The present disclosure relates to apparatus and methods for continuous analyte monitoring.
Continuous analyte monitoring (CAM), such as continuous glucose monitoring (CGM), has become a routine monitoring operation, particularly for individuals with diabetes. CAM provides real-time analyte analysis (e.g., analyte concentrations) of an individual's body fluid. In the case of CGM, real-time glucose concentrations of an individual's interstitial fluid are provided. By providing real-time glucose concentrations, therapeutic and/or clinical actions may be timelier applied to individuals being monitored, thus better controlling glycemic conditions.
Improved CAM and CGM methods and apparatus are desired.
In some embodiments, a method of displaying a projected range of future analyte concentrations is provided. The method includes determining a current analyte concentration G(t0) at a present time t0; projecting an analyte concentration G(tA) at a time tA; calculating a deviation R(tA) from the projected analyte concentration G(tA); and displaying at least one indicium indicating the deviation R(tA).
In some embodiments, a method of displaying a cone of confidence representing a projected range of future analyte concentrations is provided. The method includes determining a current analyte concentration G(t0); determining past analyte concentrations from a time tP to a present time t0; determining a slope S(t0) of the analyte concentration at the present time t0; projecting a first analyte concentration G(tA) at a time tA as G(t0)+S(t0)*tA/t0; projecting a second analyte concentration G(tB) at a time tB, which is later than the time tA, as G(t0)+S(t0)*tB/to; determining a probability P1 that the first analyte concentration G(tA) will exceed an analyte concentration GEVENT at the time tA; determining a probability P2 that the second analyte concentration G(tB) will exceed the analyte concentration GEVENT at the time tB; calculating a first deviation R (tA) from G(tA) as R (tA)=ABS (G(tA)−GEVENT)*(1−P1)*F, wherein F is a scale factor for the deviation; calculating a second deviation R(tB) from G(tB) as R(tB)=ABS(G(tB)−GEVENT)*(1-P2)*F; displaying at least one indicium indicating R(tA); and displaying at least one indicium indicating R(tB).
In some embodiments, a continuous analyte monitoring system is provided. The system includes a display and a processor configured to execute computer-readable instructions that cause the processor to: determine a current analyte concentration G(t0) at a present time t0; project an analyte concentration G(tA) at a time tA; calculate a deviation R(tA) from the projected analyte concentration G(tA); and display at least one indicium indicating the deviation R(tA).
Other features, aspects, and advantages of embodiments in accordance with the present disclosure will become more fully apparent from the following detailed description, the claims, and the accompanying drawings that describe, define, and illustrate a number of example embodiments and implementations. Various embodiments in accordance with the present disclosure may also be capable of other and different applications, and its several details may be modified in various respects, all without departing from the scope of the claims and their equivalents. Accordingly, the drawings and descriptions are to be regarded as illustrative in nature, and not as restrictive.
The drawings, described below, are for illustrative purposes only and are not necessarily drawn to scale. The drawings are not intended to limit the scope of the disclosure in any way. Like numerals are used throughout to denote the same or like elements.
The apparatus, systems, and methods disclosed herein describe continuous analyte monitoring (CAM) systems and methods implemented as continuous glucose monitoring (CGM) systems, CGM methods, CGM displays, CGM display methods, and the like. The apparatus, systems, and methods disclosed herein may also be implemented to monitor and display other analytes (e.g., analyte concentrations), such as cholesterol, lactate, uric acid, alcohol, and other analytes.
In order to more closely monitor an individual's glucose (or other analyte) concentrations and detect shifts in glucose concentrations, apparatus, systems, and methods of continuous glucose monitoring (CGM) have been developed. The apparatus, systems, and methods described herein predict analyte (e.g., glucose) concentration trends and/or events (hypo or hyper). Some CGM systems may include a sensor portion (e.g., a biosensor) that is inserted under the skin of a user, and a non-implanted processing portion that is adhered to the outer surface of the skin, for example, the abdomen or the back of the upper arm. Some of the CGM systems described herein measure glucose concentrations in interstitial fluid or in samples of non-direct capillary blood. A processor executing computer-readable instructions calculates the glucose concentrations in the blood based on the measured glucose concentrations in the interstitial fluid. Other CGM systems may use optical and/or other sensors to generate data that is used to calculate glucose concentrations.
