Patent ID: 12205718

DETAILED DESCRIPTION

Overview

Monitoring a person's glucose levels is useful for deciding how to treat diabetes. Knowing what the person's glucose levels will be in the future is more useful though. This is because it allows the person or a caregiver to take action to mitigate potentially adverse health conditions tied to changing glucose levels (e.g., hyper- or hypo-glycemia) before such health conditions occur.

Conventional approaches to glucose prediction may model glucose using linear models, such as using autoregressive linear models. Although such linear models may be capable of describing time-varying processes, the output of those models is linearly dependent on previous values. This can result in glucose predictions that have significant time delays in relation to actual, observed glucose measurements. In other words, the predictive horizons of these models may fail to match a person's actual glucose. Additionally, linear models may generate inaccurate predictions of upcoming glucose measurements because the linear dependencies of those models may not allow them to robustly cover state spaces underlying tens of millions of patient days' worth of glucose measurements. Simply, linear models may not be able to account for some of the patterns observed in such historical data. Failure of predictive horizons to match actual glucose and inaccurate predictions (or predictions of limited accuracy) may render glucose predictions generated by conventional systems unsuitable for various applications, such as for prescribing actions to mitigate dangerously (and rapidly) changing glucose levels.

To overcome these problems, glucose prediction using machine learning and time series glucose measurements is leveraged. In one or more implementations, a glucose monitoring platform includes a machine learning model trained, using historical time series glucose measurements of a user population, to predict upcoming glucose measurements for an individual user. The glucose measurements of the user population and the individual user may be provided by wearable glucose monitoring devices worn by users of the user population and the individual user. By obtaining measurements produced by these wearable glucose monitoring devices and maintaining the measurements, the glucose monitoring platform may have an enormous amount of data, e.g., tens of millions of patient days' worth of measurements. Conventional linear models may not be able to model some of the patterns observed in this wealth of historical data.

In contrast to conventional approaches, the machine learning model described herein may be configured as a non-linear model, or as an ensemble of models that includes one or more non-linear models. Such non-linear machine learning models may include, for instance, neural networks (e.g., recurrent neural networks such as long-short term memory (LSTM) networks), state machines, Markov chains, Monte Carlo methods, and particle filters, to name just a few. Such models may be capable of capturing patterns of state spaces that linear techniques simply cannot model.

Once the machine learning model is trained, it is used to predict upcoming glucose measurements for users. When predicting upcoming glucose measurements for a particular user, a time series of glucose measurements up to a time is received, e.g., a last 12 hours of glucose measurements. The glucose measurements of this time series are provided by the wearable glucose monitoring device worn by the user. Responsive to receiving the time series as input, the machine learning model predicts upcoming glucose measurements for an interval of time subsequent to the time, e.g., a next 30 minutes. The machine learning model generates this prediction based on its training with the historical time series glucose measurements of the user population. The upcoming glucose measurements are then output, such as for generating a notification about the upcoming glucose measurements. This notification may be communicated over a network to one or more computing devices, including a computing device associated with the user (e.g., for output via an application of the glucose monitoring platform), a computing device associated with a health care provider, or a computing device associated with a telemedicine service, to name just a few.

By predicting upcoming glucose measurements and notifying users, health care providers, and/or telemedicine services about the upcoming glucose measurements, the described machine learning model allows actions to be taken to mitigate potentially adverse health conditions before those health conditions occur. Advantageously, the more accurate and timely predictions of upcoming glucose provided by the described machine learning model allow users and various other parties to make better informed decisions regarding how to treat diabetes and achieve better outcomes in through the treatment. In so doing, serious damage to the heart, blood vessels, eyes, kidneys, and nerves, and death due to diabetes can be largely avoided.

In addition, for a person with diabetes, treatment decisions may be influenced by the person's impending or predicted upcoming glucose measurements. For instance, decision support services of a glucose monitoring platform (e.g., via an application, notifications, and so on) may use impending or predicted upcoming glucose measurements to inform and assist users in their treatment. By way of example, such informing and assisting can be responsive to detection of impending or possible events that are predicted to occur when patients are unable to self-monitor their glucose, e.g., when they are sleeping. While notifications such as short-term predictive alarms and threshold alerts may be able to address the need to alert patients about impending events, conventional prediction techniques are not capable of accurately predicting glucose measurements for a time horizon that is further in the future from a current time, e.g., on the scale of hours or more. Accordingly, conventional techniques are unsuitable for accurately predicting whether a patient will experience overnight hypoglycemia. The machine learning models described above and below more accurately predict glucose of a person for time horizons further into the future than conventional techniques. Accordingly, the described machine learning models can be leveraged in connection with prediction of longer-term glycemic outcomes, such as whether a patient will experience overnight hypoglycemia.

In the following discussion, an example environment is first described that may employ the techniques described herein. Example implementation details and procedures are then described which may be performed in the example environment as well as other environments. Performance of the example procedures is not limited to the example environment and the example environment is not limited to performance of the example procedures.

Example Environment

FIG.1is an illustration of an environment100in an example implementation that is operable to employ glucose prediction using machine learning and time series glucose measurements as described herein. The illustrated environment100includes person102, who is depicted wearing a wearable glucose monitoring device104, insulin delivery system106, and computing device108. The illustrated environment100also includes other users in a user population110that wear wearable glucose monitoring devices, glucose monitoring platform112, and Internet of Things114(IoT114). The wearable glucose monitoring device104, insulin delivery system106, computing device108, user population110, glucose monitoring platform112, and IoT114are communicatively coupled, including via a network116.

Alternately or additionally, one or more of the wearable glucose monitoring device104, the insulin delivery system106, and the computing device108may be communicatively coupled in other ways, such as using one or more wireless communication protocols or techniques. By way of example, the wearable glucose monitoring device104, the insulin delivery system106, and the computing device108may communicate with one another using one or more of Bluetooth (e.g., Bluetooth Low Energy links), near-field communication (NFC), 5G, and so forth. The wearable glucose monitoring device104, the insulin delivery system106, and the computing device108may leverage these types of communication to form a closed-loop system between one another. In this way, the insulin delivery system106may deliver insulin based on sequences of glucose measurements in real-time as glucose measurements are obtained by the wearable glucose monitoring device104and as future glucose measurements are predicted.

In accordance with the described techniques, the wearable glucose monitoring device104is configured to monitor glucose of the person102, e.g., continuously. In one or more implementations, the wearable glucose monitoring device104is a continuous glucose monitoring (CGM) system. As used herein, the term “continuous” when used in connection with glucose monitoring may refer to an ability of a device to produce measurements substantially continuously, such that the device may be configured to produce the glucose measurements118at intervals of time (e.g., every hour, every 30 minutes, every 5 minutes, and so forth), responsive to establishing a communicative coupling with a different device (e.g., when a computing device establishes a wireless connection with the wearable glucose monitoring device104to retrieve one or more of the measurements), and so forth. The wearable glucose monitoring device104may be configured with a glucose sensor, for instance, that continuously detects analytes indicative of the person102's glucose and enables generation of glucose measurements. In the illustrated environment100these measurements are represented as glucose measurements118. This functionality along with further aspects of the wearable glucose monitoring device104's configuration are discussed in more detail in relation toFIG.2.

In one or more implementations, the wearable glucose monitoring device104transmits the glucose measurements118to the computing device108, such as via a wireless connection. The wearable glucose monitoring device104may communicate these measurements in real-time, e.g., as they are produced using a glucose sensor. Alternately or in addition, the wearable glucose monitoring device104may communicate the glucose measurements118to the computing device108at set time intervals, e.g., every 30 seconds, every minute, every 5 minutes, every hour, every 6 hours, every day, and so forth. Further still, the wearable glucose monitoring device104may communicate these measurements responsive to a request from the computing device108, e.g., communicated to the wearable glucose monitoring device104when the computing device108predicts the person102's upcoming glucose level, causes display of a user interface having information about the person102's glucose level, updates such a display, and so forth. Accordingly, the computing device108may maintain the glucose measurements118of the person102at least temporarily, e.g., in computer-readable storage media of the computing device108.

Although illustrated as a wearable device (e.g., a smart watch), the computing device108may be configured in a variety of ways without departing from the spirit or scope of the described techniques. By way of example and not limitation, the computing device108may be configured as a different type of mobile device (e.g., a mobile phone or tablet device). In one or more implementations, the computing device108may be configured as a dedicated device associated with the glucose monitoring platform112, e.g., with functionality to obtain the glucose measurements118from the wearable glucose monitoring device104, perform various computations in relation to the glucose measurements118, display information related to the glucose measurements118and the glucose monitoring platform112, communicate the glucose measurements118to the glucose monitoring platform112, and so forth. In contrast to implementations where the computing device108is configured as a mobile phone, however, the computing device108may not include some functionality available with mobile phone or wearable configurations when configured as a dedicated glucose monitoring device, such as the ability to make phone calls, camera functionality, the ability to utilize social networking applications, and so on.

Additionally, the computing device108may be representative of more than one device in accordance with the described techniques. In one or more scenarios, for instance, the computing device108may correspond to both a wearable device (e.g., a smart watch) and a mobile phone. In such scenarios, both of these devices may be capable of performing at least some of the same operations, such as to receive the glucose measurements118from the wearable glucose monitoring device104, communicate them via the network116to the glucose monitoring platform112, display information related to the glucose measurements118, and so forth. Alternately or in addition, different devices may have different capabilities that other devices do not have or that are limited through computing instructions to specified devices.

In the scenario where the computing device108corresponds to a separate smart watch and a mobile phone, for instance, the smart watch may be configured with various sensors and functionality to measure a variety of physiological markers (e.g., heartrate, breathing, rate of blood flow, and so on) and activities (e.g., steps) of the person102. In this scenario, the mobile phone may not be configured with these sensors and functionality, or it may include a limited amount of that functionality—although in other scenarios a mobile phone may be able to provide the same functionality. Continuing with this particular scenario, the mobile phone may have capabilities that the smart watch does not have, such as a camera to capture images of meals used to predict future glucose levels and an amount of computing resources (e.g., battery and processing speed) that enables the mobile phone to more efficiently carry out computations in relation to the glucose measurements118. Even in scenarios where a smart watch is capable of carrying out such computations, computing instructions may limit performance of those computations to the mobile phone so as not to burden both devices and to utilize available resources efficiently. To this extent, the computing device108may be configured in different ways and represent different numbers of devices than discussed herein without departing from the spirit and scope of the described techniques.

