Google Inc.

Speaktoit, Inc.

Other

Pavel Aleksandrovich Sirotin

Artem Goncharuk

Ilya Gennadyevich Gelfenbeyn

Olga Aleksandrovna Gelfenbeyn

Ilya Andreevich Platonov

Ilya Genadevich Gelfenbeyn

Ilya Gelfenbeyn

Invoking an agent during a dialog between a user and an automated assistant. Some implementations are directed to receiving, during a human-to-automated assistant dialog, natural language input of the user that indicates a desire to engage an agent, but that fails to indicate a particular agent to be engaged. Those implementations are further directed to selecting a particular agent from a plurality of available agents, and transmitting an invocation request to the selected particular agent. In some implementations an agent selection model can be utilized in selecting the particular agent, such as a machine learning model. The machine learning model can be trained to enable generation of output that indicates, for each of a plurality of available agents (and optionally intent(s) for those agents), a probability that the available agent (and optionally intent) will generate appropriate responsive content.

Automated assistant invocation of appropriate agent

A method for enhancing dialog systems is disclosed herein. The method may include maintaining an online marketplace that may have a plurality of dialog system extension elements. The plurality of dialog system extension elements may include at least one of a dialog system plugin, a dialog system add-on, a dialog system update, and a dialog system upgrade. The method may further include receiving a selection of one of the plurality of dialog system extension elements from an end user. The end user may be associated with a dialog system. The method may continue with associating the one of the plurality of dialog system extension elements with the dialog system of the end user.

Enhancing functionalities of virtual assistants and dialog systems via plugin marketplace

Disclosed is the technology for computer-based 'Daily Brief' service, which includes methods and corresponding systems for proactively providing push notifications for users of chat information systems. The push notifications are dynamically generated and presented to the user based on identification of one or more triggering events, which may include predetermined time/date, current geographical location, activity of peers and friends in social media associated with the user, scheduled events, appointments, meetings, emails, instant messages, and many more. The described technology improves the interaction interface between the user and chat information system.

Proactive environment-based chat information system

Embodiments of the disclosure generally relate to a dialog system allowing for automatically reactivating a speech acquiring mode after the dialog system delivers a response to a user request. The reactivation parameters, such as a delay, depend on a number of predetermined factors and conversation scenarios. The embodiments further provide for a method of operating of the dialog system. An exemplary method comprises the steps of: activating a speech acquiring mode, receiving a first input of a user, deactivating the speech acquiring mode, obtaining a first response associated with the first input, delivering the first response to the user, determining that a conversation mode is activated, and, based on the determination, automatically re-activating the speech acquiring mode within a first predetermined time period after delivery of the first response to the user.

Dialog system with automatic reactivation of speech acquiring mode

A method for context-based natural language processing is disclosed herein. The method comprises maintaining a plurality of dialog system rules, receiving a user request from a Dialog System Interface, receiving one or more attributes associated with the user request from the Dialog System Interface or a user device, and identifying a type of context associated with the user request based on the user request and the one or more attributes. A context label is assigned to the user request associated with the type of context. Based on the context label and the user request, a particular dialog system rule is selected from the plurality of dialog system rules. A response to the user request is generated by applying the dialog system rule to at least a part of the user request.

Context-based natural language processing

A method for example-driven machine learning is disclosed herein. The method comprises maintaining a plurality of dialog system rules and a knowledge database including a plurality of intent objects and a plurality of entity objects. The plurality of intent objects and the plurality of entity objects are associated with at least one dialog system rule. An exemplary phrase is received and one or more linguistic elements are retrieved from the exemplary phrase. It is determined that at least one of the linguistic elements is directed to at least one of the plurality of intent objects of the plurality of entity objects and the at least one of the linguistic elements in association with the at least one dialog system rule is added to the knowledge database.

Example-driven machine learning scheme for dialog system engines

A method for enhancing dialog systems is disclosed herein. The method comprises maintaining an online marketplace that may include a plurality of dialog system extension elements. Each of the plurality of dialog system extension elements may include at least one of a dialog system plugin, a dialog system add-on, a dialog system update, and a dialog system upgrade. The method may further include receiving a selection of one of the plurality of dialog system extension elements from a software developer. The software developer may be associated with a dialog system. The method may continue with associating the one of the plurality of dialog system extension elements with the dialog system of the software developer.

Online marketplace of plugins for enhancing dialog systems

A method for expanding software application functionalities processing is disclosed herein. The method comprises receiving a user request within a software application. The software application is enhanced with annotations, which include metadata. The method continues with loading the metadata from the annotations to a dialog system located on a remote device, sending at least a part of the user request to the dialog system, and receiving a dialog system response to the user request from the dialog system. At least one of the annotations associated with the dialog system response is identified. A code associated with the identified annotation is invoked within the software application.

Growing community of inventors

Sunnyvale, CA, United States of America

Pavel Aleksandrovich Sirotin