Some CGM systems predict hypoglycemic and hyperglycemic events, wherein hypoglycemic events may occur when glucose concentrations are less than a predetermined glucose concentration, and hyperglycemic events may occur when glucose concentrations are greater than a predetermined glucose concentration. In the examples described herein, hypoglycemic events occur when glucose concentrations are less than 70 mg/dl, and hyperglycemic events may occur when glucose concentrations are greater than 180 mg/dl. If a user is given 15-30 minutes advanced warning, for example, of a hypoglycemic or hyperglycemic event, the user may respond (e.g., with therapeutic measures) to rectify the glucose concentration issue and avoid hypoglycemic and/or hyperglycemic events altogether.
Some known CGM systems may predict hypoglycemic and hyperglycemic events based on several variables, such as exercise and dietary intake. These CGM systems require users to input exercise preformed and foods consumed, which may include portion sizes and calories consumed, for example, to predict hypoglycemic and hyperglycemic events. Because these known CGM systems require user input, they may not accurately predict glucose concentrations 15-35 minutes in the future so that users may avoid hypoglycemic and hyperglycemic events. For example, a user may not accurately enter exercises performed or foods consumed, or may not want to be bothered entering this information at all. In other situations, users' bodies react differently in response to certain exercises and foods, which may not be taken into consideration by these CGM systems. In addition, there may be other factors that affect glucose concentrations, but are not considered by these CGM systems.
The apparatus, systems, and methods described herein provide accurate glucose (and other analyte) concentration trend or behavior predictions using unique artificial intelligence models and inputs. For example, the artificial intelligence models may be trained using data from a plurality of individuals other than a present user. In some embodiments, the individuals undergo different activities during training that may affect glucose concentrations, such as consuming different foods and/or performing different activities. The artificial intelligence models (e.g., machine learning models) may identify trends in glucose concentrations and based on the trends predict, e.g., whether future glucose concentrations cross a hypo or hyper glycemic threshold. The CGM systems described herein may monitor previously calculated glucose concentrations of a user and input these previously calculated glucose concentrations into an artificial intelligence algorithm or model, which may then predict future glucose concentration trends or behaviors of the user. The user does not need to input foods consumed or exercise performed for the artificial intelligence model to predict such future trends or behaviors.
In accordance with some embodiments of the artificial intelligence models described herein, each calculated glucose concentration is related to its adjacent calculated glucose concentrations in short term and/or long-term relationships. Because of the continuous nature of CGM, prior calculated glucose concentrations may contain information relevant to predicting future glucose concentrations. That is, each calculated glucose concentration may be related to its adjacent (e.g., previous) calculated glucose concentrations, or even glucose concentrations calculated much earlier in time. For example, certain past glucose trends may be indicative of future glucose concentrations. The relationships of a present calculated glucose concentration to many previously-calculated glucose concentrations in a continuum have been found to be useful in predicting future glucose concentrations.
The glucose concentrations may also be used to determine the “slope” of present glucose concentrations (plotted in a graph). The slope of such glucose concentrations may inform a user of present and predicted directions (“trend information”) of glucose concentrations, which may be presented to the user via a display, e.g., of an external device in communication with a wearable CGM or CAM device. In some embodiments, the slope directions may be, for example, “rising,” “steady,” and “falling.” In other embodiments, the slope directions may be, e.g., “up fast,” “up slow,” “steady,” “down slow,” and “down fast.” Some glucose concentrations are calculated and/or displayed as a continuous glucose signal, which may be noisy. The methods and apparatus described herein may smooth the glucose signals during slope calculations, which provide more accurate slope calculations.
Methods and apparatus disclosed herein use artificial intelligence, such as machine learning models, to calculate slope of glucose and/or other analytes in a user. The methods and apparatus may use similar or identical data to calculate slope of the glucose signal. Therefore, slope calculations are more accurate than slope calculations of conventional CGM systems, which are prone to error due to noise sources.
The glucose trend or behavior predictions may include a certainty (e.g., a probability) that an event, such as a hypoglycemic event or a hyperglycemic event, will occur. The certainties may, in some embodiments, be functions of time. For example, the glucose trend or behavior predictions may be very certain in the short term, but may be less certain as a function of time. Some embodiments of the CAM systems and CGM systems disclosed herein display the analyte and/or glucose concentration trend or behavior predictions with confidence indications that indicate probabilities that hypoglycemic and/or hyperglycemic events will occur.
