Siemens Medical Solutions Usa, Inc.

Acuson Corporation

Amazon Technologies, Inc.

Cisco Technology, Inc.

Microsoft Technology Licensing, LLC

Omnyway, Inc.

Siemens Corporation

Mevis Breastcare Gmbh & Co. Kg

Sriram Krishnan

R Bharat Rao

Gregory L Holley

Glenn Fung

Dorin Comaniciu

Xiang Sean Zhou

Philippe Lambin

Edward A Gardner

Romer E Rosales

Robert Matthias Steele

Donghui Zhuo

Charles Eric Dannaker

Tianming Zheng

Tarun Agarwal

Rakesh Ramakrishnan

Daniel Blick

Jun Qian

Polly Po Lee

Ralf Herbrich

Jin Li

Michael Brueckner

Bogdan Georgescu

Leo Parker Dirac

Rina Panigrahy

Shipeng Yu

Aleksandr Mikhaylovich Ingerman

Nicolle M Correa

Saman Zarandioon

Sudhakar Rao Puvvadi

Cary Dehing-Oberije

Dirk De Ruysscher

Sunil Parthasarathy

Mohammad A Khan

Eric C Young

Balaji Krishnapuram

Christopher Jude Amies

Alok Gupta

Lewis J Thomas

Richard M Bennett

Harald Steck

Zhe Yang

Ashok Narasimhan

Marianne Larissa Mueller

Punit Bhargava

Robert Jay Berger

Amitaabh Malhotra

Laura Ann Torbett Giddings

Samuel James McKelvie

Ismayil M Guracar

Samuel H Maslak

Bradley Gene Calder

Bhaskar S Ramamurthy

John W Allison

Ju Wang

Patrick J Phillips

George M Moore

Michael G Cannon

Radu Stefan Niculescu

Mirsaid Seyed Bolorforosh

Jinbo Bi

Joan Carol Main

William Alexander Landi

Farbod Rahmanian

Andreas Lubbertus Aloysius Johannes Dekker

Remy Pairault

Murat Dundar

Carl J G Evertsz

Jeffrey R Resnick

Nils Sponheim

Bradly G Wouters

Vikram Dhaneshwar

Polly Po Yee Lee

John A Davidson

Ting Lan Ji

Mark Schmidt

Aiko Nishida

Hugo J W L Aerts

Renaud G Seigneuric

Maud H W Starmans

Dimitry S A Nuyten

Arie Van Erk

Michael Gaston Magagnin

Kasper M Rouschop

Christoffel Theodorus Anthonius Evelo

Adrian Campbell Begg

Michel Oellers

Claudia Offermann

Wei Hong

Allen Dutton

Dennis O'Dell

Steven Florian Petit

A machine learning service implements programmatic interfaces for a variety of operations on several entity types, such as data sources, statistics, feature processing recipes, models, and aliases. A first request to perform an operation on an instance of a particular entity type is received, and a first job corresponding to the requested operation is inserted in a job queue. Prior to the completion of the first job, a second request to perform another operation is received, where the second operation depends on a result of the operation represented by the first job. A second job, indicating a dependency on the first job, is stored in the job queue. The second job is initiated when the first job completes.

Machine learning service

A system for facilitating virtual-to-physical secure remote payment to a physical location is described. In an embodiment, a transaction bridge server receives, from a merchant computing device of a merchant user, information for a communication session between a user computing device and the merchant user; generates a digital invitation to initiate the communication session; sends a touchpoint associated with the communication session to the user computing device; receives, from an application component of the user computing device, an indication of an interaction between the user computing device and the touchpoint, the indication comprising a request to access the digital invitation; initiates the communication session between the user computing device and the merchant computing device; generates a display page customized for a user of the user computing device, the display page comprising information for a transaction with the merchant; and sends the display page to the application component of the user computing device via communication session. The user of the user computing device and the merchant user are most probably present at different physical locations.

Virtual-to-physical secure remote payment to a physical location

A system for facilitating payment and non-payment transactions between a computing device that lacks an activated payment application and a merchant is described. In an embodiment, a transaction bridge server receives, from a merchant server, transaction information for a particular transaction between a user and a merchant. The transaction bridge server generates a URL representing the particular transaction, sends the URL to the merchant server, and receives, from a web browser of a computing device, an indication of an interaction between the computing device and a touchpoint comprising the URL, the indication comprising a request to access the URL. The transaction bridge server generates a display page customized for the user, the display page comprising the transaction information, sends the display page to the web browser of the computing device, receives an acceptance of the particular transaction, and initiates the particular transaction.

