Google Inc.

Sebastian Millius

Sandro Feuz

Matthew Nirvan Sharifi

Tom Hume

Alexa Greenberg

Jan Heinrich Althaus

Nicholas Chi-Yuen Kong

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for announcing and detecting automated conversation are disclosed. One of the methods includes initiating, over a natural language communication channel, a conversation with a communication participant using a natural language communication method that includes a dialogue of natural language communications. The communication participant is determined to be automated using a pre-defined adaptive interactive protocol that specifies natural language linguistic transformations defined in a sequence. The conversation can be transitioned to a communication method that is different form the natural language communication method in response to determining that the communication participant is automated.

Monitoring communication by a bot to determine a human or an additional bot

This specification is generally directed to techniques for automatically transitioning applications—especially those that enable exchange of messages between users—into and/or out of a private state based on a variety of signals associated with the messages and/or the participants themselves. In various implementations, an ongoing message exchange thread between two or more participants operating two or more respective message exchange clients may be examined. Based at least in part on the examining, a likelihood may be determined that message(s) directed by one of the participants to another of the participants as part of the ongoing message exchange thread would be deemed private by at least a given participant of the two or more participants. A determination may be made of whether the determined likelihood satisfies one or more thresholds, and in response, one or more of the message exchange clients may be transitioned into a private state.

Transitioning between private and non-private state

Training and/or utilizing an interaction prediction model to generate a predicted interaction value that indicates a likelihood of interaction with a corresponding application on the basis of an electronic communication. The application can be in addition to any electronic communication application that is utilized in formulating the electronic communication and/or that is utilized in rendering the electronic communication. The predicted interaction value can be generated based on processing, utilizing the interaction prediction model, of features of the electronic communication and/or of other features. The predicted interaction value can be utilized to determine whether to perform further action(s) that interact with, and/or enable efficient interaction with, the application on the basis of the electronic communication.

Training and/or utilizing an interaction prediction model to determine when to interact, and/or prompt for interaction, with an application on the basis of an electronic communication

Adaptive natural language steganography and watermarking for virtual assistants

This specification is generally directed to techniques for automatically transitioning applications&#x2014;especially those that enable exchange of messages between users&#x2014;into and/or out of a private state based on a variety of signals associated with the messages and/or the participants themselves. In various implementations, an ongoing message exchange thread between two or more participants operating two or more respective message exchange clients may be examined. Based at least in part on the examining, a likelihood may be determined that message(s) directed by one of the participants to another of the participants as part of the ongoing message exchange thread would be deemed private by at least a given participant of the two or more participants. A determination may be made of whether the determined likelihood satisfies one or more thresholds, and in response, one or more of the message exchange clients may be transitioned into a private state.

Automatically generating and/or automatically transmitting a status of a user. The status is transmitted for presentation to one or more additional users via corresponding computing device(s) of the additional user(s). Some implementations are directed to determining both: a status of a user, and a predicted duration of that status; and generating a status notification that includes the status and that indicates the predicted duration. Some implementations are additionally or alternatively directed to utilizing at least one trust criterion in determining whether to provide a status notification of a user to an additional user and/or in determining what status notification to provide to the additional user. Some implementations are additionally or alternatively directed to training and/or use of machine learning model(s) in determining a status of a user and/or a predicted duration of that status.

Automatic generation and transmission of a status of a user and/or predicted duration of the status

A computing device is described that includes at least one processor and a memory including instructions that when executed cause the at least one processor to output, for display, a graphical keyboard comprising a plurality of keys, and determine, based on an indication of a selection of one or more keys from the plurality of keys, text of an electronic communication. The instructions, when executed, further cause the at least one processor to generate, based at least in part on the text, an image based search query, and output, for display, within the graphical keyboard, a graphical indication to indicate that the computing device generated the image based search query.

Image search query predictions by a keyboard

The present disclosure provides systems and methods that leverage machine learning to implement context determination and/or text extraction in computing device applications. Particular embodiments can include and use a machine-learned text extraction model that has been trained to receive one or more messages containing text and determine one or more portions of extracted text from the one or more messages as well as a corresponding user context assigned to each of the one or more portions of extracted text. In addition, or alternatively, particular embodiments can include and use a machine-learned context determination model that has been trained to receive one or more portions of device data from one or more input sources available at the mobile computing device and determine a current user context indicative of one or more activities in which a user of the mobile computing device is currently engaged.

Context aware chat history assistance using machine-learned models

Techniques are described related to improved intercom-style communication using a plurality of computing devices distributed about an environment. In various implementations, voice input may be received, e.g., at a microphone of a first computing device of multiple computing devices, from a first user. The voice input may be analyzed and, based on the analyzing, it may be determined that the first user intends to convey a message to a second user. A location of the second user relative to the multiple computing devices may be determined, so that, based on the location of the second user, a second computing device may be selected from the multiple computing devices that is capable of providing audio or visual output that is perceptible to the second user. The second computing device may then be operated to provide audio or visual output that conveys the message to the second user.

Growing community of inventors

Zurich, Switzerland

Sebastian Millius