These and other methods, systems, and apparatus for predicting and displaying trends or behaviors of analyte (e.g., glucose) concentrations are described herein with reference to
The wearable device 102 may include a biosensor 112 that may be located subcutaneously in interstitial fluid 113 of a user and may directly or indirectly measure glucose concentrations in the interstitial fluid 113. The wearable device 102 may transmit the glucose concentrations to the external device 104, where the glucose concentrations, predicted glucose concentrations, and/or other information may be displayed on a display 114. The display 114 may be any suitable type of human-perceivable display, such as but not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, or an organic light emitting diode (OLED) display.
The display 114 may display different formats of predicted glucose concentrations, such as individual numbers, graphs, and/or tables as described below. The display 114 may also display other information, such as trends in glucose concentrations. In the example embodiment of
The graph 200 includes two parts, past glucose concentrations 200A of a user determined before a time t0 and glucose concentrations 200B of the user determined after the time t0. Glucose concentrations 200A and 200B may be calculated by a CGM system or a processor external to the CGM system, for example. CGM systems include systems that measure and/or calculate glucose concentrations in interstitial fluid via a probe located in the interstitial fluid, such as the CGM system 100. The CGM systems may include optical systems that optically measure and/or calculate users' glucose concentrations. The glucose concentrations may be obtained from other systems.
A time t0 shown on the graph 200 represents a present time at which a present or most recent glucose concentration (or other analyte concentration) was processed (e.g., measured and/or calculated). For example, the CGM system 100 or an external processor may generate and/or receive data indicative of analyte concentration measurements, such as glucose concentration measurements and may calculate the present glucose concentration at the time t0. The past glucose concentrations 200A are located to the left of the time t0. As described herein, at least some of the past glucose concentrations 200A are processed by the machine learning model (or other artificial intelligence) to predict at time t0 a future trend of glucose concentrations up to a future time tF (e.g., to predict the trend of glucose concentrations 200B, which are shown to the right of time t0) and, more particularly, to predict at time t0 whether a hypoglycemic event will occur within the time period F, such as, e.g., the actual hypoglycemic event that occurs at a time t0+12 minutes, as shown in
In some embodiments, the machine learning model may use a feedforward neural network with sixteen inputs, three hidden layers of twenty-four, ten, and five neurons each, and one output layer having a single output neuron. The single output neuron may be the certainty of an event at a given time. Other neural network architectures may be used such as neural networks having different numbers of hidden layers, different numbers of neurons per hidden layer, etc. Other artificial intelligence, trained models, and machine learning models may be used, such as gradient boosted regression trees (GBRT), linear regression, and random forests. Thus, training a model may include training a machine learning model and/or one of the above-listed models.
The graph 200 shows the past glucose concentrations 200A extending back to a time tP, which may be P minutes less than to. In some embodiments, the period P may be about thirty minutes. However, the machine learning model may analyze past glucose concentrations from longer or shorter periods than thirty minutes. For example, the machine learning model may analyze the past glucose concentrations 200A back forty-five minutes from the time t0, which may require substantial processing, but may provide accurate predicted glucose concentration trends. In some embodiments, the machine learning model may analyze the past glucose concentrations 200A back fifteen minutes from the time t0, which may not provide as accurate predicted glucose concentration trends, but may require less processing.
The predicted glucose concentration trend may be based on or include a predicted strength or certainty (e.g., probability) that the predicted glucose concentration trend will actually occur as glucose concentrations 200B. Accordingly, predicted hypoglycemic events and hyperglycemic events may be based on the predicted strength or certainty that the predicted glucose concentration trend will occur as, e.g., glucose concentrations 200B. For example, the prediction of the hypoglycemic event may be based on at least a 95% certainty that the hypoglycemic event will occur within the time period F (which actually does occur at a time t0+12 minutes, as shown in
The output of block 302 includes a prediction strength (e.g., probability) that one or more hyperglycemic events and/or one or more hyperglycemic events will occur within a predetermined time period, such as within the time period F (
In the following example, the past glucose concentrations 200A of the graph 200 of
In some embodiments, the information may include the certainty (e.g., probability) of the hypoglycemic event and that the hypoglycemic event will occur within the time period F (e.g., within 30 minutes). In some embodiments, the information may include the expected time of the hypoglycemic event, which in the graph 200 of
In embodiments where the threshold within the decision block 304 is set to greater than 95%, the probability of an event calculated in block 302 described above will not exceed the threshold. Accordingly, no action will be taken per block 308.
