Amazon Technologies, Inc.

Toshiba Medical Visualization Systems Europe, Limited

David Neil Turner

Andrew J Bradley

Alistair Francis Smith

Sebastiano Merlino

Anthony Richard McBryan

Sean Daniel Murphy

Oliver Masting

Andrew Craig Brind

Matthew J Round

Robert Philip Japp

Paul Anthony Kotas

Tom Kimpe

Pavlos Papageorgiou

Reginald Jassal

Martin Christopher Hare Robertson

Simon Lloyd Spencer

Sean Donahoe

Christopher Walton

Graham Ritchie

Brian Fergus Burns

Guy Adam Taylor

Conrad Chin

Ramesh Ravipati

Carter Yates

José Lucas Lemos Mendonça

Sebastian Koch

This disclosure describes systems, methods, and computer-readable media related to retargeting online advertisement campaign recommendations for advertisements with multiple items or services. Bids may be based on a combined advertisement creative comprising two or more items or services. Dynamically selecting multiple items at bid time using a retargeting model to determine a potential revenue generation amount associated with an event may increase the probability of a conversion event based on the creative that includes the selected items. In some embodiments, a machine-learned retargeting model may be used to select multiple items to be displayed in an advertisement. The retargeting model may be applied to items that were previously viewed by the consumer and may determine a value for each of the items using factors. A bid may be calculated for each of the selected items using the values determined by the retargeting model.

Retargeting events service for online advertising

Systems, methods, and computer-readable media are disclosed for dynamically determining relative product performance using quantitative values. In one embodiment, an example method may include determining, by one or more computer processors coupled to at least one memory, a first product of a product catalog, determining a first set of actual performance values for the first product during a first time period, generating a first expected performance model based at least in part on a first set of product attributes for the first product and the first set of actual performance values, determining a first expected performance value for the first product based at least in part on the first expected performance model and the first set of product attributes, determining a first average actual performance value for the first product over the first time period based at least in part on the first set of actual performance values, and determining a first relative performance value for the first product based at least in part on the first average actual performance value and the first expected performance value.

Dynamically determining relative product performance using quantitative values

This disclosure describes systems, methods, and computer-readable media related to online advertisement campaign recommendations. A server may obtain data from an events datastore indicative of missing time slices from a partition of events data. The server may obtain first events data for the missing time slices. The server may identify redundant events data in the first events data that is redundant to second events data stored in the events datastore. The server may delete the redundant events data from the first events data to generate a modified first events data. The server may transmit the modified first events data to the events datastore.

Events service for online advertising

Systems and methods are disclosed for providing composite content, such as video content, by compositing. A template content may be received and may have one or more color blocks. One or more drop-in content sets may each have one or more drop-in content that correspond to the one or more colors of the template content color blocks. The one or more color blocks of the template content may be replaced with its corresponding drop-in content from one of the one or more drop-in content sets to generate a composite content. Furthermore, the composite content may have demographic, geographic, and/or behavioral parameters associated with it to enable targeting the composite content, such as in the form of advertisements and/or product or service recommendations, to one or more users.

Video advertisement customization by compositing

This disclosure describes systems and associated methods that can selectively filter seed behavior, e.g., user activity used to generate item recommendations. In some embodiments, seed behaviors and catalog items are associated with categories in an electronic catalog, and a particular seed behavior is used to generate user recommendations if it is more recent than a user's last purchase in the category of the seed behavior. For example, a user's activity in the TV category, e.g., viewing various TV models, may not be used to generate recommendations if the activity occurred prior to the user's purchase of a TV. As a result, in certain embodiments, additional TVs may not appear in the user's recommendations following her purchase, reflecting that the user has fulfilled her desire to purchase a TV.

Behavioral filter for personalized recommendations

A method of processing medical imaging volume data in a computer network is described. The method comprises loading a medical imaging data set to be processed to a server computer, processing the data set on the server computer, e.g. by executing a software application, and generating corresponding server-generated results. The server-generated results, e.g. rendered images, may then be transmitted to a client computer for display to a user. This allows users to quickly view the results of the processing because they have not had to wait for the data set to be transferred to their local machine before locally processing the data. However, while this is happening, the data set itself is also transmitted, e.g. as a background operation, to the client computer. Thus eventually the client computer has access to a local copy of the data set and may start processing the data set itself, thus freeing up server resources.

Server-client architecture in medical imaging

Growing community of inventors

Edinburgh, United Kingdom

David Neil Turner