Google Inc.

Andrew C Gallagher

Utsav Prabhu

Caroline Rebecca Pantofaru

Vinay Bettadapura

Tuan Anh Nguyen

Shuchang Zhou

Jaclyn Konzelmann

Zhiheng Wang

Michael Nechyba

Teresa Ko

Keith Noah Snavely

Shrenik Lad

Hang Chu

Tsung-Lin Yang

Robert McDonald

Praveen Bommannavar

Bryce Evans

Yihui Xie

Implementations relate to an automated assistant that provides and manages output from one or more elements of output hardware of a computing device. The automated assistant manages dynamic adjustment of access permissions to the computing device according to, for example, a detected presence of one or more users. An active-user queue can be established each time a unique user enters a viewing window of a camera of the computing device when, up to that point, no user was considered active. Multiple image frames can be captured via the camera and processed to determine whether an initial user remains in the viewing window and/or whether another user has entered the viewing window. The initial user can be considered active as long as they are exclusively detected in the viewing window. Restricted content associated with the user may be rendered by the computing device whilst the user is active.

Assigning priority for an automated assistant according to a dynamic user queue and/or multi-modality presence detection

Example aspects of the present disclosure are directed to computing systems and methods that perform whole person association with face screening and/or face hallucination. In particular, one aspect of the closure is directed to a multi-headed person and face detection model that performs both face and person detection in one model. Each of the face and person detection can find landmarks or other pose information and also a confidence score. The pose information for the face and person detections can be used to select certain face and person detections to associate together as a whole person detection, which can be referred to as an 'appearance.'

Whole person association with face screening

Implementations set forth herein relate to an automated assistant that uses circumstantial condition data, generated based on circumstantial conditions of an input, to determine whether the input should affect an action been initialized by a particular user. The automated assistant can allow each user to manipulate their respective ongoing action without necessitating interruptions for soliciting explicit user authentication. For example, when an individual in a group of persons interacts with the automated assistant to initialize or affect a particular ongoing action, the automated assistant can generate data that correlates that individual to the particular ongoing action. The data can be generated using a variety of different input modalities, which can be dynamically selected based on changing circumstances of the individual. Therefore, different sets of input modalities can be processed each time a user provides an input for modifying an ongoing action and/or initializing another action.

Dynamically assigning multi-modality circumstantial data to assistant action requests for correlating with subsequent requests

Assigning priority for an automated assistant according to a dynamic user queue and/or multimodality presence detection

The orientation of imagery relative to a compass bearing may be determined based on the position of the sun or other information relating to celestial bodies captured in the image.

Determining compass orientation of imagery

A method to determine whether to perform recognition on an image or a video. The method includes detecting a person in an image or a video by determining a person image region that corresponds to boundaries of the person or a face image region that corresponds to a position of a face of the person. The method further includes analyzing pixel values that correspond to the boundaries of the person or the position of the face of the person to generate a recognition avoidance signal. The method further includes determining whether the recognition avoidance signal indicates that an action was taken to discourage recognition of the person. The method further includes responsive to the recognition avoidance signal indicating that the action was taken to discourage recognition of the person, declining to perform recognition of the person.

Detecting actions to discourage recognition

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for partitioning videos. In one aspect, a method includes obtaining a partition of a video into one or more shots. Features are generated for each shot, including visual features and audio features. The generated features for each shot are provided as input to a partitioning neural network that is configured to process the generated features to generate a partitioning neural network output. The partition of the video into one or more chapters is determined based on the partitioning neural network output, where a chapter is a sequence of consecutive shots that are determined to be taken at one or more locations that are semantically related.

Partitioning videos

A method of dynamically scoring implicit interactions can include receiving, by an interaction analysis server from an imaging system, a plurality of images of an environment captured in a period of time corresponding to display of a presentation, retrieving, by the interaction analysis server, content information corresponding to content of the presentation, and identifying, by a presence detector of the interaction analysis server, that a face appears in at least one image of the plurality of images. The method can further include matching, by a facial recognition system of the interaction analysis server, the face with a user identifier, retrieving, by a client device information retriever, client device information associated with the user identifier and corresponding to the period of time, and calculating, by a client device interaction score calculator, a client device interaction score based on one or more correspondences between the client device information and the content information.

Systems and methods for dynamically scoring implicit user interaction

Aspects of the disclosure relate to identifying visited travel destinations from a set of digital images associated with users of a social networking system. For example, one or more computing devices provide access to an individual user's account, including the individual user and other users affiliated with the individual user via the social networking system. One or more digital images are received from a computing device associated with the individual user and from one or more second computing devices associated with the other users of the social networking system. From each digital image, a geo-location is determined for each digital image. The one or more computing devices display each geo-located image on a map at a position corresponding to the determined geo-location for the geo-located image.

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Fremont, CA, United States of America

Andrew C Gallagher