The operations performed in block 312 may be performed using a machine learning model or other artificial intelligence algorithm as described herein. In some embodiments, the past glucose concentrations may be analyzed back the period P (minutes) from the time tP. As described above, higher values of the period P may use more processing time than lower values of the period P to generate the outputs of block 312, but the outputs of block 312 may be more accurate. Lower values of the period P may result in less accurate outputs of the block 302 compared to use of higher values of the period P, but using the lower values of the period P may take less processing than the higher values of the period P.
As described above, the outputs of block 312 may include prediction certainties (e.g., probabilities) that a hypoglycemic event and/or a hyperglycemic event will occur at various times within the time period F. The time period F may be set by a user or another entity. In some embodiments, the time period F is 15 minutes, which may provide accurate results. In other embodiments, the time period F is 30 minutes, which may provide less accurate results, but provides the user with a longer time frame within which to take any necessary action. The time period F may be of other durations, such as, e.g., forty-five minutes.
In decision block 314 determinations are made as to whether one or more of the probabilities exceeds a threshold. If the determination made in decision block 314 is positive, one or more reports (e.g., alerts) may be sent or reported to a user per block 316. The one or more reports may include information as to when the events are expected to occur and the certainties (e.g., probabilities) that the events will occur. For example, if the threshold is set at a 95% certainty, the user may be notified if one of the outputs predicts that a hypoglycemic or hyperglycemic event will occur with at least 95% certainty and when the event(s) will occur. If the determination in decision block 314 is negative, no hypoglycemic and/or hyperglycemic events have been predicted and no action may be taken per block 318.
When the past glucose concentrations 200A of the graph 200 of
When the data generated in block 312 is input to the decision block 314, the decision block 314 determines whether any of the probabilities exceed a predetermined threshold. If so, then processing proceeds to block 316 where the user is notified of the predicted event(s). The user may be notified of the time of the pending event(s) and, in some embodiments, the certainty that the event(s) will occur. For example, referring to the graph 200 of
An event detector (e.g., event detector 530 of
In some embodiments, the machine learning model is trained by receiving or analyzing past glucose concentrations of the individuals during various periods and correlating the past glucose concentrations with future glucose concentrations. In some embodiments, the past glucose concentrations may be received or analyzed at regular increments, such as every three minutes. Other increments, such as every two minutes or every four minutes may be used. The periods of time that the past glucose concentrations are calculated and/or measured may be long enough to develop trends to train the machine learning model. In some embodiments, the periods of time may be thirty minutes. In other embodiments, longer periods, such as forty-five or sixty minutes may be used to gather more information on glucose concentration trends.
Referring to
The differences in analyte concentrations (e.g., glucose concentrations) calculated by the event detector may be referred to as a first data set 420A and a second data set 420B. The first data set 420A includes a plurality of incremental differences in glucose concentrations. For example, the first data set 420A includes the glucose concentration differences: G(t0−NI)−G(t0−1I); G(t0−1I)−G(t0−2I); G(t0−2I)−G(t0−3I); G(t0−3I)−G(t0−4I) . . . to G(t0−NI)−G(t0−(NI-1I)). Thus, calculating the first data set 420A may include calculating differences in analyte (e.g., glucose) concentrations between consecutively measured analyte concentrations between the time tP and the time t0.
The second data set 420B may include differences in glucose concentrations all referenced from the most recently measured glucose concentration G(t0). For example, the second data set 420B set includes the glucose concentration differences: G(t0)−G(t0−1I); G(t0)−G(t0−2I); G(t0)−G(t0−3I); G(t0)−G(t0−4I) . . . to G(t0)−G(t0−NI). Thus, calculating the second data set 420B may include calculating differences in analyte (e.g., glucose) concentrations between the analyte concentration G(t0) at the time t0 and analyte concentrations at measurement times before the time t0. In some embodiments, the machine learning model of the event detector may be trained at least in part based on data of the first data set 420A and the second data set 420B from the individuals used to train the machine learning model. In some embodiments, the machine learning model may be trained by further analyzing glucose concentrations of the user.