As mentioned above, the computing device108communicates the glucose measurements118to the glucose monitoring platform112. In the illustrated environment100, the glucose measurements118are shown stored in storage device120of the glucose monitoring platform112. The storage device120may represent one or more databases and also other types of storage capable of storing the glucose measurements118. The storage device120also stores a variety of other data. In accordance with the described techniques, for instance, the person102corresponds to a user of at least the glucose monitoring platform112and may also be a user of one or more other, third party service providers. To this end, the person102may be associated with a username and be required, at some time, to provide authentication information (e.g., password, biometric data, a telemedicine service, and so forth) to access the glucose monitoring platform112using the username. This information, along with other information about the user, may be maintained in the storage device120, including, for example, demographic information describing the person102, information about a health care provider, payment information, prescription information, determined health indicators, user preferences, account information for other service provider systems (e.g., a service provider associated with a wearable, social networking systems, and so on), and so forth.

The storage device120also maintains data of the other users in the user population110. Given this, the glucose measurements118in the storage device120include the glucose measurements from a glucose sensor of the wearable glucose monitoring device104worn by the person102and also include glucose measurements from glucose sensors of wearable glucose monitoring devices worn by persons corresponding to the other users in the user population110. It follows also that the glucose measurements118of these other users are communicated by their respective devices via the network116to the glucose monitoring platform112and that these other users have respective user profiles with the glucose monitoring platform112.

The data analytics platform122represents functionality to process the glucose measurements118—alone and/or along with other data maintained in the storage device120—to generate a variety of predictions, such as by using various machine learning models. Based on these predictions, the glucose monitoring platform112may provide notifications in relation to the predictions, such as alerts, recommendations, or other information based on the predictions. For instance, the glucose monitoring platform112may provide the notifications to the user, to a medical professional associated with the user, and so forth. Although depicted as separate from the computing device108, portions or an entirety of the data analytics platform122may alternately or additionally be implemented at the computing device108. The data analytics platform122may also generate these predictions using additional data obtained via the IoT114.

It is to be appreciated that the IoT114represents various sources capable of providing data that describes the person102and the person102's activity as a user of one or more service providers and activity with the real world. By way of example, the IoT114may include various devices of the user, e.g., cameras, mobile phones, laptops, and so forth. To this end, the IoT114may provide information about interaction of the user with various devices, e.g., interaction with web-based applications, photos taken, communications with other users, and so forth. The IoT114may also include various real-world articles (e.g., shoes, clothing, sporting equipment, appliances, automobiles, etc.) configured with sensors to provide information describing behavior, such as steps taken, force of a foot striking the ground, length of stride, temperature of a user (and other physiological measurements), temperature of a user's surroundings, types of food stored in a refrigerator, types of food removed from a refrigerator, driving habits, and so forth. The IoT114may also include third parties to the glucose monitoring platform112, such as medical providers (e.g., a medical provider of the person102) and manufacturers (e.g., a manufacturer of the wearable glucose monitoring device104, the insulin delivery system106, or the computing device108) capable of providing medical and manufacturing data, respectively, that can be leveraged by the data analytics platform122. Certainly, the IoT114may include devices and sensors capable of providing a wealth of data for use in connection with glucose prediction using machine learning and time series glucose measurements without departing from the spirit or scope of the described techniques. In the context of measuring glucose, e.g., continuously, and obtaining data describing such measurements, consider the following discussion ofFIG.2.

FIG.2depicts an example implementation200of the wearable glucose monitoring device104ofFIG.1in greater detail. In particular, the illustrated example200includes a top view and a corresponding side view of the wearable glucose monitoring device104.

The wearable glucose monitoring device104is illustrated to include a sensor202and a sensor module204. In the illustrated example200, the sensor202is depicted in the side view having been inserted subcutaneously into skin206, e.g., of the person102. The sensor module204is depicted in the top view as a dashed rectangle. The wearable glucose monitoring device104also includes a transmitter208in the illustrated example200. Use of the dashed rectangle for the sensor module204indicates that it may be housed or otherwise implemented within a housing of the transmitter208. In this example200, the wearable glucose monitoring device104further includes adhesive pad210and attachment mechanism212.

In operation, the sensor202, the adhesive pad210, and the attachment mechanism212may be assembled to form an application assembly, where the application assembly is configured to be applied to the skin206so that the sensor202is subcutaneously inserted as depicted. In such scenarios, the transmitter208may be attached to the assembly after application to the skin206via the attachment mechanism212. Additionally or alternately, the transmitter208may be incorporated as part of the application assembly, such that the sensor202, the adhesive pad210, the attachment mechanism212, and the transmitter208(with the sensor module204) can all be applied at once to the skin206. In one or more implementations, this application assembly is applied to the skin206using a separate applicator (not shown). This application assembly may also be removed by peeling the adhesive pad210off of the skin206. It is to be appreciated that the wearable glucose monitoring device104and its various components as illustrated are simply one example form factor, and the wearable glucose monitoring device104and its components may have different form factors without departing from the spirit or scope of the described techniques.

In operation, the sensor202is communicatively coupled to the sensor module204via at least one communication channel which can be a “wireless” connection or a “wired” connection. Communications from the sensor202to the sensor module204or from the sensor module204to the sensor202can be implemented actively or passively and these communications can be continuous (e.g., analog) or discrete (e.g., digital).

The sensor202may be a device, a molecule, and/or a chemical which changes or causes a change in response to an event which is at least partially independent of the sensor202. The sensor module204is implemented to receive indications of changes to the sensor202or caused by the sensor202. For example, the sensor202can include glucose oxidase which reacts with glucose and oxygen to form hydrogen peroxide that is electrochemically detectable by the sensor module204which may include an electrode. In this example, the sensor202may be configured as or include a glucose sensor configured to detect analytes in blood or interstitial fluid that are indicative of glucose level using one or more measurement techniques.

In another example, the sensor202(or an additional sensor of the wearable glucose monitoring device104—not shown) can include a first and second electrical conductor and the sensor module204can electrically detect changes in electric potential across the first and second electrical conductor of the sensor202. In this example, the sensor module204and the sensor202are configured as a thermocouple such that the changes in electric potential correspond to temperature changes. In some examples the sensor module204and the sensor202are configured to detect a single analyte, e.g., glucose. In other examples, the sensor module204and the sensor202are configured to detect multiple analytes, e.g., sodium, potassium, carbon dioxide, and glucose. Alternately or additionally, the wearable glucose monitoring device104includes multiple sensors to detect not only one or more analytes (e.g., sodium, potassium, carbon dioxide, glucose, and insulin) but also one or more environmental conditions (e.g., temperature). Thus, the sensor module204and the sensor202(as well as any additional sensors) may detect the presence of one or more analytes, the absence of one or more analytes, and/or changes in one or more environmental conditions.

In one or more implementations, the sensor module204may include a processor and memory (not shown). The sensor module204, by leveraging the processor, may generate the glucose measurements118based on the communications with the sensor202that are indicative of the above-discussed changes. Based on these communications from the sensor202, the sensor module204is further configured to generate glucose monitoring device data214. The glucose monitoring device data214is a communicable package of data that includes at least one glucose measurement118. Alternately or additionally, the glucose monitoring device data214includes other data, such as multiple glucose measurements118, sensor identification216, sensor status218, and so forth. In one or more implementations, the glucose monitoring device data214may include other information such as one or more of temperatures that correspond to the glucose measurements118and measurements of other analytes. It is to be appreciated that the glucose monitoring device data214may include a variety of data in addition to at least one glucose measurement118without departing from the spirit or scope of the described techniques.

In operation, the transmitter208may transmit the glucose monitoring device data214wirelessly as a stream of data to the computing device108. Alternately or additionally, the sensor module204may buffer the glucose monitoring device data214(e.g., in memory of the sensor module204) and cause the transmitter208to transmit the buffered glucose monitoring device data214at various intervals, e.g., time intervals (every second, every thirty seconds, every minute, every five minutes, every hour, and so on), storage intervals (when the buffered glucose monitoring device data214reaches a threshold amount of data or a number of instances of glucose monitoring device data214), and so forth.

In addition to generating the glucose monitoring device data214and causing it to be communicated to the computing device108, the sensor module204may include additional functionality in accordance with the described techniques. This additional functionality may include generating predictions of glucose levels of the person102in the future and communicating notifications based on the predictions, such as by communicating warnings when the predictions indicate that the person102's level of glucose is likely to be dangerously low in the near future. This computational ability of the sensor module204may be advantageous especially where connectivity to services via the network116is limited or non-existent. In this way, a person may be alerted to a dangerous condition without having to rely on connectivity, e.g., to the Internet. This additional functionality of the sensor module204may also include calibrating the sensor202initially or on an ongoing basis as well as calibrating any other sensors of the wearable glucose monitoring device104.

With respect to the glucose monitoring device data214, the sensor identification216represents information that uniquely identifies the sensor202from other sensors, such as other sensors of other wearable glucose monitoring devices104, other sensors implanted previously or subsequently in the skin206, and so on. By uniquely identifying the sensor202, the sensor identification216may also be used to identify other aspects about the sensor,202such as a manufacturing lot of the sensor202, packaging details of the sensor202, shipping details of the sensor202, and so on. In this way, various issues detected for sensors manufactured, packaged, and/or shipped in a similar manner as the sensor202may be identified and used in different ways, e.g., to calibrate the glucose measurements118, to notify users to change defective sensors or dispose of them, to notify manufacturing facilities of machining issues, and so forth.

The sensor status218represents a state of the sensor202at a given time, e.g., a state of the sensor at a same time one of the glucose measurements118is produced. To this end, the sensor status218may include an entry for each of the glucose measurements118, such that there is a one-to-one relationship between the glucose measurements118and statuses captured in the sensor status218information. Generally speaking, the sensor status218describes an operational state of the sensor202. In one or more implementations, the sensor module204may identify one of a number of predetermined operational states for a given glucose measurement118. The identified operational state may be based on the communications from the sensor202and/or characteristics of those communications.

