US-12619953-B2 - Artificial intelligence (AI)-driven standardized intake tool

Abstract

The disclosed techniques pertain to artificial intelligence (AI) techniques. Particularly, techniques are disclosed for providing an AI-driven standardized intake tool. An intake request can be accessed and processed using a plurality of computational models to generate a set of results. The set of results and a machine-learning model can be used to predict a set of workflow actions for the intake request and a set of tools for an entity associated with the intake request can be updated based on the set of workflow actions. Updating the set of tools for the entity can initiate a workflow for incorporating a target of the intake request within the entity.

Inventors

• Nishank Jain
• Christopher Alan Wix

Assignees

• TRUIST BANK

Dates

Publication Date

20260505

Application Date

20241031

Claims (20)

1. 1 . A computer-implemented method comprising: accessing, from a configuration management database (CMDB) storing a catalogue of applications deployed within an entity, an intake request associated with a process for deploying an application within the entity, wherein the intake request identifies an intake type for the intake request; applying a rule-based model to the intake request to determine that the intake request includes a valid intake type, a target team, and an initial set of impacted applications; in response to determining that the intake request includes the valid intake type, the target team, and the initial set of impacted applications, processing the intake request by: generating a feature representation of the intake request by extracting structured attributes and vectorized metadata from the intake request and related CMDB records; executing a machine-learning model on the feature representation to predict additional impacted applications deployed by the entity, wherein the machine-learning model is executed on a computing platform co-located with the CMDB; retrieving a set of historical impacted applications by executing rules that query the CMDB for prior intake requests with the same intake type and returning prior matched applications; combining the additional impacted applications and the set of historical impacted applications to produce a consolidated set of results; inputting the consolidated set of results into a workflow prediction model that is fine-tuned on prior intake-to-deployment mappings to output a set of workflow actions comprising scheduled workflow tasks, resource reservations, and message templates for participants; and automatically updating one or more enterprise tools by programmatically: creating or updating a project entry in a project management database with a Gantt chart generated from the scheduled workflow tasks; reserving calendar resources via a resource scheduling tool for the scheduled workflow tasks to avoid scheduling conflicts; and populating a messaging tool with notification messages and participant lists derived from the message templates, wherein automatically updating the one or more enterprise tools initiate the process for deploying the application.
2. 2 . The computer-implemented method of claim 1 , further comprising: updating the rule-based model by deriving a new rule from data and information collected while processing the intake request, and storing the new rule in the CMDB.
3. 3 . The computer-implemented method of claim 1 , further comprising: fine-tuning the machine-learning model with labelled training data generated from the intake request and related CMDB records collected during processing so that the workflow prediction model is improved for future intake-to-deployment mappings.
4. 4 . The computer-implemented method of claim 1 , wherein the machine-learning model comprises a transformer-based model that accepts the feature representation and leverages learned embeddings of application metadata to predict additional impacted applications.
5. 5 . The computer-implemented method of claim 1 , further comprising: performing a capacity check by comparing schedule information for currently active projects retrieved from the project management database and resource scheduling tool against the scheduled workflow tasks; and, in response to determining that impacted projects are at capacity, automatically flagging the intake request and modifying the set of workflow actions to include an escalation or rescheduling action.
6. 6 . The computer-implemented method of claim 1 , further comprising: enforcing access controls by authenticating a user or system requesting the intake request and by verifying permissions to modify the project management database, resource scheduling tool, and messaging tool prior to automatically updating the one or more enterprise tools.
7. 7 . The computer-implemented method of claim 1 , wherein the Gannt chart comprises creating one or more visualizations that depict the scheduled workflow tasks, task dependencies, resource assignments, and timeline, and storing the visualizations in the project management database for access by participants.
8. 8 . A system comprising: one or more processors; and one or more computer-readable media storing instructions which, when executed by the one or more processors, cause the system to perform operations comprising: accessing, from a configuration management database (CMDB) storing a catalogue of applications deployed within an entity, an intake request associated with a process for deploying an application within the entity, wherein the intake request identifies an intake type for the intake request; applying a rule-based model to the intake request to determine that the intake request includes a valid intake type, a target team, and an initial set of impacted applications; in response to determining that the intake request includes the valid intake type, the target team, and the initial set of impacted applications, processing the intake request by: generating a feature representation of the intake request by extracting structured attributes and vectorized metadata from the intake request and related CMDB records; executing a machine-learning model on the feature representation to predict additional impacted applications deployed by the entity, wherein the machine-learning model is executed on a computing platform co-located with the CMDB; retrieving a set of historical impacted applications by executing rules that query the CMDB for prior intake requests with the same intake type and returning prior matched applications; combining the additional impacted applications and the set of historical impacted applications to produce a consolidated set of results; inputting the consolidated set of results into a workflow prediction model that is fine-tuned on prior intake-to-deployment mappings to output a set of workflow actions comprising scheduled workflow tasks, resource reservations, and message templates for participants; and automatically updating one or more enterprise tools by programmatically: creating or updating a project entry in a project management database with a Gantt chart generated from the scheduled workflow tasks; reserving calendar resources via a resource scheduling tool for the scheduled workflow tasks to avoid scheduling conflicts; and populating a messaging tool with notification messages and participant lists derived from the message templates, wherein automatically updating the one or more enterprise tools initiate the process for deploying the application.
9. 9 . The system of claim 8 , the operations further comprising: updating the rule-based model by deriving a new rule from data and information collected while processing the intake request, and storing the new rule in the CMDB.
10. 10 . The system of claim 8 , the operations further comprising: fine-tuning the machine-learning model with labelled training data generated from the intake request and related CMDB records collected during processing so that the workflow prediction model is improved for future intake-to-deployment mappings.
11. 11 . The system of claim 8 , wherein the machine-learning model comprises a transformer-based model that accepts the feature representation and leverages learned embeddings of application metadata to predict additional impacted applications.
12. 12 . The system of claim 8 , the operations further comprising: performing a capacity check by comparing schedule information for currently active projects retrieved from the project management database and resource scheduling tool against the scheduled workflow tasks; and, in response to determining that impacted projects are at capacity, automatically flagging the intake request and modifying the set of workflow actions to include an escalation or rescheduling action.
13. 13 . The system of claim 8 , the operations further comprising: enforcing access controls by authenticating a user or system requesting the intake request and by verifying permissions to modify the project management database, resource scheduling tool, and messaging tool prior to automatically updating the one or more enterprise tools.
14. 14 . The system of claim 8 , wherein the Gannt chart comprises creating one or more visualizations that depict the scheduled workflow tasks, task dependencies, resource assignments, and timeline, and storing the visualizations in the project management database for access by participants.
15. 15 . One or more non-transitory computer-readable media storing instructions which, when executed by one or more processors, cause a system to perform operations comprising: accessing, from a configuration management database (CMDB) storing a catalogue of applications deployed within an entity, an intake request associated with a process for deploying an application within the entity, wherein the intake request identifies an intake type for the intake request; applying a rule-based model to the intake request to determine that the intake request includes a valid intake type, a target team, and an initial set of impacted applications; in response to determining that the intake request includes the valid intake type, the target team, and the initial set of impacted applications, processing the intake request by: generating a feature representation of the intake request by extracting structured attributes and vectorized metadata from the intake request and related CMDB records; executing a machine-learning model on the feature representation to predict additional impacted applications deployed by the entity, wherein the machine-learning model is executed on a computing platform co-located with the CMDB; retrieving a set of historical impacted applications by executing rules that query the CMDB for prior intake requests with the same intake type and returning prior matched applications; combining the additional impacted applications and the set of historical impacted applications to produce a consolidated set of results; inputting the consolidated set of results into a workflow prediction model that is fine-tuned on prior intake-to-deployment mappings to output a set of workflow actions comprising scheduled workflow tasks, resource reservations, and message templates for participants; and automatically updating one or more enterprise tools by programmatically: creating or updating a project entry in a project management database with a Gantt chart generated from the scheduled workflow tasks; reserving calendar resources via a resource scheduling tool for the scheduled workflow tasks to avoid scheduling conflicts; and populating a messaging tool with notification messages and participant lists derived from the message templates, wherein automatically updating the one or more enterprise tools initiate the process for deploying the application.
16. 16 . The one or more non-transitory computer-readable media of claim 15 , further comprising: updating the rule-based model by deriving a new rule from data and information collected while processing the intake request, and storing the new rule in the CMDB.
17. 17 . The one or more non-transitory computer-readable media of claim 15 , further comprising: fine-tuning the machine-learning model with labelled training data generated from the intake request and related CMDB records collected during processing so that the workflow prediction model is improved for future intake-to-deployment mappings.
18. 18 . The one or more non-transitory computer-readable media of claim 15 , wherein the machine-learning model comprises a transformer-based model that accepts the feature representation and leverages learned embeddings of application metadata to predict additional impacted applications.
19. 19 . The one or more non-transitory computer-readable media of claim 15 , the operations further comprising: performing a capacity check by comparing schedule information for currently active projects retrieved from the project management database and resource scheduling tool against the scheduled workflow tasks; and, in response to determining that impacted projects are at capacity, automatically flagging the intake request and modifying the set of workflow actions to include an escalation or rescheduling action.
20. 20 . The one or more non-transitory computer-readable media of claim 15 , the operations further comprising: enforcing access controls by authenticating a user or system requesting the intake request and by verifying permissions to modify the project management database, resource scheduling tool, and messaging tool prior to automatically updating the one or more enterprise tools.

Description

FIELD The present disclosure relates generally to artificial intelligence techniques, and more particularly, to techniques for providing an AI-driven standardized intake tool. BACKGROUND Large and complex organizations such as business enterprises are often organized into sub-enterprises such as departments, teams, divisions, and the like. These sub-enterprises are often responsible for performing one or more work requests and/or tasks that contribute to the functioning of the organization. For example, an account management division of an organization may be responsible for managing customer accounts and internal accounts. In another example, an application department may be responsible for managing applications for running the business enterprise and client-facing applications. As part of their responsibilities, sub-enterprises of the organization often coordinate processes such as work request intake or rollout of applications, services, and capabilities with other sub-enterprises of the organization. For example, in the case of a financial services organization, intake of a digital payment service by a banking department of the organization may involve coordination with an Automated Clearing House (ACH) API of the account management division of the organization. In another example, in the case of a retail enterprise, a work request in support of rollout of an inventory control database may involve coordination with a customer management department. Often intake within an organization involves coordination between sub-enterprises using disparate intake processes of the individual sub-components of the organization. Therefore, it may be desirable to provide a tool for standardizing intake. BRIEF SUMMARY Techniques disclosed herein pertain to artificial intelligence (AI) techniques. