Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: This disclosure relates generally to optimizing computing resources and, more particularly, to systems and methods for dynamically determining an optimized infrastructure for processing data. In one embodiment, a method for dynamically determining an optimized infrastructure for processing data is disclosed, comprising: receiving a task-processing request. The method may also include identifying, based on the received task-processing request, one or more rules associated with performing the task-processing request. The method may further include accessing historical learning information associated with performing at least one past task-processing request. The method may further include allocating computing resources for performing the task-processing request based on the identified one or more rules, accessed historical learning information, and available computing resources associated with a distributed computing environment.