Dropbox, Inc.

Strato, Inc.

Asf-Keystone, Inc.

Other

Amsted Rail Company, Inc.

Thomas R Berg

David J Kriegman

Peter Neil Belhumeur

Nils Peter Welinder

Jongmin Baek

Ying Xiong

Simon Kozlov

Kevin P McGarvey

Nathan Reese

Thomas J Heyden

Christopher J Clark

Aditi Jain

Jason Reiling

Walter Kim

Larry Hixon

Ralph H Schorr

George Currier

Julius I Pershwitz

Karandeep Johar

Aaron Feldstein

Manveer Singh Chawla

Swapnil Zarekar

Jeffrey Ruback

Robert Kajic

Joe Nudell

Jenny Dong

Jared Wilson

Luke Thompson

Kevin Mcgarvey

Robin Berg

Thomas R Callahan

Anthony J Bauer

Christopher T Lesniewski-Laas

Jeffrey M Ruback

Bryan Guillemette

Panashe Machinda Fundira

David Lichtenberg

Hector Chaidez

Brandon Obas

Mingming Liu

Zachary Smetana

Vidit Bhargava

Michael Fortier

David Yuh-Shyang Lin

Kevin Li

Kevin P Mcgarvey

Jeff Ruback

Nathan J Reese

Kurt Fisher

James Myers

The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize machine-learning models to classify content items and automatically organize the content items within a file structure according to their content item classifications. For instance, a content item classification system generates one or more content item classification models to determine classifications for content items and/or folders. In some instances, the classification system detects when new content items are added to a smart folder, determines destination folders to which the content items belong based on classifying the content items, and automatically moves the content items accordingly. In various instances, the classification system generates and utilizes a classification model to organize content items into dynamically-generated folders. In example implementations, the classification system generates and utilizes a classification model to automatically organize existing content items into existing folders.

Classifying and organizing digital content items automatically utilizing content item classification models

The present disclosure is directed toward systems and methods to quickly and accurately identify boundaries of a displayed document in a live camera image feed, and provide a document boundary indicator within the live camera image feed. For example, systems and methods described herein utilize different display document detection processes in parallel to generate and provide a document boundary indicator that accurately corresponds with a displayed document within a live camera image feed. Thus, a user of the mobile computing device can easily see whether the document identification system has correctly identified the displayed document within the camera viewfinder feed.

Live document detection in a captured video stream

A remote-operated palm tree trimming system and apparatus, configured to ascend a palm tree safely via treaded tires and trim the tree via at least one saw blade. The tires are disposed in communication with actuators which apply pressure against the trunk of the tree, ensuring that the tires maintain adequate grip to overcome gravity during ascent. A remote control, operated by user on the ground, enables control of the wheels, actuators, and saw blade, facilitating manipulation of the device to effectively trim the tree remotely, ensuring the safety of the operator.

Palm tree trimmer system

The present disclosure is directed toward systems and methods that efficiently and effectively generate an enhanced document image of a displayed document in an image frame captured from a live image feed. For example, systems and methods described herein apply a document enhancement process to a displayed document in an image frame that result in an enhanced document image that is cropped, rectified, un-shadowed, and with dark text against a mostly white background. Additionally, systems and method described herein determine whether a stored digital content item includes a displayed document. In response to determining that a stored digital content item does include a displayed document, systems and methods described herein generate an enhanced document image of a displayed document included in the stored digital content item.

Enhancing documents portrayed in digital images

One or more embodiments of a digital content system allow a user to conveniently search and/or navigate through a collection of digital content items. In particular, a user can interact with a client device to search for and identify one or more digital content items within a collection of digital content items. For example, the digital content system may provide a photo from a collection of photos via a graphical user interface. The digital content system can receive a user input identifying a selection of one or more visual features within the photo. Based on the selected visual feature(s), the digital content system may identify photos from the collection of photos that include the identified visual feature(s) and provide access to the identified photos via the graphical user interface.

Navigating digital content using visual characteristics of the digital content

Computer-implemented techniques for sematic image retrieval. According to one technique, digital images are classified into N number of categories based on their visual content. The classification provides a set of N-dimensional image vectors for the digital images. Each image vector contains up to N number of probability values for up to N number of corresponding categories. An N-dimensional image match vector is generated that projects an input keyword query into the vector space of the set of image vectors by computing the vector similarities between a word vector for the input query and a word vector for each of the N number of categories. Vector similarities between the image match vectors and the set of image vectors can be computed to determine images semantically relevant to the input query.

Semantic image retrieval

Techniques are provided for delayed processing for arm policy determination for content management system messaging, including, during a delayed processing window, receiving reward data for arm actions taken, where the arm actions were chosen based on a previous version of an arm choice policy, and the previous version of the arm choice policy was determined based on a previous set of reward data for a previous set of arm actions taken. When the delayed processing window has closed, a new arm choice policy is determined based at least in part on the action-reward data, and the previous set of reward data and/or the previous arm choice policy. After a request to choose an arm choice is received, a particular arm action to take is determined based on the new arm choice policy. This chosen arm is provided in response to the request.

Delayed processing for arm policy determination for content management system messaging

Growing community of inventors

St. Louis, MO, United States of America

Thomas R Berg