Fujifilm Business Innovation Corp.

Google Inc.

Fuji Xerox Co., Ltd.

Adobe, Inc.

Hao Hu

Qiong Liu

Yanxia Zhang

Francine R Chen

Andreas Girgensohn

Daniel Avrahami

Yanjun Zhu

Zhe Lin

Lynn D Wilcox

Zhaowen Wang

Joon-Young Lee

Yin-Ying Chen

Ray Yuan

Boris Dali

Aspects of the disclosure provide for a database container orchestrator system for deploying and managing containerized databases running on a computing platform. A system can include a database daemon for translating first commands, such as remote procedure calls, into second commands, such as queries built from SQL statements, which cause the database to retrieve data or perform other functions otherwise unavailable to an application incompatible with the database. The first commands received by the database daemon can be defined by an interface of a computing platform running containers for both the database and the database daemon. The interface can be an Application Programming Interface (API), which can include definitions of commands for starting, configuring, and running a containerized database, regardless of the different codebases represented by the containerized database and other applications.

Database container orchestrator

A computer-implemented method, comprising receiving an order associated with a user, and capturing information associated with a physical attribute of the user; for the order being completed, performing a matching operation on the physical attribute of the user and information associated with a region of the user that includes the physical attribute of the user and respective physical attributes of other users, to determine a closest match; and generating an output that identifies the user associated with the closest match in the information associated with the region of the user, and providing the output to a server to deliver the order to the user.

Systems and methods for reducing service errors for restaurants

A computer-implemented method comprises receiving an input associated with the arrival of an entity, performing a classification on the input to determine a purpose of the arrival of the entity, and based on a determined classification of the purpose of the arrival of the entity, invoking an action.

Building entry management system

A computer-implemented method of learning sensory media association includes receiving a first type of nontext input and a second type of nontext input; encoding and decoding the first type of nontext input using a first autoencoder having a first convolutional neural network, and the second type of nontext input using a second autoencoder having a second convolutional neural network; bridging first autoencoder representations and second autoencoder representations by a deep neural network that learns mappings between the first autoencoder representations associated with a first modality and the second autoencoder representations associated with a second modality; and based on the encoding, decoding, and the bridging, generating a first type of nontext output and a second type of nontext output based on the first type of nontext input or the second type of nontext input in either the first modality or the second modality.

System and method for learning sensory media association without using text labels

A computer-implemented method, comprising, extracting a video sequence, the video sequence including a series of video frames, estimating current poses of one or more subjects within each video frame and determining joint locations for a joint associated with the one or more subjects within each video frame, computing optical flows from the video sequence, extracting motion features from the video sequence based on the optical flows, and predicting at least one future pose.

System and method for vision-based joint action and pose motion forecasting

A computer-implemented method, comprising extracting each frame of a video sequence, estimating current poses of a subject within each frame and determining joint locations for a joint associated with the subject within each frame, computing optical flows between each pair of consecutive video frames, extracting motion features from each video frame of the video sequence based on the optical flows, encoding state information based on the current poses and the motion features, determining a current action label for each motion feature in the state information for a first frame; predicting future action labels for each motion feature in a second frame subsequent to the first frame, based on the current pose, action label and state information, predicting future poses for each motion feature in the second frame based on the current poses and the state information; and refining the current action label, the future action labels, and the future poses.

Techniques and systems are described to generate a compact video feature representation for sequences of frames in a video. In one example, values of features are extracted from each frame of a plurality of frames of a video using machine learning, e.g., through use of a convolutional neural network. A video feature representation is generated of temporal order dynamics of the video, e.g., through use of a recurrent neural network. For example, a maximum value is maintained of each feature of the plurality of features that has been reached for the plurality of frames in the video. A timestamp is also maintained as indicative of when the maximum value is reached for each feature of the plurality of features. The video feature representation is then output as a basis to determine similarity of the video with at least one other video based on the video feature representation.

Growing community of inventors

Sunnyvale, CA, United States of America

Hao Hu