Accenture Global Solutions Limited

Accenture Global Services Gmbh

Disney Enterprises, Inc.

The Torrington Company

Sargent Manufacturing Company

Corbin Russwin, Inc.

Timken US Corporation

Assa Abloy Access and Egress Hardware Group, Inc.

David T Nguyen

Matthew Thomas Short

Leeann Chau Tuyet Dang

Mary Elizabeth Hamilton

Robert Dooley

Erika Varis Doggett

Bryan Michael Walker

Anna M Wolak

Ziqiu Li

Christopher John Hawkins

Serena Tsiao-Yi Cheng

Hyon S Chu

Erick Keyu Qi

Brian R Fournier

Todd C Zimmer

Mark I Jurras, Iii

Damon J Lenk

Richard L Lemoine

Richard W Browner

Christine E Voelker

Michael Bedford

Weichu Cui

Yingying Zhong

Andrew E Fano

Michael E Bechtel

Alan Robert Selfridge

Scott Kasper

Robert Domnitz

Paul Justin Mahler

Jordan K Buller

Sunil Shettigar

Allison Youngdahl

John H Rhodes

Jerzy Baginski

John S Hayward

Binghao Tang

Nan Ke

William Middelaer

Hailing Zhou

Keyu Qi

Ray Nolan

Mark A Fuller

Justin Harris

Patrick Lai

A door lock may include a latch bolt head and a blocking pin. The latch bolt head may be configured to move between an extended position and a retracted position to selectively engage a latch head pocket of a door jamb. The latch bolt head may also be configured to move between a first rotational position and a second rotational position, wherein the blocking pin is configured to prevent movement of the latch bolt head from the first rotational position to the second rotational position when the blocking pin is in an engaged position.

Push through latch

Implementations are directed to receiving a first set of images included in a first video captured by a camera that monitors a human performing a task; processing the first set of images using a first machine learning (ML) model to determine whether the first set of images depicts a gesture that is included in a predefined set of gestures; in response to determining that the first set of images depicts a gesture included in a predefined set of gestures, processing a second set of images included in the first video using a second ML model to determine a first gesture type of the gesture; comparing the first gesture type with a first expected gesture type to determine whether performance of the task conforms to a standard operating procedure (SOP) for the task; and providing feedback representative of a comparison result in a user interface.

Behavior-based standard operating procedure detection

According to one implementation, a system includes a computing platform having a hardware processor and a system memory storing a software code including a trained neural network (NN). The hardware processor executes the software code to receive an input image including a pixel anomaly, identify, using the trained NN, one or more salient regions of the input image, and determine whether the pixel anomaly is located inside any of the one or more salient regions. The hardware processor further executes the software code to assign a first priority to the pixel anomaly when it is determined that the pixel anomaly is located inside any of the one or more salient regions, and to assign a second priority, lower than the first priority, to the pixel anomaly when it is determined that the pixel anomaly is not located inside any of the one or more salient regions.

Saliency prioritization for image processing

A system includes a hardware processor and a memory storing a software code including a predictive model. The hardware processor executes the software code to receive an input including an image having a pixel anomaly, and image data identifying the location of the pixel anomaly in the image. The software code uses the predictive model to extract a global feature map of a global image region of the image, the pixel anomaly being located within the global image region; to extract a local feature map of a local image region of the image, the pixel anomaly being located within the local image region and the local image region being smaller than the global image region; and to predict, based on the global feature map and the local feature map, a distraction level of the pixel anomaly within the image.

Automated prediction of pixel error noticeability

A button assembly for a door lock may include a button and a shank. The button may be toollessly couplable to the shank to allow force transmission between the button and the shank. The button may transfer linear force and torque along a longitudinal axis of the button to the shank to allow a state of an associated lock body to be changed.

Adjustable button mechanism

A computer system may access an attributization rule and convert, based on the attributization rule, profile data into attributes for a robust recipient profile. The system may identify, based on tag logic, tags that are associated with the attributes. The tag logic may include instructions to compare the attributes with predetermined attributes associated with the tags. The system may map the identified tags to the recipient identifier. The system may perform communication with a mobile device based on the robust recipient profile and a real-time location of the mobile device. To perform the communication, the system may monitor the plurality of data channels for data comprising real-time location information for the mobile device. The system may determine the mobile device is proximate to a physical object that is associated with the tag and the system may transmit offer information to the mobile device.

Robust multichannel targeting

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using location data to identify and provide services in association with items appearing in captured images. One of the methods includes receiving, from a device, an image and location data representing the device's physical location, determining, based on the location data, that a particular set of one or more locations are within a threshold distance of the device's physical location, accessing, for each of the one or more locations in the particular set, item information that indicates one or more items that are associated with the location, determining, based on the accessed item information, that the image likely shows a particular item that is associated with one or more locations in the particular set, and providing, to the device, instructions for presentation of information about (i) the particular item and (ii) one or more locations in the particular set that are associated with the particular item.

Identifying items in images

A computer system constructs a decision-predictive recipient profile using sensatory data tied to an online profile of a recipient. After obtaining base sensatory data tied to the online profile of the recipient, the system may filter the base sensatory data by searching the base sensatory data for one or more machine-cognizable characteristics. The filtered sensatory data may be provided to an execution group, which may review displays of the sensatory data. Responsive to the displays of the sensatory data, the execution group may generate descriptors of the content of the filtered sensatory data and send the descriptors to the system. The system may process the descriptors to generate or augment the decision-predictive recipient profile.

Complex system architecture for sensatory data based decision-predictive profile construction and analysis

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for management of physical items. A method includes actions of receiving, for each of a plurality of users, a list that identifies types of items that are indicated as of interest to the user, determining a particular item that is likely of interest to the plurality users, receiving, for each of the plurality of users, reservation information that indicates a particular geographic area and a corresponding time that the user is scheduled to visit, determining that each of the plurality of users is scheduled to visit at a particular time period, identifying a facility physically located in or nearby the particular geographic area, determining that the particular item is not presented in a particular area of the facility at the particular time period, and generating instructions to cause the particular item to be presented.

Management of physical items based on user analytics

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that relate to obtaining data that indicates a time that a MAC address was detected at a temporary site, obtaining data that indicates a time that a MAC address was detected at a non-temporary site, obtaining data that indicates that a first interaction with a first item occurred at the temporary site, obtaining data that indicates that a second interaction with a second item occurred at the non-temporary site, determining, that the MAC addresses match, determining that the time that the MAC address was detected at the temporary site occurred before the time that a MAC address was detected at the non-temporary site, determining that the first item and the second item match, and classifying the detection of the MAC address at the non-temporary address as being caused by the temporary site.

Interactions platform

Growing community of inventors

San Jose, CA, United States of America

David T Nguyen