Claims:We claim:
1. A method of managing an event engaged by a group of participants (103), in real-time, wherein the method comprises:
receiving, by an event managing system (101), an input data (208) through multiple inputs modes associated with the event engaged by the group of participants (103), wherein the input data (208) comprises at least one of text data, audio data, image data, video data and gesture data, associated with the event and the group of participants (103);
profiling, by the event managing system (101), each participant from the group of participants (103), during the event, based on the input data (208) and a pre-stored event data (210) associated with the event;
indexing, by the event managing system (101), one or more behavioural attributes (211) of each participant from the group of participants (103), based on at least one of the input data (208), the pre-stored event data (210), and the profiling of corresponding participant;
generating, by the event managing system (101), one or more alerts (213), to provide to at least one participant from the group of participants (103), during the event, based on the profiling and the indexing of the corresponding participant, the input data (208), and the pre-stored event data (210); and
determining, by the event managing system (101), upon end of the event, an event data (214) for the event and the group of participants (103), based on the profiling and the indexing of the group of participants (103), wherein the event data (214) for the event comprises inference associated with the event, and the event data (214) for a participant from the group of participants (103) comprises progress data and action plan data for the participant.

2. The method as claimed in claim 1 and further comprising:
processing, by the event managing system (101), the input data (208) by performing one or more tasks associated with Natural Language Processing (NLP) for managing the event.

3. The method as claimed in claim 1, wherein the profiling of a participant comprises generating a participant-specific data and a group-specific data for the participant, wherein the participant-specific data comprises skills, tasks, topics and activities associated with the participant with respect to context of the event, and wherein the group-specific data indicates relationship of the participant-specific data with respect to the context of the event.

4. The method as claimed in claim 1, wherein the one or more behavioural attributes (211) comprises an interpersonal attribute, an attention attribute, and a group co-ordination attribute,
wherein the indexing of an interpersonal attribute, from the one or more behavioural attributes (211), of a participant is performed using a participant-specific data and the input data (208) associated with the participant,
wherein the indexing of an attention attribute, from the one or more behavioural attributes (211), of a participant is performed based on a participant-specific data, the input data (208) and the one or more alerts (213) associated with the participant,
wherein the indexing of a group co-ordination attribute, from the one or more behavioural attributes (211), of a participant is performed based on pre-stored event data (210), a group-specific data and the input data (208) associated with the participant.

5. The method as claimed in claim 1, wherein generating the one or more alerts (213) for the at least one participant is performed using a trained deep neural network by:
detecting occurrence of a deviation in context of the event based on the input data (208) and the pre-stored event data (210), wherein detecting the occurrence of the deviation in the context of the event comprises:
extracting features associated with one or more input keywords from the input data (208) to identify at least one on-going topic in the event;
comparing the features of the one or more input keywords with features of one or more pre-stored keywords from the pre-stored event data (210), to detect a mismatch between the one or more input keywords and the one or more pre-stored keywords; and
detecting the occurrence of the deviation in the context when the mismatch is detected;
identifying the at least one participant associated with the occurrence, using the input data (208) and a participant-specific data; and
generating the one or more alerts (213) with respect to the deviation in context, to be provided to the at least one participant.

6. An event managing system (101) for managing an event engaged by a group of participants (103), in real-time, said event monitor system comprises:
a processor (105); and
a memory (107) communicatively coupled to the processor (105), wherein the memory (107) stores processor-executable instructions, which, on execution, cause the processor (105) to:
receive an input data (208) through multiple inputs modes associated with the event engaged by the group of participants (103), wherein the input data (208) comprises at least one of text data, audio data, image data, video data and gesture data, associated with the event and the group of participants (103);
profile each participant from the group of participants (103), during the event, based on the input data (208) and a pre-stored event data (210) associated with the event;
index one or more behavioural attributes (211) of each participant from the group of participants (103), based on at least one of the input data (208), the pre-stored event data (210), and the profiling of corresponding participant;
generate one or more alerts (213), to provide to at least one participant from the group of participants (103), during the event, based on the profiling and the indexing of the corresponding participant, the input data (208), and the pre-stored event data (210); and
determine upon end of the event, an event data (214) for the event and the group of participants (103), based on the profiling and the indexing of the group of participants (103), wherein the event data (214) for the event comprises inference associated with the event, and the event data (214) for a participant from the group of participants (103) comprises progress data and action plan data for the participant..

7. The event managing system (101) as claimed in claim 6 and further comprises the processor (105) configured to:
process the input data (208) by performing one or more tasks associated with Natural Language Processing (NLP) for managing the event.

8. The event managing system (101) as claimed in claim 6, wherein a participant is profiled by generating a participant-specific data and a group-specific data for the participant, wherein the participant-specific data comprises skills, tasks, topics and activities associated with the participant with respect to context of the event, and wherein the group-specific data indicates relationship of the participant-specific data with respect to the context of the event.

9. The event managing system (101) as claimed in claim 6, wherein the one or more behavioural attributes (211) comprises an interpersonal attribute, an attention attribute, and a group co-ordination attribute,
wherein the indexing of an interpersonal attribute, from the one or more behavioural attributes (211), of a participant is performed using a participant-specific data and the input data (208) associated with the participant,
wherein the indexing of an attention attribute, from the one or more behavioural attributes (211), of a participant is performed based on a participant-specific data, the input data (208) and the one or more alerts (213) associated with the participant,
wherein the indexing of a group co-ordination attribute, from the one or more behavioural attributes (211), of a participant is performed based on pre-stored event data (210), a group-specific data and the input data (208) associated with the participant.

10. The event managing system (101) as claimed in claim 6, wherein the one or more alerts (213) for the at least one participant is generated using a trained deep neural network by:
detecting occurrence of a deviation in context of the event based on the input data (208) and the pre-stored event data (210), wherein detecting the occurrence of the deviation in the context of the event comprises:
extracting features associated with one or more input keywords from the input data (208) to identify at least one on-going topic in the event;
comparing the features of the one or more input keywords with features of one or more pre-stored keywords from the pre-stored event data (210), to detect a mismatch between the one or more input keywords and the one or more pre-stored keywords; and
detecting the occurrence of the deviation in the context when the mismatch is detected;
identifying the at least one participant associated with the occurrence, using the input data (208) and a participant-specific data; and
generating the one or more alerts (213) with respect to the deviation in context, to be provided to the at least one participant.

Dated this 28th day of December, 2018

Madhusudan S.T
Of K&S Partners
Agent for the Applicant
IN/PA-1297
, Description:TECHNICAL FIELD
The present subject matter is related in general to managing systems, more particularly, but not exclusively to a method and system for managing an event engaged by group of participants in real-time.