Cloud based system for engaging shoppers at or near physical stores

Embodiments of the present invention relate to systems, methods, and computer storage media for geographically organizing the storage of data and hosted services in a distributed computing environment. An allocation constraint is utilized to identify and select a geo-region in which an affinity group that is comprised of a hosted service and/or data is to be physically stored. Additionally, a geo-domain within the geo-region is identified for hosting the hosted service and/or data of the affinity group. A stamp, such as a storage stamp or a computation stamp, is identified on the geo-domain for storing the hosted service and/or the data. The hosted service is stored in an identified computation stamp and the data is stored in an identified storage stamp. In an additional exemplary embodiment, the identification of a geo-domain involves analyzing resource information related to potential geo-domains within a geo-region.

Geographic co-location service for cloud computing

The present disclosure involves the receipt of inventory items in a materials handling facility. In one embodiment, a listing of inventory items associated with an inventory is maintained in a memory accessible to a computing device. An attempt is made to identify a match between a first image of a receivable item and a second image of one of the inventory items. If a match is successfully identified, an identifier associated with the one of the inventory items is rendered, where the identifier is to be associated with the receivable item.

Receiving items into inventory

Functional imaging information is used to determine a probability of residual disease given a treatment. The functional imaging information shows different characteristic levels for different regions of the tumor. The probability is output for planning use and/or used to automatically determine dose by region. Using the probability, the dose may be distributed by region so that some regions receive a greater dose than other regions. This distribution by region of dose more likely treats the tumor with a same dose, allows a lesser dose to sufficient treat the tumor, and/or allows a greater dose with a lesser or no increase in risk to normal tissue. The dose plan may account for personalized tumors as each patient may have distinct tumors. Probability of dose application accuracy may also be used, so that a combined treatment probability allows efficient dose planning.

Dose distribution modeling by region from functional imaging

Medical related quality of care information is extracted and edited for reporting. Patient records are mined. The mining may include mining unstructured data to create structured information. Measures are derived automatically from the structured information. A user may then edit the measures, data points used to derive the measures, or other quality metric based on expert review. The editing may allow for a better quality report. Tools may be provided to configure reports, allowing generation of new or different reports.

Quality metric extraction and editing for medical data

A system and method for diagnosis and treatment decisions based on information maximization is disclosed. Utilizing patient information as well as clinical records from other patients can reduce the uncertainty in both diagnosis and treatment options. The information maximization may consider additional data such as risk, cost, and comfort in making a proper medical decision.

Guiding differential diagnosis through information maximization

Automated treatment planning is provided with individual specific consideration. One or more prognosis models indicate survivability as a function of patient specific information for a given dose. By determining survivability for a plurality of doses, the biological model represented by survivability as a function of dose is determined from the specific patient. Similarly, the chances of complications or side effects are determined. The chance of survivability and chance of complication are used as or instead of the tumor control probability and normal tissue complications probability, respectively. The desired tumor dosage and tolerance dosage are selected as a function of the patient specific dose distributions. The selected dosages are input to an inverse treatment planning system for establishing radiation treatment parameters.

Personalized prognosis modeling in medical treatment planning

Systems and methods for utilizing affinity groups to allocate data items and computing resources are disclosed. Upon receipt of a user preference indicating an affinity group, a token associated with that affinity group may be stored in a database. The affinity group may be associated with a geographic region or a number of data centers. Data items and computing resources may be associated with the affinity group. These data items and computing resources may be allocated to a geographic region or data center based on their association with the affinity group. These data items and computing resources may also be reallocated based on efficiency analyses or user preferences. In this way, data items and computing resources may be efficiently allocated with lower user effort.

Utilizing affinity groups to allocate data items and computing resources

A system and method for screening for coronary heart disease is provided. The method includes the steps of retrieving a test for assessing risk of coronary heart disease, the test including a plurality of data fields relating to coronary risk factors; accessing a database to populate the data fields with information of an individual patient; and calculating a risk assessment of the individual patient developing coronary heart disease. A system includes a first database including a plurality of structured computerized patient records; a second database including a knowledge base relating to coronary heart disease, the second database including at least one test for determining coronary heart disease risk; and a processor for retrieving the at least one test from the second database, populating the at least one test with patient information retrieved from the first database and calculating a risk assessment for at least one patient.

Patient data mining for cardiology screening

A computer-implemented method for privacy-preserving data mining to determine cancer survival rates includes providing a random matrix B agreed to by a plurality of entities, wherein each entity i possesses a data matrix Aof cancer survival data that is not publicly available, providing a class matrix Dfor each of the data matrices A, providing a kernel K(A, B) by each of said plurality of entities to allow public computation of a full kernel, and computing a binary classifier that incorporates said public full kernel, wherein said classifier is adapted to classify a new data vector according to a sign of said classifier.

System and method for privacy preserving predictive models for lung cancer survival analysis

Growing community of inventors

Exton, PA, United States of America

Sriram Krishnan