Abstract: Techniques are disclosed that enable dynamically adapting an automated assistant response using a dynamic familiarity measure. Various implementations process received user input to determine at least one intent, and generate a familiarity measure by processing intent specific parameters and intent agnostic parameters using a machine learning model. An automated assistant response is then determined that is based on the intent and that is based on the familiarity measure. The assistant response is responsive to the user input, and is adapted to the familiarity measure. For example, the assistant response can be more abbreviated and/or more resource efficient as the familiarity measure becomes more indicative of familiarity.

Abstract: Techniques are disclosed that enable dynamically adapting an automated assistant response using a dynamic familiarity measure. Various implementations process received user input to determine at least one intent, and generate a familiarity measure by processing intent specific parameters and intent agnostic parameters using a machine learning model. An automated assistant response is then determined that is based on the intent and that is based on the familiarity measure. The assistant response is responsive to the user input, and is adapted to the familiarity measure. For example, the assistant response can be more abbreviated and/or more resource efficient as the familiarity measure becomes more indicative of familiarity.

Abstract: Techniques are described herein for dialog-based enrollment of individual users for single- and/or multi-modal recognition by an automated assistant, as well as determining how to respond to a particular user's request based on the particular user being enrolled and/or recognized. Rather than requiring operation of a graphical user interface for individual enrollment, dialog-based enrollment enables users to enroll themselves (or others) by way of a human-to-computer dialog with the automated assistant.

Abstract: Techniques are described herein for dialog-based enrollment of individual users for single- and/or multi-modal recognition by an automated assistant, as well as determining how to respond to a particular user's request based on the particular user being enrolled and/or recognized. Rather than requiring operation of a graphical user interface for individual enrollment, dialog-based enrollment enables users to enroll themselves (or others) by way of a human-to-computer dialog with the automated assistant.

Abstract: Techniques are described herein for dialog-based enrollment of individual users for single- and/or multi-modal recognition by an automated assistant, as well as determining how to respond to a particular user's request based on the particular user being enrolled and/or recognized. Rather than requiring operation of a graphical user interface for individual enrollment, dialog-based enrollment enables users to enroll themselves (or others) by way of a human-to-computer dialog with the automated assistant.

Abstract: Techniques are described herein for dialog-based enrollment of individual users for single- and/or multi-modal recognition by an automated assistant, as well as determining how to respond to a particular user's request based on the particular user being enrolled and/or recognized. Rather than requiring operation of a graphical user interface for individual enrollment, dialog-based enrollment enables users to enroll themselves (or others) by way of a human-to-computer dialog with the automated assistant.

Abstract: Techniques are disclosed that enable dynamically adapting an automated assistant response using a dynamic familiarity measure. Various implementations process received user input to determine at least one intent, and generate a familiarity measure by processing intent specific parameters and intent agnostic parameters using a machine learning model. An automated assistant response is then determined that is based on the intent and that is based on the familiarity measure. The assistant response is responsive to the user input, and is adapted to the familiarity measure. For example, the assistant response can be more abbreviated and/or more resource efficient as the familiarity measure becomes more indicative of familiarity.

Abstract: In accordance with aspects of the disclosure, a computing device may provide a user interface for developing an interactive natural-language response system, which may include a virtual assistant. A user may interact with a system using spoken, written (e.g., text), or other input methods. The user interface may allow a user to associate sentences with intents, tag words within the sentences with concepts, and construct a grammar using the associated intents and tagged concepts. The system may use the grammar for automatically predictively associating sentences with intents and words with concepts. The system may display in the foam of a chat transcript a single branch of a tree of a discussion between the virtual assistant and a user. The user interface may graphically display variable values to assist a user to test system responses under different simulated conditions.

Abstract: Techniques are described herein for dialog-based enrollment of individual users for single- and/or multi-modal recognition by an automated assistant, as well as determining how to respond to a particular user's request based on the particular user being enrolled and/or recognized. Rather than requiring operation of a graphical user interface for individual enrollment, dialog-based enrollment enables users to enroll themselves (or others) by way of a human-to-computer dialog with the automated assistant.

Abstract: In accordance with aspects of the disclosure, a computing device may provide a user interface for developing an interactive natural-language response system, which may include a virtual assistant. A user may interact with a system using spoken, written (e.g., text), or other input methods. The user interface may allow a user to associate sentences with intents, tag words within the sentences with concepts, and construct a grammar using the associated intents and tagged concepts. The system may use the grammar for automatically predictively associating sentences with intents and words with concepts. The system may display in the foam of a chat transcript a single branch of a tree of a discussion between the virtual assistant and a user. The user interface may graphically display variable values to assist a user to test system responses under different simulated conditions.