thereof. Additionally, the words “herein,” “above,” “below,” and words of similar import, when used in this application, refer to this application as a whole and not to any particular portions of this application. Where the context permits, words using the singular or plural number may also include the plural or singular number respectively. The word “or” in reference to a list of two or more items, covers all of the following interpretations of the word: any one of the items in the list, all of the items in the list, and any combination of the items in the list. Likewise, the term “and / or” in reference to a list of two or more items, covers all of the following interpretations of the word: any one of the items in the list, all of the items in the list, and any combination of the items in the list.

[0259] Although some examples, e g., those depicted in the drawings, include a particular sequence of operations, the sequence may be altered without departing from the scope of the present disclosure. For example, some of the operations depicted may be performed in parallel or in a different sequence that does not materially affect the functions as described in the examples. In other examples, different components of an example61Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 device or system that implements an example method may perform functions at substantially the same time or in a specific sequence.

[0260] The various features, steps, and processes described herein may be used independently of one another, or may be combined in various ways. All possible combinations and subcombinations are intended to fall within the scope of this disclosure. In addition, certain method or process blocks may be omitted in some implementations.62Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01

Claims

1. Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01What is claimed is:

1. A temperature-compensated analyte sensor system comprising: an analyte sensor configured to generate a first analyte signal associated with a raw analyte measurement of a host within a first time period; a temperature sensor configured to generate a first temperature signal associated with a first temperature measurement of the host within the first time period; and sensor electronics configured to perform operations, the operations comprising: determining a first relationship between the first analyte signal and the first temperature signal; receiving a second analyte signal associated with a raw analyte measurement of the host from the analyte sensor within a second time period: receiving a second temperature signal associated with a second temperature measurement of the host from the temperature sensor within the second time period; determining a second relationship between the second analyte signal and the second temperature signal; and determining an onset of sensor sensitivity degradation of the analyte sensor based on the first and second relationships.

2. The sensor system of claim 1, wherein the first time period includes an initial activation time period of the sensor system when the analyte sensor is inserted under the skin of the host.

3. The sensor system of any of claims 1 -2, wherein the first time period includes a certain time threshold subsequent to an initial activation time period of the sensor system when the analyte sensor is disposed on a body of the host.

4. The sensor system of any of claims 1-3, wherein the analyte sensor is embedded within the skin of the host.63Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT015. The sensor system of any of claims 1-4, wherein the temperature sensor is positioned on top of the skin of the host for direct surface temperature readings of the host's skin.

6. The sensor system of any of claims 1-5, wherein the temperature sensor is positioned on the same area of a body of the host as the analyte sensor.

7. The sensor system of any of claims 1-6, wherein determining the first relationship comprises accounting for an offset of time between the first analyte signal and the temperature signal.

8. The sensor system of any of claims 1-7, wherein the offset is based on a physiological delay between changes in blood analyte levels and their detection in interstitial fluid.

9. The sensor system of any of claims 1-8, wherein the offset is based on a temporal offset responsive to an environmental or physiological change.

10. The sensor system of any of claims 1-9, wherein the first time period includes a threshold time subsequent to the sensor being positioned on the body.

11. The sensor system of any of claims 1-10, wherein the first time period includes a threshold time subsequent to an initial capture of sensor data.

12. The sensor system of any of claims 1-11, wherein the first time period includes a threshold time subsequent to initial stabilization of the sensor.

13. The sensor system of any of claims 1-12, wherein the operations further comprise: detecting a stabilization of the host's physiological state; and after detecting the stabilization of the host's physiological state, triggering receiving of the first analyte signal.64Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT0114. The sensor system of any of claims 1-13, wherein the operations further comprise detecting whether the host has eaten a meal, and initiating the receiving of the first analyte signal and the first temperature signal at the first time period subsequent to the detection of the host eating the meal.

15. The sensor system of any of claims 1-14, wherein the analyte sensor and the temperature sensor are integrated into the same temperature-compensated analyte sensor system.

16. The sensor system of any of claims 1-15, wherein the first relationship includes identifying a difference in the first analyte signal and the first temperature signal at one or more time stamps within the first time period.

17. The sensor system of any of claims 1-16, wherein determining the first relationship includes correlating the first analyte signal and the first temperature signal using a machine learning model, the machine learning model trained to infer relationships between analyte signals and temperature signals using historical analyte signal data, historical temperature signal data, and historical relationship data.

18. The sensor system of any of claims 1-17, wherein correlating the first analyte signal and the first temperature signal comprises executing the machine learning model to generate a threshold difference value between the first analyte signal and the first temperature signal, and determining the onset of the sensor sensitivity degradation includes determining that a difference between the second analyte signal and the second temperature signal is above the threshold value.

