Accenture Global Services Gmbh

Accenture Global Solutions Limited

Infosys Limited

Nataraj Kuntagod

Sanjoy Paul

Senthil Kumar Kumaresan

Venkatesh K Subramanian

Satya Sai Srinivas

Vikrant Shyamkant Kaulgud

Sanjay Podder

Anutosh Maitra

Rajendra Tanniru Prasad

Vijayaraghavan Koushik

Sandeep Rathod

Satyasai Srinivas Abbabathula

Chinmoy Mukherjee

Suparna Moitra

Rambhau Eknath Rote

Swapnajeet G Chodhury

Yogesh Mallaiah

Manisha Johar Dhiman

Satya Sai Srinivas Abbabathula

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

A system and method for dynamic risk assessment is disclosed. The system may include a data warehouse, an output device and a processor including a data capturer, a process engine and a rules engine. The data capturer may capture information pertaining to a plurality of risk factors associated with an infection risk corresponding to a plurality of data elements in an environment. The plurality of data elements may pertain to at least one of a space and a person in the space. The process engine may include at least one of a space risk profiler and a person risk profiler. The process engine may determine a risk score and a risk profile associated with the person and the space. Based on the risk profile, the processor may perform at least one of an automatic identification of a mitigation to reduce the infection risk and an automated generation of an alert.

Dynamic risk assessment

A system may receive published messages over an uplink channel. The system may, via channel control logic (CCL), delegate the messages to one or more subject-domain downlink channels (SDDCHs). The CCL may determine to create new SDDCHs to facilitate accurate placement of messages within defined subject-domains corresponding to the various SDDCHs.

Complex system for message downlink channel control

A learning system for automatically transmitting files according to user capabilities is provided. The learning system may include non-transitory memory storing instructions executable to transmit a file to a user device, and a processor circuitry configured to execute the instructions to determine a home base location of a user from at least one of a database storing user information of the user and the user device of the user. The processor circuitry further configured to calculate a travel distance from the home base location of the user to a hub circuitry of the learning system, determine a type of the file and an amount of content of the file based on the travel distance, and transmit, to the user device, the file according to the type of the file and the amount of content of the file.

Location aware learning system for content dispensation for resource-constrained edge devices

Systems, apparatuses, and methods directed to authentication. Authentication data may be determined by previous authentications of a plurality of products. Sensor data, related to a first product, may be received. A current authentication of the first product may be executed based on the sensor data and at an authentication device. The execution may include identifying the first product based on the sensor data. The execution may further include identifying whether the current authentication is substantially similar the previous authentications based on whether the first product is substantially similar to the plurality of products. The execution may further include that when the current authentication is identified as not being substantially similar to the one or more of the previous authentications, authenticating the first product according to remote data and the sensor data. Otherwise, the first product may be authenticated according to the authentication data.

Authenticity identification system

A device may receive human-related data associated with a surveyor and a surveyed person participating in an interview, and may receive environmental data. The device may determine, based on rules, that first portions of the human-related data and environmental data are more reliable than second portions, and may process the first portions of the human-related data and the environmental data, with a first model, to determine high-reliability context data. The device may process the second portions of the human-related data and the environmental data, with a second model, to determine low-reliability context data, and may process the high-reliability context data and the low-reliability context data, with a third model, to generate weighted context data. The device may process the weighted context data, with a fourth model, to calculate a total stress factor, and may perform actions based on the total stress factor.

Utilizing machine learning to determine survey questions based on context of a person being surveyed, reactions to survey questions, and environmental conditions

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

A request to perform a transaction is detected. Based on the request to perform the transaction, one or more factors related to a context in which the transaction is being requested are determined. Based on the one or more factors, one or more parameters for authenticating a user that requested performance of the transaction are determined. The user that requested performance of the transaction is authenticated based on the determined one or more parameters for authenticating the user that requested performance of the transaction. Based on to authenticating the user that requested performance of the transaction, performance of the requested transaction is allowed.

Dynamic authentication technology

A method for controlling a sensor may be performed by a mobile device including a processor and a memory. The method may include receiving raw data from the sensor, classifying the raw data into a context value, interpreting the context value into a higher-level context, receiving a predicted context based on the higher-level context, and controlling the sensor based on the predicted context. Controlling the sensor may include controlling a frequency at which raw data is received from the sensor, or an on/off state of the sensor. Control of the sensor may also be based on both the higher-level context and the predicted context.

Adaptive sensor data selection and sampling based on current and future context

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

A method for adapting execution of an application on a mobile device may be performed by a mobile device including a processor and a memory. The method may include receiving an application context, a process context, and one other context. The method also includes analyzing at least one of the application context or the process context together with the one other context. The method also includes dynamically adapting execution of the application on the mobile device based on the analysis. Adapting execution of the application may include transferring processing related to the application to a backend server for processing.

Adaptive architecture for a mobile application based on rich application, process, and resource contexts and deployed in resource constrained environments

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Bengaluru, India

Nataraj Kuntagod