Wipro Limited

Sreevidya Khatravath

Sumanta Laha

Akbar Abdulmalik Ladak

Akash Gupta

Abhishek Suman

Nick Isaacs

Manikhantan Sanakara Raman

The present disclosure relates to a method for monitoring behaviour of a patient in real-time. The method comprises receiving, by a patient monitoring device, data related to the patient from one or more sources. Thereafter, the patient monitoring device classifies the received data into one or more categories based on one or more rules. Further, the patient monitoring device correlates the categorized data to identify one or more activity patterns corresponding to the patient, wherein each of the one or more activity patterns are associated with an activity performed by the patient at predefined time intervals. The patient monitoring devices compares the activity pattern with predefined activity patterns and detects abnormal behaviour of the patient if the identified activity pattern is different from one or more predefined activity patterns corresponding to the patient.

Method for monitoring behaviour of a patient in real-time using patient monitoring device

Methods and systems for facilitating transportation of articles in a warehouse are disclosed. The method includes extracting employee information associated with a plurality of employees, vehicle information associated with a plurality of vehicles used in the warehouse, and a warehouse layout map. The method further includes analyzing camera feeds captured using a plurality of cameras installed within the warehouse to determine: current activity status of each of the plurality of employees, interaction of each of the plurality of employees with at least one of the plurality of vehicles, and current location of: the plurality of employees, the plurality of vehicles, and articles within the warehouse. The method also includes computing a task completion plan for transporting an article from an originating point to a destination point within the warehouse based on: the employee information, the vehicle information, the warehouse layout map, and analysis of the camera feeds.

Methods and systems for facilitating transportation of articles in warehouses using video analytics

Disclosed herein is a method and system for recommending one or more events based on mood of a person. The method comprises receiving activity data associated with one or more activities of the person and personal information of the person from data sources. The received activity data is classified into one or more predefined categories. An event profile of the person is generated based on the classified activity data. Thereafter, a sensitivity score of the person is determined based on impact of a current event on the person, activity score of the person and correlation of the current event with one or more events occurred simultaneously with the current event. Further, a mood score of the person is determined based on the sensitivity score and the event profile of the person. Furthermore, events are recommended to the person based on the sensitivity score and the mood score of the person.

Method and system for recommending one or more events based on mood of a person

The present disclosure relates to a method and device for estimating efficiency of an employee of an organization. In one embodiment, the input data is received from one or more data sources. The input data is classified into one of location data, video data, voice data and text data of the employee. Using the location data and the video data, the trajectory information of the employee is generated. The trajectory information, the voice data and the text data are correlated. Based on the correlation, the efficiency of the employee is estimated.

Method and device for estimating efficiency of an employee of an organization

Systems and methods for providing recommendations to obfuscate an entity context in a sensory environment are described. In one implementation, the method comprises receiving entity data and sensory environment data from a plurality of sources. Further, the method comprises analyzing the entity data to obtain categorized entity data. The categorized entity data comprises a plurality of features indicating characteristics of the entity context. Further, the method comprises analyzing the sensory environment data to obtain categorized sensory environment data. Further, the method comprises determining a correlation score for each of the plurality of features by correlating the categorized entity data and the categorized sensory environment data. Further, the method comprises recommending at least one of the plurality of features, based on the correlation score, to obfuscate the entity context in the sensory environment.

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Hyderabad Telangana, India

Sreevidya Khatravath