Accenture Global Services Gmbh

Accenture Global Solutions Limited

Wipro Limited

Senthil Kumar Kumaresan

Nataraj Kuntagod

Sanjoy Paul

Kuntal Dey

Sanjay Podder

Rajendra Tanniru Prasad

Venkatesh K Subramanian

Vijayaraghavan Koushik

Anutosh Maitra

Satya Sai Srinivas

Gurdeep Virdi

Sandeep Rathod

Satyasai Srinivas Abbabathula

Suparna Moitra

ArulKumar Karthikeyan

Aditya Bhola

Satya Sai Srinivas Abbabathula

Manisha Johar Dhiman

Satya Srinivas Sai

A method for detection of a corrupted sensor including providing a first sensor; identifying one or more correlating sensors to the first sensor; determining a correlation between the first sensor and the correlating sensors according to historical sensor values; obtaining a calculated value of the first sensor based on values of the correlating sensors and the correlation; obtaining a measured value of the first sensor; and determining whether the first sensor is corrupted according to a difference between the calculated value and the measured value of the first sensor.

Corrupted sensors detection in sensor consortium

This disclosure relates to a method and system for identifying products using digital fingerprints. The method includes receiving, by a product tracking system, a set of images corresponding to a product, wherein the set of images are received along with associated context information. The method generates a set of standardized images by pre-processing the set of images. Each standardized image of the set of standardized images are further segmented into a plurality of zones. A zone wise fingerprint is generated for each zone of the plurality of zones. A product digital fingerprint is generated for the product by combining zone wise fingerprints associated with the set of standardized images, wherein the product digital fingerprint identifies the product.

Method and system for identifying products using digital fingerprints

A method and device for generating data services from heterogeneous systems is disclosed. The method includes generating an asset data model for a parent asset based on at least one data attribute associated with the parent asset. The method further includes populating the asset data model with at least one sub-asset associated with each of the at least one data attribute and at least one event associated with the at least one sub-asset and the at least one data attribute. The method includes creating a plurality of role hierarchies across multiple organizations associated with the parent asset. The method further includes associating a plurality of stakeholders with each of the plurality of role hierarchies. The method includes associating data contracts with each of the plurality of stakeholders and each of the at least one sub-asset based on the plurality of role hierarchies and the asset data model.

Method and device for generating data services from heterogeneous systems

A device may receive sensor data from one or more sensor devices. The device may detect a trigger condition, that triggers an update to a model used to analyze the sensor data, based on receiving the sensor data. The trigger condition may include at least one of: a change in a type of data included in the sensor data, a change in an entity being monitored in association with the sensor data, or a change in a frequency with which the sensor data is received from the one or more sensor devices. The device may transmit a request to update the model based on detecting the trigger condition. The device may receive an updated model based on transmitting the request to update the model. The device may apply the updated model to the sensor data and/or subsequent sensor data received from the one or more sensor devices.

Detecting trigger conditions to dynamically update a model based on sensor data in a fog computing environment

A device may receive, from a consumer device, an application programming interface (API) request that includes information associated with a consumer of data and information associated with a producer of the data. The data may be collected by one or more data sources associated with the producer. The device may identify a consumer context, associated with the consumer, based on the information associated with the consumer. The device may identify a producer context, associated with the producer, based on the information associated with the producer. The device may generate an API response based on the consumer context and the producer context. The API response may include data in accordance with the consumer context and the producer context. The device may provide, to the consumer device, the API response to permit the consumer to view or access the data.

Context-aware application programming interface response management

The system may receive a group of project requirements from a client for development of a software application. The system may identify the client based on the received group of project requirements. The system may to determine, based on the group of project requirements of the client, a past project requirement submitted by the client or another client or created previously as a generic project requirement and corresponding to a first project requirement of the group of project requirements. The system may determine information created or used for the past project requirement. The system may create information for a second project requirement of the group of project requirements. The system may generate a prototype of the software application based on the information created or used for the past project requirement and the information created for the second project requirement. The system may output the prototype.

Automated, accelerated prototype generation system

Intelligent application back stack management may include generating a first back stack for activities of an application that have been executed by a device that executes the application. The first back stack may include a back stack size limit. A further back stack may be generated for selected ones of the activities of the application if a total number of the activities of the application and further activities of the application exceeds the back stack size limit. The first back stack may be an in-memory back stack for the device that executes the application, and the further back stack may include an external on-device back stack for the device that executes the application and/or a Cloud storage based back stack. Intelligent application back stack management may further include regenerating an activity of the selected ones of the activities that is pulled from the further back stack.

Intelligent application back stack management

Methods and systems for resource optimized, coarse and fine grained location tracking for planned journey are disclosed. A method for tracking a mobile device with respect to a point of interest (POI) using a mobile location tracker includes a conserve mode in which the mobile location tracker is turned off. The conserve mode comprises determining, by an analyzer module, whether to assess an estimated time to arrive (ETA) at the POI. If determined to assess the ETA, the conserve mode comprises performing an ETA assess operation comprising receiving the ETA; and if the ETA is not greater than a threshold time, switching from the conserve mode to an active mode, wherein in the active mode the mobile location tracker is turned on. It is determined to assess the ETA if a timer measurement is not less than the reassess time or if a trigger to reassess is received.

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Bengaluru, India

Senthil Kumar Kumaresan