Google Inc.

Carbon, Inc.

Snap Inc.

Siemens Medical Solutions Usa, Inc.

Other

National Semiconductor Corporation

Eli Lilly and Company

University of Southern California

Carbon3d, Inc.

Goolge Inc.

Molecular Targeting Technologies. Inc.

Kai Chen

Michael David Marr

Jun Huang

Michael Ronald Cieslak

Farnaz Azmoodeh

Jiayao Yu

Jason P Rolland

Zahra Ferdowsi

Gregory Sean Corrado

Jeffrey Adgate Dean

Lloyd Mahlon Robeson

Justin Poelma

Joseph M DeSimone

James Goodrich

Robert Pinschmidt

Joseph C Walsh

Hartmuth C Kolb

Vani P Mocharla

Gang Chen

Dhanalakshmi Kasi

Qianwa Liang

Abhinay Sharma

Umesh B Gangadharmath

Peter J H Scott

Samy Bengio

Matthieu Devin

Tieming Zhao

Rajat Monga

Wei Zhang

David W Sculley, Ii

Henry Clifton Padgett

Tomas Mikolov

Julian Paul Grady

Gary R Holt

Sharat Chikkerur

Farhad Karimi

Robert K Pinschmidt, Jr

Robert Kniaz

Bhavesh R Mehta

Rob Kniaz

James Wilson

Bob E Feller

James A Gallagher

Alvin Rishi Rajkomar

Anjana Sinha

Diego Federici

Andrew Gordon Wright

Nicholas Charles Fox

Scott S Benson

Brian A Duclos

Eyal Oren

Steven G Dropsho

Zhiyong Gao

Andrew E Silverman

Olamide Valerie Olatunji

Quoc V Le

Yi Zhang

Bing Wang

Dhritiman Sagar

Yingwei Cui

Mark Rose

Bahman Rabii

Andrew M Dai

Nissan Hajaj

Joerg Heilig

Eric Wang

Ke Yang

Xiaodan Song

Mu San Zhang

Dustin S Jackson

Peter Junteng Liu

Wendy McKennon

Dan Siroker

Benjamin Irvine

Shioupyn Shen

Yu Liang

James Wexler

Thomas Lee Collier

Patrik Sundberg

Richard B Merrill

Scott Davies

Jay Michael Ponte

Fedir Poliakov

Amarnag Subramanya

Anurag Agarwal

Anna Katrin Szardenings

Peter Conti

Timothy Andrew James

Zhen Cheng

Xiaoyuan Chen

Tsuwei Chen

Arthur Asuncion

Marius C Milner

Changhui Liu

Chul Yu

Angshuman Guha

Istvan Marton

Tomas Lloret Llinares

Koon Y Pak

Brian D Gray

Systems, devices, media, and methods are presented for testing the capability of a mobile device to run a particular feature of an application. Each feature may include a package of code, a call, a state, and a condition; each condition correlated with a specific state. The call is a query that includes instructions to evaluate whether the application will run successfully if a particular feature is running. If the call fails, the feature condition remains Off, resulting in a rollback of the feature. If the call succeeds, the feature condition is changed to On. The system may include an experimentation framework on each mobile device for registering features, executing calls, logging results, and maintaining states and conditions.

Feature rollback for incapable mobile devices

Systems, devices, media, and methods are presented for releasing an application feature in incremental stages while monitoring the application for anomalies. The feature includes a package of code and an action setting. The methods in some implementations include identifying active devices on which the application has been installed, monitoring the application according to a set of metrics, activating the feature by changing its action setting for a first segment of the active devices, pausing the feature if an anomaly is detected among the set of metrics, and generating a repair ticket. As long as no anomaly is detected, the activating step proceeds for subsequent segments of the active devices, iteratively, until the release is completed. A feature rank may be used to process and release a plurality of features in order of priority.

Staged release of updates with anomaly monitoring

Example embodiments relate to a method for training a predictive model from data. The method includes defining a multitude of predicates as binary functions operating on time sequences of the features or logical operations on the time sequences of the features. The method also includes iteratively training a boosting model by generating a number of new random predicates, scoring all the new random predicates by weighted information gain with respect to a class label associated with a prediction of the boosting model, selecting a number of the new random predicates with the highest weighted information gain and adding them to the boosting model, computing weights for all the predicates in the boosting model, removing one or more of the selected new predicates with the highest information gain from the boosting model in response to input from an operator. The method may include repeating the prior steps a plurality of times.

Human-in-the-loop interactive model training

A method of forming a three-dimensional object is carried out by: (a) providing a carrier and an optically transparent member having a build surface, the carrier and the build surface defining a build region therebetween; (b) filling the build region with a polymerizable liquid, the polymerizable liquid including a mixture of (i) a light polymerizable liquid first component, and (ii) a second solidifiable component that is different from the first component; (c) irradiating the build region with light through the optically transparent member to form a solid polymer scaffold from the first component and also advancing the carrier away from the build surface to form a three-dimensional intermediate having the same shape as, or a shape to be imparted to, the three-dimensional object, and containing the second solidifiable component carried in the scaffold in unsolidified and/or uncured form; and (d) concurrently with or subsequent to the irradiating step, solidifying and/or curing the second solidifiable component in the three-dimensional intermediate to form the three-dimensional object.

