Exercise determinations based on pose states

Abstract

A computing device for determining an exercise based on a sequence of pose states includes one or more memories to store instructions and one or more processors to execute the instructions to perform operations, the operations including: receiving a plurality of image frames which capture a user performing an exercise; determining, via one or more first machine-learned models, body landmark locations in the plurality of image frames; determining, via one or more second machine-learned models, a sequence of pose states in the plurality of image frames, based on the body landmark locations determined via the one or more first machine-learned models, and determining a type of the exercise performed by the user based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models.

Description

EXERCISE DETERMINATIONS BASED ON POSE STATESFIELD

[0001] This disclosure relates generally to machine learning processes and machine-learned devices and systems. More particularly, the disclosure relates to implementing one or more machine-learned models to enable the tracking of exercises performed, counting of repetitions, and the like, where feedback can be provided to a user regarding whether an exercise was performed correctly.BACKGROUND

[0002] A computer can receive input(s). The computer can execute instructions to process the input(s) to generate output(s) using a parameterized model. The computer can obtain feedback on its performance in generating the outputs with the model. The computer can generate feedback by evaluating its performance. The computer can receive feedback from an external source. The computer can update parameters of the model based on the feedback to improve its performance. In this manner, the computer can iteratively “learn” to generate the desired outputs. The resulting model is often referred to as a machine-learned model.

[0003] Methods exist for monitoring an exercise movement of a user using various machine- learned models.SUMMARY

[0004] Aspects and advantages of embodiments of the disclosure will be set forth in part in the following description, or can be learned from the description, or can be learned through practice of the embodiments.

[0005] Example aspects of the disclosure provide an example computing device that includes one or more processors and one or more example non-transitory computer-readable media storing instructions that are executable by the one or more processors to cause the computing device to perform example operations. In some implementations, the example operations can include receiving a plurality of image frames which capture a user performing an exercise, determining, via one or more first machine-learned models, body landmark locations in the plurality of image frames, determining, via one or more second machine-learned models, a sequence of pose states in the plurality of image frames, based on the body landmark locations determined via the one or more first machine-learned models, and determining atype of the exercise performed by the user based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models.

[0006] In some implementations, the operations further include determining a number of repetitions performed of the exercise based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models.

[0007] In some implementations, the operations further include determining whether the exercise is performed with correct form based on the type of the exercise, the sequence of pose states in the plurality of image frames determined via the one or more second machine- learned models, and the body landmark locations determined via the one or more first machine-learned models.

[0008] In some implementations, the operations further include, in response to determining the exercise is not performed with correct form based on the type of the exercise, the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, and the body landmark locations determined via the one or more first machine-learned models, providing feedback to the user identifying a dysfunction which indicates a body part of the user which was positioned incorrectly during the exercise.

[0009] In some implementations, the determining whether the exercise is performed with correct form based on the type of the exercise, the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, and the body landmark locations determined via the one or more first machine-learned models, comprises: determining, based on a first set of body landmark locations from among the body landmark locations associated with a first pose state among the sequence of pose states, a first location of a first body part of the user relative to a second location of a second body part of the user, and when the first location of the first body part of the user relative to the second location of the second body part of the user does not satisfy a threshold criteria, providing feedback to the user identifying a dysfunction which indicates one or more body parts of the user which was positioned incorrectly in the first pose state during the exercise.

[0010] In some implementations, the determining whether the exercise is performed with correct form based on the type of the exercise, the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, and the body landmark locations determined via the one or more first machine-learned models, comprises: determining, based on a first set of body landmark locations from among the body landmarklocations associated with a first pose state among the sequence of pose states, an angle between a first body part of the user and a second body part of the user, and when the angle does not satisfy a threshold criteria, providing feedback to the user identifying a dysfunction which indicates one or more body parts of the user which was positioned incorrectly in the first pose state during the exercise.

[0011] In some implementations, the operations further include implementing one or more third machine-learned models trained to identify a dysfunction which indicates one or more body parts of the user which was positioned incorrectly during the exercise, based on the type of the exercise, the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, and the body landmark locations determined via the one or more first machine-learned models.

[0012] In some implementations, the operations further include implementing a finite state machine to determine the type of the exercise performed by the user based on the sequence of pose states in the plurality of image frames determined via the one or more second machine- learned models.

[0013] In some implementations, determining, via the one or more first machine-learned models, the body landmark locations and determining, via the one or more second machine- learned models, the sequence of pose states, are performed in real-time.

[0014] In some implementations, determining, via the one or more first machine-learned models, the body landmark locations in the plurality of image frames comprises: extracting the body landmark locations from the plurality of image frames, and mapping the body landmark locations to a three-dimensional (3D) coordinate space; and determining, via the one or more second machine-learned models, the sequence of pose states in the plurality of image frames, based on the body landmark locations determined via the one or more first machine-learned models comprises: classifying, via the one or more second machine-learned models which implements a fully connected network with residual connections, a first pose state associated with the user for a first image frame among the plurality of image frames, based on coordinate information associated with body landmark locations mapped to the 3D coordinate space with respect to the first image frame.

[0015] In some implementations, the operations further include: when the type of the exercise performed by the user is determined to be a hold exercise based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, initiating a timer for the exercise, and when the type of the exercise performed by the user is determined to be a movement exercise based on the sequence of pose states in the plurality of image frames determined via the one or more second machine- learned models, incrementing a repetition counter after a full sequence of the movement exercise is completed according to a defined sequence of pose states associated with one repetition of the exercise.

[0016] In some implementations, determining, via the one or more second machine-learned models, the sequence of pose states in the plurality of image frames, based on the body landmark locations determined via the one or more first machine-learned models, comprises: classifying a first set of body landmark locations from a first image frame among the plurality of image frames as corresponding to a first pose state among a plurality of pose states, in response to a confidence level associated with classifying the first set of body landmark locations as corresponding to the first pose state exceeding a threshold confidence level, and ignoring the first image frame and processing a second set of body landmark locations from a second image frame from among the plurality of image frames, in response to none of the confidence levels associated with classifying the first set of body landmark locations as corresponding to one of the plurality of pose states exceeding the threshold confidence level.

[0017] Example aspects of the disclosure provide an example computing device that includes one or more processors and one or more example non-transitory computer-readable media storing instructions that are executable by the one or more processors to cause the computing device to perform example operations. In some implementations, the example operations can include one or more memories configured to store instructions; and one or more processors configured to execute the instructions to perform operations, the operations comprising: receiving information indicating an exercise a user is to perform, receiving a plurality of image frames which capture the user performing the exercise, determining, via one or more first machine-learned models, body landmark locations in the plurality of image frames, determining, via one or more second machine-learned models, a sequence of pose states in the plurality of image frames, based on the body landmark locations determined via the one or more first machine-learned models, and determining whether the exercise is being performed with correct form based on the information indicating the exercise, the sequence of pose states, and the body landmark locations.

[0018] In some implementations, the operations further comprise: determining a number of repetitions performed of the exercise based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models.

[0019] In some implementations, the information indicating the exercise the user is to perform is received via an exercise application which is configured to generate an exercise workout plan.

[0020] In some implementations, the information indicating the exercise the user is to perform is received via an input by the user to the computing device prior to performing the exercise.

[0021] In some implementations, determining whether the exercise is being performed with the correct form comprises implementing one or more third machine-learned models trained to identify a dysfunction based on the information indicating the exercise, the sequence of pose states, and the body landmark locations, wherein the dysfunction indicates one or more body parts of the user which was positioned incorrectly during the exercise.

[0022] In some implementations, the operations further comprise: when the information indicates the exercise is a hold exercise, initiating a timer for the exercise in response to receiving at least one image frame among the plurality of image frames which captures the user performing the exercise, and when the information indicates the exercise is a movement exercise, incrementing a repetition counter after a full sequence of the movement exercise is completed according to a defined sequence of pose states associated with one repetition of the exercise.

[0023] Example aspects of the disclosure provide an example computer-implemented method. In some implementations, the example computer-implemented method can include: receiving, by a computing system comprising one or more processors, a plurality of image frames which capture a user performing an exercise; determining, via one or more first machine-learned models, body landmark locations in the plurality of image frames; determining, via one or more second machine-learned models, a sequence of pose states in the plurality of image frames, based on the body landmark locations determined via the one or more first machine-learned models; and determining a type of the exercise performed by the user based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models.

[0024] In some implementations, the computer-implemented method includes determining a number of repetitions performed of the exercise based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models.

[0025] In some implementations, the computer-implemented method includes determining whether the exercise is performed with correct form based on the type of the exercise, the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, and the body landmark locations determined via the one or more first machine-learned models.

[0026] In some implementations, the computer-implemented method includes: in response to determining the exercise is not performed with correct form based on the type of the exercise, the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, and the body landmark locations determined via the one or more first machine-learned models, providing feedback to the user identifying a dysfunction which indicates a body part of the user which was positioned incorrectly during the exercise.

[0027] In some implementations, the computer-implemented method includes: implementing one or more third machine-learned models trained to identify a dysfunction which indicates one or more body parts of the user which was positioned incorrectly during the exercise, based on the type of the exercise, the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, and the body landmark locations determined via the one or more first machine-learned models.

[0028] In some implementations, determining, via the one or more first machine-learned models, the body landmark locations and determining, via the one or more second machine- learned models, the sequence of pose states, are performed in real-time.

[0029] In some implementations, the computer-implemented method includes: when the type of the exercise performed by the user is determined to be a hold exercise based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, initiating a timer for the exercise; and when the type of the exercise performed by the user is determined to be a movement exercise based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, incrementing a repetition counter after a full sequence of the movement exercise is completed according to a defined sequence of pose states associated with one repetition of the exercise.

[0030] Example aspects of the disclosure provide an example computer-implemented method. In some implementations, the example computer-implemented method can include: receiving information indicating an exercise a user is to perform; receiving a plurality of image frames which capture the user performing the exercise; determining, via one or more first machine-learned models, body landmark locations in the plurality of image frames; determining, via one or more second machine-learned models, a sequence of pose states in the plurality of image frames, based on the body landmark locations determined via the one or more first machine-learned models; and determining whether the exercise is being performed with correct form based on the information indicating the exercise, the sequence of pose states, and the body landmark locations.

[0031] In some implementations, the method includes determining a number of repetitions performed of the exercise based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models.

[0032] In some implementations, the information indicating the exercise the user is to perform is received via an exercise application which is configured to generate an exercise workout plan.

[0033] In some implementations, the information indicating the exercise the user is to perform is received via an input by the user to the computing device prior to performing the exercise.

[0034] In some implementations, determining whether the exercise is being performed with the correct form comprises implementing one or more third machine-learned models trained to identify a dysfunction based on the information indicating the exercise, the sequence of pose states, and the body landmark locations, wherein the dysfunction indicates one or more body parts of the user which was positioned incorrectly during the exercise.

[0035] In some implementations, the method includes: when the information indicates the exercise is a hold exercise, initiating a timer for the exercise in response to receiving at least one image frame among the plurality of image frames which captures the user performing the exercise, and when the information indicates the exercise is a movement exercise, incrementing a repetition counter after a full sequence of the movement exercise is completed according to a defined sequence of pose states associated with one repetition of the exercise

[0036] The computer-implemented method may execute any of the operations of the computing device as described herein.

[0037] Example aspects of the disclosure provide one or more example non-transitory computer-readable media storing instructions that are executable by one or more processors to cause a computing system to perform example operations. In some implementations, the example operations can include receiving a plurality of image frames which capture a user performing an exercise; determining, via one or more first machine-learned models, body landmark locations in the plurality of image frames; determining, via one or more second machine-learned models, a sequence of pose states in the plurality of image frames, based on the body landmark locations determined via the one or more first machine-learned models; and determining the type of the exercise performed by the user based on the sequence of pose states in the plurality of image frames determined via the one or more second machine- learned models.

[0038] Example aspects of the disclosure provide one or more example non-transitory computer-readable media storing instructions that are executable by one or more processors to cause a computing system to perform example operations. In some implementations, the example operations can include: receiving information indicating an exercise a user is to perform; receiving a plurality of image frames which capture the user performing the exercise; determining, via one or more first machine-learned models, body landmark locations in the plurality of image frames; determining, via one or more second machine- learned models, a sequence of pose states in the plurality of image frames, based on the body landmark locations determined via the one or more first machine-learned models; and determining whether the exercise is being performed with correct form based on the information indicating the exercise, the sequence of pose states, and the body landmark locations.

[0039] The non-transitory computer-readable medium may store additional instructions to execute other aspects and operations of the computing device and computer-implemented method as described herein.

[0040] Other example aspects of the disclosure are directed to other systems, methods, apparatuses, tangible non-transitory computer-readable media, and devices for performing functions described herein. These and other features, aspects, and advantages of various implementations will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate implementations of the disclosure and, together with the description, help explain the related principles.BRIEF DESCRIPTION OF THE DRAWINGS

[0041] FIG. 1 A is an example system, according to one or more example embodiments of the disclosure;

[0042] FIG. IB is an example block diagram of a computing system, according to one or more example embodiments of the disclosure;

[0043] FIGS. 2A-2B each illustrate a flow diagram of an example, non-limiting computer- implemented method, according to one or more example embodiments of the disclosure;

[0044] FIG. 3 illustrates a block diagram of an exercise monitoring application, according to one or more example embodiments of the disclosure;

[0045] FIG. 4A-4C illustrate an example collection of body landmark locations, according to one or more example embodiments of the disclosure;

[0046] FIGS. 5A-5B are example tables of exercises and associated sequences of pose states corresponding to each exercise, according to one or more example embodiments of the disclosure;

[0047] FIGS. 6A-6B illustrate example experimental results, according to one or more example embodiments of the disclosure;

[0048] FIG. 7 is a flow chart diagram illustrating an example method for training a machine- learned model according to example implementations of aspects of the disclosure;

[0049] FIG. 8 is a block diagram of an example processing flow for using machine-learned model(s) to process input(s) to generate output(s) according to example implementations of aspects of the disclosure;

[0050] FIG. 9 is a block diagram of an example sequence processing model according to example implementations of aspects of the disclosure;

[0051] FIG. 10 is a block diagram of an example technique for populating an example input sequence for processing by a sequence processing model according to example implementations of aspects of the disclosure;

[0052] FIG. 11 is a block diagram of an example model development platform according to example implementations of aspects of the disclosure;

[0053] FIG. 12 is a block diagram of an example training workflow for training a machine- learned model according to example implementations of aspects of the disclosure;

[0054] FIG. 13 is a block diagram of an inference system for operating one or more machine- learned model(s) to perform inference according to example implementations of aspects of the disclosure;

[0055] FIG. 14 is a block diagram of an example networked computing system according to example implementations of aspects of the disclosure;

[0056] FIG. 15 is a block diagram of an example computing device according to example implementations of aspects of the disclosure; and

[0057] FIG. 16 is a block diagram of an example computing device according to example implementations of aspects of the disclosure.DETAILED DESCRIPTION

[0058] Reference now will be made to embodiments of the disclosure, one or more examples of which are illustrated in the drawings, wherein like reference characters denote like elements. Each example is provided by way of explanation of the disclosure and is not intended to limit the disclosure. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made to disclosure without departing from the scope or spirit of the disclosure. For instance, features illustrated or described as part of one embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that the disclosure covers such modifications and variations as come within the scope of the appended claims and their equivalents.

[0059] Being physically fit is an important aspect of our personal health and daily activities. During the COVID-19 pandemic, access to gymnasiums and personal fitness training was restricted. People have different schedules and may not have access to personal health training programs when they have time.

[0060] Existing methods for monitoring a user exercising are capable of pose landmark estimation and repetition counting. However, some methods cannot estimate three- dimensional pose landmarks, making it difficult to create generic form assessment algorithms that work for all camera views. Other methods cannot be implemented in real-time or on mobile devices.

[0061] According to examples of the disclosure, a computing system is configured to implement an exercise application (e.g., a computer vision based system) that enhancesfitness training by automatically tracking users’ exercises and offering customized coaching advice, for example, in real-time and on-demand.

[0062] In some implementations, the computing system may be configured to implement one or more machine-learned models to perform various operations, including tracking (monitoring) a user performing exercises, determining a particular type of exercise being performed, determining the number of repetitions performed, and providing feedback (e.g., instruction) regarding the performance of the exercise (e.g., whether the user used the correct form). For example, the computing system may be configured to perform the operations in real-time and on-device. For example, the computing system may be configured to perform the operations automatically (without manual intervention) for a range of exercises in diverse environments.

[0063] In some implementations, the one or more machine-learned models are trained to identify a list of form dysfunctions for various exercises, based on training data that is obtained based on data annotated by one or more subject matter experts (e.g., professional movement specialists).

[0064] In some implementations, the computing system is configured to identify when the user is not performing an exercise correctly (e.g., the user is not going deep enough on a push up or a squat, when the user is leaning too much to the left or to the right, etc.). This capability transforms the solitary exercise experience into an interactive session, promoting improved performance, and injury prevention.

[0065] According to some examples of the disclosure, the computing system may include a camera (e.g., an RGB video camera) configured to capture a sequence of image frames. The computing system may further include one or more first machine-learned models configured to extract a set of human pose landmarks from each image frame from the camera. The computing system may further include one or more second machine-learned models (e.g., a neural network classifier) configured to classify each set of human pose landmarks into an exercise pose state to generate a sequence of exercises pose states. The computing system may further include an exercise determiner configured to identify a type of exercise based on the sequence of exercise pose states, and a repetition determiner configured to determine how many repetitions that a user has performed based on the sequence of exercise pose states. The computing system may further include an exercise form analyzer configured to identify any form dysfunctions based on the set of human pose landmarks and the sequence ofexercises pose states. A form dysfunction refers to an incorrect or improper execution of an exercise, an incorrect or improper position of the user’s body while performing the exercise, etc. The computing system may be configured to provide feedback to the user based on the results output by the exercise form analyzer (e.g., instructions to improve form, information relating to methods for injury prevention, etc.).