By way of example, the sensor module204may include (e.g., in memory or other storage) a lookup table having the predetermined number of operational states and bases for selecting one state from another. For instance, the predetermined states may include a “normal” operation state where the basis for selecting this state may be that the communications from the sensor202fall within thresholds indicative of normal operation, e.g., within a threshold of an expected time, within a threshold of expected signal strength, an environmental temperature is within a threshold of suitable temperatures to continue operation as expected, and so forth. The predetermined states may also include operational states that indicate one or more characteristics of the sensor202's communications are outside of normal activity and may result in potential errors in the glucose measurements118.

For example, bases for these non-normal operational states may include receiving the communications from the sensor202outside of a threshold expected time, detecting a signal strength of the sensor202outside a threshold of expected signal strength, detecting an environmental temperature outside of suitable temperatures to continue operation as expected, detecting that the person102has rolled (e.g., in bed) onto the wearable glucose monitoring device104, and so forth. The sensor status218may indicate a variety of aspects about the sensor202and the wearable glucose monitoring device104without departing from the spirit or scope of the described techniques.

Having considered an example environment and example wearable glucose monitoring device, consider now a discussion of some example details of the techniques for glucose prediction using machine learning and time series glucose measurements in a digital medium environment in accordance with one or more implementations.

Glucose Prediction

FIG.3depicts an example implementation300in which glucose monitoring device data, including glucose measurements, is routed to different systems in connection with glucose prediction.

The illustrated example300includes fromFIG.1the wearable glucose monitoring device104and examples of the computing device108. The illustrated example300also includes the data analytics platform122and the storage device120, which, as discussed above, stores the glucose measurements118. In this example300, the wearable glucose monitoring device104is depicted transmitting the glucose monitoring device data214to the computing device108. As discussed above in relation toFIG.2, the glucose monitoring device data214includes the glucose measurements118along with other data. The wearable glucose monitoring device104may transmit the glucose monitoring device data214to the computing device108in a variety of ways.

The illustrated example300also includes data package302. The data package302may include the glucose monitoring device data214(e.g., the glucose measurements118, the sensor identification216, and the sensor status218) and supplemental data304, or portions thereof. In this example300, the data package302is depicted being routed from the computing device108to the storage device120of the glucose monitoring platform112. Broadly speaking, the computing device108includes functionality to generate the supplemental data304based, at least in part, on the glucose monitoring device data214. The computing device108also includes functionality to package the supplemental data304together with the glucose monitoring device data214to form the data package302and communicate the data package302to the glucose monitoring platform112for storage in the storage device120, e.g., via the network116. It is to be appreciated, therefore, that the data package302may include data collected by the wearable glucose monitoring device104(e.g., the glucose measurements118sensed by the sensor202) as well as supplemental data304generated by the computing device108that acts as an intermediary between the wearable glucose monitoring device104and the glucose monitoring platform112, such as a mobile phone or a smart watch of the user.

With respect to the supplemental data304, the computing device108may generate a variety of supplemental data to supplement the glucose monitoring device data214included in the data package302. In accordance with the described techniques, the supplemental data304may describe one or more aspects of a user's context, such that correspondences of the user's context with glucose monitoring device data214(e.g., the glucose measurements118) can be identified. By way of example, the supplemental data304may describe user interaction with the computing device108, and include, for instance, data extracted from application logs describing interaction (e.g., selections made, operations performed) for particular applications. The supplemental data304may also include clickstream data describing clicks, taps, and presses performed in relation to input/output interfaces of the computing device108. As another example, the supplemental data304may include gaze data describing where a user is looking (e.g., in relation to a display device associated with the computing device108or when the user is looking away from the device), voice data describing audible commands and other spoken phrases of the user or other users (e.g., including passively listening to users), device data describing the device (e.g., make, model, operating system and version, camera type, apps the computing device108is running), and so on.

The supplemental data304may also describe other aspects of a user's context, such as environmental aspects including, for example, a location of the user, a temperature at the location (e.g., outdoor generally, proximate the user using temperature sensing functionality), weather at the location, an altitude of the user, barometric pressure, context information obtained in relation to the user via the IoT114(e.g., food the user is eating, a manner in which a user is using sporting equipment, clothes the user is wearing), and so forth. The supplemental data304may also describe health-related aspects detected about a user including, for example, steps, heart rate, perspiration, a temperature of the user (e.g., as detected by the computing device108), and so forth. To the extent that the computing device108may include functionality to detect, or otherwise measure, some of the same aspects as the wearable glucose monitoring device104, the data from these two sources may be compared, e.g., for accuracy, fault detection, and so forth. The above-discussed types of the supplemental data304are merely examples and the supplemental data304may include more, fewer, or different types of data without departing from the spirit or scope of the techniques described herein.

Regardless of how robustly the supplemental data304describes a context of a user, the computing device108may communicate the data packages302, containing the glucose monitoring device data214and the supplemental data304, to the glucose monitoring platform112for processing at various intervals. In one or more implementations, the computing device108may stream the data packages302to the glucose monitoring platform112substantially in real-time, e.g., as the wearable glucose monitoring device104provides the glucose monitoring device data214continuously to the computing device108. The computing device108may alternately or additionally communicate one or more of the data packages302to the glucose monitoring platform112at a predetermined interval, e.g., every second, every 30 seconds, every hour, and so on.

Although not depicted in the illustrated example300, the glucose monitoring platform112may process these data packages302and cause at least some of the glucose monitoring device data214and the supplemental data304to be stored in the storage device120. From the storage device120, this data may be provided to, or otherwise accessed by, the data analytics platform122, e.g., to generate predictions of upcoming glucose levels, as described in more detail below.

In one or more implementations, the data analytics platform122may also ingest data from a third party306(e.g., a third party service provider) for use in connection with generating predictions of upcoming glucose levels. By way of example, the third party306may produce its own, additional data, such as via devices that the third party306manufactures and/or deploys, e.g., wearable devices. The illustrated example300includes third party data308, which is shown being communicated from the third party306to the data analytics platform122and represents this additional data produced by or otherwise communicated from the third party306.

As mentioned above, the third party306may manufacture and/or deploy associated devices. Additionally or alternately, the third party306may obtain data through other sources, such as corresponding applications. This data may thus include user-entered data entered via corresponding third party applications, e.g., social networking applications, lifestyle applications, and so forth. Given this, the data produced by the third party306may be configured in various ways, including as proprietary data structures, text files, images obtained via mobile devices of users, formats indicative of text entered to exposed fields or dialog boxes, formats indicative of option selections, and so forth.

The third party data308may describe various aspects related to one or more services provided by a third party without departing from the spirit or scope of the described techniques. The third party data308may include, for instance, application interaction data which describes usage or interaction by users with a particular application provided by the third party306. Generally, the application interaction data enables the data analytics platform122to determine usage, or an amount of usage, of a particular application by users of the user population110. Such data, for example, may include data extracted from application logs describing user interactions with a particular application, clickstream data describing clicks, taps, and presses performed in relation to input/output interfaces of the application, and so forth. In one or more implementations, the data analytics platform122may thus receive the third party data308produced or otherwise obtained by the third party306.

The data analytics platform122is illustrated with prediction system310. In accordance with the described systems, the prediction system310is configured to generate predictions312based on the glucose measurements118. Specifically, the prediction system310is configured to generate predictions312of glucose measurements over an upcoming time interval, such as a glucose trace for the upcoming time interval. As discussed in more detail below, these predictions312are based on the glucose measurements118that have been sequenced according to timestamps to form time series glucose measurements, e.g., glucose traces. In one or more implementations, for instance, the prediction system310may generate predictions312based on both the glucose measurements118and additional data, where the additional data may include one or more portions of the glucose monitoring device data214additional to the glucose measurements118, the supplemental data304, the third party data308, data from the IoT114, and so forth. As discussed below, the prediction system310may generate such predictions312by using one or more machine learning models. These models may be trained or otherwise built using the glucose measurements118and additional data obtained from the user population110.

Based on the generated predictions312, the data analytics platform122may also generate notification314. In scenarios where the prediction system310is implemented at least partially at the computing device108, an application of the glucose monitoring platform112on the computing device may generate the notification314based on the generated predictions312. The notification314, for instance, may alert a user about an upcoming adverse health condition, such as that the user is likely to experience dysglycemia (i.e., hyper- or hypoglycemia) absent a mitigating behavior (e.g., eating, taking insulin, exercise, and so forth). The notification314may also provide support for deciding how to treat diabetes, such as by recommending a user perform an action (e.g., download an app to the computing device108, seek medical attention immediately, dose insulin, go for a walk, consume a particular food or drink), continue a behavior (e.g., continue eating a certain way or exercising a certain way), change a behavior (e.g., change eating habits or exercise habits), and so on.

In such scenarios, a communication interface (not shown) of the data analytics platform122communicates the prediction312and/or the notification314for output via the computing device108, e.g., via an application of the glucose monitoring platform112. The communication interface may be configured with various communicative couplings (wired and/or wireless) via which data can be communicated over networks. This communication interface may also be implemented using a variety of software, firmware, and hardware to cause transmission and receipt of such data. In any case, is to be appreciated that either or both of the prediction312and the notification314may be communicated to the computing device108. Additionally or alternately the prediction312and/or the notification314may be routed to a decision support platform and/or a validation platform, e.g., before the prediction312and/or notification314are allowed to be delivered to the computing device108. In the context of generating one or more predictions, consider the following discussion ofFIG.4.

FIG.4depicts an example implementation400of the prediction system310ofFIG.3in greater detail in which upcoming glucose measurements are predicted using machine learning.

In the illustrated example400, the prediction system310is shown obtaining the glucose measurements118and timestamps402, e.g., from the storage device120. Here, the glucose measurements118may correspond to the person102. Additionally, each of the glucose measurements118corresponds to one of the timestamps402. In other words, there may be a one-to-one relationship between glucose measurements118and timestamps402, such that there is a corresponding timestamp402for each individual glucose measurement118. In one or more implementations, the glucose monitoring device data214may include a glucose measurement118and a corresponding timestamp402. Accordingly, the corresponding timestamp402may be associated with the glucose measurement118at the wearable glucose monitoring device104level, e.g., in connection with producing the glucose measurement118. Regardless of how a timestamp402is associated with a glucose measurement118—or which device associates a timestamp402with a glucose measurement118—each of the glucose measurements118has a corresponding timestamp402.

