AUTOMATIC TASK GENERATION

1 . A method comprising:

accessing virtual communication data associated with multiple types of virtual communication sessions related to a user;

generating one or more task items for the user using the generative artificial intelligence (AI) model based on an instruction and the virtual communication data;

aggregating the one or more task items for the multiple types of virtual communication sessions related to the user to obtain a list of task items; and

providing the list of task items to a client device associated with the user.

2 . The method of claim 1 , further comprising:

receiving the instruction for the generative AI model from a client device associated with the user.

3 . The method of claim 1 , wherein the instruction comprises the multiple types of virtual communication sessions whose communication data can be processed for task generation, wherein the instruction further comprises a task item structure, and wherein the instruction is pre-defined by the user.

4 . The method of claim 3 , wherein the multiple types of virtual communication sessions comprise virtual conferences, phone calls, email threads, or chat sessions.

5 . The method of claim 3 , wherein the task item structure comprises a suggested action, one or more action recipients, contact information associated with the one or more action recipients.

6 . The method of claim 1 , further comprising updating a status of the task item based on a user action on the task item.

7 . The method of claim 1 , further comprising:

receiving an updated instruction for the generative AI model from the client device associated with the user.

8 . A system comprising:

a communications interface;

a non-transitory computer-readable medium; and

one or more processors communicatively coupled to the communications interface and the non-transitory computer-readable medium, the one or more processors configured to execute processor-executable instructions stored in the non-transitory computer-readable medium to:

access virtual communication data associated with multiple types of virtual communication sessions related to a user;

generate one or more task items for the user using the generative artificial intelligence (AI) model based on an instruction and the virtual communication data;

aggregate the one or more task items for the multiple types of virtual communication sessions related to the user to obtain a list of task items; and

provide the list of task items to a client device associated with the user.

9 . The system of claim 8 , wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to:

receive the instruction for the generative AI model from a client device associated with the user.

10 . The system of claim 8 , wherein the instruction comprises the multiple types of virtual communication sessions whose communication data can be processed for task generation, wherein the instruction further comprises a task item structure, and wherein the instruction is pre-defined by the user.

11 . The system of claim 10 , wherein the multiple types of virtual communication sessions comprise virtual conferences, phone calls, email threads, or chat sessions.

12 . The system of claim 10 , wherein the task item structure comprises a suggested action, one or more action recipients, contact information associated with the one or more action recipients.

13 . The system of claim 8 , wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to:

update a status of the task item based on a user action on the task item.

14 . The system of claim 8 , wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to:

receive an updated instruction for the generative AI model from the client device associated with the user.

15 . A non-transitory computer-readable medium comprising processor-executable instructions configured to cause one or more processors to:

access virtual communication data associated with multiple types of virtual communication sessions related to a user;

generate one or more task items for the user using the generative artificial intelligence (AI) model based on an instruction and the virtual communication data;

aggregate the one or more task items for the multiple types of virtual communication sessions related to the user to obtain a list of task items; and

provide the list of task items to a client device associated with the user.

16 . The non-transitory computer-readable medium of claim 15 , further comprising processor-executable instructions configured to cause one or more processors to:

receive the instruction for the generative AI model from a client device associated with the user.

17 . The non-transitory computer-readable medium of claim 15 , wherein the instruction comprises the multiple types of virtual communication sessions whose communication data can be processed for task generation, wherein the instruction further comprises a task item structure, and wherein the instruction is pre-defined by the user.

18 . The non-transitory computer-readable medium of claim 17 , wherein the multiple types of virtual communication sessions comprise virtual conferences, phone calls, email threads, or chat sessions, and wherein the task item structure comprises a suggested action, one or more action recipients, contact information associated with the one or more action recipients.

19 . The non-transitory computer-readable medium of claim 15 , further comprising processor-executable instructions configured to cause one or more processors to:

update a status of the task item based on a user action on the task item.

20 . The non-transitory computer-readable medium of claim 15 , further comprising processor-executable instructions configured to cause one or more processors to:

receive an updated instruction for the generative AI model from the client device associated with the user.