[0066] One or more technical benefits of the disclosure include the implementation of one or more machine-learned models (e.g., a pose classification model comprising a fully connected network with residual connections) which classify (in real-time) pose states of a user during an exercise based on body landmark locations of the user identified in a plurality of image frames by one or more different machine-learned models. The classification of the pose states can be used to determine an exercise type performed by the user and to determine a number of repetitions performed by the user. Further, the classification of the pose states can be used to identify whether a pose performed by the user is performed correctly (with the correct form). Therefore, the accuracy and quality of electronic monitoring of an activity (e.g., an exercise activity) can be improved through the improved detection of pose states of a user during an exercise. Further, safety can be improved by identifying when a user performs an exercise with incorrect form or performs an exercise in an unsafe manner. Further, as described herein, in some implementations computing resources can be conserved or efficiently utilized by implementing the one or more machine-learned models to determine the pose states based on body landmark location data, rather than based on an original image frame. Further, as described herein, in some implementations computing resources can be conserved or efficiently utilized by ignoring pose states associated with particular image frames where the pose state is determined with a confidence level below a threshold confidence level.

[0067] Therefore, aspects of the disclosure provide technical effects, benefits, and / or improvements in computing technology and the technology of monitoring exercises performed by a user, including determining pose states of the user, determining exercise types performed by the user, determining repetitions of an exercise performed by the user, determining whether a user performs the exercise correctly, etc., via one or more computing devices (e.g., a user computing device, a server computing system, and combinations thereof) which implement one or more machine-learned models, as described herein.

[0068] Referring now to the drawings, FIG. 1 A is an example system according to one or more example embodiments of the disclosure. FIG. 1 A illustrates an example of a system1000 which includes a computing device 100, an external computing device 200, a server computing system 300, and external content 500, which may be in communication with one another over a network 400. For example, the computing device 100 and the external computing device 200 can include any of a personal computer, a smartphone, a tablet computer, a laptop, a global positioning service device, a smartwatch, and the like. The network 400 may include any type of communications network including a wired or wireless network, or a combination thereof. The network 400 may include a local area network (LAN), wireless local area network (WLAN), wide area network (WAN), personal area network (PAN), virtual private network (VPN), or the like. For example, wireless communication between elements of the example embodiments may be performed via a wireless LAN, Wi-Fi, Blutooth, ZigBee, Wi-Fi direct (WFD), ultra wideband (UWB), infrared data association (IrDA), Blutooth low energy (BLE), near field communication (NFC), a radio frequency (RF) signal, and the like. For example, wired communication between elements of the example embodiments may be performed via a pair cable, a coaxial cable, an optical fiber cable, an Ethernet cable, and the like. Communication over the network 400 can use a wide variety of communication protocols (e.g., TCP / IP, HTTP, SMTP, FTP), encodings or formats (e.g., HTML, XML), and / or protection schemes (e.g., VPN, secure HTTP, SSL).

[0069] As will be explained in more detail below, in some implementations the computing device 100 and / or server computing system 300 may form part of an application system which can provide a tool for users to track or monitor their exercise activity, via one or more machine-learned models.

[0070] In some example embodiments, the server computing system 300 may obtain data from one or more of an image data store 350, an exercise data store 360, and a machine- learned model data store 370, to implement various operations and aspects of the application system as disclosed herein. The image data store 350, exercise data store 360, and machine- learned model data store 370 may be integrally provided with the server computing system 300 (e.g., as part of the one or more memory devices 320 of the server computing system 300) or may be separately (e.g., remotely) provided. Further, image data store 350, exercise data store 360, and machine-learned model data store 370 can be combined as a single data store (database) or may include a plurality of respective data stores. Data stored in one data store (e.g., the image data store 350) may overlap with some data stored in another data store (e.g., exercise data store 360). In some implementations, one data store (e.g., the machine-learned model data store 370) may reference data that is stored in another data store (e.g., the image data store 350).

[0071] In some implementations, the image data store 350 can store images or videos associated with an exercise. In some implementations, the information stored in the image data store 350 can be associated with and / or stored according to a particular user or a plurality of users. In some implementations, the information stored in the image data store 350 can be associated with and / or stored according to a particular type of exercise, a particular environment (e.g., outdoor, indoor, under certain weather conditions, etc.) in which the image was captured, a particular orientation from which the image was captured (e.g., in landscape and portrait orientations), a particular camera position or angle from which the image was captured (e.g., an overhead camera angle, a perspective view, etc.), a particular lighting condition in which the image was captured, a particular background in which the image was captured, etc. In some implementations, the information stored in the image data store 350 can be associated with and / or stored according to a particular application that is associated with the image (e.g., a particular software application that was used to capture the image), a particular duration of a video (e.g., videos less than 30 seconds, about one minute long, about two minutes long, etc.).

[0072] In some implementations, the exercise data store 360 can store data associated with the performance of an exercise. In some implementations, the information stored in the exercise data store 360 can be associated with and / or stored according to a particular user or a plurality of users. In some implementations, the information stored in the exercise data store 360 can be associated with and / or stored according to a particular type of exercise (e.g., a particular movement exercise, a particular hold exercise), a particular type of pose that forms part of the exercise, a duration of the exercise, a number of repetitions performed during the exercise, particular muscle groups that are associated with the exercise, a particular environment (e.g., outdoor, indoor, under certain weather conditions, etc.) in which the exercise was performed, a particular lighting condition in which the exercise was performed, a particular background in which the exercise was performed, etc.

[0073] Machine-learned model data store 370 can store machine-learned models which can be retrieved and implemented by the server computing system 300 for generating distilled or fine-tuned machine-learned models (e.g., distilled or fine-tuned generative machine-learned models) that, in some implementations, can also be provided to the computing device 100. Machine-learned model data store 370 can also store distilled or fine-tuned machine-learnedmodels (e.g., distilled or fine-tuned generative machine-learned models) which can be retrieved and implemented by the computing device 100. In some implementations, the computing device 100 can retrieve and implement machine-learned models which are large parameter models that have not been fine-tuned or distilled. The machine-learned models (including large parameter models and distilled or fine-tuned models) stored at the machine- learned model data store 370 can include generative machine-learned models respectively associated with different types of applications, types of exercises, types of poses, etc. that may be implemented across a variety of domains (e.g., healthcare, gaming, engineering / science, entertainment, etc.). The machine-learned models may include large language models (e.g., the Bidirectional Encoder Representations from Transformers (BERT) large language model) and general, multimodal models (e.g., Gemini). The machine-learned models may include generative artificial intelligence (Al) models (e.g., Bard) which may implement generative adversarial networks (GANs), transformers, variational autoencoders (VAEs), neural radiance fields (NeRFs), and the like.

[0074] External content 500 can be any form of external content including news articles, webpages, video files, audio files, written descriptions, ratings, game content, social media content, photographs, commercial offers, transportation method, weather conditions, sensor data obtained by various sensors, or other suitable external content. The computing device 100, external computing device 200, and server computing system 300 can access external content 500 over network 400. External content 500 can be searched by computing device 100, external computing device 200, and server computing system 300 according to known searching methods and search results can be ranked according to relevance, popularity, or other suitable attributes, including location-specific filtering or promotion.

[0075] FIG. IB is an example block diagram of a computing system, according to one or more example embodiments of the disclosure. Referring now to FIG. IB, example block diagrams of a system 1100 including a computing device 100 and server computing system 300 according to one or more example embodiments of the disclosure will now be described. Although computing device 100 is represented in FIG. IB, features of the computing device 100 described herein are also applicable to the external computing device 200.

[0076] The computing device 100 may include one or more processors 110, one or more memory devices 120, an application system 130, a position determination device 140, an input device 150, a display device 160, an output device 170, and a capture device 180. Theserver computing system 300 may include one or more processors 310, one or more memory devices 320, and an application system 330.

[0077] For example, the one or more processors 110, 310 can be any suitable processing device that can be included in a computing device 100 or server computing system 300. For example, the one or more processors 110, 310 may include one or more of a processor, processor cores, a controller and an arithmetic logic unit, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an image processor, a microcomputer, a field programmable array, a programmable logic unit, an applicationspecific integrated circuit (ASIC), a microprocessor, a microcontroller, etc., and combinations thereof, including any other device capable of responding to and executing instructions in a defined manner. The one or more processors 110, 310 can be a single processor or a plurality of processors that are operatively connected, for example in parallel.

[0078] The one or more memory devices 120, 320 can include one or more non-transitory computer-readable storage mediums, including a Read Only Memory (ROM), Programmable Read Only Memory (PROM), Erasable Programmable Read Only Memory (EPROM), and flash memory, a USB drive, a volatile memory device including a Random Access Memory (RAM), a hard disk, floppy disks, a Blu-ray disk, or optical media such as CD ROM discs and DVDs, and combinations thereof. However, examples of the one or more memory devices 120, 320 are not limited to the above description, and the one or more memory devices 120, 320 may be realized by other various devices and structures as would be understood by those skilled in the art.

[0079] For example, the one or more memory devices 120 can also include data 122 and instructions 124 that can be retrieved, manipulated, created, or stored by the one or more processors 110. In some example embodiments, such data can be accessed and used as input to implement exercise monitoring application 132, and to execute the instructions to perform operations including: receiving a plurality of image frames which capture a user performing an exercise, determining, via one or more first machine-learned models, body landmark locations in the plurality of image frames, determining, via one or more second machine- learned models, a sequence of pose states in the plurality of image frames, based on the body landmark locations determined via the one or more first machine-learned models, and determining a type of the exercise performed by the user based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, as described according to examples of the disclosure.

[0080] For example, the one or more memory devices 320 can also include data 322 and instructions 324 that can be retrieved, manipulated, created, or stored by the one or more processors 310. In some example embodiments, such data can be accessed and used as input to implement exercise monitoring application 332, and to execute the instructions to perform operations including: receiving a plurality of image frames which capture a user performing an exercise, determining, via one or more first machine-learned models, body landmark locations in the plurality of image frames, determining, via one or more second machine- learned models, a sequence of pose states in the plurality of image frames, based on the body landmark locations determined via the one or more first machine-learned models, and determining a type of the exercise performed by the user based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, as described according to examples of the disclosure.

[0081] In some example embodiments, the computing device 100 includes an application system 130. For example, the application system 130 may include the exercise monitoring application 132. The application system 130 can include various other applications including biometric activity applications, health applications, gaming applications, document applications, text messaging applications, email applications, dictation applications, virtual keyboard applications, browser applications, map applications, social media applications, navigation applications, etc.

[0082] According to examples of the disclosure, the exercise monitoring application 132 (or biometric activity application) may be executed by the computing device 100 to provide a user of the computing device 100 a way to monitor or track an activity (e.g., a biometric activity, an exercise, etc.) and to provide feedback or instructions (guidance) to a user regarding the user’s performance of the exercise, via one or more machine-learned models. In some implementations, the exercise monitoring application 132 may be part of another application (e.g., a biometric activity application, health application, gaming application, etc.) or may be a standalone application. The exercise monitoring application 132 may be configured to be dynamically interactive according to various user inputs. Example implementations of the exercise monitoring application 132 are described herein, however the disclosure is not limited to these examples as various modifications may be made to the embodiments described herein.

[0083] In some examples, one or more aspects of the exercise monitoring application 132 may be implemented by the exercise monitoring application 332 of the server computingsystem 300 which may be remotely located, to provide a user of the computing device 100 a way to monitor or track an activity (e.g., a biometric activity, an exercise, etc.) and / or to provide feedback or instructions (guidance) to a user regarding the user’s performance of the exercise, via one or more machine-learned models, in response to receiving an input from a user via the computing device 100. In some examples, one or more aspects of the exercise monitoring application 332 may be implemented by the exercise monitoring application 132 of the computing device 100, to monitor or track an activity (e.g., a biometric activity, an exercise, etc.) and / or to provide feedback or instructions (guidance) to a user regarding the user’s performance of the exercise, via one or more machine-learned models, in response to receiving an input from a user via the computing device 100.

[0084] In some example embodiments, the computing device 100 includes a position determination device 140. Position determination device 140 can determine a current geographic location of the computing device 100 and communicate the geographic location to the server computing system 300 over network 400. The position determination device 140 can be any device or circuitry for analyzing the position of the computing device 100. For example, the position determination device 140 can determine actual or relative position by using a satellite navigation positioning system (e.g. a GPS system, a Galileo positioning system, the GLObal Navigation satellite system (GLONASS), the BeiDou Satellite Navigation and Positioning system), an inertial navigation system, a dead reckoning system, based on an IP address, by using triangulation and / or proximity to cellular towers or WiFi hotspots, and / or other suitable techniques for determining a position of the computing device 100. For example, in some implementations the exercise monitoring application 132 may be configured to utilize position information determined by the position determination device 140 to monitor or track a position of the user (e.g., in conjunction with an image of the user captured by capture device 180).

[0085] The computing device 100 may include an input device 150 configured to receive an input from a user and may include, for example, one or more of a keyboard (e.g., a physical keyboard, virtual keyboard, etc.), a mouse, a joystick, a button, a switch, an electronic pen or stylus, a gesture recognition sensor (e.g., to recognize gestures of a user including movements of a body part), an input sound device or speech recognition sensor (e.g., a microphone to receive a voice input such as a voice command or a voice query), a track ball, a remote controller, a portable (e.g., a cellular or smart) phone, a tablet PC, a pedal or footswitch, a virtual -reality device, and so on. The input device 150 may also be embodied by a touch-sensitive display having a touchscreen capability, for example. For example, the input device 150 may be configured to receive an input from a user associated with the input device 150 for executing the exercise monitoring application 132, for capturing an image via the capture device 180, for providing feedback to the exercise monitoring application 132, for communicating with other users, for accepting or declining suggestions or recommendations provided by the computing device 100 with respect to a pose or an exercise, etc.

[0086] The computing device 100 may include a display device 160 which displays information viewable by the user (e.g., a user interface screen). For example, the display device 160 may be a non-touch sensitive display or a touch-sensitive display. The display device 160 may include a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, active matrix organic light emitting diode (AMOLED), flexible display, 3D display, a plasma display panel (PDP), a cathode ray tube (CRT) display, and the like, for example. However, the disclosure is not limited to these example displays and may include other types of displays. The display device 160 can be used by the application system 130 provided at the computing device 100 to display information to a user relating to the performance of an exercise, relating to a pose position of a user, relating to feedback or guidance for performing an exercise, etc. The display device 160 can be configured to provide, for presentation to a user, one or more user interface screens having user interface elements which are selectable by the user for monitoring or tracking an activity (e.g., a biometric activity, an exercise, etc.) and / or for providing feedback or instructions (guidance) to a user regarding the user’s performance of the exercise.

[0087] The computing device 100 may include an output device 170 to provide an output to the user and may include, for example, one or more of an audio device (e.g., one or more speakers), a haptic device to provide haptic feedback to a user (e.g., a vibration device), a light source (e.g., one or more light sources such as LEDs which provide visual feedback to a user), a thermal feedback system, and the like. For example, the output device 170 may provide information relating to monitoring or tracking an activity (e.g., a biometric activity, an exercise, etc.) and / or for providing feedback or instructions (guidance) to a user regarding the user’s performance of the exercise.

[0088] The computing device 100 may include a capture device 180 that is capable of capturing media content, according to various examples of the disclosure. For example, the capture device 180 can include an image capturer 182 (e.g., a camera) which is configured to capture images (e.g., photos, video, and the like). For example, the image capturer 182 caninclude one or more cameras having an imaging sensor (e.g., a complementary metal -oxide- semiconductor (CMOS) or charge-coupled device (CCD)) to capture, detect, or recognize a user's behavior, figure, expression, pose, position, rate of movement of the user, rate of movement of a body part of the user, etc. For example, the capture device 180 can include a sound capturer 184 (e.g., a microphone) which is configured to capture sound or audio (e.g., an audio recording). The media content captured by the capture device 180 may be transmitted to one or more of the server computing system 300, image data store 350, exercise data store 360, and machine-learned model data store 370, for example, via network 400. For example, in some implementations, content which is captured by the capture device 180 may be provided as an input to one or more machine-learned models to monitor or track an activity (e.g., a biometric activity, an exercise, etc.) and / or to provide feedback or instructions (guidance) to a user regarding the user’s performance of the exercise.

[0089] The computing device 100 may include one or more sensors 190. For example, the one or more sensors 190 may include an inertial measurement unit which includes one or more accelerometers and / or one or more gyroscopes. The one or more accelerometers may be used to capture motion information with respect to the computing device 100. The one or more gyroscopes may also be used additionally or alternatively to capture motion information with respect to the computing device 100. For example, the inertial measurement unit may be configured as a six-axis or six-dimensional inertial measurement unit (e.g., a tri-axial accelerometer and a tri-axial gyroscope). The motion information obtained via the inertial measurement unit may be associated with the user when the computing device 100 is worn or carried by the user.

[0090] For example, the one or more sensors 190 may include one or more optical sensors (e.g., one or more photoplethysmography (PPG) sensors) which can be used to monitor or detect the heart rate of the user according to known methods. The one or more optical sensors (e.g., one or more PPG sensors) may include one or more emitters (e.g., lightemitting diodes (LEDs)) and one or more detectors (e.g., photodiodes). Additionally, the one or more optical sensors may be configured to provide information about heart rate variability (HRV), blood oxygen saturation (SpO2) levels, and the like. For example, the one or more sensors 190 may include one or more ECG sensors which can also be used to monitor the heart rate of the user according to known methods. For example, an ECG sensor may include a plurality of electrodes that are disposed to contact different areas of a user’s body. For example, the electrodes may be disposed at various locations that can come into contact withthe user’s skin (e.g., a band of a smartwatch, one or more sides of the body of the computing device 100, integrated as part of a display screen of the display device 160, etc.). The one or more sensors 190 may also include other sensors such as a magnetometer, proximity sensor, Hall effect sensor, and the like.

[0091] In accordance with example embodiments of the disclosure, the server computing system 300 can include one or more processors 310 and one or more memory devices 320 as described herein. The server computing system 300 may also include an application system 330 which is similar to the application system 130 described herein.

[0092] For example, the application system 330 may include the exercise monitoring application 332 which performs functions similar to those described herein with respect to exercise monitoring application 132. In some implementations, one or more machine-learned models (e.g., generative machine-learned models, large language models, etc.) associated with the application system 330 may be configured to monitor or track an activity (e.g., a biometric activity, an exercise, etc.) and / or to provide feedback or instructions (guidance) to a user regarding the user’s performance of the exercise.

