Provided are techniques for unified cognition for a virtual personal cognitive assistant. A personal cognitive agent creates an association with an entity and a personalized embodied cognition manager that includes an entity agent registry, wherein the personal cognitive agent comprises a virtual personal cognitive assistant. Selection of a first cognitive assistant agent from a first domain and a second cognitive assistant agent from a second domain are received. Input from the entity is received. A goal based on the input is identified. Unified cognition is provided by coordinating the first cognitive assistant agent of the first domain and the second cognitive assistant agent of the second domain to generate one or more actions to meet the goal. A response is provided to the input with an indication of the goal.

Systems and methods for AI assisted analysis of a user experience study are provided. A study objective (a goal of the study) and data relating to all possible navigation routes within a digital interface are received. Simulated clickstreams for navigating from any state of the digital interface to the study objective are generated. This simulated clickstream data is then used to train one or more machine learning models to determine a most efficient path to achieve the study objective from any state of the digital interface. Subsequently, study results from many different participants is received. Key events are then identified within the study results. Additionally, the likelihood of failure for each of the plurality of study results is predicted using the machine learning model, and information density of the plurality of study results is determined.

The present disclosure relates to systems, methods, and non-transitory computer-readable media for utilizing a parameterized notebook to adaptively generate visualizations regarding machine-learning models. In particular, the disclosed systems can generate a parameterized notebook based on a user-defined visualization recipe and provide parameter values that correspond to the machine-learning model to the parameterized notebook. Upon execution of the user-defined visualization recipe via the parameterized notebook, the disclosed systems can extract visualization data corresponding to the machine-learning model from the parameterized notebook. In addition, the disclosed systems can generate visualizations based on the visualization data and provide the generated visualizations for display in a graphical user interface.

The present disclosure relates to systems, methods, and non-transitory computer-readable media for utilizing a parameterized notebook to adaptively generate visualizations regarding machine-learning models. In particular, the disclosed systems can generate a parameterized notebook based on a user-defined visualization recipe and provide parameter values that correspond to the machine-learning model to the parameterized notebook. Upon execution of the user-defined visualization recipe via the parameterized notebook, the disclosed systems can extract visualization data corresponding to the machine-learning model from the parameterized notebook. In addition, the disclosed systems can generate visualizations based on the visualization data and provide the generated visualizations for display in a graphical user interface.

A system and method are disclosed for creating solution design blueprints. A solution design blueprint is a machine-readable data structure that includes a conceptual design model for an application framework. A user interface is configured to receive a plain language textual request from a user that describes a desired application or solution to a problem. Artificial Intelligence is leveraged to fit the textual request to semantic data models and map elements of the textual request to components of a design library. The resulting solution design blueprint can be presented to a user, and the user interface can be used to provide feedback related to the solution design blueprint that can be utilized to update machine learning algorithms and/or neural networks. In some embodiments, the solution design blueprint can be converted to an application framework that is provided to the use in an integrated development environment of the user interface.

A system and method are disclosed for creating solution design blueprints. A solution design blueprint is a machine-readable data structure that includes a conceptual design model for an application framework. A user interface is configured to receive a plain language textual request from a user that describes a desired application or solution to a problem. Artificial Intelligence is leveraged to fit the textual request to semantic data models and map elements of the textual request to components of a design library. The resulting solution design blueprint can be presented to a user, and the user interface can be used to provide feedback related to the solution design blueprint that can be utilized to update machine learning algorithms and/or neural networks. In some embodiments, the solution design blueprint can be converted to an application framework that is provided to the use in an integrated development environment of the user interface.

Shared variable binding and parallel execution of a process and robot workflow activities for robotic process automation (RPA) are disclosed. An RPA robot may be “bound” to a variable that is accessed by and displayed in an application. When the RPA robot is triggered, the RPA robot performs potentially conditional logic that may result in modifications to one or more bound variables. The RPA robot may lookup data, perform calculations, check on the status of other processes, and/or perform any other logical operations. The RPA robot may then modify, delete, or otherwise change the value of one or more bound variables, causing the application associated with those variables to display the results (e.g., when the user interface (UI) of the application is refreshed).

The present invention provides a system and method for automation of the creation of a software application as a combination of microservices by extraction of discrete elements of software functionality from a monolithic architectures using dynamic and static analysis of code and non-code-related artefacts; forming them into microservices such that the software application is recreated as serverless hardware infrastructure while also validating the transformed code. In many cases, serverless microservices will significantly reduce the hardware requirement for software systems.

Generally described, one or more aspects of the present application relate to improving the process of generating and deploying software applications in a network environment, particularly software applications that incorporate or rely upon machine learning models. More specifically, the present disclosure provides specific user interface features and associated computer-implemented features that may effectively, from a user's perspective, remove most of the complexities associated with writing and deploying code and developing and improving machine learning models. For example, the present disclosure may provide user-friendly visual building blocks that allow users to build and customize machine learning workflows that can then be turned into a full software application and optimized and deployed at target destinations of the users' choice.

Techniques facilitating quantum software developer kit and framework as a service are provided. A system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an execution component that executes, on a quantum device located within a cloud computing environment, a code based on an identification of the code received from a communication device. A quantum software development kit can execute on the communication device.