Sap Se

General Electric Company

Ge Precision Healthcare LLC

Sai Hareesh Anamandra

Kavitha Krishnan

Rohit Jalagadugula

Gopi Kishan

Akash Srivastava

Hariharan Ravishankar

Rahul Venkataramani

Rakesh Mullick

Sandeep Suryanarayana Kaushik

Prasad Sudhakar

Martin Wezowski

Parthasarathy Menon

Alexander Schaefer

Diwakar Maurya

Vinay George Roy

Hans-Martin Will

Jayanthi Subramanian

A method includes receiving, at a search toolbar, a search query from a machine in a network. The machine has an associated machine profile for participating in the network as an entity. The machine profile includes a machine identifier and machine metadata. A query type is determined from the search query. A search context for the machine is determined using a semantic graph of the network. From a set of services for the network, one or more relevant services to respond to the search query are identified based on the query type and the search context. The search query is applied to the one or more relevant services to obtain a set of responses. A set of relevant results for the search query is determined from the set of responses. The set of relevant results is transmitted to the machine.

Search service for a network of social machines

A computer-implemented method can receive a message sent from a source entity, perform first pre-processing operations for verifying validity of the message, perform second pre-processing operations for determining a category of the message, extract metadata from the message, generate an enriched message comprising the metadata and the determined category, perform post-processing operations for classifying the enriched message into one of a plurality of event types, broadcast the enriched message to a message broker, and routing, by the message broker, the enriched message to one or more target entities registered an event type into which the message is classified.

Message platform for autonomous entities

A method includes receiving a message query from an entity identifier participating in a social network. The message query specifies one or more entities, one or more requirements, and one or more constraints. A set of message query parameters is generated based on the message query. A set of queries for a semantic graph of the social network is generated based on the set of message query parameters. The set of queries is applied to the semantic graph to obtain a set of query results. A message context of the entity identifier is determined based on the set of query results and the set of message query parameters. A set of messages from a message repository is determined based on the message context. The set of messages can be presented on a client computer associated with the entity identifier.

Message query service and message generation for a social network

Technologies and solutions are provided for improving process efficiency/identifying efficient paths of process steps. A target outcome can be identified, which can be a particular status, such as a stage (or status/step) in a process, or a target outcome can be an identification of particular process statuses that can be reached, such as given a particular set of constraints. Proceeding between process steps involves the use of resources, where a process step can be reached, or having an increased chance of being reached, when the resources have been obtained. Various paths can exist for obtaining a resource, where some paths can be more efficient than others. Based on resource paths and paths between steps of a process, one or more paths can be suggested for reaching the target outcome, including providing information about the process step paths or the resources paths for reaching the target outcome.

Automated optimization of branched processes providing improved process efficiency

A method may include collecting data from a variety of data sources associated with a user. The data sources may include personal data sources, corporate data sources, and public data source. The data collected from the variety of data sources may be enriched through categorization and aggregation. For example, browser history may be categorized based on types of website and aggregated to reflect the quantity of interactions with each category of website. A multi-dimensional digital profile may be generated based on the enriched data. For instance, the digital profile may include a social, emotional, spiritual, environmental, occupational, intellectual, and physical dimension. One or more recommendation corresponding to one or more of a burnout prediction, wellness recommendation, learning plan, skill gap, and personality type may be generated based on the digital profile. Related systems and computer program products are also provided.

Multimodal data framework for digital system representation

A system is presented. The system includes an acquisition subsystem configured to obtain images corresponding to a target domain. Moreover, the system includes a processing subsystem in operative association with the acquisition subsystem and including a memory augmented domain adaptation platform configured to compute one or more features of an input image corresponding to a target domain, identify a set of support images based on the features of the input image, where the set of support images corresponds to the target domain, augment an input to a machine-learnt model with a set of features, a set of masks, or both corresponding to the set of support images to adapt the machine-learnt model to the target domain, and generate an output based at least on the set of features, the set of masks, or both. Additionally, the system includes an interface unit configured to present the output for analysis.

System and method for memory augmented domain adaptation

The present approach relates to a system capable of life-long learning in a deep learning context. The system includes a deep learning network configured to process an input dataset and perform one or more tasks from among a first set of tasks. As an example, the deep learning network may be part of an imaging system, such as a medical imaging system, or may be used in industrial applications. The system further includes a learning unit communicatively coupled to the deep learning networkand configured to modify the deep learning network so as to enable it to perform one or more tasks in a second task list without losing the ability to perform the tasks from the first list.

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Bengaluru, India

Sai Hareesh Anamandra