[0093] For example, one or more machine-learned models (e.g., generative machine-learned models, large language models, etc.) associated with the application system 330 may be configured to perform a first action (e.g., to extract a set of human pose landmarks from each image frame captured by the camera), while the computing device 100 may be configured to perform a second action (e.g., to classify each set of human pose landmarks into an exercise pose state to generate a sequence of exercises pose states). For example, one or more machine-learned models (e.g., generative machine-learned models, large language models, etc.) associated with the application system 130 may be configured to perform a first action (e.g., to identify the exercise being performed and to count repetitions of the exercise), while the server computing system 300 may be configured to perform a second action (e.g., to identify dysfunctions based on the set of human pose landmarks and the sequence of exercises pose states).

[0094] Examples of the disclosure are directed to computer implemented methods for monitoring or tracking an activity (e.g., a biometric activity, an exercise, etc.) and / or for providing feedback or instructions (guidance) to a user regarding the user’s performance of the exercise, via one or more machine-learned models.

[0095] The flow diagram of FIG. 2A illustrates a method 2100 for monitoring or tracking an activity (e.g., a biometric activity, an exercise, etc.) and / or for providing feedback or instructions (guidance) to a user regarding the user’s performance of the exercise, by implementing one or more machine-learned models. Although shown in a particular sequence or order, unless otherwise specified, the order of the processes can be modified. Thus, the illustrated embodiments should be understood only as examples, and the illustrated processes can be performed in a different order, and some processes can be performed in parallel. Additionally, one or more processes can be omitted in various embodiments. Thus, not all processes are required in every embodiment. Other process flows are possible.

[0096] The flow diagram of FIG. 2B illustrates a method 2200 for determining a pose state, by implementing one or more second machine-learned models. Although shown in a particular sequence or order, unless otherwise specified, the order of the processes can be modified. Thus, the illustrated embodiments should be understood only as examples, and the illustrated processes can be performed in a different order, and some processes can be performed in parallel. Additionally, one or more processes can be omitted in various embodiments. Thus, not all processes are required in every embodiment. Other process flows are possible.

[0097] The operations of FIGS. 2 A and 2B will be explained with reference to FIGS. 3-5B. FIG. 3 illustrates a block diagram 3000 of an exercise monitoring application, according to one or more example embodiments of the disclosure. FIG. 4A-4C illustrate the collection of landmark points, according to one or more example embodiments of the disclosure. FIGS. 5A-5B are example tables of exercises and associated sequences of poses corresponding to each exercise, according to one or more example embodiments of the disclosure.

[0098] Referring to FIG. 2 A, at operation 2110 the method 2000 includes a computing device receiving a plurality of image frames. As described herein, the computing device may be embodied as computing device 100, server computing system 300, or combinations thereof. For example, the image frames can be captured by one or more cameras (e.g., via capture device 180). According to examples of the disclosure, one or more capture devices 180 may be positioned to capture a user performing an exercise. The one or more capture devices 180 can be positioned to capture an overhead view of a user performing an exercise, a side view, a front view, a rear view, etc. The image frames may be captured at various frame rates (e.g., 24 frames per second (fps), 30 fps, 60 fps, etc.). In some implementations, the image frames can be captured in association with an exercise application, a fitness tracking application, orvarious other types of applications (e.g., a media (audio, visual) application, browser application, social media application, etc.). For example, the one or more capture devices 180 may be configured to capture the image frames in response to an input provided by the user via the input device 150. For example, the input providing the prompt may be provided by the user via a voice input, the selection of a user interface element associated with an application, etc. In some implementations, the image frames may be captured at the computing device 100 and transmitted to the server computing system 300. In some implementations, the image frames may be captured by the external computing device 200 and transmitted to the computing device 100 and / or server computing system 300. In some implementations, the image frames may be captured by the computing device 100 and processed (e.g., at any of operations 2120, 2130, 2140, 2150, 2160, 2170) at the computing device 100 in real-time. For example, in FIG. 3, the exercise monitoring application 3200 may be configured to receive the input image frames 3100. The exercise monitoring application 3200 may correspond to exercise monitoring application 132 and / or exercise monitoring application 332. Though not depicted in FIG. 3, exercise monitoring application 3200 may also be configured to receive other input data (e.g., motion data associated with the user, sensor data associated with the user and / or environment, etc.) that can be collected via the capture device 180 and / or the one or more sensors 190.

[0099] At operation 2120 the method 2100 includes determining body landmark locations via one or more first machine-learned models. For example, the one or more first machine- learned models may correspond to the one or more first machine-learned models 3210 from FIG. 3. In some implementations, the one or more first machine-learned models 3210 may be configured to perform a human pose estimation operation, and output two-dimensional (2D) and / or three-dimensional (3D) information (e.g., coordinate information which reflects a body position of a human in 2D or 3D space). The 2D and 3D information may be based on pose landmarks associated with the human body (e.g., particular body parts of the user). In some implementations, the one or more first machine-learned models 3210 may be configured to determine a plurality of body landmark locations (e.g., 10 locations, 20 locations, 40 locations, etc.) which respectively represent the approximate location of particular body parts (e.g., a nose, eye, ear, mouth, shoulder, elbow, wrist, finger, hip, knee, ankle, heel, foot, etc.). The plurality of body landmark locations can be extracted from a static image (e.g., an image frame), for example. The one or more first machine-learned models 3210 may be configured to process the image frames with high accuracy at real-time(e.g., greater than 30 FPS), which can be used for on-device real-time video processing. For example, according to examples of the disclosure, operation 2120 may be performed at the same computing device which captures and / or receives the image frames (e.g., computing device 100) in real-time.

[0100] For example, as shown in the image 4100 of FIG. 4A, the one or more first machine- learned models 3210 may be configured to identify a plurality of body landmark locations 4110 including a first body landmark location (e.g., an eye 4120), a second body landmark location (e.g., an elbow 4130), a third body landmark location (e.g., a heel 4140), etc. In some implementations, some body landmark locations may be readily visible and identified by the one or more first machine-learned models 3210 while other body landmark locations which are not visible (e.g., partly or fully occluded, out of the camera view, etc.) may be inferred, estimated, or hallucinated by the one or more first machine-learned models 3210.

[0101] In some implementations, the one or more first machine-learned models 3210 may determine the body landmark locations in the plurality of image frames by: extracting the body landmark locations from the plurality of image frames, and mapping the body landmark locations to a three-dimensional (3D) coordinate space. For example, as shown in the graph 4200 of FIG. 4B, the one or more first machine-learned models 3210 may be configured to plot the plurality of body landmark locations 4110 in a coordinate space (e.g., a 2D coordinate space and / or 3D coordinate space. For example, the plurality of body landmark locations 4110 may be mapped to the 3D coordinate space as shown in the graph 4200, including the first body landmark location 4220 (e.g., corresponding to the eye 4120), the second body landmark location 4230 (e.g., corresponding to the elbow 4130), the third body landmark location 4240 (e.g., corresponding to the heel 4140), etc. In some implementations, some body landmark locations may be readily visible and identified by the one or more first machine-learned models 3210 while other body landmark locations which are not visible (e.g., partly or fully occluded, out of the camera view, etc.) may be inferred, estimated, or hallucinated by the one or more first machine-learned models 3210 and mapped to the coordinate space. The one or more first machine-learned models 3210 may be configured to interconnect the plurality of body landmark locations 4110 to form a skeletal representation of the user’s pose in the image frame.

[0102] For example, as shown in the image 4300 of FIG. 4C, the one or more first machine- learned models 3210 may be configured to generate a model of the user which depicts the pose of the user in the image frame, based on the determined plurality of body landmarklocations which can be extracted from the image frame or inferred based on other information available to the one or more first machine-learned models 3210.

[0103] At operation 2130 the method 2100 includes the computing device determining, via one or more second machine-learned models, one or more pose states, based on the plurality of body landmark locations determined at operation 2120. For example, the one or more second machine-learned models may correspond to the one or more second machine-learned models 3220 from FIG. 3. The one or more second machine-learned models 3220 may determine the sequence of pose states in the plurality of image frames, based on the body landmark locations determined via the one or more first machine-learned models 3210 includes: classifying, via the one or more second machine-learned models 3220 which implements a fully connected network with residual connections, a first pose state associated with the user for a first image frame among the plurality of image frames, based on coordinate information associated with body landmark locations mapped to the 3D coordinate space with respect to the first image frame. In some implementations, the one or more second machine-learned models 3220 may be configured to employ a particular network architecture to classify each set of the determined body landmark locations (detected human pose landmarks) into an exercise pose state (e.g., an up or a down state of a push-up exercise, a left leg or a right leg extension of a lunge, etc.). The one or more second machine-learned models 3220 may also be configured to output a confidence score associated with a classified exercise pose state. In some implementations, 3D pose landmarks may be utilized by the one or more second machine-learned models 3220 instead of 2D landmarks as 3D landmarks are view-invariant and require less training data. For example, the 2D landmarks of a user doing squats with their body facing the camera, compared to their side facing the camera, will be quite different, whereas 3D landmarks of the two views will be similar, hence making it easier for the one or more second machine-learned models 3220 to learn that pose. For example, 3D pose landmarks may include coordinate space information associated with each body landmark location in a particular image frame (e.g., left elbow: xl, yl, zl; right elbow: x2, y2, z2; left hip: x3, y3, z3; right_hip: x4, y4, z4; etc.).

[0104] FIG. 2B illustrates a flow diagram of an example, non-limiting computer- implemented method 2200 for implementing the one or more second machine-learned models at operation 2130. For example, at operation 2210 the one or more second machine-learned models 3220 may receive, as an input, pose landmark input data. The pose landmark input data may correspond to the output of the one or more first machine-learned models 3210from operation 2120 of FIG. 2 A. As described herein, the pose landmark input data may include coordinate space information associated with each body landmark location in a particular image frame (e.g., left_elbow: xl, yl, zl; right_elbow: x2, y2, z2; left hip: x3, y3, z3; right hip: x4, y4, z4; etc.). In the example of FIG. 2B, 33 pose landmark locations are detected in operation 2120 and described in a 3D coordinate space to form a matrix of size 3 x 33. In some implementations, the model may use both 2D and 3D pose landmark positions for high accuracy

[0105] At operation 2220 of method 2200, the one or more second machine-learned models 3220 may be configured to implement (perform) a flattening operation with respect to the pose landmark input data (e.g., the input image frame represented as a three-dimensional array). The flattening operation can include converting a multi-dimensional array (which represents the input data) into a one-dimensional array that can be processed by fully connected layers (also known as dense layers) in a neural network. In the example of FIG. 2B, the input matrix of size 3 x 33 may be flattened to a 99 element one-dimensional array, then passed to the fully connected layers.

[0106] In some implementations, the one or more second machine-learned models 3220 may be configured to include a fully connected network (FCN) 2230, also known as a dense neural network, where each neuron in one layer is connected to every neuron in the next layer. The FCN 2230 may include an input layer, hidden layers, and an output layer. As illustrated in the example of FIG. 2B, the FCN 2230 may include a batch normalization layer 2232 which is configured to normalize (standardize) the inputs to a layer for each mini -batch (e.g., by fixing the means and variances of each layer’s inputs), to mitigate issues such as the vanishing and exploding gradient problem. The FCN 2230 may also include a swish activation layer 2234 which is configured to apply a swish function on the layer inputs. For example, the swish operation may be represented as f(x) = x / (1 + e'px). Activation layers such as swish layers can improve the training accuracy for some applications (e.g., for classification problems) and can be provided subsequent to a normalization layer. In some implementations, other activation functions may be implemented instead of the swish activation layer (e.g., rectified linear unit (ReLU), softmax, etc.).

[0107] The FCN 2230 may also include a dropout layer 2236 which is configured to randomly drop one or more nodes of the neural network (e.g., input nodes, hidden nodes) by setting the weight of the randomly selected node(s) to zero, to prevent overfitting of themodel. The dropout layer 2236 may be implemented only during training or may be implemented for both training the model and at inference time.

[0108] The FCN 2230 also includes a plurality of neurons. In FIG. 2B the neurons are represented as 100 dense 2238 which denotes there are 100 neurons or nodes in the fully connected network. As illustrated in the example of FIG. 2B, an input vector (e.g., the flattened one-dimensional vector obtained at operation 2220) may be fed to the FCN 2230, and the FCN 2230 transforms the input vector via various operations (e.g., via an activation function) to produce a transformed output. In some implementations, the transformed output may be added via an adding operation 2239 to the original input vector via a residual connection 2231 to obtain the final output of the FCN 2230. The FCN 2230 may be repeated for n blocks (e.g., 5 blocks) and the output layer may include nodes (e.g., neurons) which correspond to the N classes that the models is trained to recognize and classify input data into. In some implementations, the output layer may include a probability or probability distribution associated with the likelihood that the input data corresponds to a particular pose among the possible N poses represented by the N classes 2240. For example, the one or more second machine-learned models 3220 may be trained to recognize a plurality of different poses and pose states (e.g., 20 pose states, 30 pose states, 50 pose states, etc.). The pose states can be associated with one or more exercises, and a sequence of one or more pose states may define or be associated with a particular exercise. The one or more second machine-learned models 3220 may be configured to process the landmark input data with high accuracy at real-time (e.g., greater than 30 FPS), which can be used for on-device realtime video processing. For example, according to examples of the disclosure, operation 2130 (and thus corresponding method 2200) may be performed at the same computing device which captures and / or receives the image frames (e.g., computing device 100) in real-time and which includes the one or more first machine-learned models 3210 that outputs the landmark input data.

[0109] In some implementations, a training computing system may be configured to train the one or more second machine-learned models 3220 (e.g., including a pose classification model) based on a diverse dataset. For example, the dataset may include hundreds of videos (e.g., of varying length, for example, two to three minutes each) for each exercise type. The videos may be captured from different orientations (e.g., in landscape and portrait orientations), different environments (e.g., indoor and outdoor settings), from different camera positions and angles, different user types (e.g., different genders), varying lightingconditions, varying backgrounds, etc. For example, the image data (e.g., videos, images, etc.) may be stored in image data store 350. In some implementations, human annotators (which may include subject matter experts) may identify and classify segments of the videos into one of the predefined pose states (e.g., using the known ELAN annotation tool). Some segments of a video, such as intermediate frames between different states of an exercise (e.g., between up and down states of a push-up), may not be labeled.

[0110] The training computing system may be configured to extract pose landmarks from the frames of the labeled segments by using the one or more first machine-learned models 3210 and save the detected pose landmarks with their human-annotated pose states as the training and evaluation datasets. The training computing system may be configured to train the one or more second machine-learned models 3220 (e.g., including the pose classification model) using only the pose landmarks as inputs, and not the image frames, and the pose states as labels. During model training, the training computing system may be configured to implement a scalable hyperparameter tuning service, to search over a range of network depths, learning rates, decays of learning rates, batch sizes, and optimizer choices.

[0111] Referring back to FIG. 2A, at operation 2140 the computing device may be configured to determine an exercise type and at operation 2150 the computing device may be configured to determine the number of repetitions performed during the exercise. For example, the exercise determiner 3240 may be configured to determine the exercise type based on the output of the one or more second machine-learned models 3220 and the repetition determiner 3230 may be configured to determine the number of repetitions performed during the exercise based on the output of the one or more second machine- learned models 3220. The repetition determiner 3230 and exercise determiner 3240 may be configured to process the output of the one or more second machine-learned models 3220 (e.g., the classified pose states) at real-time (e.g., greater than 30 FPS), which can be used for on-device real-time video processing. For example, according to examples of the disclosure, operations 2140 and 2150 may be performed at the same computing device which performs operations 2110, 2120, and 2130 in real-time.

[0112] In some implementations, the exercise determiner 3240 may be configured to identify a type of exercise based on a sequence of exercise pose states determined by the one or more second machine-learned models 3220 according to a plurality of image frames. The repetition determiner 3230 may also be configured to determine how many repetitions that a user has performed based on the sequence of exercise pose states determined by the one ormore second machine-learned models 3220 and according to the type of exercise as determined by the exercise determiner 3240. In some implementations, the exercise determiner 3240 and the repetition determiner 3230 may be embodied by a finite state machine where each state in the finite state machine can represent a particular pose or a transitional pose in the exercise sequence. In some implementations, exercises may be grouped into movement exercises and hold exercises. Each movement exercise (e.g., pushups, squats, etc.) may be defined by a sequence of pose states. For example, the exercise determiner 3240 may be configured to determine the type of the exercise performed by the user to be a hold exercise based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, and to initiate a timer for the exercise. For example, the exercise determiner 3240 may be configured to determine the type of the exercise performed by the user to be a hold exercise based on information indicating the exercise (e.g., via an input by the user to the computing device, via an exercise application, etc.), and to initiate a timer for the exercise in response to receiving at least one image frame among the plurality of image frames which captures the user performing the exercise. For example, the exercise determiner 3240 may be configured to determine the type of the exercise performed by the user to be a movement exercise based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, and the repetition determiner 3230 may be configured to increment a repetition counter after a full sequence of the movement exercise is completed according to a defined sequence of pose states associated with one repetition of the exercise. For example, the exercise determiner 3240 may be configured to determine the type of the exercise performed by the user to be a movement exercise based on information indicating the exercise (e.g., via an input by the user to the computing device, via an exercise application, etc.), and the repetition determiner 3230 may be configured to increment a repetition counter after a full sequence of the movement exercise is completed according to a defined sequence of pose states associated with one repetition of the exercise. The user may perform multiple exercises in a single workout, and the computing device (e.g., repetition determiner 3230) may be configured to recognize and track the repetition counts of each exercise.

[0113] The exercise determiner 3240 may be configured to recognize a hold exercise (e.g., plank, one leg stand, etc.) based on the detected pose state. If a pose state associated with a hold exercise is detected, the exercise determiner 3240 may be configured to start (initiate) ahold timer for the exercise. Once a different pose state is detected, the hold timer may be paused.

