Sun Microsystems, Inc.

Amazon Technologies, Inc.

Mobile Iron, Inc.

Google Inc.

Motorola Corporation

Goldman Sachs &co. LLC

Rahul Sharma

Sanjeev Krishnan

Vladimir Matena

Masood Mortazavi

John S Drewry

Venkata Sastry Akella

Beryl Tomay

Sandeep Bhatia

Xiaodan Song

Siddharth Vivek Joshi

Vijay Dheeraj Reddy Mandadi

Gunja Agrawal

Xuerui Wang

Vinoo Vasudevan

Jordan Barry Brest

Vikram Madan

Roberto R Chinnici

Pronob Ashwin

Venkata Keerthana Atchutuni

Jie Han

Daniel Li

Gary Cribb

Kevin Lee Davenport, Jr

Swaminathan Sivasubramanian

Nafea Bshara

Stefano Stefani

Wei Xiao

Sudipta Sengupta

Leo Parker Dirac

Amjad Hussain

Andrea Olgiati

John David Bruner

Pietro Perona

Paul Burchard

Poorna Chand Srinivas Perumalla

Matthew James Wood

Diwakar Chakravarthy

Mark W Hapner

David Robert Thomas

William A Shannon

Thulasiraman Jeyaraman

Nagaraju Shiramshetti

Fedor Zhdanov

Dominic Rajeev Divakaruni

James Robert Blair

Charles H Bell

Bratin Saha

Jalaja C Kurubarahalli

Prabhu Anand Nakkeeran

Pragya Agarwal

Sindhu Chejerla

Rashmi Murthy

Kenneth David Saks

Dominic Dotterrer

Asif Hussain

Arvind Jayasundar

Babu Srinivasan

Timothy Wells Hunt

Harish Prabandham

Rajankumar Singh

Douglas C Kohlert

Lachlan Maxwell

Olivier Robert Munn

Christopher T George

Marco Argenti

Raj Mahajan

Fabiano Romeiro

Jianliang Zhao

Phillip B Goodwin

Qingqing Ouyang

Kathleen A Stout

Hareesh Lakshmi Narayanan

Zdravko Pantic

Mark Klunder

Umang Wadhwa

Daniel Evans

Karan Kothari

Eftiquar Shaikh

Jun Wu

Cory Pruce

Samuel Oshin

Zahid Rahman

Sankalp Srivastava

A method for compliance with Know Your Customer (KYC) and other regulations includes a pseudonymous globally unique identifier stored on a blockchain that associates a pseudonymous first party address with a globally unique identifier representing the vetted identity of the owner of the address. The method also includes a trusted third party issuing a verifiable credential for a first pseudonymous party to a proposed transaction to a second pseudonymous party to the transaction.

Pseudonymous transactions on blockchains compliant with know your customer regulations and reporting requirements

Systems and methods provide for submission, verification, and validation of one or more code packages associated with provisioning logic for third-party applications. A user may request verification as a publisher and submit a code package for use with a third-party application within a resource provider environment. The code package may be tested and, upon passing, be published to a public repository for discovery and use by other users. The code package may form part of an extension that is integrated into a template that enables automated provisioning of resources and applications.

Third-party extension integration, verification, and publication for distributed environments

Generating granular clusters for real-time processing is provided. The systems can identify tokens based on aggregating input from computing devices over a time interval. The systems can identify, based on metrics, a subset of tokens for cluster generation. The systems can generate, via a clustering technique, token clusters from the subset of the tokens, each of the token clusters comprising two or more tokens from the subset of the tokens. The systems can apply a de-duplication technique to each of the token clusters. The systems can apply a filtering technique to the token clusters to remove tokens erroneously grouped in a token cluster. The systems can assign, based on a selection process, a label for each of the token clusters. The systems can activate, based on a number of remaining tokens in each of the token clusters, a subset of the token clusters for real-time content selection.

Granular cluster generation for real-time processing

This application is directed to presenting a unified user interface on a network-connected television device. The unified user interface displays media content recommendations selected and organized based on knowledge of a user (e.g., search queries, search results, watch history, purchase history, physical activities). The unified user interface also includes a focus area for displaying a series of media content items (e.g., an advertisement) sequentially according to a temporal order for the purposes of promoting a media content item, product, event or service. In some implementations, playback of a media content item presented on the unified user interface relies on a collaborative implementation of a corresponding media player application and a media content casting application, particularly when the play involves a restricted mode. The content casting application is enabled to play the media content item at the restrict mode when the media player application does not operate in the restricted mode.

Mixing of media content items for display on a focus area of a network-connected television device

Machine learning based automatic audience segment in ad targeting

Techniques for active learning-based data labeling are described. An active learning-based data labeling service enables a user to build and manage large, high accuracy datasets for use in various machine learning systems. Machine learning may be used to automate annotation and management of the datasets, increasing efficiency of labeling tasks and reducing the time required to perform labeling. Embodiments utilize active learning techniques to reduce the amount of a dataset that requires manual labeling. As subsets of the dataset are labeled, this label data is used to train a model which can then identify additional objects in the dataset without manual intervention. The process may continue iteratively until the model converges. This enables a dataset to be labeled without requiring each item in the data set to be individually and manually labeled by human labelers.

