Fuji Xerox Co., Ltd.

Fujifilm Business Innovation Corp.

Chidansh Amitkumar Bhatt

Matthew L Cooper

David Ayman Shamma

Chelhwon Kim

Hideto Oda

Jian Zhao

Patrick Chiu

Laurent Denoue

Scott Carter

Qiong Liu

Donald G Kimber

David Michael Hilbert

Miteshkumar Patel

Yanxia Zhang

Shinji Onishi

Kandha Sankarapandian

Steven Cebertowicz

Ram Sriram

A computer-implemented method, comprising receiving one or more inputs associated with a master services agreement (MSA), and generating an output that includes smart contract information; performing a simulation and receiving user input to interactively adjust the smart contract information and implement a smart purchase agreement (SPA) in blockchain and in a state engine; and automatically generating a smart purchase order (SPO) under the MSA, wherein the SPO is automatically validated for a conflict against the MSA in real-time, and for the case of a conflict, a conflict warning is provided.

System for simulating and creating smart purchase agreements from master service agreements

Example implementations described herein are directed to an interface for a worker dispatch system configured to dispatch routes and schedules for workers on a manufacturing floor. Example implementations can involve a system configured to execute a lot ordering and route optimization for each of a plurality of workers; determine rack placement of the plurality of racks and total travel distance for each worker; generate a deployment plan for each worker from the lot ordering, the route optimization, the rack placement, and the total travel distance, the deployment plan involving a pick-up route and a schedule for each worker; provide the deployment plan to an interface configured to receive modifications to the deployment plan; and upon receiving instructions to deploy the deployment plan through the interface, provide the deployment plan to a device of each worker.

Optimized parts pickup list and routes for efficient manufacturing using frequent pattern mining and visualization

Example implementations described herein are directed to systems and methods for skill assessment, such as hand washing compliance in hospitals, or assembling products in factories. Example implementations involve body part tracking (e.g., hands), skeleton tracking and deep neural networks to detect and recognize sub-tasks and to assess the skill on each sub-task. Furthermore, the order of the sub-tasks is checked for correctness. Beyond monitoring individual users, example implementations can be used for analyzing and improving workflow designs with multiple sub-tasks.

Methods for real-time skill assessment of multi-step tasks performed by hand movements using a video camera

A computer-implemented method of localization for an indoor environment is provided, including receiving, in real-time, a dynamic query from a first source, and static inputs from a second source; extracting features of the static inputs by applying a metric learning convolutional neural network (CNN), and aggregating the extracted features of the static inputs to generate a feature transformation; and iteratively extracting features of the dynamic query on a deep CNN as an embedding network and fusing the feature transformation into the deep CNN, and applying a triplet loss function to optimize the embedding network and provide a localization result.

Indoor localization using real-time context fusion of visual information from static and dynamic cameras

A method of visualizing recommended pieces of media content is provided. The method includes identifying at least one piece of media content associated with a received content feature associated with a viewed piece of media content, selecting at least one additional piece of media content linked to the viewed piece of media content by a sequential relationship, generating a two-dimensional visualization based on a content similarity between the identified at least one piece of media content, the viewed piece of media content, and the selected at least one additional piece of media content and the sequential relationship; and displaying the generated two-dimensional visualization.

System and method for visualizing and recommending media content based on sequential context

Example implementations are directed to systems and methods for developing a topic model for a set of video series that include overlapping topics, wherein each video series includes segments directed to one or more of topics, wherein the topic model is based on topic distributions detected from the segments; analyzing auxiliary information, for each video series, to determine sequence information for the segments of the video series; and generating an array for topic transitions using sequence pattern mining on the distributions and the sequence information, wherein a next segment is determined for a selected segment based on an alignment decision using the array and sequence based scoring.

Systems and methods for topic guidance in video content using sequence mining

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Mountain View, CA, United States of America

Chidansh Amitkumar Bhatt