Systems and methods for electronically creating and modifying a fitness plan are disclosed. The method may include receiving electronic user data, collecting electronic fitness data, and displaying a suggestion for a fitness activity based on the electronic user data and the electronic fitness data.

Systems and methods of automatically distilling concepts from math problems and dynamically constructing and testing the creation of math problems from a collection of math concepts comprising: providing a user interface to a user; receiving as input: a math problem; one or more math concepts; and/or a user data packet; extracting and compiling a concept cloud of one or more CLIs that comprise the mathematical concepts embodied in the input, describe the operation of the one or more math concepts, or relate to the UDP, respectively; generating one or more math problem building blocks from the concept cloud CLIs; applying a mathematical rules engine to the one or more math problem building blocks to build one or more additional math problems; and returning to the user, through the user interface, the one or more additional math problems built from the CLIs that define the concept cloud extracted from the input.

Systems and methods of automatically distilling concepts from math problems and dynamically constructing and testing the creation of math problems from a collection of math concepts comprising: providing a user interface to a user; receiving as input: a math problem; one or more math concepts; and/or a user data packet; extracting and compiling a concept cloud of one or more CLIs that comprise the mathematical concepts embodied in the input, describe the operation of the one or more math concepts, or relate to the UDP, respectively; generating one or more math problem building blocks from the concept cloud CLIs; applying a mathematical rules engine to the one or more math problem building blocks to build one or more additional math problems; and returning to the user, through the user interface, the one or more additional math problems built from the CLIs that define the concept cloud extracted from the input.

An exercise feedback system receives exercise data such as images or video captured by client devices of users performing exercises. The exercise feedback system may access a machine learning model trained using image of a population of users. The images used for training may be labeled, for example, as having proper or improper musculoskeletal form. The exercise feedback system may determine a metrics describing the musculoskeletal form of a user by applying the trained machine learning model to images of the user as input features. The exercise feedback system may generate feedback for a certain exercise using the metrics based on output predictions of the model. The feedback can be provided to a client device of the user or a physical therapist for presentation.

A method can include obtaining personal data of a user and generating, based at least in part on the personal data of the user, a taste profile of the user. The taste profile can include a set of food characteristics that corresponds to one or more food preferences of the user. The method can include obtaining contextual data that corresponds to a location of the user. The method can include generating, based at least in part on the taste profile and the contextual data, a food recommendation. The food recommendation can include a predicted food preference of the user. The method can include transmitting the food recommendation to the user.

A user computing entity executes application program code to cause display of an IUI via a user interface of the user computing entity. The IUI comprises an action list comprising one or more action items corresponding to one or more team members of a team. The action items are automatically ordered based on one or more action priorities. At least one of the action items corresponds to a coaching opportunity and a recommendation for responding thereto. The coaching opportunity is automatically identified using a recommendation model trained using machine learning based at least in part on performance data corresponding to a plurality of key performance indicator metrics. The recommendation for responding to the coaching opportunity is determined using the recommendation model and based on the performance data. The recommendation model is trained using information regarding previous handlings of coaching opportunities and corresponding outcome indicators for a cluster of teams.

A user computing entity executes application program code to cause display of an IUI via a user interface of the user computing entity. The IUI comprises an action list comprising one or more action items corresponding to one or more team members of a team. The action items are automatically ordered based on one or more action priorities. At least one of the action items corresponds to a coaching opportunity and a recommendation for responding thereto. The coaching opportunity is automatically identified using a recommendation model trained using machine learning based at least in part on performance data corresponding to a plurality of key performance indicator metrics. The recommendation for responding to the coaching opportunity is determined using the recommendation model and based on the performance data. The recommendation model is trained using information regarding previous handlings of coaching opportunities and corresponding outcome indicators for a cluster of teams.

Apparatus and methods for proactively and preemptively communicating with a user interacting with a software application are provided. The apparatus and methods may include an artificial intelligence/machine learning communication engine monitoring and tracking a user's interactions. The apparatus and methods may include the communication engine determining if the user requires further training, if the interaction is fraudulent, and pre-empting requests for information the user may commence. The apparatus and methods may include the communication engine creating and displaying training materials for the user to complete, revoking access if fraud is present, and proactively providing information before the user requests the information.

Method, apparatus and computer program for providing a personalized study plan to a learner through cognitive and behavioral diagnosis of the learner. A learner who uses a data input device such as a smart pen and a stylus pen by using data obtained from the data input device. The method, apparatus and computer program relate to technology for obtaining input data based on information inputted by a user for at least one question with the data input device, creating test behavior data on the user from the obtained input data, analyzing cognition and behavior of the user based on at least one of metadata on the at least one question and the created test behavior data, and providing a personalized study plan to the user through an algorithm using machine learning based on the cognition and behavior analysis.

Method, apparatus and computer program for providing a personalized study plan to a learner through cognitive and behavioral diagnosis of the learner. A learner who uses a data input device such as a smart pen and a stylus pen by using data obtained from the data input device. The method, apparatus and computer program relate to technology for obtaining input data based on information inputted by a user for at least one question with the data input device, creating test behavior data on the user from the obtained input data, analyzing cognition and behavior of the user based on at least one of metadata on the at least one question and the created test behavior data, and providing a personalized study plan to the user through an algorithm using machine learning based on the cognition and behavior analysis.