Accenture Global Solutions Limited

Satya Sai Srinivas

Nataraj Kuntagod

Sanjay Podder

Venkatesh K Subramanian

Anutosh Maitra

Vikrant Shyamkant Kaulgud

Senthil Kumar Kumaresan

Yogesh Mallaiah

Venkatesh Subramaniam

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 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.

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Bengaluru, India

Satya Sai Srinivas