Abstract: Disclosed herein are system, method, and computer program product embodiments for providing a natural, pen-and-paper user experience that harnesses cutting edge artificial intelligence and machine learning to enhance user interactions with data in a suite of software applications. The pen-and-paper user experience may mimic the time-honored human tradition of writing with pen and paper, allowing users to work in an intuitive, productive, familiar fashion. The user experience may automatically retrieve objects, e.g., data, charts, tables, graphs, etc. from backend systems and display the objects in the user experience. The user experience may allow users to provide additional natural language directly on or around the objects and update the displayed results accordingly.

Abstract: The present disclosure involves systems, software, and computer implemented methods for generating a unified semantic model identifying a user's intentions or goals within operating the system. By determining the intent and desires of the user, tasks and queries associated with or requested by the user can be more effectively optimized, automated, or otherwise improved. In some computing environments, multiple users access a database which includes a collection of software objects. The objects within the database can sometimes form a hierarchy, with a parent-child relationship among them. Traditionally, a developer creating the database and the objects within can only determine a user's intent based on the actions taken by the user within the database. The present solution provides the developer with a higher level model representing different users' intents based on observed data from a number of sources in addition to the user's interaction with the database.

Abstract: The present disclosure involves systems, software, and computer implemented methods for generating a unified semantic model identifying a user's intentions or goals within operating the system. By determining the intent and desires of the user, tasks and queries associated with or requested by the user can be more effectively optimized, automated, or otherwise improved. In some computing environments, multiple users access a database which includes a collection of software objects. The objects within the database can sometimes form a hierarchy, with a parent-child relationship among them. Traditionally, a developer creating the database and the objects within can only determine a user's intent based on the actions taken by the user within the database. The present solution provides the developer with a higher level model representing different users' intents based on observed data from a number of sources in addition to the user's interaction with the database.

Abstract: Disclosed herein are system, method, and computer program product embodiments for providing a natural, pen-and-paper user experience that harnesses cutting edge artificial intelligence and machine learning to enhance user interactions with data in a suite of software applications. The pen-and-paper user experience may mimic the time-honored human tradition of writing with pen and paper, allowing users to work in an intuitive, productive, familiar fashion, while incorporating artificial intelligence and machine learning techniques to interpret a user's natural language and other pen-strokes in order to better interpret a user's natural language and resolve ambiguity in user inputs. A pen-and-paper-based user experience may be extended and enhanced to allow a user to interact with a suite of applications, a launchpad, a key performance indicator tool, a digital boardroom, or other application using the same pen-and-paper-based design paradigm.

Abstract: A system, method, and computer program product embodiment related to a digital paper-based interaction to system data. An embodiment operates by receiving a written instruction from a user; analyzing the written instruction to determine a user intent and one or more parameters; retrieving a set of data from a data application; rendering a representation of the set of data in the user experience; receiving a second written instruction from the user in response to a condition in the representation of the set of data; deriving a conclusion based on the written instruction and the second written instruction; building a flow comprising a sequence of actions undertaken, by the user experience, in response to a user action; and adding the flow to a knowledge base.

Abstract: The present disclosure involves systems, software, and computer implemented methods for generating a unified semantic model identifying a user's intentions or goals within operating the system. By determining the intent and desires of the user, tasks and queries associated with or requested by the user can be more effectively optimized, automated, or otherwise improved. In some computing environments, multiple users access a database which includes a collection of software objects. The objects within the database can sometimes form a hierarchy, with a parent-child relationship among them. Traditionally, a developer creating the database and the objects within can only determine a user's intent based on the actions taken by the user within the database. The present solution provides the developer with a higher level model representing different users' intents based on observed data from a number of sources in addition to the user's interaction with the database.

Abstract: Configuring a graphical user interface according to a user's preferences is described herein. Systems and methods are provided for generating a dynamically varying themed interface for a user environment; defining appropriate color palettes associated within a brand definition of the user environment; and, providing one or more configuration settings for the rendering engine.

Abstract: A system for answering a natural language query (NLQ) directed to a database system. The system includes a query receiver receives the NLQ and models the NLQ as a tuple of entities including at least a subject entity and an intention entity. The system further includes a service discovery component and a query transformation unit. The service discovery component discovers a specific database system asset for answering the NLQ by evaluating system model descriptions of database system assets against the NLQ. The query transformation unit transforms the NLQ into a native service call for the specific database system asset, and invokes the native service call for callback of the specific database system asset to answer the NLQ.

Abstract: Disclosed herein are system, method, and computer program product embodiments for providing a natural, pen-and-paper user experience that harnesses cutting edge artificial intelligence and machine learning to enhance user interactions with data in a suite of software applications. The pen-and-paper user experience may mimic the time-honored human tradition of writing with pen and paper, allowing users to work in an intuitive, productive, familiar fashion. The user experience may automatically retrieve objects, e.g., data, charts, tables, graphs, etc. from backend systems and display the objects in the user experience. The user experience may allow users to provide additional natural language directly on or around the objects and update the displayed results accordingly.