[0114] In some implementations, if the pose state predicted by the one or more second machine-learned models 3220 (e.g., a pose classification model) is below a given confidence score threshold (e.g., less than 40%, less than 30%, etc.), the one or more second machine- learned models 3220 may be configured to determine that the associated image frame did not include a known pose state, and body landmark data from a next frame can be processed. For example, the one or more second machine-learned models 3220 may be configured to classify a first set of body landmark locations from a first image frame among the plurality of image frames as corresponding to a first pose state among a plurality of pose states, in response to a confidence level associated with classifying the first set of body landmark locations as corresponding to the first pose state exceeding a threshold confidence level. For example, the one or more second machine-learned models 3220 may be configured to ignore the first image frame and process a second set of body landmark locations from a second image frame from among the plurality of image frames, in response to none of the confidence levels associated with classifying the first set of body landmark locations as corresponding to one of the plurality of pose states exceeding the threshold confidence level.

[0115] The exercise determiner 3240 may ignore image frames having unknown pose states (e.g., without changes to the state machine) and process a next image frame to determine the exercise type. In some implementations, the computing device may be configured to notify or alert the user when their body is out of frame (or partially out of frame) of the camera so that errors in identifying pose landmarks and in identifying repetition counts can be reduced or prevented. For example, the computing device may be configured to notify or alert the user to reposition the camera or the user, or change a field of view or zoom level of the camera, in response to a confidence level regarding a respective body landmark location for one or more body parts being below a threshold level (e.g., below 80%, below 70%, etc.)). For example, the computing device may be configured to notify or alert the user to reposition the camera or the user, or change a field of view or zoom level of the camera, in response to determining one or more body parts are not visible in the image or in response to determining one or more body parts necessary for determining a pose of a user with a sufficient confidence level, are not visible in the image.

[0116] In some implementations, the exercise determiner 3240 may be configured to distinguish between exercise types that share one or more pose states (e.g., a “Common Up”pose state for a burpee exercise and a jumping jack exercise) or between exercises that are a combination of exercises (e.g., a burpee exercise including a squat, pushup, and jumping jack). For example, the exercise determiner 3240 may be configured to distinguish between the exercise types based on the exercise type that has the longest detected sequence. The exercise determiner 3240 may also limit a duration of time between different pose states to delineate between an exercise that includes a combination of exercises compared to discrete exercises performed sequentially. For some hold exercises (e.g., the one leg stand), the exercise determiner 3240 may be configured to increment the hold timer based on the different pose states associated with the exercise (e.g., a left leg stand compared to a right leg stand).

[0117] In some implementations, the exercise determiner 3240 and the repetition determiner 3230 may be embodied as (include) one or more third machine-learned models (e.g., a neural network based model) configured to identify the exercise being performed and to count repetitions of the exercise. For example, the training data for training the one or more third machine-learned models can include data which is associated with sequences of pose landmarks of each exercise repetition and a specified duration of time that is associated with the exercise. For example, the training data (e.g., videos, images, etc.) can be labeled with each type of exercise, the number of repetitions, the pose states depicted, etc., and the one or more third machine-learned models (e.g., the neural network based model) can be trained to detect the exercise type and number of repetitions performed.

[0118] FIGS. 5A-5B are example tables of exercises and associated sequences of poses corresponding to each exercise, according to one or more example embodiments of the disclosure. Referring to FIG. 5A, the table 5100 illustrates example exercises that can be detected by the exercise determiner 3240 (e.g., a burpee, jumping jack, push up, squat, triceps dip). The table 5100 of FIG. 5A is merely an example and other exercises may be recognized by the computing device and exercise determiner 3240. The table 5100 of FIG. 5A illustrates example movement exercises. For example, the burpee exercise may correspond to the sequence of pose states of: {Common Up, Squat Down, PushUp Up, PushUp Down, PushUp Up, Squat Down, JumpingJack Up, Common Up}. For example, the jumping jack exercise may correspond to the sequence of pose states of: {Common Up, JumpingJack Up, Common Up}. For example, the push up exercise may correspond to the sequence of pose states of: {PushUp Up, PushUp Down, PushUp Up}. For example, the squat exercise may correspond to the sequence of pose states of: {Squat Up, Squat Down, Squat Up}. Forexample, the triceps dip exercise may correspond to the sequence of pose states of: { TricepsDip Up, TricepsDip Down, TricepsDip Up}.

[0119] Referring to FIG. 5B, the table 5200 illustrates example exercises that can be detected by the exercise determiner 3240 (e.g., a one leg stand, plank). The table 5200 of FIG. 5B is merely an example, and other exercises may be recognized by the computing device and exercise determiner 3240. The table 5200 of FIG. 5B illustrates example hold exercises. For example, the one leg stand exercise may correspond to the sequence of pose states of: {OneLegStand Left } or {OneLegStand Right}. For example, the plank exercise may correspond to the pose state of: {Plank}.

[0120] Referring back to FIG. 2A, in some implementations, the exercise the user is performing may be known prior to the user performing the exercise and the determination of the exercise at operation 2140 may be based on receiving information indicating an exercise the user is to perform (or is performing). For example, the information indicating the exercise the user is to perform may be received via an exercise application which is configured to generate an exercise workout plan. For example, the exercise application may be provided by the computing device (e.g., as part of application system 130) and be configured to generate or provide a workout plan that the user follows. As another example, the information indicating the exercise the user is to perform may be received via an input by the user (e.g., via input device 150) to the computing device prior to performing the exercise. In the implementation that the exercise is known beforehand or predetermined, the exercise determiner 3240 may determine the exercise (or type of exercise) based on the information indicating the exercise rather than the output of the one or more second machine-learned models. The information indicating the exercise may be received via the input device 150, the exercise application, etc., and provided to the exercise determiner 3240, for example. Similar to discussed above, in the implementation that the exercise is known beforehand or predetermined, the repetition determiner 3230 may also be configured to determine how many repetitions that a user has performed based on the sequence of exercise pose states determined by the one or more second machine-learned models 3220 and according to the type of exercise as determined by the exercise determiner 3240.

[0121] Referring back to FIG. 2A, at operation 2160 the computing device may be configured to determine or evaluate the presence of dysfunctions (or lack thereof) with respect to an exercise being performed by a user. For example, the form analyzer 3250 may be configured to determine whether the user performs an exercise correctly (e.g., with properform, at a proper speed, without overexerting oneself, etc.), or if at least some portion of the exercise is performed incorrectly and a dysfunction is identified (e.g., with improper form, at an improper speed, in which one overexerts themself, etc.). For example, the form analyzer 3250 may be configured to determine whether the exercise is performed with correct form based on the type of the exercise (e.g., as determined by the exercise determiner 3240 based on the output of the one or more second machine-learned models, an input received from a user, an input received from an exercise application, etc.), the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models 3220, and the body landmark locations determined via the one or more first machine-learned models 3210.

[0122] In some implementations, the form analyzer 3250 may be configured to identify any form dysfunctions based on the set of human pose landmarks and the sequence of exercises pose states. The form analyzer 3250 may include a plurality of detectors that each identify a single movement dysfunction (e.g., completing a push up without reaching the proper depth, allowing medial deviation of the knee during a squat greater than a threshold level, etc.), based on the landmark information (e.g., the set of human pose landmarks) received from the one or more first machine-learned models 3210 and exercise classification signals (indicating the sequence of pose states) received from the one or more second machine-learned models 3220. In some implementations, the form analyzer 3250 may be configured to identify or determine the dysfunction based on relative locations of particular body parts (e.g., relative locations of a hand to a shoulder in a push-up exercise). When the relative locations of the particular body parts does not satisfy particular criteria (e.g., exceeds a threshold value, outside of a particular threshold range, etc.), the form analyzer 3250 may be configured to identify the dysfunction with respect to the pose and / or with respect to the particular body parts (e.g., the user’s hands are too close together, the user’s hands are too spaced apart, the user’s torso is not low enough, the user’s back is arched excessively, etc.). In some implementations, the form analyzer 3250 may be configured to identify or determine the dysfunction based on a ratio of locations of particular body parts (e.g., the distance between the knees divided by the distance between the ankles in a squat exercise). When the ratio of locations of particular body parts does not satisfy particular criteria (e.g., is less than a threshold value, outside of a particular threshold range, etc.), the form analyzer 3250 may be configured to identify the dysfunction with respect to the pose and / or with respect to the particular body parts (e.g., if the user’s knees are too close together and / or the user’s anklesare too far apart, then the ratio may be less than a threshold value indicating improper form, or if the user’s knees are too far apart and / or the user’s ankles are too close together, then the ratio may be more than another threshold value indicating improper form). In some implementations, the form analyzer 3250 may be configured to identify or determine the dysfunction based on an angle of particular body parts (e.g., angle at the knee and hip of a user during a squat exercise). When the angle of the particular body parts does not satisfy particular criteria (e.g., exceeds a threshold value, outside of a particular threshold range, etc.), the form analyzer 3250 may be configured to identify the dysfunction with respect to the pose and / or with respect to the particular body parts (e.g., the user’s knees are bent too far).

[0123] In some implementations, a detector may be dormant until it identifies a dysfunction, at which point the detector outputs a detection event until the dysfunction is resolved. In some implementations, the detectors may be stateless. For example, the medial deviation of the knees for squats can be detected based on the formula: exercise = SQUAT and (knee distance / ankle distance) less than a threshold level. In some implementations, the detectors may be stateful, and use real-time information. For example, a squat detector may be configured to be triggered (output a dysfunction event) in response to the real-time depth measured during the Squat Down state corresponding to a dysfunction and if a Squat Up state is subsequently detected.

[0124] In some implementations, the form analyzer 3250 may be embodied as or include one or more fourth machine-learned models (e.g., a dysfunction classification model) which is configured to identify dysfunctions based on the landmark information (e.g., the set of human pose landmarks) received from the one or more first machine-learned models 3210 and exercise classification signals (indicating the sequence of pose states) received from the one or more second machine-learned models 3220. In some implementations, the form analyzer 3250 may implement one or more fourth machine-learned models (which are trained to identify a dysfunction which indicates one or more body parts of the user which was positioned incorrectly during the exercise), based on the type of the exercise (e.g., as determined by the exercise determiner 3240 based on the output of the one or more second machine-learned models, an input received from a user, an input received from an exercise application, etc.), the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, and the body landmark locations determined via the one or more first machine-learned models. For example, the training datafor training the one or more fourth machine-learned models can include data which is associated with correct form (posture) for each type of exercise and for each pose in the sequences of poses that form the exercise. For example, the training data for training the one or more fourth machine-learned models can also include data which is associated with incorrect form (posture) for each type of exercise and for each pose in the sequences of poses that form the exercise that are associated with dysfunctions. The training data (e.g., videos, images, etc.) can be labeled with each type of dysfunction (or lack thereof), the type of exercise, the pose states depicted, etc., and the one or more fourth machine-learned models (e.g., the dysfunction classification model) can be trained to detect the dysfunctions.

[0125] In some implementations, the form analyzer 3250 may be configured to receive other input data (e.g., motion data associated with movement of the user, sensor data associated with the user and / or environment, etc.) that can be collected via the capture device 180 and / or the one or more sensors 190. For example, the sensor data can include biometric data associated with the user (e.g., a heart rate, SpO2 level, body temperature, etc.). In some implementations, the form analyzer 3250 may be configured to detect whether movement of a body part of the user exceeds a threshold movement level for a particular exercise, based on the pose state information and timing associated with each pose, based on a movement speed of the body part measured by the one or more sensors 190, etc. In some implementations, the form analyzer 3250 may be configured to detect whether a biometric of the user exceeds a threshold level such that the form analyzer 3250 determines the exercise is being performed unsafely or improperly which can cause the biometric of the user to be outside of a specified tolerance (e.g., a heart rate of the user exceeds a specified heart rate value).

[0126] At operation 2170, the method 2100 may include the computing device (e.g., form analyzer 3250) providing or generating feedback regarding the presence (or lack thereof) of the dysfunctions detected at operation 2160. For example, the form analyzer 3250 may be configured to, in response to determining the exercise is not performed with correct form based on the type of the exercise (e.g., as determined by the exercise determiner 3240 based on the output of the one or more second machine-learned models, an input received from a user, an input received from an exercise application, etc.), the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, and the body landmark locations determined via the one or more first machine-learned models, provide feedback to the user identifying a dysfunction which indicates a body part of the user which was positioned incorrectly during the exercise. The computing device (via theform analyzer 3250) may be configured to provide feedback to users in real-time according to which dysfunctions are identified. For example, the feedback may indicate that a squat pose was not low enough, that a pushup exercise was performed with the hands too far apart, etc. In some implementations, information regarding the presence (or lack thereof) of the dysfunctions detected at operation 2160 may be stored in exercise data store 360 or at the computing device. In some implementations, information regarding the performance of the exercise (e.g., the type of exercise(s) performed, the number of repetitions performed for each exercise, and associated data such as the time, day, location, weather, environment, etc.), may be stored in exercise data store 360 or at the computing device.

[0127] The form analyzer 3250 may be configured to process the received image frames, the output of the one or more first machine-learned models 3110 (e.g., the body landmark location data), the output of the one or more second machine-learned models 3220 (e.g., the classified pose states), and the output of the exercise determiner 3240 (e.g., the determined exercise type), at real-time (e.g., greater than 30 FPS), which can be used for on-device realtime video processing. For example, according to examples of the disclosure, operations 2160 and 2170 may be performed at the same computing device which performs operations 2110, 2120, 2130, and 2140 in real-time.

[0128] As indicated in FIG. 3, the exercise monitoring application 3200 may include a user interface generator 3260 which is configured to generate a user interface associated with performing an exercise. In some implementations, the user interface generator 3260 may receive the input image frames 3100, outputs from the one or more first machine-learned models 3210, the one or more second machine-learned models 3220, the repetition determiner 3230, the exercise determiner 3240, and the form analyzer 3250. The user interface generator 3260 may be configured to render images relating to the received information. For example, images may be rendered (in real-time) which correspond to the user performing an exercise based on the determined body landmark locations, determined pose states, determined exercise type, and determined repetitions. For example, information may be provided on the user interface screen visually (in real-time) regarding whether the user performs the exercise with the correct form, and if dysfunctions are identified, information may be provided on the user interface screen visually (in real-time) regarding the identified dysfunction and actions which can be taken to correct the form. The user interface screen may be provided via the display device 160 and / or the output device 170 (e.g., where the output device 170 includes a display). Information relating to the exercise may also beprovided audibly through a speaker, haptic device, etc. For example, in some implementations the output device 170 may be configured to provide an output to the computing device and / or to an external computing device which causes the computing device and / or the external computing device to notify or alert the user that the exercise is being performed correctly or incorrectly. For example, the user may wear one or more devices (e.g., a vibration device, a thermal device, etc.) on certain body parts of the user and if the particular body part is determined to be out of position in a particular pose state, the one or more devices may be activated to alert the user that the body part is out of position. If the particular body part is determined to be in position in the particular pose state, the one or more devices may remain in an inactive state. In addition, or alternatively, the user may wear one or more devices (e.g., virtual or augmented reality goggles, headphones, earbuds, etc.) and if the particular body part is determined to be out of position in a particular pose state, the one or more devices may be activated to alert the user that the body part is out of position (e.g., through a graphic display, a particular sound, etc.). If the particular body part is determined to be in position in the particular pose state, the one or more devices may remain in an inactive state or the user may be notified they are performing the exercise correctly (e.g., through a graphic display, a particular sound, etc.).

[0129] FIGS. 6A-6B illustrate example experimental results, according to one or more example embodiments of the disclosure. FIG. 6A includes a table 6100 which shows experimental results relating to the predictive power of the one or more second machine- learned models 3220 in predicting the correct pose state. As shown in FIG. 6A, it can be seen that implementation of the computing device (and the one or more second machine-learned models 3220) described herein resulted in a reasonably high predictive power based on metrics relating to the precision, recall, and Fl score for various pose states. The precision of the Squat Down pose is lower than most other poses, as this pose is sometimes confused with a standing pose (e.g., sharing the Common Up state) in a frontal view. Similarly, the Pushup Up pose is sometimes confused with a plank pose when the camera is positioned high and pointed down towards the floor. However, overall, the results indicate that the one or more second machine-learned models 3220 can generally predict the correct pose state.

[0130] The performance of the repetition determiner 3230 is shown in the table 6200 of FIG. 6B. The experimental results were obtained based on 10 videos for each exercise type, with each video about 2-3 minutes long. The Absolute Percentage Error (APE) between the ground truth and the predicted repetition counts for each video was computed and then theMean of APEs (MAPE) was computed across each exercise type. Higher error rates were observed for some exercise types because certain body parts can easily occlude other body parts, thus sometimes leading to incorrect pose landmarks and higher errors in repetition counts. However, overall, the results indicate that the repetition determiner 3230 can generally determine number of repetitions for an exercise with a low error rate.

[0131] FIG. 7 depicts a flowchart of a method 700 for training one or more machine-learned models according to aspects of the disclosure. For instance, an example machine-learned model can include one or more of a LLM, a generative machine-learned model, etc. For example, the one or more machine-learned models may be configured to implement the operations of FIGS. 2A and 2B, of the exercise monitoring application 3200, etc., as described herein.

[0132] FIG. 7 is a flow chart diagram illustrating an example method for training a machine- learned model according to example implementations of aspects of the disclosure. One or more portion(s) of example method 700 can be implemented by a computing system that includes one or more computing devices such as, for example, computing systems described with reference to the other drawings. Each respective portion of example method 700 can be performed by any (or any combination) of one or more computing devices. Moreover, one or more portion(s) of example method 700 can be implemented on the hardware components of the device(s) described herein, for example, to train one or more systems or models. FIG. 7 depicts elements performed in a particular order for purposes of illustration and discussion. Those of ordinary skill in the art, using the disclosures provided herein, will understand that the elements of any of the methods discussed herein can be adapted, rearranged, expanded, omitted, combined, or modified in various ways without deviating from the scope of the disclosure. FIG. 7 is described with reference to elements / terms described with respect to other systems and drawings for exemplary illustrated purposes and is not meant to be limiting. One or more portions of example method 700 can be performed additionally, or alternatively, by other systems.

[0133] At 702, example method 700 can include obtaining a training instance. A set of training data can include a plurality of training instances divided between multiple datasets (e.g., a training dataset, a validation dataset, or testing dataset). A training instance can be labeled or unlabeled. Although referred to in example method 700 as a “training” instance, it is to be understood that runtime inferences can form training instances when a model is trained using an evaluation of the model’s performance on that runtime instance (e.g., onlinetraining / learning). Example data types for the training instance and various tasks associated therewith are described throughout the disclosure.