19. The sensor system of any of claim 1-18, wherein correlating the first analyte signal and the first temperature signal comprises executing the machine learning model to generate an indication of the onset of sensor sensitivity degradation, the machine learning model receiving as input a continuous stream of analyte signals and temperature signals, the machine learning model determining a plurality of relationships between pairs of the analyte signals and the temperature signals from the continuous stream of analyte signals and temperature signals.65Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT0120. The sensor system of any of claims 1-19, wherein determining the onset of sensor sensitivity degradation comprises executing a machine learning model using continuous relationships between analyte signals and temperature signals to generate an indication of the onset, the machine learning model being trained to identify onsets of sensor sensitivity degradation from relationships between analyte signals and temperature signals.

21. The sensor system of any of claims 1-20, wherein the raw analyte measurement includes a raw glucose measurement.

22. A method comprising: receiving a first analyte signal associated with a raw analyte measurement of a host from an analyte sensor within a first time period; receiving a first temperature signal associated with a first temperature measurement of the host from a temperature sensor within the first time period; determining a first relationship between the first analyte signal and the first temperature signal; receiving a second analyte signal associated with a raw analyte measurement of the host from the analyte sensor within a second time period; receiving a second temperature signal associated with a second temperature measurement of the host from the temperature sensor within the second time period; determining a second relationship between the second analyte signal and the second temperature signal; and determining an onset of sensor sensitivity degradation of the analyte sensor based on the first and second relationships.

23. The method of claim 22, wherein the first time period includes an initial activation time period of the analyte sensor when the analyte sensor is inserted under the skin of the host.66Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT0124. The method of any of claim 22-23, wherein the first time period includes a certain time threshold subsequent to an initial activation time period of the analyte sensor when the analyte sensor is disposed on a body of the host.

25. The method of any of claim 22-24, wherein the analyte sensor is embedded within the skin of the host.

26. The method of any of claim 22-25, wherein the temperature sensor is positioned on top of the skin of the host for direct surface temperature readings of the host's skin.

27. The method of any of claim 22-26, wherein the temperature sensor is positioned on the same area of a body of the host as the analyte sensor.

28. The method of any of claim 22-27, wherein determining the first relationship comprises accounting for an offset of time between the first analyte signal and the temperature signal.

29. The method of any of claim 22-28. wherein the offset is based on a physiological delay between changes in blood analyte levels and their detection in interstitial fluid.

30. The method of any of claim 22-29. wherein the offset is based on a temporal offset responsive to an environmental or physiological change.

31. The method of any of claim 22-30, wherein the first time period includes a threshold time subsequent to the sensor being positioned on the body.

32. The method of any of claim 22-31, wherein the first time period includes a threshold time subsequent to an initial capture of sensor data.

33. The method of any of claim 22-32, wherein the first time period includes a threshold time subsequent to initial stabilization of the sensor.

34. The method of any of claim 22-33. wherein the operations further comprise: detecting a stabilization of the host's physiological state; and67Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 after detecting the stabilization of the host's physiological state, triggering receiving of the first analyte signal.

35. The method of any of claim 22-34, wherein the operations further comprise detecting whether the host has eaten a meal, and initiating the receiving of the first analyte signal and the first temperature signal at the first time period subsequent to the detection of the host eating the meal.

36. The method of any of claim 22-35, wherein the analyte sensor and the temperature sensor are integrated into the same temperature-compensated analyte sensor system.

37. The method of any of claim 22-36, wherein the first relationship includes identifying a difference in the first analyte signal and the first temperature signal at one or more time stamps within the first time period.

38. The method of any of claim 22-37, wherein determining the first relationship includes correlating the first analyte signal and the first temperature signal using a machine learning model, the machine learning model trained to infer relationships between analyte signals and temperature signals using historical analyte signal data, historical temperature signal data, and historical relationship data.

39. The method of any of claim 22-38, wherein correlating the first analyte signal and the first temperature signal comprises executing the machine learning model to generate a threshold difference value between the first analyte signal and the first temperature signal, and determining the onset of the sensor sensitivity degradation includes determining that a difference between the second analyte signal and the second temperature signal is above the threshold value.

40. The method of any of claim 22-39, wherein correlating the first analyte signal and the first temperature signal comprises executing the machine learning model to generate an indication of the onset of sensor sensitivity degradation, the machine learning model receiving as input a continuous stream of analyte signals and temperature signals, the machine learning68Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01 model determining a plurality of relationships between pairs of the analyte signals and the temperature signals from the continuous stream of analyte signals and temperature signals.

41. The method of any of claim 22-40, wherein determining the onset of sensor sensitivity degradation comprises executing a machine learning model using continuous relationships between analyte signals and temperature signals to generate an indication of the onset, the machine learning model being trained to identify onsets of sensor sensitivity' degradation from relationships between analyte signals and temperature signals.

42. The method of any of claim 22-41, wherein the raw analyte measurement includes a raw glucose measurement.69Atty Docket No. 4855.137WO1 Client Ref. No. 0947-PCT01

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