Abstract: Disclosed herein are system, method, and computer program product embodiments for providing a natural, pen-and-paper user experience that harnesses cutting edge artificial intelligence and machine learning to enhance user interactions with data in a suite of software applications. The pen-and-paper user experience may mimic the time-honored human tradition of writing with pen and paper, allowing users to work in an intuitive, productive, familiar fashion. The user experience may further incorporate artificial intelligence and machine learning techniques in order to provide real-time insights, ad-hoc analyses, anticipate user actions, and improve overall usability based on prior interactions.

Abstract: Disclosed herein are system, method, and computer program product embodiments for providing a natural, pen-and-paper user experience that harnesses cutting edge artificial intelligence and machine learning to enhance user interactions with data in a suite of software applications. The pen-and-paper user experience may mimic the time-honored human tradition of writing with pen and paper, allowing users to work in an intuitive, productive, familiar fashion, while incorporating artificial intelligence and machine learning techniques to interpret a user's natural language and other pen-strokes in order to better interpret a user's natural language and resolve ambiguity in user inputs. A pen-and-paper-based user experience may be extended and enhanced to allow a user to interact with a suite of applications, a launchpad, a key performance indicator tool, a digital boardroom, or other application using the same pen-and-paper-based design paradigm.

Abstract: In an example embodiment, first user input including handwriting input and non-alphanumeric symbolic input is detected. The non-alphanumeric symbolic input is input into a first machine learning model trained to output a set of possible actions corresponding to the non-alphanumeric symbolic input and a probability score assigned to each action in the set of possible actions. A combination of the action having the highest probability score and textual input from the handwriting input is input into a second machine learning model trained to select a service from a plurality of services based on the textual input and the selected action by referencing a service model corresponding to each service in the plurality of services. The combination of the textual input and the selected action is transformed into a native request for the selected service based on the service model for the selected service.

Abstract: A system for answering a natural language query (NLQ) directed to a database system. The system includes a query receiver receives the NLQ and models the NLQ as a tuple of entities including at least a subject entity and an intention entity. The system further includes a service discovery component and a query transformation unit. The service discovery component discovers a specific database system asset for answering the NLQ by evaluating system model descriptions of database system assets against the NLQ. The query transformation unit transforms the NLQ into a native service call for the specific database system asset, and invokes the native service call for callback of the specific database system asset to answer the NLQ.

Abstract: Configuring a graphical user interface according to a user's preferences is described herein. Systems and methods are provided for generating a dynamically varying themed interface for a user environment; defining appropriate color palettes associated within a brand definition of the user environment; and, providing one or more configuration settings for the rendering engine.

Abstract: Configuring a graphical user interface according to a user's preferences is described herein. Systems and methods are provided for generating a dynamically varying themed interface for a user environment; defining appropriate color palettes associated within a brand definition of the user environment; and, providing one or more configuration settings for the rendering engine.

Abstract: In an example embodiment, first user input including handwriting input and non-alphanumeric symbolic input is detected. The non-alphanumeric symbolic input is input into a first machine learning model trained to output a set of possible actions corresponding to the non-alphanumeric symbolic input and a probability score assigned to each action in the set of possible actions. A combination of the action having the highest probability score and textual input from the handwriting input is input into a second machine learning model trained to select a service from a plurality of services based on the textual input and the selected action by referencing a service model corresponding to each service in the plurality of services. The combination of the textual input and the selected action is transformed into a native request for the selected service based on the service model for the selected service.

Abstract: The present disclosure provides methods, devices, systems, and computer program products for providing fine-grain gesture-based control of wearable applications. Methods are provided for multi-step gesture-based control systems of wearable applications with an initial, easy to recognize gesture being used to place the device in a state that subtle gestures can be identified that can control navigation and interactivity on the device that rely on the user being able to view the device. Systems and methods are provided for utilizing data obtained from sensor(s) to determine a gesture to place a wearable device in a state of fine-grain gesture-based control.

Abstract: Configuring a graphical user interface according to a user's preferences is described herein. Systems and methods are provided for generating a dynamically varying themed interface for a user environment; defining appropriate color palettes associated within a brand definition of the user environment; and, providing one or more configuration settings for the rendering engine.

Abstract: In one embodiment, a method determines a role for a user for access to software code. A marker associated with an instruction in the software code is determined. An access level for the user based on the marker and the role is then determined. The access level is enforced based on the instruction associated with the marker for the software code.

Abstract: In one embodiment, a method determines a role for a user for access to software code. A marker associated with an instruction in the software code is determined. An access level for the user based on the marker and the role is then determined. The access level is enforced based on the instruction associated with the marker for the software code.