Abstract: A visual bot designer displays a user interface that has a visual authoring canvas and a property pane. It can also have a serialized file display pane. A user can provide authoring inputs on any of the user interfaces, and the visual bot designer computing system generates and displays updates on the other parts of the user interface.

Abstract: A chatbot designer user interface (UI) has a visual authoring canvas that shows visual elements indicative of a logical flow of processing in a chatbot application. The designer UI receives an authoring input identifying a language processing system that is to be used during runtime. The chatbot application is configured to use the identified language processing system.

Abstract: A chat bot computing system includes a bot controller and a natural language processor. The natural language processor receives a first textual input and identifies concepts represented by the first textual input. An indication of the concepts is output to the bot controller which generates a response to the first textual input. The concepts output by the natural language processor are also fed back into the input to the natural language processor, as context information, along with an expiration indicator when a second textual input is received. The natural language processor then identifies concepts represented in the second textual input, based on the second natural language, textual input and unexpired context information.

Abstract: A visual trigger node, indicative of a trigger, is displayed on a first display portion. A trigger configuration user interface is also displayed. A property input, indicative of a property that activates the trigger, is detected and code is generated to map the trigger to the property.

Abstract: A visual bot designer displays a user interface that has a visual authoring canvas and a property pane. It can also have a serialized file display pane. A user can provide authoring inputs on any of the user interfaces, and the visual bot designer computing system generates and displays updates on the other parts of the user interface.

Abstract: A visual bot designer displays a user interface that has a visual authoring canvas and a property pane. It can also have a serialized file display pane. A user can provide authoring inputs on any of the user interfaces, and the visual bot designer computing system generates and displays updates on the other parts of the user interface.

Abstract: A chatbot designer user interface (UI) has a visual authoring canvas that shows visual elements indicative of a logical flow of processing in a chatbot application. The designer UI receives an authoring input identifying a language processing system that is to be used during runtime. The chatbot application is configured to use the identified language processing system.

Abstract: A chatbot designer user interface (UI) has a visual authoring canvas that shows visual elements indicative of a logical flow of processing in a chatbot application. The designer UI receives an authoring input identifying a language processing system that is to be used during runtime. The chatbot application is configured to use the identified language processing system.

Abstract: A visual bot designer displays a user interface that has a visual authoring canvas and a property pane. It can also have a serialized file display pane. A user can provide authoring inputs on any of the user interfaces, and the visual bot designer computing system generates and displays updates on the other parts of the user interface.

Abstract: A chatbot designer user interface (UI) has a visual authoring canvas that shows visual elements indicative of a logical flow of processing in a chatbot application. The designer UI receives an authoring input identifying a language processing system that is to be used during runtime. The chatbot application is configured to use the identified language processing system.

Abstract: A visual trigger node, indicative of a trigger, is displayed on a first display portion. A trigger configuration user interface is also displayed. A property input, indicative of a property that activates the trigger, is detected and code is generated to map the trigger to the property.

Abstract: One disclosed example provides a computing system configured to receive input defining a machine conversation dialog flow, display in an editing user interface a first representation of the machine conversation dialog flow in the form of a symbolic representation, receive input requesting display of a second representation of the machine conversation dialog flow, and in response to the request display in the editing user interface the machine conversation dialog flow in the character-based representation. The computing system is further configured to, based upon the machine conversation dialog flow, update a machine conversation schema template to form an updated machine conversation schema, and form an agent definition file based upon the updated machine conversation schema for use in executing the machine conversation dialog flow.

Abstract: A chat bot computing system includes a bot controller and a natural language processor. The natural language processor receives a first textual input and identifies concepts represented by the first textual input. An indication of the concepts is output to the bot controller which generates a response to the first textual input. The concepts output by the natural language processor are also fed back into the input to the natural language processor, as context information, along with an expiration indicator when a second textual input is received. The natural language processor then identifies concepts represented in the second textual input, based on the second natural language, textual input and unexpired context information.

Abstract: A chat bot computing system includes a bot controller and a natural language processor. The natural language processor receives a first textual input and identifies concepts represented by the first textual input. An indication of the concepts is output to the bot controller which generates a response to the first textual input. The concepts output by the natural language processor are also fed back into the input to the natural language processor, as context information, along with an expiration indicator when a second textual input is received. The natural language processor then identifies concepts represented in the second textual input, based on the second natural language, textual input and unexpired context information.

Abstract: A chat bot computing system includes a bot controller and a natural language processor. The natural language processor receives a first textual input and identifies concepts represented by the first textual input. An indication of the concepts is output to the bot controller which generates a response to the first textual input. The concepts output by the natural language processor are also fed back into the input to the natural language processor, as context information, along with an expiration indicator when a second textual input is received. The natural language processor then identifies concepts represented in the second textual input, based on the second natural language, textual input and unexpired context information.

Abstract: Various embodiments provide a frequent sites module which is designed to generate frequent sites for a user that include websites that are relevant to the user based on browsing patterns of the user. In one or more embodiments, the frequent sites are generated from user-engagement data that indicates engagement by the user with websites identified in the user's browsing history. A web platform, e.g., a web browser, can display the frequent sites for the user in a frequent sites user interface container to enable the user to efficiently navigate to the websites that are relevant to the user by selection of websites from the frequent sites user interface container. Various embodiments describe other aspects of frequent sites based on browsing patterns, including multiple-device frequent sites, device-specific frequent sites, domain-specific frequent sites, URL-specific frequent sites, decaying of frequent sites, recent frequent sites, and contextual frequent sites.

Abstract: An electronic device can receive user input via voice or text that includes tasks to be performed. A digital personal assistant infrastructure service can control to which registered action provider the task is assigned. Per-task action provider preferences can be stored. If a preferred action provider is not able to complete the task, the task can still be performed by a registered action provider that has appropriate capabilities. Machine learning can determine a user's preferences. Resource conservation and effective user interaction can result.

Abstract: One disclosed example provides a computing system configured to receive input defining a machine conversation dialog flow, display in an editing user interface a first representation of the machine conversation dialog flow in the form of a symbolic representation, receive input requesting display of a second representation of the machine conversation dialog flow, and in response to the request display in the editing user interface the machine conversation dialog flow in the character-based representation. The computing system is further configured to, based upon the machine conversation dialog flow, update a machine conversation schema template to form an updated machine conversation schema, and form an agent definition file based upon the updated machine conversation schema for use in executing the machine conversation dialog flow.

Abstract: Examples are disclosed that relate to a conversation runtime for automating transitions of conversational user interfaces. One example provides a computing system comprising a logic subsystem and a data-holding subsystem. The data-holding subsystem comprises instructions executable by the logic subsystem to execute a conversation runtime configured to receive one or more agent definitions for a conversation robot program, each agent definition defining a state machine including a plurality of states, detect a conversation trigger condition, select an agent definition for a conversation based on the conversation trigger condition, and execute a conversation dialog with a client computing system using the agent definition selected for the conversation and automatically transition the state machine between different states of the plurality of states during execution of the conversation dialog.

Abstract: A computing device is provided, which may include a display, an input device and a processor configured to execute an application program including an application user interface presented via the display, the application user interface including a session state of a current session of a user, and execute a bot client program configured to execute a dialog with a user, the bot client program including a conversation canvas presented via the display, wherein the bot client program is configured to receive a query in the dialog from the user via the input device and conversation canvas, determine that the query is directed to content related to the state of the application program, send a context request to the application program, receive context data from the application program, the context data being derived from the state of the application program, and determine a response to the query.