In some embodiments, a method comprises receiving, at a processor of an autonomous vehicle and from at least one sensor, sensor data distributed within a time window. A first event being a first event type occurring at a first time in the time window is identified by the processor using a software model based on the sensor data. At least one first attribute associated with the first event is extracted by the processor. A second event being the first event type occurring at a second time in the time window is identified by the processor based on the at least one first attribute. In response to determining that the second event is not yet recognized as being the first event type, a first label for the second event is generated by the processor.

Various embodiments of the teachings herein include methods for amending or adding machine learning capabilities to an automation device in an automation system. The method may include: 1) providing a capability model of the automation device semantically representing capabilities of the device; 2) providing a machine learning model for semantically representing a machine learning functionality and including a semantic model of a neural network; 3) deploying the machine learning model within the automation device; 4) interpreting a semantic part of the machine learning model using a semantic reasoner and matching requirements of the machine learning model with device capabilities inferred by the capability model; and 5) executing the machine learning functionalities on the automation device.

A solution providing for the dynamic design, use, and modification of models is provided. The solution can receive an electronic communication identifying a request or event and process the electronic communication in a runtime environment by binding a model of the collection of models to dynamically construct an implementation of the model. Collective properties of the set of related models can emerge dynamically. The binding can comprise late-binding of an application associated with the collection of models to enable at least one user to perform at least one interaction using the environment without disrupting any of the environment or the application.

A solution providing for the dynamic design, use, and modification of models is provided. The solution can receive an electronic communication identifying a request or event and process the electronic communication in a runtime environment by binding a model of the collection of models to dynamically construct an implementation of the model. Collective properties of the set of related models can emerge dynamically. The binding can comprise late-binding of an application associated with the collection of models to enable at least one user to perform at least one interaction using the environment without disrupting any of the environment or the application.

In one aspect, a computerized method for generating a personalized digital software application comprising: providing an application modeler engine. With the application modeler engine the method provides a graphical display of a palate that comprises a list all the nodes that are available to include in an application definition file(s). The application modeler engine receives a set of nodes via a drag and drop operation into the application definition file. The application modeler engine defines and integrates a chatbot into the personalized digital software application. The application modeler engine automatically creates the application definition file to run on a mobile device of the patient, wherein the application definition file follows a protocol and logic created in the application by a care team and the drag and dropped nodes. The application modeler engine uses the application definition file to generate the application from the application definition file. The application modeler engine deploys the application to a user's mobile device.

A method of generating a user interface for presentation to a user. The method comprises executing a first application computer program to provide a user interface, executing agent computer program code to interrogate and modify said user interface during execution of said first application computer program, and presenting said modified user interface. The first application computer program may be run on a server, while the modified user interface may be presented to a user at a client connected to said server.

A method of generating a user interface for presentation to a user. The method comprises executing a first application computer program to provide a user interface, executing agent computer program code to interrogate and modify said user interface during execution of said first application computer program, and presenting said modified user interface. The first application computer program may be run on a server, while the modified user interface may be presented to a user at a client connected to said server.

A system may receive a request from a user to build a custom application specific to an operating system (OS) platform. The system may include an application gateway and the custom application may be a user-customized variation of an application gateway client. The system may prepare a build request and send it to a bonding client running on a work station specific to the OS platform. The bonding client may execute build scripts with user-provided configuration information to create the custom application. Instead of submitting the custom application to a third-party digital distribution platform, the bonding client may store the custom application in a storage location accessible by the system. The system, responsive to the request from the user, may send a response to the computing device, the response containing a link to the storage location of the custom application.

A system may receive a request from a user to build a custom application specific to an operating system (OS) platform. The system may include an application gateway and the custom application may be a user-customized variation of an application gateway client. The system may prepare a build request and send it to a bonding client running on a work station specific to the OS platform. The bonding client may execute build scripts with user-provided configuration information to create the custom application. Instead of submitting the custom application to a third-party digital distribution platform, the bonding client may store the custom application in a storage location accessible by the system. The system, responsive to the request from the user, may send a response to the computing device, the response containing a link to the storage location of the custom application.

Embodiments are disclosed for a method. The method includes identifying a prefix updated by a searcher of a machine learning model. The machine learning model is configured to generate source code in a programming language. The method also includes determining whether the prefix violates a semantic correctness property of the programming language. Additionally, the method includes instructing the searcher, in response to the determination, to prune the prefix from a set of prefixes under consideration by the searcher.