In this example400, the prediction system310is depicted including sequencing manager404and machine learning model406, which are configured to generate predicted upcoming glucose measurements408based on the glucose measurements118and the timestamps402. Although the prediction system310is depicted including these two components, it is to be appreciated that the prediction system310may have more, fewer, and/or different components to generate the predicted upcoming glucose measurements408based on the glucose measurements118and the timestamps402without departing from the spirit or scope of the described techniques.

Broadly speaking, the sequencing manager404is configured to generate time series glucose measurements410based on the glucose measurements118and the timestamps402. Although the glucose measurements118may generally be received in order, e.g., by the glucose monitoring platform112from the wearable glucose monitoring device104and/or the computing device108, in some instances, one or more of the glucose measurements118may not be received in a same order in which the glucose measurements118are produced. For instance, packets with the glucose measurements118may be received out of order. Thus, the order of receipt may not chronologically match the order in which the glucose measurements118are produced by the wearable glucose monitoring device104. In addition or alternately, the communications including one or more of the glucose measurements118may be corrupted. Indeed, there may be a variety of reasons why the glucose measurements118, as obtained by the prediction system310, are not entirely in time order.

To generate the time series glucose measurements410, the sequencing manager404determines a time-ordered sequence of the glucose measurements118according to the respective timestamps402. Due to corruption and communication errors, the glucose measurements118obtained by the prediction system310may not only be out of time order but may also be missing one or more measurements—there may be gaps in the time-ordered sequence where one or more measurements are expected. In these instances, the sequencing manager404interpolates the missing glucose measurements and incorporates them into the time-ordered sequence.

The sequencing manger404outputs the time-ordered sequence of the glucose measurements118as the time series glucose measurements410. The time series glucose measurements410may be configured as or otherwise referred to as a “glucose trace.” In contrast to the predicted upcoming glucose measurements408, the time series glucose measurements410are a trace of glucose measurements that have been observed by a wearable glucose monitoring device, such as by the wearable glucose monitoring device104worn by the person102. Glucose measurements observed in this way contrast with glucose measurements predicted, e.g., by the machine learning model406.

For example, the time series glucose measurements410may be a trace of the glucose measurements118observed for the person102over a previous 12 hours from a time the prediction is initiated. In contrast, the predicted upcoming glucose measurements408may be configured as an additional trace of glucose measurements spanning from a time the prediction is initiated to a time 30 minutes into the future. It is to be appreciated that the time series glucose measurements410and the predicted upcoming glucose measurements408may correspond to different time intervals than 12 hours and 30 minutes, respectively, without departing from the spirit or scope of the described techniques.

In accordance with the described techniques, the time series glucose measurements410are provided as input to the machine learning model406. Responsive to receiving the time series glucose measurements410as input, the machine learning model406is configured to generate and output the predicted upcoming glucose measurements408. Although the machine learning model406is generally described as generating the predicted upcoming glucose measurements408from input of the time series glucose measurements410, in one or more implementations, the machine learning model406may receive additional inputs in order to generate the predicted upcoming glucose measurements408. By way of example, the machine learning model406may receive as input a patient-specific correction factor (e.g., specific to the person102) along with the time series glucose measurements410. The prediction system310may determine a patient-specific correction factor for the person102based on historical glucose measurements118of the person102, device data of the wearable glucose monitoring device104and other previously worn glucose monitoring devices, interaction data describing interactions of the person102with the wearable glucose monitoring device104and an application of the glucose monitoring platform112, and health (or status) of the person102's wearable glucose monitoring device104, to name just a few. The machine learning model406may receive other data as input without departing from the spirit or scope of the described techniques.

Regardless of the particular data received as input, the machine learning model406is trained to output the predicted upcoming glucose measurements408. By way of example, the machine learning model406may be trained, or an underlying model may be learned, based on one or more training approaches and using historical time series glucose measurements, such as time series glucose measurements generated from the glucose measurements118of the user population110. Such training may utilize a large amount of training data generated from the glucose measurements118of the user population110, such as by forming training data comprising vectors of users' individual glucose measurements118over fixed time intervals (e.g., hours, days, or weeks) from the user population110data maintained in the storage device120. This data may be used, in part, for testing and validation of the machine learning model406. Training the machine learning model406is discussed in more detail in relation toFIG.8.

In contrast to conventional glucose prediction approaches, the machine learning model406is configured as a non-linear model. Conventional approaches to glucose prediction may model glucose using linear models, such as with autoregressive linear models. Although such linear models may be capable of describing time-varying processes, the output of the models is linearly dependent on previous values. This can result in glucose predictions that have significant time delays in relation to actual, observed glucose measurements.

By way of example, a conventionally configured linear model may output a prediction that is intended to indicate a person's glucose measurement 30 minutes into the future from a current time. However, the person's observed glucose may correspond to the predicted measurement a mere five minutes into the future. To this end, the conventionally configured linear model's prediction is 25 minutes delayed—the predictive horizon of the conventional model thus fails to match the person's actual glucose. Additionally, linear models may generate less accurate predictions of upcoming glucose measurements than a non-linear model as described herein. This is because linear models may not be able to account for some patterns observed in historical data, which can be captured using non-linear approaches. Failure of the predictive horizon to match actual glucose and less accurate predictions may render glucose predictions generated by conventional systems unsuitable for various applications, such as for prescribing actions to mitigate dangerously (and rapidly) changing glucose levels.

Instead, the machine learning model406may be configured as a non-linear model or as an ensemble of models that includes one or more non-linear models. The machine learning model406may be configured as a generative model, which extrapolates a sequence of glucose measurements, e.g., multiple hours into the future. Non-linear and generative machine learning models may include, for instance, neural networks (e.g., recurrent neural networks such as long-short term memory (LSTM) network), state machines, Markov chains (e.g., hidden Markov models), Monte Carlo methods, and particle filters, to name just a few. Generally speaking, these types of models may be configured to learn patterns in data that correspond to long-term trends, enabling them to learn dynamics of glucose measurements through sequence recognition. It is to be appreciated that the machine learning model406may be configured as or otherwise include one or more different types of non-linear machine learning models without departing from the spirit or scope of the described techniques. As one example of a non-linear machine learning model, considerFIG.5.

FIG.5depicts an example implementation500in which a machine learning model predicts upcoming glucose measurements with iterative predictions.

The illustrated example500includes the time series glucose measurements410and the predicted upcoming glucose measurements408. Here, the time series glucose measurements410and the predicted upcoming glucose measurements408are depicted as input to and output from, respectively, steps of the machine learning model406. In particular, the illustrated example500includes a plurality of steps of the machine learning model406(1)-(5). This may represent a scenario in which the machine learning model406is configured as a recurrent neural network, such as an LSTM network. In scenarios where the machine learning model406is configured as an LSTM network, for example, the steps of the machine learning model406(1)-(5) represent repeating modules of the network.

The illustrated example500also includes glucose traces502-510, including a first glucose trace502, a second glucose trace504, a third glucose trace506, an (n−1)thglucose trace508, and an nthglucose trace510as well as visualizations of those glucose traces and a visualization of the time series glucose measurements410. The visualizations of the time series glucose measurements410and the glucose traces502-510are depicted in greater detail inFIGS.6and7. The discussion of the illustrated example500refers to details of the visualizations as depicted inFIGS.6and7.

Specifically,FIG.6depicts example visualizations600of observed and predicted glucose traces, including visualizations of the time series glucose measurements410, the first glucose trace502, and the second glucose trace504.FIG.7depicts example visualizations700of predicted glucose traces, including visualizations of the third glucose trace506, the (n−1)thglucose trace508, and the nthglucose trace510.

In the illustrated example500, the step of the machine learning model406(1) is shown receiving the time series glucose measurements410as input. With reference to the example visualizations600, the visualization of the time series glucose measurements410includes a plurality of points that represent observed glucose measurements and that are disposed within input window602. This represents that the glucose measurements represented by those points are input to the step of the machine learning model406(1).

In the illustrated examples600,700each of the visualizations includes the input window602. Broadly speaking, the input window identifies which of the glucose measurements are predicted and which measurements are used as input to a next step. In these examples600,700, for instance, the glucose measurements within the input window602are input to a step of the machine learning model406. In one or more implementations, the glucose measurements that are not within the input window602are not used as input to the next step. For instance, crossed-out points604of the first glucose trace502may not be input to the step of the machine learning model406(2).

Although a size of the input window602is depicted remaining the same across the example visualizations600,700—representing that a same amount of time of time series glucose measurements is input to the machine learning model406at each step (e.g., 12 hours' worth of measurements)—it is to be appreciated that in one or more implementations, the input window602may not remain a same size. Instead, the input window may expand at each step to add an amount of time that corresponds to a timestep of the step's prediction. For example, if the time series glucose measurements410correspond to 12 hours' worth of data and the machine learning model406predicts five minutes' worth of glucose measurements at each step, then the first glucose trace502corresponds to 12 hours and 5 minutes' worth of data, and this 12 hours and 5 minutes' worth of data is input to the machine learning model at the second step. Continuing with this example, such input may produce the second glucose trace504as 12 hours and 10 minutes' worth of data.

Despite this and similar such approaches in various implementations, in the following discussion, an implementation is described in which the input window602remains a same size (e.g., in terms of amount of time's worth of time series glucose measurements). Also, it is to be appreciated that although a size of the input window602may remain the same across the different steps, different implementations may leverage an input window of a different size than discussed in the following, i.e., a different amount of time. In one or more implementations, the input window602may correspond to 12 hours' worth of glucose measurements, e.g., the time series glucose measurements410may correspond to 12 hours of the glucose measurements118that span back from a time at which generation of the predicted upcoming glucose measurements408is initiated. In other implementations, the input window602may have a different size, such that the time series glucose measurements410correspond, for example, to a last day's worth of glucose measurements, a last two days' worth of glucose measurements, a last 6 hours' worth of glucose measurements, or a last hour's worth of measurements, to name just a few.

