Systems and methods for processing tasks delegated to a task facilitation service

1. A computer-implemented method, comprising:

generating task data associated with a user, wherein the task data identifies a task to be delegated by a user;

generating a feature vector corresponding to the task data;

applying one or more parameter prediction models to the feature vector to generate task parameters associated with the task;

transmitting the task data and the task parameters, wherein, when the task data and the task parameters are received, a delegation indicator associated with the task is generated;

receiving the delegation indicator, wherein the delegation indicator corresponds to the user delegating the task for completion by a task facilitation service;

applying a communication policy for the task responsive to receiving the delegation indicator, wherein the communication policy inhibits transmission of communications to a computing device associated with the user;

detecting a request to establish a real-time messaging session being transmitted across a communication network, wherein the real-time messaging session is associated with the computing device associated with the user;

intercepting the request from being transmitted over the communication network, wherein the interception is determined based on the communication policy, and wherein the interception results in inhibiting the transmission of communication data to the computing device associated with the user;

receiving feedback generated by the user after completion of the task; and

updating the one or more parameter prediction models based on the feedback, wherein updating the one or more parameter prediction models increases a likelihood of future task parameters being consistent with the feedback.

2. The computer-implemented method of claim 1 , wherein the communication policy inhibits the transmission of communications by requiring confirmation at the computing device associated with a representative before permitting a transmission of a communication to the computing device corresponding to the user.

3. The computer-implemented method of claim 1 , wherein applying the one or more parameter prediction models includes:

identifying a missing value for a parameter of the task; and

determining the missing value based on the task parameters.

4. The computer-implemented method of claim 1 , wherein applying the one or more parameter prediction models includes:

identifying a missing value for a parameter of the task; and

transmitting a request for the task parameters, wherein, when the request is received, the one or more parameter prediction models are applied to the feature vector.

5. The computer-implemented method of claim 1 , further comprising generating a proposal for completion of the task using a proposal recommendation system, wherein generating the proposal includes providing the task parameters to the proposal recommendation system.

6. The computer-implemented method of claim 1 further comprising generating a proposal for completion of the task using a proposal recommendation system, wherein the proposal recommendation system selects a proposal template based on the task parameters.

7. The computer-implemented method of claim 1 , wherein the communication policy is a first communication policy, the computer-implemented method further comprising:

receiving a revocation indicator for the task, wherein the revocation indicator indicates that the task is to be no longer delegated for completion by the task facilitation service; and

determining a second communication policy for the task in response to receiving the revocation indicator, the second communication policy being more permissive than the first communication policy.

8. The computer-implemented method of claim 1 , wherein applying the one or more parameter prediction models includes:

accessing a user model associated with the user, wherein the user model identifies one or more characteristics specific to the user;

generating a weighting vector using the user model; and

adjusting parameters of the one or more parameter prediction models based on the weighting vector, wherein the parameters are adjusted to increase a likelihood of the future task parameters to correspond to the one or more characteristics of the user.

9. A computing device comprising:

one or more data processors; and

a non-transitory computer-readable storage medium containing instructions which, when executed by the one or more data processors, cause the one or more data processors to perform operations including:

generating task data associated with a user, wherein the task data identifies a task to be delegated by a user:

generating a feature vector corresponding to the task data;

applying one or more parameter prediction models to the feature vector to generate task parameters associated with the task;

transmitting the task data and the task parameters, wherein, when the task data and the task parameters are received, a delegation indicator associated with the task is generated;

receiving the delegation indicator, wherein the delegation indicator corresponds to the user delegating the task for completion by a task facilitation service;

applying a communication policy for the task responsive to receiving the delegation indicator, wherein the communication policy inhibits transmission of communications to a computing device associated with the user;

detecting a request to establish a real-time messaging session being transmitted across a communication network, wherein the real-time messaging session is associated with the computing device associated with the user;

intercepting the request from being transmitted over the communication network, wherein the interception is determined based on the communication policy, and wherein the interception results in inhibiting the transmission of communication data to the computing device associated with the user;

receiving feedback generated by the user after completion of the task; and

updating the one or more parameter prediction models based on the feedback, wherein updating the one or more parameter prediction models increases a likelihood of future task parameters being consistent with the feedback.

10. The computing device of claim 9 , wherein the communication policy inhibits the transmission of communications by requiring confirmation at the computing device associated with a representative before permitting a transmission of a communication to the computing device corresponding to the user.