Methods of producing three-dimensional objects with apparatus having feed channels

A method of forming a three-dimensional object is described. The method may use a polymerizable liquid, or resin, useful for the production by additive manufacturing of a three-dimensional object, comprising a mixture of (i) a light polymerizable liquid first component, and (ii) a second solidifiable component that is different from said first component.

Methods for producing three-dimensional objects

Aspects of the present disclosure involve systems and methods for performing operations comprising providing a messaging application comprising a feature to a client device, the feature being implemented by operations having alternative complexity levels, wherein a first complexity level represents a first amount of device resources consumed by a first set of operations, and wherein a second complexity level represents a second amount of device resources consumed by a second set of operations; determining that the first configuration rule is satisfied by a first property of the client device; and in response to determining that the first configuration rule is satisfied by the first property of the client device, causing the feature to be implemented on the client device by the first set of operations having the first complexity level that consume a greater amount of device resources than the second set of operations having the second complexity level.

Automated scaling of application features based on rules

In general, in one aspect, a method includes compiling user interaction statistics for a set of content items displayed in association with a first target media document having a non-textual portion, at least some of the content items associated with one or more keywords, based on the interaction statistics, associating the first target media document with at least some of the keywords associated with the content items, and based on a common attribute of the first target media document and a second target media document having a non-textual portion, associating the second target media document with at least some of the keywords assigned to the first target media document. Other aspects include corresponding systems, apparatus, and computer programs stored on computer storage devices.

Content keyword identification

Provided herein are methods of recycling additively manufactured objects (e.g., stereolithographically produced objects), the additively manufactured objects comprising linear polyurethane, branched polyurethane, linear polyurea, branched polyurea, a copolymer thereof, or a combination thereof. Compositions comprising the same and methods of use thereof are also provided.

Sustainable additive manufacturing resins and methods of recycling

Systems, devices, media, and methods are presented for assigning configuration parameters to unknown devices by comparing characteristics of unknown devices to characteristics of known devices. Characteristics of an unknown device are compared to characteristics of known devices to identify a similar device having known configuration parameters. The unknown devise is then assigned the configuration parameters of the identified similar device.

Device configuration parameter determination

Provided is a method of recycling an additively manufactured object into a reusable thermoplastic polymer, which method in some embodiments may include: (a) providing at least one additively manufactured object produced by stereolithography from a dual cure resin, the object comprising (i) a light polymerized polymer, and (ii) a heat polymerized polymer intermixed with said light polymerized polymer; (b) comminuting the object to produce a particulate material therefrom; (c) contacting the particulate material to a polar, aprotic solvent for a time and at a temperature sufficient to extract the heat polymerized polymer from the particulate material into said solvent, leaving residual particulate material comprising said light polymerized polymer in solid form; (d) separating the residual particulate material from said solvent; and then (e) separating the heat polymerized polymer from said solvent to provide a reusable thermoplastic polymer material in solid form.

Chemical recycling of additively manufactured objects

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using embedded function with a deep network. One of the methods includes receiving an input comprising a plurality of features, wherein each of the features is of a different feature type; processing each of the features using a respective embedding function to generate one or more numeric values, wherein each of the embedding functions operates independently of each other embedding function, and wherein each of the embedding functions is used for features of a respective feature type; processing the numeric values using a deep network to generate a first alternative representation of the input, wherein the deep network is a machine learning model composed of a plurality of levels of non-linear operations; and processing the first alternative representation of the input using a logistic regression classifier to predict a label for the input.

Using embedding functions with a deep network

A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order. An electronic device configured with a healthcare provider-facing interface displays the predicted one or more future clinical events and the pertinent past medical events of the patient.

System and method for predicting and summarizing medical events from electronic health records

A polymerizable liquid useful for the production of a three-dimensional object comprised of silicone, or a copolymer thereof, which includes at least one constituent selected from the group consisting of (i) a blocked or reactive blocked siloxane-containing prepolymer, (ii) a blocked or reactive blocked siloxane-containing polyisocyanate, and (iii) a blocked or reactive blocked siloxane-containing polyisocyanate chain extender. Methods of using the same in additive manufacturing processes such as continuous liquid interface production are also described.

Blocked silicone dual cure resins for additive manufacturing

A method of forming a three-dimensional object (e.g. comprised of polyurethane, polyurea, or copolymer thereof) is carried out by: (a) providing a carrier and an optically transparent member having a build surface, the carrier and the build surface defining a build region therebetween; (b) filling the build region with a polymerizable liquid, the polymerizable liquid comprising a mixture of: (i) a light polymerizable liquid first component, and (ii) a second solidifiable component that is different from the first component; (c) irradiating the build region with light through the optically transparent member to form a solid blocked polymer scaffold and advancing the carrier away from the build surface to form a three-dimensional intermediate having the same shape as, or a shape to be imparted to, the three-dimensional object, with the intermediate containing the second solidifiable component; and then (d) contacting the three-dimensional intermediate to water to form the three-dimensional object.

Methods for producing three-dimensional objects with apparatus having feed channels

Systems, devices, media, and methods are presented for determining performance metrics of client devices on a network using benchmark applications. Benchmark applications are distributed to client devices to produce performance metrics for the client devices. Performance metrics of the client devices received from the client devices are grouped to obtain performance metrics for different types of client devices.

Network device performance metrics determination

Growing community of inventors

San Bruno, CA, United States of America

Kai Chen