When performing the method 300 of
A second display embodiment 540B shows example information that may be displayed on the display 114 in response to processing the past glucose concentrations 200A (
In addition to the foregoing display embodiments, the display 114 may also display portions of the graph 200 (
The first display embodiment 540A and/or the second display embodiment 540B may be displayed in any of a plurality of locations. In some embodiments, the first display embodiment 540A and/or the second display embodiment 540B may be displayed on the display 114 (
In some embodiments, the processor 532 may inform the user of a predicted hypoglycemic event and/or a hyperglycemic event via an audio signal and/or a tactile signal. The audio signal may be a voice informing the user of the information in the first display embodiment 540A and/or the second display embodiment 540B. Other audio signals, such as alarms may be used. The tactile signal may provide the information in Braille or other tactile formats, such as vibration of the external device 104.
In the embodiment of
The transceiver 646 may be electrically coupled to the event detector 530. In some embodiments where the glucose concentrations are calculated by the processor 640 in the wearable device 102, the event detector 530 may function in a similar manner as described in
In the embodiment of
The event detector 530 may receive the glucose concentrations and predict hypoglycemic and/or hyperglycemic events as described above. The predictions may be transmitted to the external device 104 by way of the transceiver 644. The external device 104 may receive the predictions by way of the transceiver 646 and may display the predictions on the display 114 as described herein. In the embodiment of
In the embodiments of
In some embodiments, the user or another entity may select the threshold for the certainty or probability that hypoglycemic events and/or hyperglycemic events are detected (e.g., detection rates). Higher thresholds may yield lower detection rates and lower false alarm rates than with lower thresholds. Lower thresholds, however, may provide a higher number of earlier warnings of hypoglycemic events and hyperglycemic events, but may have higher false alarm rates. Thus, there is a tradeoff between earlier warnings and receiving more false alarms. In some embodiments, the user may be given options to choose a threshold that the user is comfortable with while still presenting information about the probability of events occurring in the future.
Tables 1 and 2 below each show example performances of outputs of a trained model, such as a trained machine learning model, having different thresholds for detecting hypoglycemic and/or hyperglycemic events. Table 1 shows an example analysis using a high threshold (e.g., 95%), and Table 2 shows the analysis with the same data, but using a lower threshold (e.g., 90%).
As described herein, the CGM system 100 (
In some embodiments, the machine learning model used to calculate slope may use a feedforward neural network with sixteen inputs, three hidden layers of twenty-four, ten, and five neurons each, and one output layer having a single output neuron. The single output neuron may be the slope S(t) at a given time. Other neural network architectures may be used such as neural networks having different numbers of hidden layers, different numbers of neurons per hidden layer, etc. Other artificial intelligence, trained models, and machine learning models may be used, such as gradient boosted regression trees (GBRT), linear regression, and random forests.
Returning to
The memory 554 may store the machine learning model 556 or other artificial intelligence as described above that calculates the slope S(t) based at least in part on past glucose concentrations. The slope calculations may also predict slopes of the predicted glucose concentrations. Accordingly, the slope calculations may be based on past glucose concentrations to project into the future. The slope calculator 550 may output the slope S(t) or an indication of the slope S(t), such as to the display 114. In the embodiment of
The input to the machine learning model 556 shown in
The first data set 420A and the second data set 420B may be based on glucose concentrations that go back a period P to the time tP, which may be, e.g., twenty-four minutes from the present time t0. Using the period P of twenty-four minutes may enable accurate slope calculations without overloading processors that execute the machine learning model 556 or other artificial intelligence. Other time periods for the period P may be used, such as fifteen minutes, thirty minutes, or forty-five minutes.