Turning now to a stepwise discussion of the illustrated example500. In this example500, the first step of the machine learning model406(1) is depicted receiving as input the time series glucose measurements410. The first step of the machine learning model406(1) is depicted outputting the first glucose trace502. As depicted in the illustrated examples600, the first glucose trace502includes a first timestep of glucose measurements606. Generally speaking, each step of the machine learning model406is configured to predict a timestep of glucose measurements given an input window of glucose measurements. Accordingly, the first step machine learning model406(1) predicts the first timestep of glucose measurements606based on the model's training and on one or more patterns in the time series glucose measurements410. The first timestep of glucose measurements606includes glucose measurements predicted for a timestep that spans from initiation of the prediction to a subsequent time that corresponds to an amount of time of the timestep. The first step machine learning model406(1) may append the first timestep of glucose measurements606to a terminal end of the time series glucose measurements410and also remove glucose measurements from a beginning of the time series, e.g., a timestep's worth of the glucose measurements. Alternately or additionally, the first step machine learning model406may simply predict the first timestep of glucose measurements606and additional logic (not shown) may perform the appending and removing. By predicting the first timestep of glucose measurements606and performing the appending and removing, the prediction system310forms the first glucose trace502.

Consider an example of the predicting, appending, and removing where the input window corresponds to 12 hours and the timestep corresponds to 5 minutes. Here, the first step of the machine learning model406(1) generates the first timestep of glucose measurements606as a 5-minute prediction of upcoming glucose measurements. This 5-minute prediction is appended to a terminal end of the time series glucose measurements410, which is 12 hours of glucose measurements, thereby forming 12 hours and 5 minutes of glucose measurements. Then, 5 minutes of glucose measurements are removed from a beginning of this trace, forming the first glucose trace502as a 12-hour trace of glucose measurements. The first glucose trace502, therefore, includes both observed glucose measurements and predicted glucose measurements. The first glucose trace502is then input to the second step of the machine learning model406(2).

In this example500, the second step of the machine learning model406(2) is depicted outputting the second glucose trace504. As depicted in the illustrated examples600, the second glucose trace504includes the first timestep of glucose measurements606and a second timestep of glucose measurements608. Here, the second step of the machine learning model406(2) predicts the second timestep of glucose measurements608based on the model's training and on one or more patterns in the first glucose trace502. The second timestep of glucose measurements608includes glucose measurements predicted for a timestep that spans from a time corresponding to one timestep's worth of time to a subsequent time corresponding to two timesteps' worth of time, e.g., glucose measurements for 5-10 minutes in the future.

In a similar manner as the preceding step, the second step of the machine learning model406(2) may append the second timestep of glucose measurements608to a terminal end of the first glucose trace502and also remove glucose measurements from a beginning of the trace, e.g., a timestep's worth of the glucose measurements. Alternately or additionally, the above-mentioned additional logic (not shown) may perform the appending and removing. By predicting the second timestep of glucose measurements608and performing the appending and removing, the prediction system310forms the second glucose trace504. The second glucose trace504is then input to the third step of the machine learning model406(3).

In this example500, the third step of the machine learning model406(3) is depicted outputting the third glucose trace506. As depicted in the illustrated examples700, the third glucose trace506includes the first timestep of glucose measurements606, the second timestep of glucose measurements608, and a third timestep of glucose measurements702. Here, the third step of the machine learning model406(3) predicts the third timestep of glucose measurements702based on the model's training and on one or more patterns in the second glucose trace504. The third timestep of glucose measurements702includes glucose measurements predicted for a timestep that spans from a time corresponding to two timesteps' worth of time to a subsequent time corresponding to three timesteps' worth of time, e.g., glucose measurements for 10-15 minutes in the future.

In a similar manner as with the preceding timesteps, the third step of the machine learning model406(3) may append the third timestep of glucose measurements702to a terminal end of the second glucose trace504and also remove glucose measurements from a beginning of the trace, e.g., a timestep's worth of the glucose measurements. Alternately or additionally, the above-mentioned additional logic (not shown) may perform the appending and removing. By predicting the third timestep of glucose measurements702and performing the appending and removing, the prediction system310forms the third glucose trace506. The third glucose trace506is then input to a next step of the machine learning model406.

The illustrated example500includes ellipses to indicate that there may be one or more steps between the third step of the machine learning model406(3) and the illustrated fourth step of the machine learning model406(4). If the machine learning model406generates the predicted upcoming glucose measurements408for a 30-minute interval and the timestep of the prediction at each step is 5 minutes, then there are 6 steps of the machine learning model406(and only one not shown step between the third and fourth steps of the machine learning model406(3),(4)). However, there may be more steps in one or more implementations, such as with 5-minute timesteps for an hour interval, 3-minute timesteps for a 30-minute interval, and so on. It is to be appreciated that there may be more or fewer steps than illustrated in this example500without departing from the spirit or scope of the techniques.

In any case, the fourth step of the machine learning model406(4) receives a glucose trace from an immediately preceding step of the machine learning model406as input. Where there are n steps, for instance, the fourth step of the machine learning model406(4) receives the (n−2)thglucose trace as input. Here, the fourth step of the machine learning model406(4) is depicted outputting the (n−1)thglucose trace508. As depicted in the illustrated examples700, the (n−1)thglucose trace508includes the first timestep of glucose measurements606, the second timestep of glucose measurements608, the third timestep of glucose measurements702, and an (n−1)thtimestep of glucose measurements704. Here, the fourth step of the machine learning model406(4) predicts the (n−1)thtimestep of glucose measurements704based on the model's training and on one or more patterns in a glucose trace from an immediately preceding step of the machine learning model406.

Although not illustrated in the example visualizations700, it is to be appreciated that if there are additional steps between the third and fourth steps of the machine learning model406(3),(4), then there are also a corresponding number of additional timesteps of glucose measurements between the third timestep of glucose measurements702and the (n−1)thtimestep of glucose measurements704. The (n−1)thtimestep of glucose measurements704includes glucose measurements predicted for a timestep that spans from a time corresponding to (n−2) timesteps' worth of time to a subsequent time corresponding to (n−1) timesteps' worth of time, e.g., glucose measurements for 20-25 minutes in the future.

In a similar manner as with the preceding timesteps, the fourth step of the machine learning model406(4) may append the (n−1)thtimestep of glucose measurements704to a terminal end of the immediately preceding glucose trace and also remove glucose measurements from a beginning of the trace, e.g., a timestep's worth of the glucose measurements. Alternately or additionally, the above-mentioned additional logic (not shown) may perform the appending and removing. By predicting the (n−1)thtimestep of glucose measurements704and performing the appending and removing, the prediction system310forms the (n−1)thglucose trace508. The (n−1)thglucose trace508is then input to a next step of the machine learning model406, e.g., the illustrated fifth step of the machine learning model406(5).

In this example500, the fifth step of the machine learning model406(5) is depicted outputting the nthglucose trace510. As depicted in the illustrated examples700, the nthglucose trace510includes the first timestep of glucose measurements606, the second timestep of glucose measurements608, the third timestep of glucose measurements702, the (n−1)thtimestep of glucose measurements704, and an nthtimestep of glucose measurements706. Here, the fifth step of the machine learning model406(5) predicts the nthtimestep of glucose measurements706based on the model's training and on one or more patterns in the (n−1)thglucose trace508. The nthtimestep of glucose measurements706includes glucose measurements predicted for a timestep that spans from a time corresponding to (n−1) timesteps' worth of time to a subsequent time corresponding to n timesteps' worth of time, e.g., glucose measurements for 25-30 minutes in the future.

In a similar manner as with the preceding timesteps, the fifth step of the machine learning model406(5) may append the nthtimestep of glucose measurements706to a terminal end of the (n−1)thglucose trace508and also remove glucose measurements from a beginning of the trace, e.g., a timestep's worth of the glucose measurements. Alternately or additionally, the above-mentioned additional logic (not shown) may perform the appending and removing. By predicting the nthtimestep of glucose measurements706and performing the appending and removing, the prediction system310forms the nthglucose trace510.

The illustrated example500includes dashed lines extending from the predicted upcoming glucose measurements408and a dashed box around a portion of the nthglucose trace510. Notably, the dashed box is illustrated around the first timestep of glucose measurements606, the second timestep of glucose measurements608, the third timestep of glucose measurements702, the (n−1)thtimestep of glucose measurements704, and the nthtimestep of glucose measurements706. This represents that the predicted upcoming glucose measurements408may correspond to the combination of glucose measurements as predicted in those timesteps606,608,702,704,706. The glucose measurements predicted in the timesteps606,608,702,704,706are distinguished from the glucose measurements of the time series glucose measurements410because the time series glucose measurements410are actually observed (e.g., produced by the wearable glucose monitoring device104while worn by the person102) rather than predicted.

Although the machine learning model406may be configured to generate the predicted upcoming glucose measurements408iteratively, in timesteps as described in relation toFIGS.6and7, in one or more implementations the machine learning model406may instead generate the predicted upcoming glucose measurements408in a single step—without using multiple iterations. In other words, rather than generate six five-minute timesteps of predictions—to predict 30 minutes' worth of predicted upcoming glucose measurements408—the machine learning model406may instead simply generate a 30-minute prediction of the predicted upcoming glucose measurements408in a single step. For example, the machine learning model406may receive 12 hours' worth of the glucose measurements118as input and generate the 30 minutes' worth of the predicted upcoming glucose measurements408in one step—rather than doing so in iterations involving predicting, appending, and then inputting an augmented trace to the machine learning model406.

In the illustrated examples400,500, the machine learning model406is depicted receiving the time series glucose measurements410as input only, and is not depicted receiving data describing other aspects that may impact a person's glucose in the future, such as insulin administered, carbohydrates consumed, exercise, and stress. Although in some implementations the machine learning model406may be limited to receiving time series glucose measurements410(and information about the time series glucose measurements410such as confidences), in one or more implementations, the machine learning model406may also receive data as input describing one or more other aspects that impact a person's glucose in the future.

