Abstract: Computing systems, database systems, and related conversational updating methods are provided for dynamically updating graphical user interfaces responsive to conversational user inputs received via a chatbot or other conversational interactions with a user.

Abstract: Computing systems, database systems, and related conversational updating methods are provided for dynamically updating graphical user interfaces responsive to conversational user inputs received via a chatbot or other conversational interactions with a user.

Abstract: Computing systems, database systems, and related conversational updating methods are provided for dynamically updating graphical user interfaces responsive to conversational user inputs received via a chatbot or other conversational interactions with a user.

Abstract: A method of training a predictive model to predict a likely field value for one or more user selected fields within an application. The method comprises providing a user interface for user selection of the one or more user selected fields within the application; analyzing a pre-existing, user provided data set of objects; training, based on the analysis, the predictive model; determining, for each user selected field based on the analysis, a confidence function for the predictive model that identifies the percentage of cases predicted correctly at different applied confidence levels, the percentage of cases predicted incorrectly at different applied confidence levels, and the percentage of cases in which the prediction model could not provide a prediction at different applied confidence levels; and providing a user interface for user review of the confidence functions for user selection of confidence threshold levels to be used with the predictive model.

Abstract: Technologies are provided for tracking learning progress of an end user of a cloud computing platform when the end user is not registered with an external learning platform that provides an external learning application. In response to the end user interacting with virtual learning entities provided at the cloud computing platform, learning progress information with respect to the external learning application is generated. The interaction by the end user takes place in the context of an application provided by the cloud computing platform while in an active web session. The virtual learning entities represent content of the external learning application (e.g., learning content and contextual user information of the external learning application). When a user service of the external learning platform receives a request to persist the learning progress information for the end user, the user service can determine whether a corresponding user account (e.g.

Abstract: Technologies are provided for integrating learning data provided by an external learning platform (ELP) to create a custom learner experience within a context of an application provided by a cloud computing platform (CCP). The system can include the CCP, the ELP, learner APIs that expose a common learning data schema on the CCP, and a user interface platform (UIP). The UIP can include a compiler that transforms source code of UICs of a componentized learner user interface for usage on the CCP, and a bundler that generates a package of UI components (UICs) that are compatible for usage on the CCP. The UICs are specific to the learning data schema shared with the learner APIs. The UIP exports the package to the CCP, which composes the learning data provided via learner APIs and UICs from the package to provide the custom learner experience within the context of the application.

Abstract: Computing systems, database systems, and related conversational updating methods are provided for dynamically updating graphical user interfaces responsive to conversational user inputs received via a chatbot or other conversational interactions with a user.

Abstract: A method of training a predictive model to predict a likely field value for one or more user selected fields within an application. The method comprises providing a user interface for user selection of the one or more user selected fields within the application; analyzing a pre-existing, user provided data set of objects; training, based on the analysis, the predictive model; determining, for each user selected field based on the analysis, a confidence function for the predictive model that identifies the percentage of cases predicted correctly at different applied confidence levels, the percentage of cases predicted incorrectly at different applied confidence levels, and the percentage of cases in which the prediction model could not provide a prediction at different applied confidence levels; and providing a user interface for user review of the confidence functions for user selection of confidence threshold levels to be used with the predictive model.

Abstract: Computing systems, database systems, and related conversational updating methods are provided for dynamically updating graphical user interfaces responsive to conversational user inputs received via a chatbot or other conversational interactions with a user.

Abstract: A method of training a predictive model to predict a likely field value for one or more user selected fields within an application. The method comprises providing a user interface for user selection of the one or more user selected fields within the application; analyzing a pre-existing, user provided data set of objects; training, based on the analysis, the predictive model; determining, for each user selected field based on the analysis, a confidence function for the predictive model that identifies the percentage of cases predicted correctly at different applied confidence levels, the percentage of cases predicted incorrectly at different applied confidence levels, and the percentage of cases in which the prediction model could not provide a prediction at different applied confidence levels; and providing a user interface for user review of the confidence functions for user selection of confidence threshold levels to be used with the predictive model.

Abstract: In accordance with embodiments, there are provided mechanisms and methods for facilitating client-based control and experience of application programming interfaces in an on-demand services environment according to one embodiment. In one embodiment and by way of example, a method comprises offering, by application programming interface (API)-usage server device (“API-usage device”), a client interface package including a user interface for facilitating client-based control of APIs at a client computing device over a network. The method may further include facilitating, by the API-usage device, the client-based control of the APIs using an API menu accessible through the user interface, where the API menu is selectively populated with one or more of documentation, working code samples, and application protocol clients.

Abstract: Computing systems, database systems, and related conversational updating methods are provided for dynamically updating graphical user interfaces responsive to conversational user inputs received via a chatbot or other conversational interactions with a user.

Abstract: A method of training a predictive model to predict a likely field value for one or more user selected fields within an application. The method comprises providing a user interface for user selection of the one or more user selected fields within the application; analyzing a pre-existing, user provided data set of objects; training, based on the analysis, the predictive model; determining, for each user selected field based on the analysis, a confidence function for the predictive model that identifies the percentage of cases predicted correctly at different applied confidence levels, the percentage of cases predicted incorrectly at different applied confidence levels, and the percentage of cases in which the prediction model could not provide a prediction at different applied confidence levels; and providing a user interface for user review of the confidence functions for user selection of confidence threshold levels to be used with the predictive model.

Abstract: In accordance with embodiments, there are provided mechanisms and methods for facilitating client-based control and experience of application programming interfaces in an on-demand services environment according to one embodiment. In one embodiment and by way of example, a method comprises offering, by application programming interface (API)-usage server device (“API-usage device”), a client interface package including a user interface for facilitating client-based control of APIs at a client computing device over a network. The method may further include facilitating, by the API-usage device, the client-based control of the APIs using an API menu accessible through the user interface, where the API menu is selectively populated with one or more of documentation, working code samples, and application protocol clients.