[0134] At 704, example method 700 can include processing, using one or more machine- learned models, the training instance to generate an output. The output can be directly obtained from the one or more machine-learned models or can be a downstream result of a chain of processing operations that includes an output of the one or more machine-learned models.

[0135] At 706, example method 700 can include receiving an evaluation signal associated with the output. The evaluation signal can be obtained using a loss function. Various determinations of loss can be used, such as mean squared error, likelihood loss, cross entropy loss, hinge loss, contrastive loss, or various other loss functions. The evaluation signal can be computed using known ground-truth labels (e.g., supervised learning), predicted or estimated labels (e.g., semi- or self-supervised learning), or without labels (e.g., unsupervised learning). The evaluation signal can be a reward (e.g., for reinforcement learning). The reward can be computed using a machine-learned reward model configured to generate rewards based on output(s) received. The reward can be computed using feedback data describing human feedback on the output(s).

[0136] At 708, example method 700 can include updating the machine-learned model using the evaluation signal. For example, values for parameters of the machine-learned model(s) can be learned, in some embodiments, using various training or learning techniques, such as, for example, backwards propagation. For example, the evaluation signal can be backpropagated from the output (or another source of the evaluation signal) through the machine-learned model(s) to update one or more parameters of the model(s) (e.g., based on a gradient of the evaluation signal with respect to the parameter value(s)). For example, system(s) containing one or more machine-learned models can be trained in an end-to-end manner. Gradient descent techniques can be used to iteratively update the parameters over a number of training iterations. In some implementations, performing backwards propagation of errors can include performing truncated backpropagation through time. Example method 700 can include implementing a number of generalization techniques (e.g., weight decays, dropouts, etc.) to improve the generalization capability of the models being trained.

[0137] In some implementations, example method 700 can be implemented for training a machine-learned model from an initialized state to a fully trained state (e.g., when the model exhibits a desired performance profile, such as based on accuracy, precision, recall, etc.).

[0138] In some implementations, example method 700 can be implemented for particular stages of a training procedure. For instance, in some implementations, example method 700 can be implemented for pre-training a machine-learned model. Pre-training can include, for instance, large-scale training over potentially noisy data to achieve a broad base of performance levels across a variety of tasks / data types. In some implementations, example method 700 can be implemented for fine-tuning a machine-learned model. Fine-tuning can include, for instance, smaller-scale training on higher-quality (e.g., labeled, curated, etc.) data. Fine-tuning can affect all or a portion of the parameters of a machine-learned model. For example, various portions of the machine-learned model can be “frozen” for certain training stages. For example, parameters associated with an embedding space can be “frozen” during fine-tuning (e.g., to retain information learned from a broader domain(s) than present in the fine-tuning dataset(s)). An example fine-tuning approach includes reinforcement learning. Reinforcement learning can be based on user feedback on model performance during use.Example Machine-Learned Models

[0139] FIG. 8 is a block diagram of an example processing flow for using machine-learned model(s) 1 to process input(s) 2 to generate output(s) 3.

[0140] Machine-learned model(s) 1 can be or include one or multiple machine-learned models or model components. Example machine-learned models can include neural networks (e.g., deep neural networks). Example machine-learned models can include nonlinear models or linear models. Example machine-learned models can use other architectures in lieu of or in addition to neural networks. Example machine-learned models can include decision tree based models, support vector machines, hidden Markov models, Bayesian networks, linear regression models, k-means clustering models, etc.

[0141] Example neural networks can include feed-forward neural networks, recurrent neural networks (RNNs), including long short-term memory (LSTM) based recurrent neural networks, convolutional neural networks (CNNs), diffusion models, generative-adversarial networks, or other forms of neural networks. Example neural networks can be deep neural networks. Some example machine-learned models can leverage an attention mechanism suchas self-attention. For example, some example machine-learned models can include multiheaded self-attention models.

[0142] Machine-learned model(s) 1 can include a single or multiple instances of the same model configured to operate on data from input(s) 2. Machine-learned model(s) 1 can include an ensemble of different models that can cooperatively interact to process data from input(s) 2. For example, machine-learned model(s) 1 can employ a mixture-of-experts structure. See, e.g., Zhou et al., Mixture-of-Experts with Expert Choice Routing, ARXIV:2202.09368V2 (Oct. 14, 2022).

[0143] Input(s) 2 can generally include or otherwise represent various types of data. Input(s) 2 can include one type or many different types of data. Output(s) 3 can be data of the same type(s) or of different types of data as compared to input(s) 2. Output(s) 3 can include one type or many different types of data.

[0144] Example data types for input(s) 2 or output(s) 3 include natural language text data, software code data (e.g., source code, object code, machine code, or any other form of computer-readable instructions or programming languages), machine code data (e.g., binary code, assembly code, or other forms of machine-readable instructions that can be executed directly by a computer's central processing unit), assembly code data (e.g., low-level programming languages that use symbolic representations of machine code instructions to program a processing unit), genetic data or other chemical or biochemical data, image data, audio data, audiovisual data, haptic data, biometric data, medical data, financial data, statistical data, geographical data, astronomical data, historical data, sensor data generally (e.g., digital or analog values, such as voltage or other absolute or relative level measurement values from a real or artificial input, such as from an audio sensor, light sensor, displacement sensor, etc.), and the like. Data can be raw or processed and can be in any format or schema.

[0145] In multimodal inputs 2 or outputs 3, example combinations of data types include image data and audio data, image data and natural language data, natural language data and software code data, image data and biometric data, sensor data and medical data, etc. It is to be understood that any combination of data types in an input 2 or an output 3 can be present.

[0146] An example input 2 can include one or multiple data types, such as the example data types noted above. An example output 3 can include one or multiple data types, such as the example data types noted above. The data type(s) of input 2 can be the same as or different from the data type(s) of output 3. It is to be understood that the example data types notedabove are provided for illustrative purposes only. Data types contemplated within the scope of the disclosure are not limited to those examples noted above.Example Machine-Learned Sequence Processing Models

[0147] FIG. 9 is a block diagram of an example implementation of an example machine- learned model configured to process sequences of information. For instance, an example implementation of machine-learned model(s) 1 can include machine-learned sequence processing model(s) 4. An example system can pass input(s) 2 to sequence processing model(s) 4. Sequence processing model(s) 4 can include one or more machine-learned components. Sequence processing model(s) 4 can process the data from input(s) 2 to obtain an input sequence 5. Input sequence 5 can include one or more input elements 5-1, 5-2, . . . , 5-A , etc. obtained from input(s) 2. Sequence processing model 4 can process input sequence 5 using prediction layer(s) 6 to generate an output sequence 7. Output sequence 7 can include one or more output elements 7-1, 7-2, . . . , 7-A, etc. generated based on input sequence 5. The system can generate output(s) 3 based on output sequence 7.

[0148] Sequence processing model(s) 4 can include one or multiple machine-learned model components configured to ingest, generate, or otherwise reason over sequences of information. For example, some example sequence processing models in the text domain are referred to as “Large Language Models,” or LLMs. See, e.g., PaLM 2 Technical Report, GOOGLE, https: / / ai.google / static / documents / palm2techreport.pdf (n.d.). Other example sequence processing models can operate in other domains, such as image domains, see, e.g., Dosovitskiy et al., An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, ARXIV:2010.11929v2 (Jun. 3, 2021), audio domains, see, e.g., Agostinelli et al., MusicLM: Generating Music From Text, ARXIV:2301.1 1325V1 (Jan. 26, 2023), biochemical domains, see, e.g., Jumper et al., Highly accurate protein structure prediction with AlphaFold, 596 Nature 583 (Aug. 26, 2021), by way of example. Sequence processing model(s) 4 can process one or multiple types of data simultaneously. Sequence processing model(s) 4 can include relatively large models (e.g., more parameters, computationally expensive, etc.), relatively small models (e.g., fewer parameters, computationally lightweight, etc.), or both.

[0149] In general, sequence processing model(s) 4 can obtain input sequence 5 using data from input(s) 2. For instance, input sequence 5 can include a representation of data from input(s) 2 in a format understood by sequence processing model(s) 4. One or more machine- learned components of sequence processing model(s) 4 can ingest the data from input(s) 2,parse the data into pieces compatible with the processing architectures of sequence processing model(s) 4 (e.g., via “tokenization”), and project the pieces into an input space associated with prediction layer(s) 6 (e.g., via “embedding”).

[0150] Sequence processing model(s) 4 can ingest the data from input(s) 2 and parse the data into a sequence of elements to obtain input sequence 5. For example, a portion of input data from input(s) 2 can be broken down into pieces that collectively represent the content of the portion of the input data. The pieces can provide the elements of the sequence.

[0151] Elements 5-1, 5-2, . . . , 5-M can represent, in some cases, building blocks for capturing or expressing meaningful information in a particular data domain. For instance, the elements can describe “atomic units” across one or more domains. For example, for textual input source(s), the elements can correspond to groups of one or more words or sub-word components, such as sets of one or more characters.

[0152] For example, elements 5-1, 5-2, . . . , 5-M can represent tokens obtained using a tokenizer. For instance, a tokenizer can process a given portion of an input source and output a series of tokens (e.g., corresponding to input elements 5-1, 5-2, . . . , 5-M) that represent the portion of the input source. Various approaches to tokenization can be used. For instance, textual input source(s) can be tokenized using a byte-pair encoding (BPE) technique. See, e.g., Kudo et al., SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing, PROCEEDINGS OF THE 2018 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (System Demonstrations), pages 66-71 (October 31-November 4, 2018), https: / / aclanthology.org / D18-2012.pdf. Imagebased input source(s) can be tokenized by extracting and serializing patches from an image.

[0153] In general, arbitrary data types can be serialized and processed into input sequence 5. It is to be understood that element(s) 5-1, 5-2, . . . , 5-M depicted in FIG. 9 can be the tokens or can be the embedded representations thereof.

[0154] Prediction layer(s) 6 can predict one or more output elements 7-1, 7-2, . . . , 1-N based on the input elements. Prediction layer(s) 6 can include one or more machine-learned model architectures, such as one or more layers of learned parameters that manipulate and transform the input(s) to extract higher-order meaning from, and relationships between, input element(s) 5-1, 5-2, . . . , 5-M. In this manner, for instance, example prediction layer(s) 6 can predict new output element(s) in view of the context provided by input sequence 5.

[0155] Prediction layer(s) 6 can evaluate associations between portions of input sequence 5 and a particular output element. These associations can inform a prediction of the likelihood that a particular output follows the input context. For example, consider the textual snippet, “The carpenter’s toolbox was small and heavy. It was full of .” Example prediction layer(s) 6 can identify that “It” refers back to “toolbox” by determining a relationship between the respective embeddings. Example prediction layer(s) 6 can also link “It” to the attributes of the toolbox, such as “small” and “heavy.” Based on these associations, prediction layer(s) 6 can, for instance, assign a higher probability to the word “nails” than to the word “sawdust.”

[0156] A transformer is an example architecture that can be used in prediction layer(s) 4. See, e.g., Vaswani et al., Attention Is All You Need, ARXIV: 1706.03762V7 (Aug. 2, 2023). A transformer is an example of a machine-learned model architecture that uses an attention mechanism to compute associations between items within a context window. The context window can include a sequence that contains input sequence 5 and potentially one or more output element(s) 7-1, 7-2, . . . , 1-N. A transformer block can include one or more attention layer(s) and one or more post-attention layer(s) (e.g., feedforward layer(s), such as a multilayer perceptron).

[0157] Prediction layer(s) 6 can include other machine-learned model architectures in addition to or in lieu of transformer-based architectures. For example, recurrent neural networks (RNNs) and long short-term memory (LSTM) models can also be used, as well as convolutional neural networks (CNNs). In general, prediction layer(s) 6 can leverage various kinds of artificial neural networks that can understand or generate sequences of information.

[0158] Output sequence 7 can include or otherwise represent the same or different data types as input sequence 5. For instance, input sequence 5 can represent textual data, and output sequence 7 can represent textual data. Input sequence 5 can represent image, audio, or audiovisual data, and output sequence 7 can represent textual data (e.g., describing the image, audio, or audiovisual data). It is to be understood that prediction layer(s) 6, and any other interstitial model components of sequence processing model(s) 4, can be configured to receive a variety of data types in input sequence(s) 5 and output a variety of data types in output sequence(s) 7.

[0159] Output sequence 7 can have various relationships to input sequence 5. Output sequence 7 can be a continuation of input sequence 5. Output sequence 7 can becomplementary to input sequence 5. Output sequence 7 can translate, transform, augment, or otherwise modify input sequence 5. Output sequence 7 can answer, evaluate, confirm, or otherwise respond to input sequence 5. Output sequence 7 can implement (or describe instructions for implementing) an instruction provided via input sequence 5.

[0160] Output sequence 7 can be generated autoregressively. For instance, for some applications, an output of one or more prediction layer(s) 6 can be passed through one or more output layers (e.g., softmax layer) to obtain a probability distribution over an output vocabulary (e.g., a textual or symbolic vocabulary) conditioned on a set of input elements in a context window. In this manner, for instance, output sequence 7 can be autoregressively generated by sampling a likely next output element, adding that element to the context window, and re-generating the probability distribution based on the updated context window, and sampling a likely next output element, and so forth.

[0161] Output sequence 7 can also be generated non-autoregressively. For instance, multiple output elements of output sequence 7 can be predicted together without explicit sequential conditioning on each other. See, e.g., Saharia et al., Non-Autoregressive Machine Translation with Latent Alignments, ARXIV:2004.07437V3 (NOV. 16, 2020).

[0162] Output sequence 7 can include one or multiple portions or elements. In an example content generation configuration, output sequence 7 can include multiple elements corresponding to multiple portions of a generated output sequence (e.g., a textual sentence, values of a discretized waveform, computer code, etc.). In an example classification configuration, output sequence 7 can include a single element associated with a classification output. For instance, an output “vocabulary” can include a set of classes into which an input sequence is to be classified. For instance, a vision transformer block can pass latent state information to a multilayer perceptron that outputs a likely class value associated with an input image.

[0163] FIG. 10 is a block diagram of an example technique for populating an example input sequence 8. Input sequence 8 can include various functional elements that form part of the model infrastructure, such as an element 8-0 obtained from a task indicator 9 that signals to any model(s) that process input sequence 8 that a particular task is being performed (e.g., to help adapt a performance of the model(s) to that particular task). Input sequence 8 can include various data elements from different data modalities. For instance, an input modality 10-1 can include one modality of data. A data-to- sequence model 11-1 can process data frominput modality 10-1 to project the data into a format compatible with input sequence 8 (e.g., one or more vectors dimensioned according to the dimensions of input sequence 8) to obtain elements 8-1, 8-2, 8-3. Another input modality 10-2 can include a different modality of data. A data-to-sequence model 11-2 can project data from input modality 10-2 into a format compatible with input sequence 8 to obtain elements 8-4, 8-5, 8-6. Another input modality 10-3 can include yet another different modality of data. A data-to-sequence model 11-3 can project data from input modality 10-3 into a format compatible with input sequence 8 to obtain elements 8-7, 8-8, 8-9.

[0164] Input sequence 8 can be the same as or different from input sequence 5. Input sequence 8 can be a multimodal input sequence that contains elements that represent data from different modalities using a common dimensional representation. For instance, an embedding space can have P dimensions. Input sequence 8 can be configured to contain a plurality of elements that have P dimensions. In this manner, for instance, example implementations can facilitate information extraction and reasoning across diverse data modalities by projecting data into elements in the same embedding space for comparison, combination, or other computations therebetween.

[0165] For example, elements 8-0, . . . , 8-9 can indicate particular locations within a multidimensional embedding space. Some elements can map to a set of discrete locations in the embedding space. For instance, elements that correspond to discrete members of a predetermined vocabulary of tokens can map to discrete locations in the embedding space that are associated with those tokens. Other elements can be continuously distributed across the embedding space. For instance, some data types can be broken down into continuously defined portions (e.g., image patches) that can be described using continuously distributed locations within the embedding space.

[0166] In some implementations, the expressive power of the embedding space may not be limited to meanings associated with any particular set of tokens or other building blocks. For example, a continuous embedding space can encode a spectrum of high-order information. An individual piece of information (e.g., a token) can map to a particular point in that space: for instance, a token for the word “dog” can be projected to an embedded value that points to a particular location in the embedding space associated with canine-related information. Similarly, an image patch of an image of a dog on grass can also be projected into the embedding space. In some implementations, the projection of the image of the dog can be similar to the projection of the word “dog” while also having similarity to a projection of theword “grass,” while potentially being different from both. In some implementations, the projection of the image patch may not exactly align with any single projection of a single word. In some implementations, the projection of the image patch can align with a combination of the projections of the words “dog” and “grass.” In this manner, for instance, a high-order embedding space can encode information that can be independent of data modalities in which the information is expressed.

[0167] Task indicator 9 can include a model or model component configured to identify a task being performed and inject, into input sequence 8, an input value represented by element 8-0 that signals which task is being performed. For instance, the input value can be provided as a data type associated with an input modality and projected along with that input modality (e.g., the input value can be a textual task label that is embedded along with other textual data in the input; the input value can be a pixel-based representation of a task that is embedded along with other image data in the input; etc.). The input value can be provided as a data type that differs from or is at least independent from other input(s). For instance, the input value represented by element 8-0 can be a learned within a continuous embedding space.

[0168] Input modalities 10-1, 10-2, and 10-3 can be associated with various different data types (e.g., as described above with respect to input(s) 2 and output(s) 3).

[0169] Data-to-sequence models 11-1, 11-2, and 11-3 can be the same or different from each other. Data-to-sequence models 11-1, 11-2, and 11-3 can be adapted to each respective input modality 10-1, 10-2, and 10-3. For example, a textual data-to-sequence model can subdivide a portion of input text and project the subdivisions into element(s) in input sequence 8 (e.g., elements 8-1, 8-2, 8-3, etc.). An image data-to-sequence model can subdivide an input image and project the subdivisions into element(s) in input sequence 8 (e.g., elements 8-4, 8-5, 8-6, etc.). An arbitrary datatype data-to-sequence model can subdivide an input of that arbitrary datatype and project the subdivisions into element(s) in input sequence 8 (e.g., elements 8-7, 8-8, 8-9, etc.).

