Apple Inc.

Sift Science, Inc.

International Business Machines Corporation

Dropbox, Inc.

Adobe, Inc.

Amazon Technologies, Inc.

Chang Liu

Lan H Hoang

Daniel D Sunshine

Christopher Schultz

David M Kindlon

Michael B Nussbaum

Andrew L Rosenberg

Mark J Beesley

Andrew Sterian

Breton M Saunders

David A Bolt

Peter W Mash

Pranav Piyush

Xiaoyi Wang

Wei Liu

Steven J Keating

Yuyang Guo

Khurram Zia

Angela Li

Pradhan Bagur Umesh

Matt Bond

Lawson Fulton

Yuan Yuan Li

Takayoshi Katahira

Hui Wang

Shuo Wu

Nicholas Benavides

Yanqing Bao

Yuan Zhuang

Kang Xu

Bian Li

Yunting Wang

Ning Wang

Paul John Asente

Steven P Cardinali

Qi Rong Wang

Lin Lin

Ying Zhou

Dennis R Pyper

Zhenyu Yan

Zhen Xiao

Meng Chi Lee

Amir Salehi

Raghunandan Chaware

Xiao F Wang

Jun He

Michal Lukac

Siddharth Nangia

Wyeman Chen

Wuyang Dai

Vu Thanh Vo

Helen Marushchenko

Chun Lin Zhang

Xinyue Liu

Shengyun Peng

Shayan Chandrashekar

Sangeeta Varma

Xiaolei Zhou

Frederick Ian St Johnston

Suofei Wu

Natasha Sehgal

Disclosed are systems, methods, and non-transitory computer-readable storage media for managing tasks in a content management system. For example, the content management system can collect, aggregate, and/or store task data assigned to a user from across projects and/or content items. The user can select to view tasks for a particular project and/or from across all projects. The content management system can generate a single graphical user interface for presenting the task data associated with the user. The content management system can generate a graphical user interface the allows the user to move tasks from one task status graphical element (e.g., task status container) to another task status graphical element to change the status of a task associated with the user. Thus, the user can quickly and easily view the tasks assigned to the user and update the status of tasks in an easy to use graphical user interface.

Managing tasks in a content management system

A machine learning-based method for accelerating a generation of automated fraud or abuse detection workflows in a digital threat mitigation platform includes identifying a plurality of distinct digital event features indicative of digital fraud; automatically deriving a plurality of distinct digital event decisioning criteria based on the plurality of distinct digital event features and a digital event data corpus associated with a target subscriber; automatically constructing a probationary automated fraud or abuse detection workflow based on the plurality of distinct digital event decisioning criteria, and deploying the probationary automated fraud or abuse detection workflow to a target digital fraud prevention environment associated with the target subscriber.

Systems and methods for generating a probationary automated-decisioning workflow in a machine learning-task oriented digital threat or digital abuse mitigation system

A system and method for accelerated anomaly detection and replacement of an anomaly-experiencing machine learning-based ensemble includes identifying a machine learning-based digital threat scoring ensemble having an anomalous drift behavior in digital threat score inferences computed by the machine learning-based digital threat scoring ensemble for a target period; executing a tiered anomaly evaluation for the machine learning-based digital threat scoring ensemble that includes identifying at least one errant machine learning-based model of the machine learning-based digital threat scoring ensemble contributing to the anomalous drift behavior, and identifying at least one errant feature variable of the at least one machine learning-based model contributing to the anomalous drift behavior; generating a successor machine learning-based digital threat scoring ensemble to the machine learning-based digital threat scoring ensemble based on the tiered anomaly evaluation; and replacing the machine learning-based digital threat scoring ensemble with the successor machine learning-based digital threat scoring ensemble.

