Microsoft Technology Licensing, LLC

Amazon Technologies, Inc.

Mohammed Nazeeruddin

Arshish C Kapadia

Targo Tennisberg

Tarkan Sevilmis

Ignacio Omar Venado Estrada

Murali Venkatrao

Ji Tang

R P Huang

Jonathan Campbell

Todd Haugen

Rolando Jimenez Salgado

David Koronthaly

Peter Kenneth Harwood

Wei-Lun Lo

Munir Mahmood

Howard Morrison Crow, Iii

Jiajun Hua

Sundar Paranthaman

Eray Chou

Kaushik Raj

Bin Zhu

Howard M Crow, Iii

Mohamed Thahir Peer Mohamed

Ricard Roma-Dalfo

Christopher Foster Johnson

Risto Fermin Rangel Kuoppa

Faizi Siddiqui

Sergio Damo De Lemos

Peter K Harwood

Howard M Iii Crow

A system for processing a seller provided input image depicting an object. The system may process the input images to be in compliance with an online shopping website's image requirements. The image processing service may execute multiple modules on the input image. An edge detection module may detect one or more edges of the image of the object and determine border data indicative of a border around the object. An object of interest module may extract the image of the object within the border to generate an object of interest image. A background extraction module may create one or more closed shapes to separate the image of the object from background pixels. A hole detection module may traverse the one or more closed shapes to determine a presence of background pixels, remove the background pixels, and generate a resulting image.

System and apparatus for image processing

A method for providing per-user aggregation of content accessible via a personalized user site may generally include receiving a request for metadata and determining whether the requested metadata is expired. The method may further include returning at least a portion of queried metadata prior to verifying whether the metadata is expired. The method may then include querying a change log for metadata modifications. The method may further include, if metadata has been modified since a previous query, sending a query to a community site for metadata that meets change criteria. The method may also include receiving the changed metadata and returning the changed metadata to a content group container within the personalized user site.

Per-user aggregation of database content

Influential token combinations corresponding to various classes associated with a machine learning model trained to classify text collections expressed in a first language are identified. A similarity metric between one or more influential token combinations and a classification candidate text collection (expressed in a second language) is determined. A substitute text collection corresponding to the classification candidate is generated based at least partly on the similarity metric. The substitute text collection is used as input to one or more machine learning models to identify a predicted class of the classification candidate.

Artificial intelligence system for automated adaptation of text-based classification models for multiple languages

A software application written for a server environment is transformed into one which runs in a rich client environment. An entity model defines data sources as they are accessed from the server and as they are accessed from the clients. An application programming interface defines stereotyped interfaces which provide the same functionality on the server and the clients. A metadata model describes the components which make up the application and defines the differences in structure between the server and clients. The metadata model also defines mappings between components used on the server and clients. Settings and context information which tailor the activation and functionality of the components is also captured in the metadata model. Automated transformation is performed by using the mappings to identify the component set to use for the clients, selecting the client version of the application programming interfaces, and selecting the appropriate settings and context information.

Projecting software and data onto client

A computing device having a dynamic metadata model includes an interface module, an algorithm module and a registration module. The interface module includes an interface that defines one or more API functions for obtaining metadata using an algorithm. At least one of the API functions is used with a plurality of algorithms. Each of the plurality of algorithms is associated with a business system. The algorithm module includes one or more algorithms that implement the API functions defined in the interface. The one or more algorithms provide metadata without accessing a metadata database. Each of the one or more algorithms is associated with a business system. The metadata is associated with API functions of the business system. The registration module registers the one or more algorithms on the computing device.

Dynamic metadata

A computing device includes instructions that, when executed by a processing unit, cause the processing unit to: create a business data catalog module that accesses and manipulates data residing on an internal data store and an external data store, wherein the business data catalog module receives a request for data, determines whether the data resides in the internal data store or the external data store, and access the data residing on the external data store, and create a external data provisioning module that provisions data accessed from the external data store, wherein the external data provisioning module maps external data from the external data store for presentation on a client computing device. A user can access and manipulate the data from the external data store in a same manner as accessing and manipulating data from the internal data store.

Extending collaboration capabilities to external data

Architecture for uninterrupted access to business data by enabling work on rich client machines to be stored in a local business data cache that is always available. A dispatching mechanism executes read/write requests against the local cache transparently to a client application, an automatic synchronization mechanism propagates changes bi-directionally when the client goes online and a live connection with the corresponding backend data source is available. A declarative data access model is employed based on operation stereotyping and grouping that enables client applications to transparently execute create, read, update, delete, query (CRUDQ) and association operations against the local cache as if executed against the actual business data source. The architecture supports the offline creation of entities and sets of related entities when the entity identifiers are generated by the business data source and offline navigation of entity associations when foreign keys are not present in the related entities.

Business data access client for online/offline client use

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Bellevue, WA, United States of America

Mohammed Nazeeruddin