[0170] Data-to-sequence models 11-1, 11-2, and 11-3 can form part of machine-learned sequence processing model(s) 4. Data-to-sequence models 11-1, 11-2, and 11-3 can be jointly trained with or trained independently from machine-learned sequence processing model(s) 4. Data-to-sequence models 11-1, 11-2, and 11-3 can be trained end-to-end with machine-learned sequence processing model(s) 4.Example Machine-Learned Model Development Platform

[0171] FIG. 11 is a block diagram of an example model development platform 12 that can facilitate creation, adaptation, and refinement of example machine-learned models (e.g., machine-learned model(s) 1, sequence processing model(s) 4, etc.). Model development platform 12 can provide a number of different toolkits that developer systems can employ in the development of new or adapted machine-learned models.

[0172] Model development platform 12 can provide one or more model libraries 13 containing building blocks for new models. Model libraries 13 can include one or more pretrained foundational models 13-1, which can provide a backbone of processing power across various tasks. Model libraries 13 can include one or more pre-trained expert models 13-2, which can be focused on performance in particular domains of expertise. Model libraries 13 can include various model primitives 13-3, which can provide low-level architectures or components (optionally pre-trained), which can be assembled in various arrangements as desired.

[0173] Model development platform 12 can receive selections of various model components 14. Model development platform 12 can pass selected model components 14 to a workbench15 that combines selected model components 14 into a development model 16.

[0174] Workbench 15 can facilitate further refinement and adaptation of development model16 by leveraging a number of different toolkits integrated with model development platform 12. For example, workbench 15 can facilitate alignment of the development model 16 with a desired performance profile on various tasks using a model alignment toolkit 17.

[0175] Model alignment toolkit 17 can provide a number of tools for causing development model 16 to generate outputs aligned with desired behavioral characteristics. Alignment can include increasing an accuracy, precision, recall, etc. of model outputs. Alignment can include enforcing output styles, schema, or other preferential characteristics of model outputs. Alignment can be general or domain-specific. For instance, a pre-trained foundational model 13-1 can begin with an initial level of performance across multiple domains. Alignment of the pre-trained foundational model 13-1 can include improving a performance in a particular domain of information or tasks (e.g., even at the expense of performance in another domain of information or tasks).

[0176] Model alignment toolkit 17 can integrate one or more dataset(s) 17-1 for aligning development model 16. Curated dataset(s) 17-1 can include labeled or unlabeled trainingdata. Dataset(s) 17-1 can be obtained from public domain datasets. Dataset(s) 17-1 can be obtained from private datasets associated with one or more developer system(s) for the alignment of bespoke machine-learned model(s) customized for private use-cases.

[0177] Pre-training pipelines 17-2 can include a machine-learned model training workflow configured to update development model 16 over large-scale, potentially noisy datasets. For example, pre-training can leverage unsupervised learning techniques (e.g., de-noising, etc.) to process large numbers of training instances to update model parameters from an initialized state and achieve a desired baseline performance. Pre-training pipelines 17-2 can leverage unlabeled datasets in dataset(s) 17-1 to perform pre-training. Workbench 15 can implement a pre-training pipeline 17-2 to pre-train development model 16.

[0178] Fine-tuning pipelines 17-3 can include a machine-learned model training workflow configured to refine the model parameters of development model 16 with higher-quality data. Fine-tuning pipelines 17-3 can update development model 16 by conducting supervised training with labeled dataset(s) in dataset(s) 17-1. Fine-tuning pipelines 17-3 can update development model 16 by conducting reinforcement learning using reward signals from user feedback signals. Workbench 15 can implement a fine-tuning pipeline 17-3 to fine-tune development model 16.

[0179] Prompt libraries 17-4 can include sets of inputs configured to induce behavior aligned with desired performance criteria. Prompt libraries 17-4 can include few-shot prompts (e.g., inputs providing examples of desired model outputs for prepending to a desired runtime query), chain-of-thought prompts (e.g., inputs providing step-by-step reasoning within the exemplars to facilitate thorough reasoning by the model), and the like.

[0180] Example prompts can be retrieved from an available repository of prompt libraries 17- 4. Example prompts can be contributed by one or more developer systems using workbench 15.

[0181] In some implementations, pre-trained or fine-tuned models can achieve satisfactory performance without exemplars in the inputs. For instance, zero-shot prompts can include inputs that lack exemplars. Zero-shot prompts can be within a domain within a training dataset or outside of the training domain(s).

[0182] Prompt libraries 17-4 can include one or more prompt engineering tools. Prompt engineering tools can provide workflows for retrieving or learning optimized prompt values. Prompt engineering tools can facilitate directly learning prompt values (e.g., input elementvalues) based one or more training iterations. Workbench 15 can implement prompt engineering tools in development model 16.

[0183] Prompt libraries 17-4 can include pipelines for prompt generation. For example, inputs can be generated using development model 16 itself or other machine-learned models. In this manner, for instance, a first model can process information about a task and output an input for a second model to process in order to perform a step of the task. The second model can be the same as or different from the first model. Workbench 15 can implement prompt generation pipelines in development model 16.

[0184] Prompt libraries 17-4 can include pipelines for context injection. For instance, a performance of development model 16 on a particular task can improve if provided with additional context for performing the task. Prompt libraries 17-4 can include software components configured to identify desired context, retrieve the context from an external source (e.g., a database, a sensor, etc.), and add the context to the input prompt. Workbench 15 can implement context injection pipelines in development model 16.

[0185] Although various training examples described herein with respect to model development platform 12 refer to “pre-training” and “fine-tuning,” it is to be understood that model alignment toolkit 17 can generally support a wide variety of training techniques adapted for training a wide variety of machine-learned models. Example training techniques can correspond to the example training method 700 described above.

[0186] Model development platform 12 can include a model plugin toolkit 18. Model plugin toolkit 18 can include a variety of tools configured for augmenting the functionality of a machine-learned model by integrating the machine-learned model with other systems, devices, and software components. For instance, a machine-learned model can use tools to increase performance quality where appropriate. For instance, deterministic tasks can be offloaded to dedicated tools in lieu of probabilistically performing the task with an increased risk of error. For instance, instead of autoregressively predicting the solution to a system of equations, a machine-learned model can recognize a tool to call for obtaining the solution and pass the system of equations to the appropriate tool. The tool can be a traditional system of equations solver that can operate deterministically to resolve the system of equations. The output of the tool can be returned in response to the original query. In this manner, tool use can allow some example models to focus on the strengths of machine-learned models — e.g., understanding an intent in an unstructured request for a task — while augmenting theperformance of the model by offloading certain tasks to a more focused tool for rote application of deterministic algorithms to a well-defined problem.

[0187] Model plugin toolkit 18 can include validation tools 18-1. Validation tools 18-1 can include tools that can parse and confirm output(s) of a machine-learned model. Validation tools 18-1 can include engineered heuristics that establish certain thresholds applied to model outputs. For example, validation tools 18-1 can ground the outputs of machine-learned models to structured data sources (e.g., to mitigate “hallucinations”).

[0188] Model plugin toolkit 18 can include tooling packages 18-2 for implementing one or more tools that can include scripts or other executable code that can be executed alongside development model 16. Tooling packages 18-2 can include one or more inputs configured to cause machine-learned model(s) to implement the tools (e.g., few-shot prompts that induce a model to output tool calls in the proper syntax, etc.). Tooling packages 18-2 can include, for instance, fine-tuning training data for training a model to use a tool.

[0189] Model plugin toolkit 18 can include interfaces for calling external application programming interfaces (APIs) 18-3. For instance, in addition to or in lieu of implementing tool calls or tool code directly with development model 16, development model 16 can be aligned to output instruction that initiate API calls to send or obtain data via external systems.

[0190] Model plugin toolkit 18 can integrate with prompt libraries 17-4 to build a catalog of available tools for use with development model 16. For instance, a model can receive, in an input, a catalog of available tools, and the model can generate an output that selects a tool from the available tools and initiates a tool call for using the tool.

[0191] Model development platform 12 can include a computational optimization toolkit 19 for optimizing a computational performance of development model 16. For instance, tools for model compression 19-1 can allow development model 16 to be reduced in size while maintaining a desired level of performance. For instance, model compression 19-1 can include quantization workflows, weight pruning and sparsification techniques, etc. Tools for hardware acceleration 19-2 can facilitate the configuration of the model storage and execution formats to operate optimally on different hardware resources. For instance, hardware acceleration 19-2 can include tools for optimally sharding models for distributed processing over multiple processing units for increased bandwidth, lower unified memory requirements, etc. Tools for distillation 19-3 can provide for the training of lighter- weight models based on the knowledge encoded in development model 16. For instance,development model 16 can be a highly performant, large machine-learned model optimized using model development platform 12. To obtain a lightweight model for running in resource-constrained environments, a smaller model can be a “student model” that learns to imitate development model 16 as a “teacher model.” In this manner, for instance, the investment in learning the parameters and configurations of development model 16 can be efficiently transferred to a smaller model for more efficient inference.

[0192] Workbench 15 can implement one, multiple, or none of the toolkits implemented in model development platform 12. Workbench 15 can output an output model 20 based on development model 16. Output model 20 can be a deployment version of development model 16. Output model 20 can be a development or training checkpoint of development model 16. Output model 20 can be a distilled, compressed, or otherwise optimized version of development model 16.

[0193] FIG. 12 is a block diagram of an example training flow for training a machine-learned development model 16. One or more portion(s) of the example training flow can be implemented by a computing system that includes one or more computing devices such as, for example, computing systems described with reference to the other drawings. Each respective portion of the example training flow can be performed by any (or any combination) of one or more computing devices. Moreover, one or more portion(s) of the example training flow can be implemented on the hardware components of the device(s) described herein, for example, to train one or more systems or models. FIG. 12 depicts elements performed in a particular order for purposes of illustration and discussion. Those of ordinary skill in the art, using the disclosures provided herein, will understand that the elements of any of the methods discussed herein can be adapted, rearranged, expanded, omitted, combined, or modified in various ways without deviating from the scope of the disclosure. FIG. 12 is described with reference to elements / terms described with respect to other systems and drawings for exemplary illustrated purposes and is not meant to be limiting. One or more portions of the example training flow can be performed additionally, or alternatively, by other systems.

[0194] Initially, development model 16 can persist in an initial state as an initialized model 21. Development model 16 can be initialized with weight values. Initial weight values can be random or based on an initialization schema. Initial weight values can be based on prior pre-training for the same or for a different model.

[0195] Initialized model 21 can undergo pre-training in a pre-training stage 22. Pre-training stage 22 can be implemented using one or more pre-training pipelines 17-2 over data from dataset(s) 17-1. Pre-training can be omitted, for example, if initialized model 21 is already pre-trained (e.g., development model 16 contains, is, or is based on a pre-trained foundational model or an expert model).

[0196] Pre-trained model 23 can then be a new version of development model 16, which can persist as development model 16 or as a new development model. Pre-trained model 23 can be the initial state if development model 16 was already pre-trained. Pre-trained model 23 can undergo fine-tuning in a fine-tuning stage 24. Fine-tuning stage 24 can be implemented using one or more fine-tuning pipelines 17-3 over data from dataset(s) 17-1. Fine-tuning can be omitted, for example, if a pre-trained model as satisfactory performance, if the model was already fine-tuned, or if other tuning approaches are preferred.

[0197] Fine-tuned model 29 can then be a new version of development model 16, which can persist as development model 16 or as a new development model. Fine-tuned model 29 can be the initial state if development model 16 was already fine-tuned. Fine-tuned model 29 can undergo refinement with user feedback 26. For instance, refinement with user feedback 26 can include reinforcement learning, optionally based on human feedback from human users of fine-tuned model 25. As reinforcement learning can be a form of fine-tuning, it is to be understood that fine-tuning stage 24 can subsume the stage for refining with user feedback 26. Refinement with user feedback 26 can produce a refined model 27. Refined model 27 can be output to downstream system(s) 28 for deployment or further development.

[0198] In some implementations, computational optimization operations can be applied before, during, or after each stage. For instance, initialized model 21 can undergo computational optimization 29-1 (e.g., using computational optimization toolkit 19) before pre-training stage 22. Pre-trained model 23 can undergo computational optimization 29-2 (e.g., using computational optimization toolkit 19) before fine-tuning stage 24. Fine-tuned model 25 can undergo computational optimization 29-3 (e.g., using computational optimization toolkit 19) before refinement with user feedback 26. Refined model 27 can undergo computational optimization 29-4 (e.g., using computational optimization toolkit 19) before output to downstream system(s) 28. Computational optimization(s) 29-1, . . . , 29-4 can all be the same, all be different, or include at least some different optimization techniques.Example Machine-Learned Model Inference System

[0199] FIG. 13 is a block diagram of an inference system for operating one or more machine- learned model(s) 1 to perform inference (e.g., for training, for deployment, etc.). A model host 31 can receive machine-learned model(s) 1. Model host 31 can host one or more model instance(s) 31-1, which can be one or multiple instances of one or multiple models. Model host 31 can host model instance(s) 31-1 using available compute resources 31-2 associated with model host 31.

[0200] Model host 31 can perform inference on behalf of one or more client(s) 32. Client(s) 32 can transmit an input request 33 to model host 31. Using input request 33, model host 31 can obtain input(s) 2 for input to machine-learned model(s) 1. Machine-learned model(s) 1 can process input(s) 2 to generate output(s) 3. Using output(s) 3, model host 31 can return an output payload 34 for responding to input request 33 from client(s) 32. Output payload 34 can include or be based on output(s) 3.

[0201] Model host 31 can leverage various other resources and tools to augment the inference task. For instance, model host 31 can communicate with tool interfaces 35 to facilitate tool use by model instance(s) 31-1. Tool interfaces 35 can include local or remote APIs. Tool interfaces 35 can include integrated scripts or other software functionality.Model host 31 can engage online learning interface(s) 36 to facilitate ongoing improvements to machine-learned model(s) 1. For instance, online learning interface(s) 36 can be used within reinforcement learning loops to retrieve user feedback on inferences served by model host 31. Model host 31 can access runtime data source(s) 37 for augmenting input(s) 2 with additional contextual information. For instance, runtime data source(s) 37 can include a knowledge graph 37-1 that facilitates structured information retrieval for information associated with input request(s) 33 (e.g., a search engine service). Runtime data source(s) 37 can include public or private, external or local database(s) 37-2 that can store information associated with input request(s) 33 for augmenting input(s) 2. Runtime data source(s) 37 can include account data 37-3 which can be retrieved in association with a user account corresponding to a client 32 for customizing the behavior of model host 31 accordingly.

[0202] Model host 31 can be implemented by one or multiple computing devices or systems. Client(s) 2 can be implemented by one or multiple computing devices or systems, which can include computing devices or systems shared with model host 31.

[0203] For example, model host 31 can operate on a server system that provides a machinelearning service to client device(s) that operate client(s) 32 (e.g., over a local or wide-area network). Client device(s) can be end-user devices used by individuals. Client device(s) can be server systems that operate client(s) 32 to provide various functionality as a service to downstream end-user devices.

[0204] In some implementations, model host 31 can operate on a same device or system as client(s) 32. Model host 31 can be a machine-learning service that runs on-device to provide machine-learning functionality to one or multiple applications operating on a client device, which can include an application implementing client(s) 32. Model host 31 can be a part of a same application as client(s) 32. For instance, model host 31 can be a subroutine or method implemented by one part of an application, and client(s) 32 can be another subroutine or method that engages model host 31 to perform inference functions within the application. It is to be understood that model host 31 and client(s) 32 can have various different configurations.

[0205] Model instance(s) 31-1 can include one or more machine-learned models that are available for performing inference. Model instance(s) 31-1 can include weights or other model components that are stored on in persistent storage, temporarily cached, or loaded into high-speed memory. Model instance(s) 31-1 can include multiple instance(s) of the same model (e.g., for parallel execution of more requests on the same model). Model instance(s) 31-1 can include instance(s) of different model(s). Model instance(s) 31-1 can include cached intermediate states of active or inactive model(s) used to accelerate inference of those models. For instance, an inference session with a particular model may generate significant amounts of computational results that can be re-used for future inference runs (e.g., using a KV cache for transformer-based models). These computational results can be saved in association with that inference session so that session can be executed more efficiently when resumed.

[0206] Compute resource(s) 31-2 can include one or more processors (central processing units, graphical processing units, tensor processing units, machine-learning accelerators, etc.) connected to one or more memory devices. Compute resource(s) 31-2 can include a dynamic pool of available resources shared with other processes. Compute resource(s) 31-2 can include memory devices large enough to fit an entire model instance in a single memory instance. Compute resource(s) 31-2 can also shard model instance(s) across multiple memory devices (e.g., using data parallelization or tensor parallelization, etc.). This can bedone to increase parallelization or to execute a large model using multiple memory devices which individually might not be able to fit the entire model into memory.

[0207] Input request 33 can include data for input(s) 2. Model host 31 can process input request 33 to obtain input(s) 2. Input(s) 2 can be obtained directly from input request 33 or can be retrieved using input request 33. Input request 33 can be submitted to model host 31 via an API.

[0208] Model host 31 can perform inference over batches of input requests 33 in parallel. For instance, a model instance 31-1 can be configured with an input structure that has a batch dimension. Separate input(s) 2 can be distributed across the batch dimension (e.g., rows of an array). The separate input(s) 2 can include completely different contexts. The separate input(s) 2 can be multiple inference steps of the same task. The separate input(s) 2 can be staggered in an input structure, such that any given inference cycle can be operating on different portions of the respective input(s) 2. In this manner, for instance, model host 31 can perform inference on the batch in parallel, such that output(s) 3 can also contain the batch dimension and return the inference results for the batched input(s) 2 in parallel. In this manner, for instance, batches of input request(s) 33 can be processed in parallel for higher throughput of output payload(s) 34.