Systems and methods for accelerated detection and replacement of anomalous machine learning-based ensembles and intelligent generation of anomalous artifacts for anomalous ensembles

Systems and methods for accelerated detection and replacement of anomalous machine learning-based digital threat scoring ensembles and intelligent generation of anomalous artifacts for anomalous ensembles

Methods, systems, and computer-readable media for catalog item selection based on visual similarity are disclosed. A similarity detection system selects candidate items for an unavailable item in a catalog. The candidate items are selected from one or more item categories in which the unavailable item is classified. The system determines respective similarity scores for at least some of the candidate items with respect to the unavailable item. For a particular candidate item, the similarity score is determined based at least in part on a visual similarity between an image of the unavailable item and an image of the particular candidate item. The system selects, from the candidate items, a set of visually similar items to the unavailable item based at least in part on the similarity scores. The visually similar items are available to the user.

Catalog item selection based on visual similarity

A system and method for adaptively sampling a corpus of data samples for improving an accuracy of a predictive machine learning model includes: identifying the corpus of data samples, wherein each data sample of the corpus of data samples is associated with a machine learning-derived threat inference value; stratifying the corpus of data samples into a plurality of distinct strata based on the machine learning-derived threat inference value associated with each data sample of the corpus of data samples; adaptively sampling the plurality of distinct strata; constructing a machine learning training corpus comprising a plurality of data samples based on the adaptive sampling of the plurality of distinct strata; and training the predictive machine learning model based on the machine learning training corpus.

Systems and methods for an adaptive sampling of unlabeled data samples for constructing an informative training data corpus that improves a training and predictive accuracy of a machine learning model

An item may include fabric having insulating and conductive yarns or other strands of material. The conductive strands may form signal paths. Electrical components can be mounted to the fabric. Each electrical component may have an electrical device such as a semiconductor die that is mounted on an interposer substrate. The interposer may have contacts that are soldered to the conductive strands. A protective cover may encapsulate portions of the electrical component. To create a robust connection between the electrical component and the fabric, the conductive strands may be threaded through recesses in the electrical component. The recesses may be formed in the interposer or may be formed in a protective cover on the interposer. Conductive material in the recess may be used to electrically and/or mechanically connect the conductive strand to a bond pad in the recess. Thermoplastic material may be used to seal the solder joint.

Electrical components attached to fabric

Introduced here are approaches for identifying the optimal send time for messages by accounting for hidden confounders, such as the content of those messages, delivery channel, etc. These approaches use a causal inference framework to discover and then remove the impact of hidden confounders. These approaches may be employed by a marketing and analytics platform (or simply 'marketing platform') that may be used to design, implement, or review digital marketing campaigns. The marketing platform can consider the send time as a treatment and then employ machine learning (ML) models that consider the send time, features of the recipient, and hidden confounders to produce a ranked series of send times with the effect of the hidden confounders marginalized. Approaches to performing offline evaluations that mimic A/B tests using data related to existing field experiments are also introduced here.

Optimization of send time of messages

Methods and systems disclosed herein relate generally to systems and methods for transforming document elements in response to modifications to a layout of a document. A layout-modification application identifies, from a first document having a first document layout, a first set of measurements of a document element and a first location of the document element within the first document. Based on an aspect-ratio difference between the first document layout and a second document layout, the layout-modification application selects a set of transformation rules that specify, for the document element, a second set of measurements and a second location within a second document. To select the particular set of transformation rules, the layout-modification application uses the determined aspect-ratio difference. The layout-modification application applies the selected set of transformation rules to the document element.

Transforming document elements for modified document layouts

Frames and other structures for system-in-package modules that may allow components on boards in the modules to communicate with each other.

Interconnect frames for SIP modules

Readily manufactured structures for sealing or encapsulating devices in system-in-a-package modules, such that the modules are easily assembled, have a low-profile, and are space efficient. One example may provide readily manufactured covers for SIP modules. These modules may be easily assembled by attaching the cover to a top side of a substrate. These SIP modules may have a low-profile, for example when their height is reduced using one or more recesses in a bottom surface of a top of the recess, where the one or more recesses are arranged to accept one or more components. These SIP modules may be made space efficient by placing an edge of a cover near an edge of the substrate and connecting the plating of the cover using side plating on, or vias through, the substrate.

Low-profile space-efficient shielding for SIP module

Growing community of inventors

San Francisco, CA, United States of America

Chang Liu