Accenture Global Services Gmbh

Hewlett-Packard Development Company, L.p.

Accenture Global Solutions Limited

Pramila Mullan

Paul Barsamian

Michael Mui

Robert Fenney

Walid Negm

Edy S Liongosari

Brian Richards

Ankur Mathur

Daniel Garrison

Sang-Ik Kim

Juan Carlos Mendez

Puneet Sharma

Sung-Ju Lee

Sujata Banerjee

Sujoy Basu

Dadong Wan

Anuraag Chintalapally

Brad H Ruderman

Erin Maneri

Shishir Garg

Jitendra Kavathekar

Sukesh Garg

Cindy Au

Alice Tsing

Paul M Barsamian

Daniel P Garrison

Juan C Mendez

According to an example, collaborative healthcare may include retrieving healthcare data for a patient from at least one data source, and generating a plurality of distinct healthcare programs for the patient based on the healthcare data. Collaborative healthcare may further include reconciling the plurality of distinct healthcare programs to generate a universal patient healthcare plan for the patient. The universal patient healthcare plan may include a universal view of the overall healthcare for the patient and healthcare provider-specific views for the patient. The reconciling may include detecting conflicts for predetermined components of the healthcare programs, and in response to the detection of the conflicts, eliminating errors related to the detected conflicts.

Collaborative healthcare

A device intelligence architecture configures and controls on-site devices and performs environment monitoring to facilitate effective device functionality. The architecture facilitates efficient use of devices (e.g., robotics) in an unstructured and dynamic environment, which will allow deployments in many more environments than is currently possible. The architecture stores and shares information between segregated devices to avoid the silo effect of vendor specific stacks. The architecture also models capabilities of devices and maps actions to intelligence packages to deploy a specific intelligence package to the device. The architecture also implements distributed processing. For instance, computationally intensive tasks may be offloaded to the back end processing, with action updates resulting from the processing pushed to the devices.

Self-adaptive device intelligence as a service enterprise infrastructure for sensor-rich environments

Unmanned vehicle (UV) movement and data control may include controlling a UV according to a movement plan. Formatted movement and status metadata may be received from a sensor of the UV during movement of the UV. The movement and status metadata may include time and location information for the UV during the movement of the UV. An unformatted data stream may be received from the sensor of the UV. The time and location information may be injected into metadata of the unformatted data stream to generate a time and location correlated (TLC) stream. The TLC stream may be analyzed to identify an event related to the UV, and a notification related to the event may be generated.

Unmanned vehicle (UV) movement and data control system

Unmanned vehicle (UV) control may include receiving a UV work order and generating a mission request based on the UV work order. The mission request may identify an objective of a mission, assign a UV and a sensor to the mission from a fleet of UVs and sensors, and assign a first movement plan to the mission based on the identified objective of the mission. The assigned UV may be controlled according to the assigned first movement plan, and communication data may be received from the assigned sensor. The communication data may be analyzed to identify an event related to the mission. The identified event and the first movement plan may be analyzed to assign a second movement plan to the mission based on the analysis of the identified event and the first movement plan to meet the identified objective of the mission.

Unmanned vehicle (UV) control system

A device may receive sensor data from a sensor device located on a particular farm. The device may identify an alert, associated with the particular farm, based on the sensor data and using a model. The model may be created based on imagery data and numeric data relating to a group of farms. The device may determine, using the model, a recommended course of action to address the alert, and provide, to a user device associated with the particular farm, the recommended course of action.

Precision agriculture system

One embodiment relates to a system for linking together multiple registries and aggregating data from the multiple registries. The system includes a plurality of local resource or service (resource/service) registries, a distributed index storage and retrieval system, and at least one proxy registry. The proxy registry is configured to receive a search query from a client system, retrieve information from the distributed index storage and retrieval system as to which of the local resource/service registries are relevant to the search query, and obtain pertinent entries from said relevant resource/service registries. Other embodiments are also disclosed.

Discovery across multiple registries

Growing community of inventors

Los Gatos, CA, United States of America

Pramila Mullan