Custom labeling workflows in an active learning-based data labeling service

Techniques for partitioning data flow operations between execution on a compute instance and an attached accelerator instance are described. A set of operations supported by the accelerator is obtained. A set of operations associated with the data flow is obtained. An operation in the set of operations associated with the data flow is identified based on the set of operations supported by the accelerator. The accelerator executes the first operation.

Framework integration for instance-attachable accelerator

Techniques for active learning-based data labeling are described. An active learning-based data labeling service enables a user to build and manage large, high accuracy datasets for use in various machine learning systems. Machine learning may be used to automate annotation and management of the datasets, increasing efficiency of labeling tasks and reducing the time required to perform labeling. Embodiments utilize active learning techniques to reduce the amount of a dataset that requires manual labeling. As subsets of the dataset are labeled, this label data is used to train a model which can then identify additional objects in the dataset without manual intervention. The label data can be added to an augmented manifest, the augmented manifest can be used to filter the dataset to perform further labeling jobs on the same or different subsets of the dataset.

Active learning-based data labeling service using an augmented manifest

Technologies are provided for managing computer system resources using natural language statements. A natural language statement can be received from a user computing device by a management service. The natural language statement can be analyzed to identify an executable command, and the command can be executed against one or more system resources. If the system resources are located in separate computing environments, different operations can be used to target the system resources in the separate computing environments. A script repository can be searched to identify an executable script containing the command referenced by the received natural language statement. A message can be transmitted to the user device, recommending execution of the identified script. In a different or further embodiment, if a given user is not authorized to execute a given command or script, a request for authorization can be sent to a supervisor of the given user.

System management using natural language statements

Techniques for active learning-based data labeling are described. An active learning-based data labeling service enables a user to build and manage large, high accuracy datasets for use in various machine learning systems. Machine learning may be used to automate annotation and management of the datasets, increasing efficiency of labeling tasks and reducing the time required to perform labeling. Embodiments utilize active learning techniques to reduce the amount of a dataset that requires manual labeling. As subsets of the dataset are labeled, this label data is used to train a model which can then identify additional objects in the dataset without manual intervention. The process may continue iteratively until the model converges. This enables a dataset to be labeled without requiring each item in the dataset to be individually and manually labeled by human labelers.

Active learning loop-based data labeling service

Systems and methods are described for management of data transmitted between computing devices in a communication network. An administrative component can configure one or more devices in the communication path of messages to be exchanged by devices to interpret codes embedded in the communication messages. A receiving device can review incoming messages for one or more processing codes or instructions that are embedded in the portion of the communication typically utilized solely to identify the subject matter of the communication, generally referred to as the topic portion of the communication. The receiving devices can then process the embedded codes to determine how the communication message will be routed or otherwise processed.

Identity management for coordinated devices in a networked environment

Techniques for generating and utilizing machine learning based adaptive instructions for annotation are described. An annotation service can use models to identify edge case data elements predicted to elicit differing annotations from annotators, 'bad' data elements predicted to be difficult to annotate, and/or 'good' data elements predicted to elicit matching or otherwise high-quality annotations from annotators. These sets of data elements can be automatically incorporated into annotation job instructions provided to annotators, resulting in improved overall annotation results via having efficiently and effectively &#x201c;trained&#x201d; the annotators how to perform the annotation task.

Machine learning based adaptive instructions for annotation

Techniques for migrating servers from customer networks into service provider networks are described. A backup proxy can be deployed in a customer's network and associated with one or more servers in the customer's network and with a server migration service of a service provider network. A customer can identify a server in the customer's network to migrate and the server migration service coordinates the migration with the backup proxy. The backup proxy can be instructed to obtain replication data for the server, obtain configuration data associated with the server, and upload the replication data and configuration data to the service provider network. The service provider network uses the replication data and configuration data to create a migrated copy of the server at the service provider network.

Migrating servers from user networks to a user-selected type of hypervisor at a service provider network

Localized identity management in limited communication networks

Disclosed are approaches for using overlapping geo-fences to confirm delivery of a shipment. A first client computing device and a second client computing device may be in data communication with a server computing device. The server computing device may receive a delivery notification from the first client computing device. The server computing device may receive a first position of the first client computing device and a second position of the second client computing device. The server computing device may then determine that the second position is within a threshold distance of the first position or vice versa. Finally, the server computing device may generate a delivery confirmation in response to a first determination that the second position is within a threshold distance of the first position and a second determination that the first position is within a threshold distance of the second position.

Delivery confirmation using overlapping geo-fences

Growing community of inventors

San Jose, CA, United States of America

Rahul Sharma