Beyond insulin administration, carbohydrate consumption, exercise, and stress, further examples of aspects that may be indicative of a person's glucose in the future include accelerometer data of a mobile device or smart watch (e.g., indicating that that the person has viewed a user interface of the device and thus has likely seen an alert or information related to glucose measurements), application data (e.g., clickstream data describing user interfaces displayed and user interactions with applications via the user interfaces), environmental temperature, barometric pressure, and the presence or absence of various health conditions (e.g., pregnancy), to name just a few.

In the context of training the machine learning model406to predict upcoming glucose measurements based on time series of observed glucose measurements, consider the following discussion ofFIG.8.

FIG.8depicts an example implementation800of the prediction system310in greater detail in which a machine learning model is trained to predict upcoming glucose measurements. As inFIG.3, the prediction system310is included as part of the data analytics platform122, although in other scenarios the prediction system310may also or alternately be, in part or entirely, included in other devices such as the computing device108.

In the illustrated example800, the prediction system310includes model manager802, which manages the machine learning model406, which as mentioned above is configured as or includes one or more non-linear models, e.g., a recurrent neural network such as an LSTM. It is to be appreciated that the machine learning model406may be configured as or include other types of non-linear models without departing from the spirit or scope of the described techniques. These different machine learning models may be built or trained (or the model otherwise learned), respectively, using different algorithms due, at least in part, to different architectures. Accordingly, it is to be appreciated that the following discussion of the model manager802's functionality is applicable to a variety of non-linear machine learning models. For explanatory purposes, however, the functionality of the model manager802will be described generally in relation to training a neural network.

Broadly speaking, the model manager802is configured to manage machine learning models, including the machine learning model406. This model management includes, for example, building the machine learning model406, training the machine learning model406, updating this model, and so on. In one or more implementations, updating this model may include transfer learning to personalize the machine learning model406—to personalize it from a state as trained with training data of the user population110to an updated state trained with additional training data or (update data) describing one or more aspects of the person102and/or describing one or more aspects of a subset of the user population110determined similar to the person102. Specifically, the model manager802is configured to carry out model management using, at least in part, the wealth of data maintained in the storage device120of the glucose monitoring platform112. As illustrated, this data includes the glucose measurements118and timestamps402of the user population110. Said another way, the model manager802builds the machine learning model406, trains the machine learning model406(or otherwise learns an underlying model), and updates this model using the glucose measurements118and the timestamps402of the user population110. In implementations where the machine learning model406receives data in addition to time series glucose measurements as input, the model manager802also uses this other data of the user population110to build, train, and update the machine learning model406.

Unlike conventional systems, the glucose monitoring platform112stores (e.g., in the storage device120) or otherwise has access to glucose measurements118obtained using the wearable glucose monitoring device104for hundreds of thousands of users of the user population110(e.g., 500,000 or more). Moreover, these measurements are taken by sensors of the wearable glucose monitoring device104at a continuous rate. As a result, the glucose measurements118available to the model manager802, for model building and training, number in the millions, or even billions. With such a robust amount of data, the model manager802can build and train the machine learning model406to accurately predict upcoming glucose of a person based on patterns in their observed glucose measurements.

Absent the robustness of the glucose monitoring platform112's glucose measurements118, conventional systems simply cannot build or train models to cover state spaces in a manner that suitably represents how patterns in glucose levels are indicative of future glucose levels. Failure to suitably cover these state spaces can result in glucose predictions that are inaccurate (e.g., in terms of an amount of glucose actually present in the person's blood or in terms of a timing of the prediction), which can lead to recommending unsafe actions or behaviors that could cause death. Given the gravity of generating inaccurate and untimely predictions, it is important to build the machine learning model406using an amount of glucose measurements118that is robust against rare events.

In one or more implementations, the model manager802generates training data to train the machine learning model406. Initially, generating the training data includes forming training time series of glucose measurements from the glucose measurements118and the corresponding timestamps402of the user population110. The model manager802may leverage the functionality of the sequencing manager404to form those training time series, for instance, in a similar manner as discussed in relation to forming the time series glucose measurements410.

For each of the training time series, the model manager802may then generate an input portion of the training time series and an expected output portion of the training time series, i.e., a ground truth for comparison to the model's output during training. Accordingly, each instance of training data may include a training input portion and an expected output portion extracted from a training time series. The model manager802may generate those portions by segmenting a training time series, such as by selecting an input window's worth of the training time series for the input portion and also by using, as the expected output portion, a portion of the training time series that follows the selected portion subsequent in time. In scenarios where the machine learning model406generates predictions in steps, as discussed in relation toFIG.5, the expected output portion may correspond to a timestep's worth of the training time series subsequent to the selected portion.

To demonstrate, consider again the example in which the machine learning model406is designed to receive 12 hours of time series glucose measurements as input and in which steps of the machine learning model406are trained to generate 5-minute predictions of upcoming glucose measurements. In this example, assume also that the training time series are 24 hours of time-ordered glucose measurements (24-hour glucose traces)—certainly the model manager802may use training time series of different lengths of time without departing from the spirit or scope of the described techniques. By way of example, a particular training time series may span from 12:00:00 PM on Apr. 8, 2020 to 12:00:00 PM on Apr. 9, 2020. As the input portion of this particular training time series, the model manager802may select a 12-hour portion, such as from 1:59:00 PM on Apr. 8, 2020 to 1:59:00 AM on Apr. 9, 2020. It follows then that the expected output portion of this particular training time series spans from 1:59:00 AM on Apr. 9, 2020 to 2:04:00 AM on Apr. 9, 2020. In scenarios in which the machine learning model406is not configured to generate the predicted upcoming glucose measurements408in a stepwise manner—but rather in a single pass through the model—the model manager802may use, for the expected output portion, a portion of the training time series that corresponds to an entire amount of time of the predicted upcoming glucose measurements408, e.g., 30 minutes following the input portion. Accordingly, once built, the machine learning model406is configured to predict traces of glucose measurements that correspond in amount of time to the expected output portions of the training time series.

The model manager802uses the training input portions along with the respective expected output portions to train the machine learning model406. In the context of training, the model manager802may train the machine learning model406by providing an instance of data from the set of training input portions to the machine learning model406. Responsive to this, the machine learning model406generates a prediction of upcoming glucose measurements, such as by predicting a timestep of upcoming glucose measurements in stepped implementations (e.g., LSTM) or predicting an entire interval in non-stepped implementations (e.g., other types of neural networks). The model manager802obtains this training prediction from the machine learning model406as output and compares the training prediction to the expected output portion that corresponds to the training input portion. Based on this comparison, the model manager adjusts internal weights of the machine learning model406so that the machine learning model can substantially reproduce the expected output portion when the respective training input portion is provided as input in the future.

This process of inputting instances of the training input portions into the machine learning model406, receiving training predictions from the machine learning model406, comparing the training predictions to the expected output portions (observed) that correspond to the input instances (e.g., using a loss function such as mean squared error), and adjusting internal weights of the machine learning model406based on these comparisons, can be repeated for hundreds, thousands, or even millions of iterations—using an instance of training data per iteration.

The model manager802may perform such iterations until the machine learning model406is able to generate predictions that consistently and substantially match the expected output portions. The capability of a machine learning model to consistently generate predictions that substantially match expected output portions may be referred to as “convergence.” Given this, it may be said that the model manger802trains the machine learning model406until it “converges” on a solution, e.g., the internal weights of the model have been suitably adjusted due to training iterations so that the model consistently generates predictions that substantially match the expected output portions.

As noted above, the machine learning model406may be configured to receive input in addition to an interval (e.g., an input window) of time series glucose measurements in one or more implementations. In such implementations, the model manager802may form training instances that include the training input portion, the respective expected output portion and also additional input data describing any other aspects of the user population110being used to predict upcoming glucose measurements, e.g., insulin administrations, carbohydrate consumption, exercise, and/or stress. This additional data as well as the training input portion may be processed by the model manger802according to one or more known techniques to produce an input vector. This input vector, describing the training input portion as well as the other aspects, may then be provided to the machine learning model406. In response, the machine learning model406may generate a prediction of upcoming glucose measurements in a similar manner as discussed above, such that the prediction can be compared to the expected output portion of the training instance and weights of the model adjusted based on the comparison.

As also noted above, management of the machine learning model406may include personalizing the machine learning model406using transfer learning. In such scenarios, the model manager802may initially train the machine learning model406at the global level, as described in detail above using instances of training data generated from the data of the user population110. In transfer learning scenarios, the model manager802may then create an instance of this globally trained model for a particular user, such that a copy of the globally trained model is generated for the person102and other copies of the globally trained model are generated for other users on a per-user basis.

This globally trained model may then be updated (or further trained) using data specific to the person102. For example, the model manager802may create instances of training data using the glucose measurements118of the person102, and further train the globally trained version of model in a similar manner as described above, e.g., by providing training input portions of the person102's training data to the machine learning model406, receiving training predictions of upcoming glucose measurements, comparing those predictions to respective output portions of the training data, and adjusting internal weights of the machine learning model406. Based on this further training, the machine learning model406is trained at a personal level, creating a personally trained machine learning model406.

It is to be appreciated that the personalizing may be less granular than on a per-user basis, in one or more implementations. For example, the globally trained model may be personalized at a user segment level, i.e., a set of similar users of the user population110that is less than an entirety of the user population110. In this way, the model manager802may create copies of the globally trained machine learning model406on a per-segment basis and train the global versions at the segment level, creating segment specific machine learning models406.

In one or more implementations, the model manager802may personalize the machine learning model406at the server level, e.g., at servers of the glucose monitoring platform112. This model may then be maintained at the server level and/or communicated to the computing device108, e.g., for integration with an application of the glucose monitoring platform112at the computing device108. Alternately or additionally, at least a portion of the model manager802may be implemented at the computing device108, such that the globally trained version of the machine learning model406is communicated to the computing device108and the transfer learning (i.e., the further training discussed above to personalize the model) is carried out at the computing device108. Although transfer learning may be leveraged in one or more scenarios, it is to be appreciated that in other scenarios such personalization may not be utilized and the described techniques may be implemented using globally trained versions of the machine learning model406.