11. The computing device of claim 9 , wherein applying the one or more parameter prediction models includes:

identifying a missing value for a parameter of the task; and

determining the missing value based on the task parameters.

12. The computing device of claim 9 , wherein the operations further include generating a proposal for completion of the task using a proposal recommendation system, and wherein generating the proposal includes providing the task parameters to the proposal recommendation system.

13. The computing device of claim 9 , wherein applying the one or more parameter prediction models includes:

identifying a missing value for a parameter of the task; and

transmitting a request for the task parameters, wherein, when the request is received, the one or more parameter prediction models are applied to the feature vector.

14. The computing device of claim 9 , wherein the operations further include generating a proposal for completion of the task using a proposal recommendation system, and wherein the proposal recommendation system selects a proposal template based on the task parameters.

15. The computing device of claim 9 , wherein the communication policy is a first communication policy, and wherein the operations further include:

receiving a revocation indicator for the task, wherein the revocation indicator indicates that the task is to be no longer delegated for completion by the task facilitation service; and

determining a second communication policy for the task in response to receiving the revocation indicator, the second communication policy being more permissive than the first communication policy.

16. The computing device of claim 9 , wherein applying the one or more parameter prediction models includes:

accessing a user model associated with the user, wherein the user model identifies one or more characteristics specific to the user;

generating a weighting vector using the user model; and

adjusting parameters of the one or more parameter prediction models based on the weighting vector, wherein the parameters are adjusted to increase a likelihood of the future task parameters to correspond to the one or more characteristics of the user.

17. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause a computing device to perform operations including:

generating task data associated with a user, wherein the task data identifies a task to be delegated by a user;

generating a feature vector corresponding to the task data;

applying one or more parameter prediction models to the feature vector to generate task parameters associated with the task;

transmitting the task data and the task parameters, wherein, when the task data and the task parameters are received, a delegation indicator associated with the task is generated;

receiving the delegation indicator, wherein the delegation indicator corresponds to the user delegating the task for completion by a task facilitation service;

applying a communication policy for the task responsive to receiving the delegation indicator, wherein the communication policy inhibits transmission of communications to a computing device associated with the user;

detecting a request to establish a real-time messaging session being transmitted across a communication network, wherein the real-time messaging session is associated with the computing device associated with the user;

intercepting the request from being transmitted over the communication network, wherein the interception is determined based on the communication policy, and wherein the interception results in inhibiting the transmission of communication data to the computing device associated with the user;

receiving feedback generated by the user after completion of the task; and

updating the one or more parameter prediction models based on the feedback, wherein updating the one or more parameter prediction models increases a likelihood of future task parameters being consistent with the feedback.

18. The computer-program product of claim 17 , wherein the communication policy inhibits the transmission of communications by requiring confirmation at the computing device associated with a representative before permitting a transmission of a communication to the computing device corresponding to the user.

19. The computer-program product of claim 17 , wherein applying the one or more parameter prediction models includes:

identifying a missing value for a parameter of the task; and

determining the missing value based on the task parameters.

20. The computer-program product of claim 17 , wherein applying the one or more parameter prediction models includes:

identifying a missing value for a parameter of the task; and

transmitting a request for the task parameters, wherein, when the request is received, the one or more parameter prediction models are applied to the feature vector.

21. The computer-program product of claim 17 , wherein the operations further include generating a proposal for completion of the task using a proposal recommendation system, and wherein generating the proposal includes providing the task parameters to the proposal recommendation system.

22. The computer-program product of claim 17 , wherein the operations further include generating a proposal for completion of the task using a proposal recommendation system, and wherein the proposal recommendation system selects a proposal template based on the task parameters.

23. The computer-program product of claim 17 , wherein the communication policy is a first communication policy, and wherein the operations further include:

receiving a revocation indicator for the task, wherein the revocation indicator indicates that the task is to be no longer delegated for completion by the task facilitation service; and

determining a second communication policy for the task in response to receiving the revocation indicator, the second communication policy being more permissive than the first communication policy.

24. The computer-program product of claim 17 , wherein applying the one or more parameter prediction models includes:

accessing a user model associated with the user, wherein the user model identifies one or more characteristics specific to the user;

generating a weighting vector using the user model; and

adjusting parameters of the one or more parameter prediction models based on the weighting vector, wherein the parameters are adjusted to increase a likelihood of the future task parameters to correspond to the one or more characteristics of the user.