APPARATUSES, METHODS, AND COMPUTER PROGRAM PRODUCTS FOR GENERATING AUTOMATED RESOLUTION ACTIONS USING INTENT PROVIDED BY A TRAINED MACHINE LEARNING MODEL

1 . An apparatus comprising one or more processors and one or more storage devices storing instructions that are operable, when executed by the one or more processors, to cause the one or more processors to:

receive a service message object via a communication channel of a plurality of communication channels, wherein the service message object defines a feature dataset associated with a service request for an application framework;

apply a machine learning model trained for intent recognition to the feature dataset to generate an intent support label for the service message object;

correlate the intent support label to a resolution data object related to a service resolution for the service request; and

based on whether the resolution data object satisfies defined resolution criteria for the intent support label,

initiate a first resolution action for the service request based at least in part on first resolution information related to a searchable database system or a generative system, or

initiate a second resolution action for the service request based at least in part on second resolution information related to an automated response message object.

2 . The apparatus of claim 1 , wherein machine learning model is a first machine learning model, and wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:

apply a second machine learning model trained for automated service resolution to the intent support label to generate the resolution data object.

3 . The apparatus of claim 1 , wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:

query a set of predefined resolution data objects to select the resolution data object from the set of predefined resolution data objects based at least in part on the intent support label.

4 . The apparatus of claim 1 , wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:

select the resolution data object from a ranking of resolution data objects configured based at least in part on the intent support label.

5 . The apparatus of claim 1 , wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:

generate a response message object for a client device via a communication channel of the plurality of communication channels in response to a determination that the feature dataset does not satisfy defined intent criteria for the machine learning model;

receive an additional service message object via the communication channel, wherein the additional service message object defines an additional feature dataset associated with the service request; and

apply the machine learning model trained for intent recognition to the feature dataset and the additional feature dataset to generate the intent support label for the service message object.

6 . The apparatus of claim 1 , wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:

in response to a determination the resolution data object does not satisfy the defined resolution criteria for the intent support label, utilize a generative machine learning model to determine the first resolution information.

7 . The apparatus of claim 1 , wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:

in response to a determination the resolution data object does not satisfy the defined resolution criteria for the intent support label, query a knowledge base system to determine the first resolution information.

8 . The apparatus of claim 1 , wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:

transmit a resolution message object associated with the first resolution information or the second resolution information to a client device via the communication channel.

9 . The apparatus of claim 1 , wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:

route a resolution ticket data object associated with the first resolution information or the second resolution information to a support device.

10 . The apparatus of claim 1 , wherein the communication channel corresponds to an email communication channel, a network portal interface communication channel, a user interface widget communication channel, a chat communication channel, or an application programming interface (API) communication channel.

11 . A computer-implemented method, comprising:

receiving a service message object via a communication channel of a plurality of communication channels, wherein the service message object defines a feature dataset associated with a service request for an application framework;

applying a machine learning model trained for intent recognition to the feature dataset to generate an intent support label for the service message object;

correlating the intent support label to a resolution data object related to a service resolution for the service request; and

based on whether the resolution data object satisfies defined resolution criteria for the intent support label,

initiating a first resolution action for the service request based at least in part on first resolution information related to a searchable database system or a generative system, or

initiating a second resolution action for the service request based at least in part on second resolution information related to an automated response message object.

12 . The computer-implemented method of claim 11 , wherein machine learning model is a first machine learning model, and the computer-implemented method further comprising:

applying a second machine learning model trained for automated service resolution to the intent support label to generate the resolution data object.

13 . The computer-implemented method of claim 11 , further comprising:

querying a set of predefined resolution data objects to select the resolution data object from the set of predefined resolution data objects based at least in part on the intent support label.

14 . The computer-implemented method of claim 11 , further comprising:

selecting the resolution data object from a ranking of resolution data objects configured based at least in part on the intent support label.

15 . The computer-implemented method of claim 11 , further comprising:

generating a response message object for a client device via a communication channel of the plurality of communication channels in response to a determination that the feature dataset does not satisfy defined intent criteria for the machine learning model;

receiving an additional service message object via the communication channel, wherein the additional service message object defines an additional feature dataset associated with the service request; and

applying the machine learning model trained for intent recognition to the feature dataset and the additional feature dataset to generate the intent support label for the service message object.

16 . The computer-implemented method of claim 11 , further comprising:

in response to a determination the resolution data object does not satisfy the defined resolution criteria for the intent support label, utilizing a generative machine learning model to determine the first resolution information.

17 . The computer-implemented method of claim 11 , further comprising:

in response to a determination the resolution data object does not satisfy the defined resolution criteria for the intent support label, querying a knowledge base system to determine the first resolution information.

18 . The computer-implemented method of claim 11 , further comprising:

transmitting a resolution message object associated with the first resolution information or the second resolution information to a client device via the communication channel.

19 . The computer-implemented method of claim 11 , further comprising:

routing a resolution ticket data object associated with the first resolution information or the second resolution information to a support device.

20 . A computer program product, stored on a computer readable medium, comprising instructions that when executed by one or more computers cause the one or more computers to:

receive a service message object via a communication channel of a plurality of communication channels, wherein the service message object defines a feature dataset associated with a service request for an application framework;

apply a machine learning model trained for intent recognition to the feature dataset to generate an intent support label for the service message object;

correlate the intent support label to a resolution data object related to a service resolution for the service request; and

based on whether the resolution data object satisfies defined resolution criteria for the intent support label,

initiate a first resolution action for the service request based at least in part on first resolution information related to a searchable database system or a generative system, or

initiate a second resolution action for the service request based at least in part on second resolution information related to an automated response message object.