As also noted above, the machine learning model406may be configured as an LSTM network in one or more implementations. With reference toFIG.5, each of the steps of the machine learning model406(1)-(5) may correspond to a cell of the LSTM network. In this context, the model manger802during training may adjust weights of the different layers of the LSTM network, including weights of a sigmoid layer referred to as the “forget gate layer,” weights of a second sigmoid layer referred to as the “input gate layer,” weights of a tanh layer that creates a vector of candidate values, and weights of a third sigmoid layer referred to as the “output layer.” To adjust these weights, the model manager802may back-propagate error values from the output layers, such that the error remains in the LSTM's cell. By doing so, the model manager802continuously feeds error back to each of the LSTM cell's layers, until the layers learn to cut off the value during training.

FIG.9depicts an example visualization900of glucose traces with predicted glucose measurements and confidences in the predictions.

The illustrated example900depicts a glucose trace902having observed glucose measurements904and predicted glucose measurements906, e.g., the combination forming an “augmented” glucose trace. Additionally, the illustrated example includes visualizations of confidence908. In particular, each visualization of confidence908represents a confidence of the respective predicted glucose measurement906, e.g., a confidence that the prediction is correct. In this example900, the visualizations of confidence908become larger in size the further away (in terms of time) the predicted glucose measurements906are from the observed glucose measurements904. This reflects that the further in time the predicted glucose measurements906are from the observed glucose measurements904, the less confident the machine learning model406is in those predictions. The visualizations of confidence908may represent, for instance, a range of glucose within which the machine learning model406is 70% confident an observed glucose measurement will be produced at the respective time.

In one or more implementations, the machine learning model406may output one or more measures of confidence along with the stepwise glucose traces502-510and/or the predicted upcoming glucose measurements408. In connection with implementations in which the machine learning model406outputs measures of confidence with the stepwise glucose traces, the machine learning model406may also be configured to receive those measures as input at each step. The machine learning model406may use the input measures of confidence to compute the measures of confidence for the next step. In addition or alternately, the machine learning model406may be configured to generate a glucose trace at a next step as long the measures of confidence satisfy a threshold. If the glucose trace generated at a previous step is associated with confidence measures that fail to satisfy the threshold, however, then the machine learning model406may not generate any further glucose traces.

By using confidence measures in this way, a length of the predicted upcoming glucose measurements408may be based on a confidence in the stepwise predictions and not a predetermined interval of time. Nonetheless, one or more implementations may generate glucose traces in the above-discussed stepwise manner for a fixed interval of time.

As discussed above in relation toFIG.3, the data analytics platform122may generate and deliver notifications314based on a prediction312, such as based on the predicted upcoming glucose measurements408. As noted above, the machine learning model406is capable of predicting glucose accurately for further predictive horizons from a current time than conventional techniques. These more accurate predictions of glucose can in turn be used to predict whether a patient will experience an upcoming glycemic event, such as overnight hypoglycemia. To this end, the glucose predictions generated by the machine learning model406may serve as input to one or more decision support models, which can alert or otherwise inform users about upcoming glycemic events. For instance, the data analytics platform122may deliver an alert or support for deciding how to treat diabetes. In this context, considerFIG.10which depicts example notifications.

FIG.10depicts example implementations1000of user interfaces displayed for notifying a user based on predictions of upcoming glucose measurements. In particular, the example implementations1000include the computing device108depicted in an alert scenario1002and a decision support scenario1004.

In both the alert and decision support scenarios1002,1004, the computing device108displays a user interface1006. The user interface1006may correspond to an interface of an application, e.g., an interface of an application of the glucose monitoring platform112. Alternately or additionally, the user interface1006may correspond to a notification “center”, such as a lock screen or other operating-level screen.

In the alert scenario1002, the user interface1006displays an alert notification314via a display device of the computing device108. This notification314may be configured to alert a user about an upcoming adverse health condition, such as that the user is likely to experience dysglycemia (i.e., hyper- or hypo-glycemia) absent a mitigating behavior (e.g., eating, taking insulin, exercise, and so forth). In accordance with the described techniques, this notification314is based on the predicted upcoming glucose measurements408, which in this example may be processed to identify a predicted hypoglycemic episode in 22 minutes. It is to be appreciated that notifications may cause computing device to output different signals in addition to displaying information, including, for instance, audio signals via speakers, vibrations or other haptics via haptic systems, and so forth.

In the decision support scenario1004, the user interface1006displays a support notification314via the display device of the computing device108. This notification314may be configured to provide support for deciding how to treat diabetes, such as by recommending a user perform an action (e.g., download an app to the computing device108, seek medical attention immediately, dose insulin, go for a walk, consume a particular food or drink), continue a behavior (e.g., continue eating a certain way or exercising a certain way), change a behavior (e.g., change eating habits or exercise habits), and so on. In accordance with the described techniques, this notification314is also based on the predicted upcoming glucose measurements408.

Although notifications to a user are shown, it is to be appreciated that in one or more implementations, notifications generated based on the predicted upcoming glucose measurements408may alternately or additionally be communicated to other entities, such as a health care provider of the person102(e.g., a doctor), a caregiver of the person102(e.g., a parent or a child), a telemedicine service, and so forth. Further, it is to be appreciated that a variety of other services in addition or alternate to notification services may be provided based on the predicted upcoming glucose measurements408without departing from the spirit or scope of the described techniques.

Having discussed example details of the techniques for glucose prediction using machine learning and time series glucose measurements, consider now some example procedures to illustrate additional aspects of the techniques.

Example Procedures

This section describes example procedures for glucose prediction using machine learning and time series glucose measurements. Aspects of the procedures may be implemented in hardware, firmware, or software, or a combination thereof. The procedures are shown as a set of blocks that specify operations performed by one or more devices and are not necessarily limited to the orders shown for performing the operations by the respective blocks. In at least some implementations the procedures are performed by a prediction system, such as the prediction system310that makes use of the sequencing manager404, the machine learning model406, and the model manager802.

FIG.11depicts a procedure1100in an example implementation in which a non-linear machine learning model predicts upcoming glucose measurements based on time series glucose measurements.

A time series of glucose measurements up to a time is received (block1102). In accordance with the principles discussed herein, the glucose measurements are provided by a wearable glucose monitoring device worn by a user. By way of example, the machine learning model406receives the time series glucose measurements410, and the glucose measurements are provided by the wearable glucose monitoring device104worn by the person102. In particular, the wearable glucose monitoring device104includes the sensor202, which is inserted subcutaneously into skin of the person102and used to measure glucose in the person102's blood. As discussed above, the wearable glucose monitoring device104may be configured as a continuous glucose monitoring (CGM) system.

Upcoming glucose measurements are predicted for an interval of time subsequent to the time (block1104). In accordance with the principles discussed herein, the upcoming glucose measurements are predicted by processing the time series glucose measurements using a non-linear machine learning model generated based on historical time series of glucose measurements of a user population. By way of example, the machine learning model406generates the predicted upcoming glucose measurements408. The machine learning model406generates this prediction by processing the time series glucose measurements410based on patterns, learned during training, of time series of the glucose measurements118of the user population110. As noted above, the user population110includes users that wear wearable glucose monitoring device, such as the wearable glucose monitoring device104.

The upcoming glucose measurements are output (block1106). By way of example, the prediction system310outputs the predicted upcoming glucose measurements408, such as for processing by additional logic (e.g., to generate recommendations or notifications), for storing in the storage device120, for communication to one or more computing devices, or for display, to name just a few.

A notification is generated based on the upcoming glucose measurements (block1108). By way of example, the data analytics platform122generates the notification314based on the predicted upcoming glucose measurements408. For instance, the notification314may alert a user (or a health care provider or telemedicine service) about an upcoming adverse health condition, such as that the user is likely to experience dysglycemia (i.e., hyper- or hypoglycemia) absent a mitigating behavior (e.g., eating, taking insulin, exercise, and so forth). In addition or alternately, the notification314may provide support for deciding how to treat diabetes, such as by recommending a user (or a health care provider or telemedicine service) perform an action (e.g., download an app to the computing device108, seek medical attention immediately, dose insulin, go for a walk, consume a particular food or drink), continue a behavior (e.g., continue eating a certain way or exercising a certain way), change a behavior (e.g., change eating habits or exercise habits), and so on.

The notification is communicated, over a network, to one or more computing device for output (block1110). By way of example, a communication interface of the data analytics platform122communicates the notification314over the network116to the computing device108of the person102, e.g., for output via an application of the glucose monitoring platform112. Alternately or in addition, the data analytics platform122communicates the notification314over the network116to a computing device associated with a health care provider (not shown) and/or a computing device associated with a telemedicine service (not shown), e.g., for output via a provider portal.

FIG.12depicts a procedure1200in an example implementation in which a non-linear machine learning model iteratively predicts upcoming glucose measurements until an interval of time of the measurements is predicted.

Glucose measurements are obtained that have been provided by a wearable glucose monitoring device worn by a user (block1202). By way of example, the sequencing manager404obtains the glucose measurements118of the person102and the timestamps402of those measurements.

A time series of the glucose measurements up to a time is formed (block1204). In accordance with the principles discussed herein, the time series of glucose measurements is formed by ordering the glucose measurements according to respective timestamps and by interpolating missing measurements. By way of example, the sequencing manager404forms the time series glucose measurements410(e.g., up to a measurement last received from the wearable glucose monitoring device104) by ordering the glucose measurements118according to the timestamps402. The sequencing manager404also interpolates missing measurements, such as measurements missing due to data corruption or communication errors.

A timestep of upcoming glucose measurements is predicted based on the time series of glucose measurements using a non-linear machine learning model (block1206). By way of example, the machine learning model406(1) generates the first timestep of glucose measurements606based on the time series glucose measurements410.

The timestep of upcoming glucose measurements is appended to a terminal end of the time series of glucose measurements to form an augmented time series of glucose measurements (block1208). By way of example, the machine learning model406(1) (or additional logic) appends the first timestep of glucose measurements606to a terminal end of the time series glucose measurements410to form the first glucose trace502, an augmented trace of glucose measurements.

Optionally, a timestep's worth of the glucose measurements are removed from a beginning of the augmented trace (block1210). By way of example, the machine learning model406(1) (or the additional logic) removes measurements corresponding to the crossed-out points604from the first glucose trace502.

Unless an amount of time corresponding to the upcoming glucose measurements of the augmented trace comprises at least a predetermined interval time, a next timestep of upcoming glucose measurements is predicted based on the augmented trace using the non-linear machine learning model (block1212). By way of example, the machine learning model406(2) generates the second timestep of glucose measurements608based on the first glucose trace502.

The blocks1208-1212are repeated until the timesteps of upcoming glucose measurements of the augmented trace in combination span at least the predetermined interval of time. By way of example, the subsequent steps of the machine learning model406repeat the steps of blocks1208-1212until the timesteps in combination span at least the predetermined interval of time, e.g., six 5-minute timesteps combined span a predetermined 30-minute time interval.

FIG.13depicts a procedure1300in an example implementation in which a non-linear machine learning model is trained to predict upcoming glucose measurements based on historical time series glucose measurements of a user population.

Glucose measurements provided by wearable glucose monitoring devices worn by users of a user population are obtained (block1302). By way of example, the sequencing manager404obtains the glucose measurements118of users of the user population110and the timestamps402of those measurements.

Time series of the glucose measurements are formed (block1304). In accordance with the principles discussed herein, the time series of glucose measurements are formed by ordering the glucose measurements according to respective timestamps and by interpolating missing measurements. By way of example, the sequencing manager404forms time series of the user population110's glucose measurements118by ordering the user population110's glucose measurements118according to the respective timestamps402. The sequencing manager404also interpolates missing measurements, such as measurements missing due to data corruption or communication errors.

Instances of training data are generated by segmenting each time series into a training input portion and an expected output portion (block1306). In accordance with the principles discussed herein, the training input portion spans up to a point in time of the time series and the expected output portion begins substantially at the point in time and spans to a subsequent time of the time series. By way of example, the model manager generates instances of training data by segmenting each of the time series formed at block1304into a training input portion and an expected output portion.

Here, blocks1308-1314may be repeated until a non-linear machine learning model is suitably trained, such as until the non-linear machine learning model “converges” on a solution, e.g., the internal weights of the model have been suitably adjusted due to training iterations so that the model consistently generates predictions that substantially match the expected output portions. Alternately or in addition, the blocks1308-1314may be repeated for a number of instances (e.g., all instances) of the training data.

A training input portion of an instance of training data is provided as input to the non-linear machine learning model (block1308). By way of example, the model manager802provides a training input portion of an instance of training data generated at block1306as input to the machine learning model406.

A prediction of glucose measurements is received as output from the non-linear machine learning model (block1310). In accordance with the principles discussed herein, the prediction of glucose measurements is predicted for an interval of time spanning substantially from the point in time to the subsequent time. By way of example, the machine learning model406predicts a timestep of upcoming glucose measurements based on the training input portion provided at block1308, and the model manager802receives the timestep of upcoming glucose measurements as output of the machine learning model406.

The prediction of glucose measurements is compared to the expected output portion of the instance of training data (block1312). By way of example, the model manager compares the timestep of upcoming glucose measurements predicted at block1310to the expected output portion of the training instance generated at block1306, e.g., by using a loss function such as mean squared error (MSE). It is to be appreciated that the model manager802may use other loss functions during training, to compare the predictions of the machine learning model406to the expected output, without departing from the spirit or scope of the described techniques.

Weights of the non-linear machine learning model are adjusted based on the comparison (block1314). By way of example, the model manager802may adjust internal weights of the machine learning model406based on the comparing. In one or more implementations, the model manager802may optionally leverage one or more hyperparameter optimization techniques (e.g., a Bayesian-optimized grid search) during training to tune hyperparameters of the learning algorithm employed.

Having described example procedures in accordance with one or more implementations, consider now an example system and device that can be utilized to implement the various techniques described herein.

Example System and Device

FIG.14illustrates an example system generally at1400that includes an example computing device1402that is representative of one or more computing systems and/or devices that may implement the various techniques described herein. This is illustrated through inclusion of the glucose monitoring platform112. The computing device1402may be, for example, a server of a service provider, a device associated with a client (e.g., a client device), an on-chip system, and/or any other suitable computing device or computing system.

The example computing device1402as illustrated includes a processing system1404, one or more computer-readable media1406, and one or more I/O interfaces1408that are communicatively coupled, one to another. Although not shown, the computing device1402may further include a system bus or other data and command transfer system that couples the various components, one to another. A system bus can include any one or combination of different bus structures, such as a memory bus or memory controller, a peripheral bus, a universal serial bus, and/or a processor or local bus that utilizes any of a variety of bus architectures. A variety of other examples are also contemplated, such as control and data lines.

The processing system1404is representative of functionality to perform one or more operations using hardware. Accordingly, the processing system1404is illustrated as including hardware elements1410that may be configured as processors, functional blocks, and so forth. This may include implementation in hardware as an application specific integrated circuit or other logic device formed using one or more semiconductors. The hardware elements1410are not limited by the materials from which they are formed or the processing mechanisms employed therein. For example, processors may be comprised of semiconductor(s) and/or transistors (e.g., electronic integrated circuits (ICs)). In such a context, processor-executable instructions may be electronically-executable instructions.

The computer-readable media1406is illustrated as including memory/storage1412. The memory/storage1412represents memory/storage capacity associated with one or more computer-readable media. The memory/storage component1412may include volatile media (such as random access memory (RAM)) and/or nonvolatile media (such as read only memory (ROM), Flash memory, optical disks, magnetic disks, and so forth). The memory/storage component1412may include fixed media (e.g., RAM, ROM, a fixed hard drive, and so on) as well as removable media (e.g., Flash memory, a removable hard drive, an optical disc, and so forth). The computer-readable media1406may be configured in a variety of other ways as further described below.

Input/output interface(s)1408are representative of functionality to allow a user to enter commands and information to computing device1402, and also allow information to be presented to the user and/or other components or devices using various input/output devices. Examples of input devices include a keyboard, a cursor control device (e.g., a mouse), a microphone, a scanner, touch functionality (e.g., capacitive or other sensors that are configured to detect physical touch), a camera (e.g., which may employ visible or non-visible wavelengths such as infrared frequencies to recognize movement as gestures that do not involve touch), and so forth. Examples of output devices include a display device (e.g., a monitor or projector), speakers, a printer, a network card, tactile-response device, and so forth. Thus, the computing device1402may be configured in a variety of ways as further described below to support user interaction.

Various techniques may be described herein in the general context of software, hardware elements, or program modules. Generally, such modules include routines, programs, objects, elements, components, data structures, and so forth that perform particular tasks or implement particular abstract data types. The terms “module,” “functionality,” and “component” as used herein generally represent software, firmware, hardware, or a combination thereof. The features of the techniques described herein are platform-independent, meaning that the techniques may be implemented on a variety of commercial computing platforms having a variety of processors.

An implementation of the described modules and techniques may be stored on or transmitted across some form of computer-readable media. The computer-readable media may include a variety of media that may be accessed by the computing device1402. By way of example, and not limitation, computer-readable media may include “computer-readable storage media” and “computer-readable signal media.”

“Computer-readable storage media” may refer to media and/or devices that enable persistent and/or non-transitory storage of information in contrast to mere signal transmission, carrier waves, or signals per se. Thus, computer-readable storage media refers to non-signal bearing media. The computer-readable storage media includes hardware such as volatile and non-volatile, removable and non-removable media and/or storage devices implemented in a method or technology suitable for storage of information such as computer readable instructions, data structures, program modules, logic elements/circuits, or other data. Examples of computer-readable storage media may include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, hard disks, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other storage device, tangible media, or article of manufacture suitable to store the desired information and which may be accessed by a computer.

“Computer-readable signal media” may refer to a signal-bearing medium that is configured to transmit instructions to the hardware of the computing device1402, such as via a network. Signal media typically may embody computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as carrier waves, data signals, or other transport mechanism. Signal media also include any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media.

As previously described, hardware elements1410and computer-readable media1406are representative of modules, programmable device logic and/or fixed device logic implemented in a hardware form that may be employed in some embodiments to implement at least some aspects of the techniques described herein, such as to perform one or more instructions. Hardware may include components of an integrated circuit or on-chip system, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a complex programmable logic device (CPLD), and other implementations in silicon or other hardware. In this context, hardware may operate as a processing device that performs program tasks defined by instructions and/or logic embodied by the hardware as well as a hardware utilized to store instructions for execution, e.g., the computer-readable storage media described previously.

Combinations of the foregoing may also be employed to implement various techniques described herein. Accordingly, software, hardware, or executable modules may be implemented as one or more instructions and/or logic embodied on some form of computer-readable storage media and/or by one or more hardware elements1410. The computing device1402may be configured to implement particular instructions and/or functions corresponding to the software and/or hardware modules. Accordingly, implementation of a module that is executable by the computing device1402as software may be achieved at least partially in hardware, e.g., through use of computer-readable storage media and/or hardware elements1410of the processing system1404. The instructions and/or functions may be executable/operable by one or more articles of manufacture (for example, one or more computing devices1402and/or processing systems1404) to implement techniques, modules, and examples described herein.

The techniques described herein may be supported by various configurations of the computing device1402and are not limited to the specific examples of the techniques described herein. This functionality may also be implemented all or in part through use of a distributed system, such as over a “cloud”1414via a platform1416as described below.

The cloud1414includes and/or is representative of a platform1416for resources1418. The platform1416abstracts underlying functionality of hardware (e.g., servers) and software resources of the cloud1414. The resources1418may include applications and/or data that can be utilized while computer processing is executed on servers that are remote from the computing device1402. Resources1418can also include services provided over the Internet and/or through a subscriber network, such as a cellular or Wi-Fi network.

The platform1416may abstract resources and functions to connect the computing device1402with other computing devices. The platform1416may also serve to abstract scaling of resources to provide a corresponding level of scale to encountered demand for the resources1418that are implemented via the platform1416. Accordingly, in an interconnected device embodiment, implementation of functionality described herein may be distributed throughout the system1400. For example, the functionality may be implemented in part on the computing device1402as well as via the platform1416that abstracts the functionality of the cloud1414.

Conclusion

Although the systems and techniques have been described in language specific to structural features and/or methodological acts, it is to be understood that the systems and techniques defined in the appended claims are not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as example forms of implementing the claimed subject matter.