In some embodiments, the machine learning model 556 may be stored in the memory used to store other programs and may be executed on another processor. For example, the machine learning model 556 may be stored in the memory 534 and executed on the processor 532. In other embodiments, the machine learning model may be stored and executed on a computer or the like that is external to the CGM system 100 (
A conventional slope measuring system has generated the conventionally-calculated slope 764, which is marked with x's. As shown in
A graph of machine learning (ML) predicted slope 762 is marked with circles and is generated by the machine learning model 536 (
The cone of confidence 804 may include a first line 806 and a second line 808, which, in some embodiments, are boundaries of the cone of confidence 804. As described herein, the cone of confidence 804 may provide a user with a visual indication of the probabilities that projected glucose concentrations will occur as glucose concentrations 802B. As shown in
The cone of confidence 804 enables users to quickly visualize confidence of the projected glucose concentrations. For example, the cone of confidence 804 enables users to visualize likelihoods of glycemic events in the future. In the embodiment described in
In the embodiment of
Different methods may be employed to calculate or generate the cone of confidence 804. In some embodiments, the cone of confidence 804 is calculated using probabilities of future hypo and/or hyper glycemic events, present slope, and current and projected glucose concentrations. The probabilities of future glycemic events may be received from the event detector 530 (
The following describes an embodiment of generating the cone of confidence 804. Other methods may be used to generate the cone of confidence 804. In embodiments where the event detector 530 (
In embodiments where the event detector 530 (
For each probability PN(tN, t0), the value of t on the graph, which may be referred to as the value X (value on the x-axis of the graph 800), is equal to t0+tN. The glucose concentration, which may be referred to as Y, is equal to G(t0)+slope(t0)*(tN/TI), wherein T1 is the period between time intervals tN. For example, in the embodiment of
The following provides an example of generating a cone of confidence, such as the cone of confidence 804. In the following example, the present glucose concentration G(t0) has been measured or calculated to be 100 mg/dl. The glucose concentration for a hypoglycemic event is 70 mg/dl, so GEVENT is equal to 70. The event detector 530 and/or methods thereof have predicted a 70% chance of a hypoglycemic event occurring in fifteen minutes and a 90% chance of a hypoglycemic event occurring in thirty minutes. Thus, PA(15, t0) is equal to 0.7, PB(30, t0) is equal to 0.9, and the interval I is equal to 15 minutes. Based on the foregoing, tA is equal to 15 and tB is equal to 30. G(tA), which corresponds to the projected glucose concentration at time tA, is equal to 100-25(15/15), which equals 75. G(tB), which corresponds to the projected glucose concentration at time tB, is equal to 100-25(30/15), which equals 50. Based on the foregoing, the center of the first circle 812 or other indicator or graphic is at time (x-axis) of 15 minutes and glucose concentration (y-axis) of 75. The center of the second circle 814 or other indicator is at time (x-axis) of 30 minutes and glucose concentration (y-axis) of 50. The radius R(tA) of the first circle or other indicator, which may be referred to as the distance R(tA), is equal to (75-70)*(1-0.7)*5, wherein F is equal to 5, so the radius R(tA) is equal to 7.5. The radius R(tB) of the second circle 814 or other indicator, which may be referred to as the distance R(tB), is equal to |(50-70)|*(1-0.9)*5, wherein F is equal to 5, so the radius R(tA) is equal to 12.5.
The cone of confidence 804 may use indicators other than circles, such as ellipses or vertical lines. An embodiment of an ellipse may have a vertically-extending major axis twice the distance R(tA) and centered at a point indicative of G(tA). Referring now to
As described above, the graphics and indicium in the cone of confidence may have many forms. In some embodiments, R(tA) is represented by a distance and at least one indicium is displayed that includes at least one graphic a distance R(tA) from a point indicative of G(tA). In some embodiments, R(tA) is represented by a distance and at least one indicium is displayed including at least one graphic a vertical distance R(tA) from a point indicative of G(tA). In some embodiments, R(tA) is represented by a distance and at least one indicium is displayed including at least one graphic extending a distance R(tA) from a point indicative of G(tA). In some embodiments, R(tA) is represented by a distance and at least one indicium is displayed including at least one graphic extending a vertical distance R(tA) from a point indicative of G(tA). In some embodiments, R(tA) is represented by a distance and at least one indicium is displayed as a first graphic a distance R(tA) above a point indicative of G(tA) and a second graphic a distance R(tA) below the point indicative of G(tA).
The cone of confidence 804 will continually change as the past glucose concentrations 802A change. However, the cone of confidence 804 provides users with a quick visual aid of a projected range of future glucose concentrations. As shown in
The foregoing description discloses only example embodiments. Modifications of the above-disclosed apparatus and methods which fall within the scope of this disclosure will be readily apparent to those of ordinary skill in the art.
This claims the benefit of U.S. Provisional Patent Application No. 63/112,153, filed Nov. 10, 2020, the disclosure of which is hereby incorporated by reference herein in its entirety for all purposes.
Number | Date | Country | |
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63112153 | Nov 2020 | US |