[0209] Output payload 34 can include or be based on output(s) 3 from machine-learned model(s) 1. Model host 31 can process output(s) 3 to obtain output payload 34. This can include chaining multiple rounds of inference (e.g., iteratively, recursively, across the same model(s) or different model(s)) to arrive at a final output for a task to be returned in output payload 34. Output payload 34 can be transmitted to client(s) 32 via an API.

[0210] Online learning interface(s) 36 can facilitate reinforcement learning of machine- learned model(s) 1. Online learning interface(s) 36 can facilitate reinforcement learning with human feedback (RLHF). Online learning interface(s) 36 can facilitate federated learning of machine-learned model(s) 1.

[0211] Model host 31 can execute machine-learned model(s) 1 to perform inference for various tasks using various types of data. For example, various different input(s) 2 and output(s) 3 can be used for various different tasks. In some implementations, input(s) 2 can be or otherwise represent image data. Machine-learned model(s) 1 can process the image data to generate an output. As an example, machine-learned model(s) 1 can process the image data to generate an image recognition output (e.g., a recognition of the image data, a latentembedding of the image data, an encoded representation of the image data, a hash of the image data, etc.). As another example, machine-learned model(s) 1 can process the image data to generate an image segmentation output. As another example, machine-learned model(s) 1 can process the image data to generate an image classification output. As another example, machine-learned model(s) 1 can process the image data to generate an image data modification output (e.g., an alteration of the image data, etc.). As another example, machine-learned model(s) 1 can process the image data to generate an encoded image data output (e.g., an encoded and / or compressed representation of the image data, etc.). As another example, machine-learned model(s) 1 can process the image data to generate an upscaled image data output. As another example, machine-learned model(s) 1 can process the image data to generate a prediction output.

[0212] In some implementations, the task is a computer vision task. In some cases, input(s) 2 includes pixel data for one or more images and the task is an image processing task. For example, the image processing task can be image classification, where the output is a set of scores, each score corresponding to a different object class and representing the likelihood that the one or more images depict an object belonging to the object class. The image processing task may be object detection, where the image processing output identifies one or more regions in the one or more images and, for each region, a likelihood that region depicts an object of interest. As another example, the image processing task can be image segmentation, where the image processing output defines, for each pixel in the one or more images, a respective likelihood for each category in a predetermined set of categories. For example, the set of categories can be foreground and background. As another example, the set of categories can be object classes. As another example, the image processing task can be depth estimation, where the image processing output defines, for each pixel in the one or more images, a respective depth value. As another example, the image processing task can be motion estimation, where the network input includes multiple images, and the image processing output defines, for each pixel of one of the input images, a motion of the scene depicted at the pixel between the images in the network input.

[0213] In some implementations, input(s) 2 can be or otherwise represent natural language data. Machine-learned model(s) 1 can process the natural language data to generate an output. As an example, machine-learned model(s) 1 can process the natural language data to generate a language encoding output. As another example, machine-learned model(s) 1 can process the natural language data to generate a latent text embedding output. As anotherexample, machine-learned model(s) 1 can process the natural language data to generate a translation output. As another example, machine-learned model(s) 1 can process the natural language data to generate a classification output. As another example, machine-learned model(s) 1 can process the natural language data to generate a textual segmentation output. As another example, machine-learned model(s) 1 can process the natural language data to generate a semantic intent output. As another example, machine-learned model(s) 1 can process the natural language data to generate an upscaled text or natural language output (e.g., text or natural language data that is higher quality than the input text or natural language, etc.). As another example, machine-learned model(s) 1 can process the natural language data to generate a prediction output (e.g., one or more predicted next portions of natural language content).

[0214] In some implementations, input(s) 2 can be or otherwise represent speech data (e.g., data describing spoken natural language, such as audio data, textual data, etc.). Machine- learned model(s) 1 can process the speech data to generate an output. As an example, machine-learned model(s) 1 can process the speech data to generate a speech recognition output. As another example, machine-learned model(s) 1 can process the speech data to generate a speech translation output. As another example, machine-learned model(s) 1 can process the speech data to generate a latent embedding output. As another example, machine-learned model(s) 1 can process the speech data to generate an encoded speech output (e.g., an encoded and / or compressed representation of the speech data, etc.). As another example, machine-learned model(s) 1 can process the speech data to generate an upscaled speech output (e.g., speech data that is higher quality than the input speech data, etc.). As another example, machine-learned model(s) 1 can process the speech data to generate a textual representation output (e.g., a textual representation of the input speech data, etc.). As another example, machine-learned model(s) 1 can process the speech data to generate a prediction output.

[0215] In some implementations, input(s) 2 can be or otherwise represent latent encoding data (e.g., a latent space representation of an input, etc.). Machine-learned model(s) 1 can process the latent encoding data to generate an output. As an example, machine-learned model(s) 1 can process the latent encoding data to generate a recognition output. As another example, machine-learned model(s) 1 can process the latent encoding data to generate a reconstruction output. As another example, machine-learned model(s) 1 can process the latent encoding data to generate a search output. As another example, machine-learnedmodel(s) 1 can process the latent encoding data to generate a reclustering output. As another example, machine-learned model(s) 1 can process the latent encoding data to generate a prediction output.

[0216] In some implementations, input(s) 2 can be or otherwise represent statistical data. Statistical data can be, represent, or otherwise include data computed and / or calculated from some other data source. Machine-learned model(s) 1 can process the statistical data to generate an output. As an example, machine-learned model(s) 1 can process the statistical data to generate a recognition output. As another example, machine-learned model(s) 1 can process the statistical data to generate a prediction output. As another example, machine- learned model(s) 1 can process the statistical data to generate a classification output. As another example, machine-learned model(s) 1 can process the statistical data to generate a segmentation output. As another example, machine-learned model(s) 1 can process the statistical data to generate a visualization output. As another example, machine-learned model(s) 1 can process the statistical data to generate a diagnostic output.

[0217] In some implementations, input(s) 2 can be or otherwise represent sensor data. Machine-learned model(s) 1 can process the sensor data to generate an output. As an example, machine-learned model(s) 1 can process the sensor data to generate a recognition output. As another example, machine-learned model(s) 1 can process the sensor data to generate a prediction output. As another example, machine-learned model(s) 1 can process the sensor data to generate a classification output. As another example, machine-learned model(s) 1 can process the sensor data to generate a segmentation output. As another example, machine-learned model(s) 1 can process the sensor data to generate a visualization output. As another example, machine-learned model(s) 1 can process the sensor data to generate a diagnostic output. As another example, machine-learned model(s) 1 can process the sensor data to generate a detection output.

[0218] In some implementations, machine-learned model(s) 1 can be configured to perform a task that includes encoding input data for reliable and / or efficient transmission or storage (and / or corresponding decoding). For example, the task may be an audio compression task. The input may include audio data and the output may comprise compressed audio data. In another example, the input includes visual data (e.g., one or more images or videos), the output comprises compressed visual data, and the task is a visual data compression task. In another example, the task may comprise generating an embedding for input data (e.g., input audio or visual data). In some cases, the input includes audio data representing a spokenutterance and the task is a speech recognition task. The output may comprise a text output which is mapped to the spoken utterance. In some cases, the task comprises encrypting or decrypting input data. In some cases, the task comprises a microprocessor performance task, such as branch prediction or memory address translation.

[0219] In some implementations, the task is a generative task, and machine-learned model(s) 1 can be configured to output content generated in view of input(s) 2. For instance, input(s) 2 can be or otherwise represent data of one or more modalities that encodes context for generating additional content.

[0220] In some implementations, the task can be a text completion task. Machine-learned model(s) 1 can be configured to process input(s) 2 that represent textual data and to generate output(s) 3 that represent additional textual data that completes a textual sequence that includes input(s) 2. For instance, machine-learned model(s) 1 can be configured to generate output(s) 3 to complete a sentence, paragraph, or portion of text that follows from a portion of text represented by input(s) 2.

[0221] In some implementations, the task can be an instruction following task. Machine- learned model(s) 1 can be configured to process input(s) 2 that represent instructions to perform a function and to generate output(s) 3 that advance a goal of satisfying the instruction function (e.g., at least a step of a multi-step procedure to perform the function). Output(s) 3 can represent data of the same or of a different modality as input(s) 2. For instance, input(s) 2 can represent textual data (e.g., natural language instructions for a task to be performed) and machine-learned model(s) 1 can process input(s) 2 to generate output(s) 3 that represent textual data responsive to the instructions (e.g., natural language responses, programming language responses, machine language responses, etc.). Input(s) 2 can represent image data (e.g., image-based instructions for a task to be performed, optionally accompanied by textual instructions) and machine-learned model(s) 1 can process input(s) 2 to generate output(s) 3 that represent textual data responsive to the instructions (e.g., natural language responses, programming language responses, machine language responses, etc.). One or more output(s) 3 can be iteratively or recursively generated to sequentially process and accomplish steps toward accomplishing the requested functionality. For instance, an initial output can be executed by an external system or be processed by machine-learned model(s) 1 to complete an initial step of performing a function. Multiple steps can be performed, with a final output being obtained that is responsive to the initial instructions.

[0222] In some implementations, the task can be a question answering task. Machine-learned model(s) 1 can be configured to process input(s) 2 that represent a question to answer and to generate output(s) 3 that advance a goal of returning an answer to the question (e.g., at least a step of a multi-step procedure to perform the function). Output(s) 3 can represent data of the same or of a different modality as input(s) 2. For instance, input(s) 2 can represent textual data (e.g., natural language instructions for a task to be performed) and machine-learned model(s) 1 can process input(s) 2 to generate output(s) 3 that represent textual data responsive to the question (e.g., natural language responses, programming language responses, machine language responses, etc.). Input(s) 2 can represent image data (e.g., image-based instructions for a task to be performed, optionally accompanied by textual instructions) and machine-learned model(s) 1 can process input(s) 2 to generate output(s) 3 that represent textual data responsive to the question (e.g., natural language responses, programming language responses, machine language responses, etc.). One or more output(s) 3 can be iteratively or recursively generated to sequentially process and accomplish steps toward answering the question. For instance, an initial output can be executed by an external system or be processed by machine-learned model(s) 1 to complete an initial step of obtaining an answer to the question (e.g., querying a database, performing a computation, executing a script, etc.). Multiple steps can be performed, with a final output being obtained that is responsive to the question.

[0223] In some implementations, the task can be an image generation task. Machine-learned model(s) 1 can be configured to process input(s) 2 that represent context regarding a desired portion of image content. The context can include text data, image data, audio data, etc. Machine-learned model(s) 1 can be configured to generate output(s) 3 that represent image data that depicts imagery related to the context. For instance, machine-learned model(s) 1 can be configured to generate pixel data of an image. Values for channel(s) associated with the pixels in the pixel data can be selected based on the context (e.g., based on a probability determined based on the context).

[0224] In some implementations, the task can be an audio generation task. Machine-learned model(s) 1 can be configured to process input(s) 2 that represent context regarding a desired portion of audio content. The context can include text data, image data, audio data, etc. Machine-learned model(s) 1 can be configured to generate output(s) 3 that represent audio data related to the context. For instance, machine-learned model(s) 1 can be configured to generate waveform data in the form of an image (e.g., a spectrogram). Values for channel(s)associated with pixels of the image can be selected based on the context. Machine-learned model(s) 1 can be configured to generate waveform data in the form of a sequence of discrete samples of a continuous waveform. Values of the sequence can be selected based on the context (e.g., based on a probability determined based on the context).

[0225] In some implementations, the task can be a data generation task. Machine-learned model(s) 1 can be configured to process input(s) 2 that represent context regarding a desired portion of data (e.g., data from various data domains, such as sensor data, image data, multimodal data, statistical data, etc.). The desired data can be, for instance, synthetic data for training other machine-learned models. The context can include arbitrary data type(s). Machine-learned model(s) 1 can be configured to generate output(s) 3 that represent data that aligns with the desired data. For instance, machine-learned model(s) 1 can be configured to generate data values for populating a dataset. Values for the data object(s) can be selected based on the context (e.g., based on a probability determined based on the context).Example Computing Systems and Devices

[0226] FIG. 14 is a block diagram of an example networked computing system that can perform aspects of example implementations of the disclosure. The system can include a number of computing devices and systems that are communicatively coupled over a network 49. An example computing device 50 is described to provide an example of a computing device that can perform any aspect of the disclosure (e.g., implementing model host 31, client(s) 32, or both). An example server computing system 60 is described as an example of a server computing system that can perform any aspect of the disclosure (e.g., implementing model host 31, client(s) 32, or both). Computing device 50 and server computing system(s) 60 can cooperatively interact (e.g., over network 49) to perform any aspect of the disclosure (e.g., implementing model host 31, client(s) 32, or both). Model development platform system 70 is an example system that can host or serve model development platform(s) 12 for development of machine-learned models. Third-party system(s) 80 are example system(s) with which any of computing device 50, server computing system(s) 60, or model development platform system(s) 70 can interact in the performance of various aspects of the disclosure (e.g., engaging third-party tools, accessing third-party databases or other resources, etc.).

[0227] Network 49 can be any type of communications network, such as a local area network (e.g., intranet), wide area network (e.g., Internet), or some combination thereof and caninclude any number of wired or wireless links. In general, communication over network 49 can be carried via any type of wired or wireless connection, using a wide variety of communication protocols (e.g., TCP / IP, HTTP, SMTP, FTP), encodings or formats (e.g., HTML, XML), or protection schemes (e.g., VPN, secure HTTP, SSL). Network 49 can also be implemented via a system bus. For instance, one or more devices or systems of FIG. 14 can be co-located with, contained by, or otherwise integrated into one or more other devices or systems.

[0228] Computing device 50 can be any type of computing device, such as, for example, a personal computing device (e.g., laptop or desktop), a mobile computing device (e.g., smartphone or tablet), a gaming console or controller, a wearable computing device, an embedded computing device, a server computing device, a virtual machine operating on a host device, or any other type of computing device. Computing device 50 can be a client computing device. Computing device 50 can be an end-user computing device. Computing device 50 can be a computing device of a service provided that provides a service to an end user (who may use another computing device to interact with computing device 50).

[0229] Computing device 50 can include one or more processors 51 and a memory 52. Processor(s) 51 can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. Memory 52 can include one or more non-transitory computer-readable storage media, such as HBM, RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. Memory 52 can store data 53 and instructions 54 which can be executed by processor(s) 51 to cause computing device 50 to perform operations. The operations can implement any one or multiple features described herein. The operations can implement example methods and techniques described herein.

[0230] Computing device 50 can also include one or more input components that receive user input. For example, a user input component can be a touch-sensitive component (e.g., a touch-sensitive display screen or a touch pad) that is sensitive to the touch of a user input object (e.g., a finger or a stylus). The touch-sensitive component can serve to implement a virtual keyboard. Other example user input components include a microphone, camera, LIDAR, a physical keyboard or other buttons, or other means by which a user can provide user input.

[0231] Computing device 50 can store or include one or more machine-learned models 55. Machine-learned models 55 can include one or more machine-learned model(s) 1, such as a sequence processing model 4. Machine-learned models 55 can include one or multiple model instance(s) 31-1. Machine-learned model(s) 55 can be received from server computing system(s) 60, model development platform system 70, third party system(s) 80 (e.g., an application distribution platform), or developed locally on computing device 50. Machine- learned model(s) 55 can be loaded into memory 52 and used or otherwise implemented by processor(s) 51. Computing device 50 can implement multiple parallel instances of machine- learned model(s) 55.

[0232] Server computing system(s) 60 can include one or more processors 61 and a memory 62. Processor(s) 61 can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. Memory 62 can include one or more non-transitory computer-readable storage media, such as HBM, RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. Memory 62 can store data 63 and instructions 64 which can be executed by processor(s) 61 to cause server computing system(s) 60 to perform operations. The operations can implement any one or multiple features described herein. The operations can implement example methods and techniques described herein.

[0233] In some implementations, server computing system 60 includes or is otherwise implemented by one or multiple server computing devices. In instances in which server computing system 60 includes multiple server computing devices, such server computing devices can operate according to sequential computing architectures, parallel computing architectures, or some combination thereof.

[0234] Server computing system 60 can store or otherwise include one or more machine- learned models 65. Machine-learned model(s) 65 can be the same as or different from machine-learned model(s) 55. Machine-learned models 65 can include one or more machine- learned model (s) 1, such as a sequence processing model 4. Machine-learned models 65 can include one or multiple model instance(s) 31-1. Machine-learned model(s) 65 can be received from computing device 50, model development platform system 70, third party system(s) 80, or developed locally on server computing system(s) 60. Machine-learned model(s) 65 can be loaded into memory 62 and used or otherwise implemented byprocessor(s) 61. Server computing system(s) 60 can implement multiple parallel instances of machine-learned model(s) 65.

[0235] In an example configuration, machine-learned models 65 can be included in or otherwise stored and implemented by server computing system 60 to establish a client-server relationship with computing device 50 for serving model inferences. For instance, server computing system(s) 60 can implement model host 31 on behalf of client(s) 32 on computing device 50. For instance, machine-learned models 65 can be implemented by server computing system 60 as a portion of a web service (e.g., remote machine-learned model hosting service, such as an online interface for performing machine-learned model operations over a network on server computing system(s) 60). For instance, server computing system(s) 60 can communicate with computing device 50 over a local intranet or internet connection. For instance, computing device 50 can be a workstation or endpoint in communication with server computing system(s) 60, with implementation of machine-learned models 65 being managed by server computing system(s) 60 to remotely perform inference (e.g., for runtime or training operations), with output(s) returned (e.g., cast, streamed, etc.) to computing device 50. Machine-learned models 65 can work cooperatively or interoperatively with machine- learned models 55 on computing device 50 to perform various tasks.

[0236] Model development platform system(s) 70 can include one or more processors 71 and a memory 72. Processor(s) 71 can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. Memory 72 can include one or more non-transitory computer-readable storage media, such as HBM, RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. Memory 72 can store data 73 and instructions 74 which can be executed by processor(s) 71 to cause model development platform system(s) 70 to perform operations. The operations can implement any one or multiple features described herein. The operations can implement example methods and techniques described herein. Example operations include the functionality described herein with respect to model development platform 12. This and other functionality can be implemented by developer tool(s) 75.

[0237] Third-party system(s) 80 can include one or more processors 81 and a memory 82. Processor(s) 81 can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. Memory 82 can includeone or more non-transitory computer-readable storage media, such as HBM, RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. Memory 82 can store data 83 and instructions 84 which can be executed by processor(s) 81 to cause third-party system(s) 80 to perform operations. The operations can implement any one or multiple features described herein. The operations can implement example methods and techniques described herein. Example operations include the functionality described herein with respect to tools and other external resources called when training or performing inference with machine-learned model(s) 1, 4, 16, 20, 55, 65, etc. (e.g., third-party resource(s) 85).

[0238] FIG. 14 illustrates one example arrangement of computing systems that can be used to implement the disclosure. Other computing system configurations can be used as well. For example, in some implementations, one or both of computing system 50 or server computing system(s) 60 can implement all or a portion of the operations of model development platform system 70. For example, computing system 50 or server computing system(s) 60 can implement developer tool(s) 75 (or extensions thereof) to develop, update / train, or refine machine-learned models 1, 4, 16, 20, 55, 65, etc. using one or more techniques described herein with respect to model alignment toolkit 17. In this manner, for instance, computing system 50 or server computing system(s) 60 can develop, update / train, or refine machine- learned models based on local datasets (e.g., for model personalization / customization, as permitted by user data preference selections).

[0239] FIG. 15 is a block diagram of an example computing device 98 that performs according to example embodiments of the disclosure. Computing device 98 can be a user computing device or a server computing device (e.g., computing device 50, server computing system(s) 60, etc.). Computing device 98 can implement model host 31. For instance, computing device 98 can include a number of applications (e.g., applications 1 through N). Each application can contain its own machine learning library and machine-learned model(s). For example, each application can include a machine-learned model. Example applications include a text messaging application, an email application, a dictation application, a virtual keyboard application, a browser application, a social media application, a chat application, an exercise monitoring application, etc. As illustrated in FIG. 15, each application can communicate with a number of other components of the computing device, such as, for example, one or more sensors, a context manager, a device state component, or additional components. In some implementations, each application can communicate with each devicecomponent using an API (e.g., a public API). In some implementations, the API used by each application is specific to that application.

[0240] FIG. 16 is a block diagram of an example computing device 99 that performs according to example embodiments of the disclosure. Computing device 99 can be the same as or different from computing device 98. Computing device 99 can be a user computing device or a server computing device (e.g., computing device 50, server computing system(s) 60, etc.). Computing device 98 can implement model host 31. For instance, computing device 99 can include a number of applications (e.g., applications 1 through N). Each application can be in communication with a central intelligence layer. Example applications include a text messaging application, an email application, a dictation application, a virtual keyboard application, a browser application, a social media application, a chat application, an exercise monitoring application, etc. In some implementations, each application can communicate with the central intelligence layer (and model(s) stored therein) using an API (e.g., a common API across all applications).

[0241] The central intelligence layer can include a number of machine-learned models. For example, as illustrated in FIG. 16, a respective machine-learned model can be provided for each application and managed by the central intelligence layer. In other implementations, two or more applications can share a single machine-learned model. For example, in some implementations, the central intelligence layer can provide a single model for all of the applications. In some implementations, the central intelligence layer is included within or otherwise implemented by an operating system of computing device 99.

[0242] The central intelligence layer can communicate with a central device data layer. The central device data layer can be a centralized repository of data for computing device 99. As illustrated in FIG. 16, the central device data layer can communicate with a number of other components of the computing device, such as, for example, one or more sensors, a context manager, a device state component, or additional components. In some implementations, the central device data layer can communicate with each device component using an API (e.g., a private API).Additional Disclosure

[0243] The technology discussed herein makes reference to servers, databases, software applications, and other computer-based systems, as well as actions taken and information sentto and from such systems. The inherent flexibility of computer-based systems allows for a great variety of possible configurations, combinations, and divisions of tasks and functionality between and among components. For instance, processes discussed herein can be implemented using a single device or component or multiple devices or components working in combination. Databases and applications can be implemented on a single system or distributed across multiple systems. Distributed components can operate sequentially or in parallel.

[0244] Aspects of the disclosure have been described in terms of illustrative embodiments thereof. Any and all features in the following claims can be combined or rearranged in any way possible, including combinations of claims not explicitly enumerated in combination together, as the example claim dependencies listed herein should not be read as limiting the scope of possible combinations of features disclosed herein. Accordingly, the scope of the disclosure is by way of example rather than by way of limitation, and the subject disclosure does not preclude inclusion of such modifications, variations or additions to the disclosure as would be readily apparent to one of ordinary skill in the art. Moreover, terms are described herein using lists of example elements joined by conjunctions such as “and,” “or,” “but,” etc. It should be understood that such conjunctions are provided for explanatory purposes only. Clauses and other sequences of items joined by a particular conjunction such as “or,” for example, can refer to “and / or,” “at least one of’, “any combination of’ example elements listed therein, etc. Terms such as “based on” should be understood as “based at least in part on.”

[0245] Terms used herein are used to describe the example embodiments and are not intended to limit and / or restrict the disclosure. The singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. In this disclosure, terms such as "including", "having", “comprising”, and the like are used to specify features, numbers, steps, operations, elements, components, or combinations thereof, but do not preclude the presence or addition of one or more of the features, elements, steps, operations, elements, components, or combinations thereof.

[0246] The term "and / or" includes a combination of a plurality of related listed items or any item of the plurality of related listed items. For example, the scope of the expression or phrase "A and / or B" includes the item "A", the item "B", and the combination of items "A and B”.

[0247] In addition, the scope of the expression or phrase "at least one of A or B" is intended to include all of the following: (1) at least one of A, (2) at least one of B, and (3) at least one of A and at least one of B. Likewise, the scope of the expression or phrase "at least one of A, B, or C" is intended to include all of the following: (1) at least one of A, (2) at least one of B, (3) at least one of C, (4) at least one of A and at least one of B, (5) at least one of A and at least one of C, (6) at least one of B and at least one of C, and (7) at least one of A, at least one of B, and at least one of C.

[0248] It will be understood that, although the terms first, second, third, etc., may be used herein to describe various elements, the elements are not limited by these terms. Instead, these terms are used to distinguish one element from another element. For example, without departing from the scope of the disclosure, a first element may be termed as a second element, and a second element may be termed as a first element.

[0249] The term “can” should be understood as referring to a possibility of a feature in various implementations and not as prescribing an ability that is necessarily present in every implementation. For example, the phrase “X can perform Y” should be understood as indicating that, in various implementations, X has the potential to be configured to perform Y, and not as indicating that in every instance X must always be able to perform Y. It should be understood that, in various implementations, X might be unable to perform Y and remain within the scope of the disclosure.

[0250] The term “may” should be understood as referring to a possibility of a feature in various implementations and not as prescribing an ability that is necessarily present in every implementation. For example, the phrase “X may perform Y” should be understood as indicating that, in various implementations, X has the potential to be configured to perform Y, and not as indicating that in every instance X must always be able to perform Y. It should be understood that, in various implementations, X might be unable to perform Y and remain within the scope of the disclosure.

[0251] To the extent terms including "module", and "unit," and the like are used herein, these terms may refer to, but are not limited to, a software or hardware component or device, such as a Field Programmable Gate Array (FPGA) or Application Specific Integrated Circuit (ASIC), which performs certain tasks. A module or unit may be configured to reside on an addressable storage medium and configured to execute on one or more processors. Thus, a module or unit may include, by way of example, components, such as software components,object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. The functionality provided for in the components and modules / units may be combined into fewer components and modules / units or further separated into additional components and modules.

[0252] Aspects of the above-described example embodiments may be recorded in non- transitory computer-readable media including program instructions to implement various operations embodied by a computer. The media may also include, alone or in combination with the program instructions, data files, data structures, and the like. Examples of non- transitory computer-readable media include magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD ROM disks, Blu-Ray disks, and DVDs; magneto-optical media such as optical discs; and other hardware devices that are specially configured to store and perform program instructions, such as semiconductor memory, readonly memory (ROM), random access memory (RAM), flash memory, USB memory, and the like. Examples of program instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter. The program instructions may be executed by one or more processors. The described hardware devices may be configured to act as one or more software modules in order to perform the operations of the above-described embodiments, or vice versa. In addition, a non-transitory computer-readable storage medium may be distributed among computer systems connected through a network and computer-readable codes or program instructions may be stored and executed in a decentralized manner. In addition, the non- transitory computer-readable storage media may also be embodied in at least one application specific integrated circuit (ASIC) or Field Programmable Gate Array (FPGA).

[0253] Each block of the flowchart illustrations may represent a unit, module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks may occur out of order. For example, two blocks shown in succession may in fact be executed substantially concurrently (simultaneously) or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

[0254] Further to the descriptions above, a user may be provided with controls allowing the user to make an election as to both if and when systems, programs, or features describedherein may enable collection of user information (e.g., information about a user’s social network, social actions, or activities, profession, a user’s preferences, or a user’s current location), and if the user is sent content or communications from a server. In addition, certain data may be treated in one or more ways before it is stored or used, so that personally identifiable information is removed. For example, a user’s identity may be treated so that no personally identifiable information can be determined for the user, or a user’s geographic location may be generalized where location information is obtained (such as to a city, ZIP code, or state level), so that a particular location of a user cannot be determined. Thus, the user may have control over what information is collected about the user, how that information is used, and what information is provided to the user.

[0255] While the disclosure has been described with respect to various example embodiments, each example is provided by way of explanation, not limitation of the disclosure. Those skilled in the art, upon attaining an understanding of the foregoing, can readily produce alterations to, variations of, and equivalents to such embodiments. Accordingly, the disclosure does not preclude inclusion of such modifications, variations and / or additions to the disclosed subject matter as would be readily apparent to one of ordinary skill in the art. For example, features illustrated or described as part of one embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that the disclosure covers such alterations, variations, and equivalents.

Claims

WHAT IS CLAIMED IS:

1. A computing device, comprising: one or more memories configured to store instructions; and one or more processors configured to execute the instructions to perform operations, the operations comprising: receiving a plurality of image frames which capture a user performing an exercise, determining, via one or more first machine-learned models, body landmark locations in the plurality of image frames, determining, via one or more second machine-learned models, a sequence of pose states in the plurality of image frames, based on the body landmark locations determined via the one or more first machine-learned models, and determining a type of the exercise performed by the user based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models.

2. The computing device of claim 1, wherein the operations further comprise: determining a number of repetitions performed of the exercise based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models.

3. The computing device of claim 1, wherein the operations further comprise: determining whether the exercise is performed with correct form based on the type of the exercise, the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, and the body landmark locations determined via the one or more first machine-learned models.

4. The computing device of claim 3, wherein the operations further comprise: in response to determining the exercise is not performed with correct form based on the type of the exercise, the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, and the body landmark locations determined via the one or more first machine-learned models, providing feedback to the user identifying a dysfunction which indicates a body part of the user which was positioned incorrectly during the exercise.

5. The computing device of claim 3, wherein determining whether the exercise is performed with correct form based on the type of the exercise, the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, and the body landmark locations determined via the one or more first machine- learned models, comprises: determining, based on a first set of body landmark locations from among the body landmark locations associated with a first pose state among the sequence of pose states, a first location of a first body part of the user relative to a second location of a second body part of the user, and when the first location of the first body part of the user relative to the second location of the second body part of the user does not satisfy a threshold criteria, providing feedback to the user identifying a dysfunction which indicates one or more body parts of the user which was positioned incorrectly in the first pose state during the exercise.

6. The computing device of claim 3, wherein determining whether the exercise is performed with correct form based on the type of the exercise, the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, and the body landmark locations determined via the one or more first machine- learned models, comprises: determining, based on a first set of body landmark locations from among the body landmark locations associated with a first pose state among the sequence of pose states, an angle between a first body part of the user and a second body part of the user, and when the angle does not satisfy a threshold criteria, providing feedback to the user identifying a dysfunction which indicates one or more body parts of the user which was positioned incorrectly in the first pose state during the exercise.

7. The computing device of claim 1, wherein the operations further comprise implementing one or more third machine-learned models trained to identify a dysfunction which indicates one or more body parts of the user which was positioned incorrectly during the exercise, based on the type of the exercise, the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, and the body landmark locations determined via the one or more first machine-learned models.

8. The computing device of claim 1, wherein the operations further comprise implementing a finite state machine to determine the type of the exercise performed by the user based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models.

9. The computing device of claim 1, wherein determining, via the one or more first machine-learned models, the body landmark locations and determining, via the one or more second machine-learned models, the sequence of pose states, are performed in real-time.

10. The computing device of claim 1, wherein determining, via the one or more first machine-learned models, the body landmark locations in the plurality of image frames comprises: extracting the body landmark locations from the plurality of image frames, and mapping the body landmark locations to a three-dimensional (3D) coordinate space; and determining, via the one or more second machine-learned models, the sequence of pose states in the plurality of image frames, based on the body landmark locations determined via the one or more first machine-learned models comprises: classifying, via the one or more second machine-learned models which implements a fully connected network with residual connections, a first pose state associated with the user for a first image frame among the plurality of image frames, based on coordinate information associated with body landmark locations mapped to the 3D coordinate space with respect to the first image frame.

11. The computing device of claim 1, wherein the operations further comprise: when the type of the exercise performed by the user is determined to be a hold exercise based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, initiating a timer for the exercise, and when the type of the exercise performed by the user is determined to be a movement exercise based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, incrementing a repetition counter after a full sequence of the movement exercise is completed according to a defined sequence of pose states associated with one repetition of the exercise.

12. The computing device of claim 1, wherein determining, via the one or more second machine-learned models, the sequence of pose states in the plurality of image frames, based on the body landmark locations determined via the one or more first machine-learned models, comprises: classifying a first set of body landmark locations from a first image frame among the plurality of image frames as corresponding to a first pose state among a plurality of pose states, in response to a confidence level associated with classifying the first set of body landmark locations as corresponding to the first pose state exceeding a threshold confidence level, and ignoring the first image frame and processing a second set of body landmark locations from a second image frame from among the plurality of image frames, in response to none of the confidence levels associated with classifying the first set of body landmark locations as corresponding to one of the plurality of pose states exceeding the threshold confidence level.

13. A computer-implemented method, comprising: receiving, by a computing system comprising one or more processors, a plurality of image frames which capture a user performing an exercise; determining, via one or more first machine-learned models, body landmark locations in the plurality of image frames; determining, via one or more second machine-learned models, a sequence of pose states in the plurality of image frames, based on the body landmark locations determined via the one or more first machine-learned models; and determining a type of the exercise performed by the user based on the sequence of pose states in the plurality of image frames determined via the one or more second machine- learned models.

14. The computer-implemented method of claim 13, further comprising: determining a number of repetitions performed of the exercise based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models.

15. The computer-implemented method of claim 13, further comprising: determining whether the exercise is performed with correct form based on the type of the exercise, the sequence of pose states in the plurality of image frames determined via theone or more second machine-learned models, and the body landmark locations determined via the one or more first machine-learned models.

16. The computer-implemented method of claim 15, further comprising: in response to determining the exercise is not performed with correct form based on the type of the exercise, the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, and the body landmark locations determined via the one or more first machine-learned models, providing feedback to the user identifying a dysfunction which indicates a body part of the user which was positioned incorrectly during the exercise.

17. The computer-implemented method of claim 13, further comprising: implementing one or more third machine-learned models trained to identify a dysfunction which indicates one or more body parts of the user which was positioned incorrectly during the exercise, based on the type of the exercise, the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, and the body landmark locations determined via the one or more first machine- learned models.

18. The computer-implemented method of claim 13, wherein determining, via the one or more first machine-learned models, the body landmark locations and determining, via the one or more second machine-learned models, the sequence of pose states, are performed in real-time.

19. The computer-implemented method of claim 13, further comprising: when the type of the exercise performed by the user is determined to be a hold exercise based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, initiating a timer for the exercise; and when the type of the exercise performed by the user is determined to be a movement exercise based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models, incrementing a repetition counter after a full sequence of the movement exercise is completed according to a defined sequence of pose states associated with one repetition of the exercise.

20. A non-transitory computer readable medium storing instructions which, when executed by a processor, cause the processor to perform operations for determining a type of exercise, the operations comprising: receiving a plurality of image frames which capture a user performing an exercise; determining, via one or more first machine-learned models, body landmark locations in the plurality of image frames; determining, via one or more second machine-learned models, a sequence of pose states in the plurality of image frames, based on the body landmark locations determined via the one or more first machine-learned models; and determining the type of the exercise performed by the user based on the sequence of pose states in the plurality of image frames determined via the one or more second machine- learned models.

21. A computing device, comprising: one or more memories configured to store instructions; and one or more processors configured to execute the instructions to perform operations, the operations comprising: receiving information indicating an exercise a user is to perform, receiving a plurality of image frames which capture the user performing the exercise, determining, via one or more first machine-learned models, body landmark locations in the plurality of image frames, determining, via one or more second machine-learned models, a sequence of pose states in the plurality of image frames, based on the body landmark locations determined via the one or more first machine-learned models, and determining whether the exercise is being performed with correct form based on the information indicating the exercise, the sequence of pose states, and the body landmark locations.

22. The computing device of claim 21, wherein the operations further comprise: determining a number of repetitions performed of the exercise based on the sequence of pose states in the plurality of image frames determined via the one or more second machine-learned models.

23. The computing device of claim 21, wherein the information indicating the exercise the user is to perform is received via an exercise application which is configured to generate an exercise workout plan.

24. The computing device of claim 21, wherein the information indicating the exercise the user is to perform is received via an input by the user to the computing device prior to performing the exercise.

25. The computing device of claim 21, wherein determining whether the exercise is being performed with the correct form comprises implementing one or more third machine- learned models trained to identify a dysfunction based on the information indicating the exercise, the sequence of pose states, and the body landmark locations, wherein the dysfunction indicates one or more body parts of the user which was positioned incorrectly during the exercise.

26. The computing device of claim 21, wherein the operations further comprise: when the information indicates the exercise is a hold exercise, initiating a timer for the exercise in response to receiving at least one image frame among the plurality of image frames which captures the user performing the exercise, and when the information indicates the exercise is a movement exercise, incrementing a repetition counter after a full sequence of the movement exercise is completed according to a defined sequence of pose states associated with one repetition of the exercise.

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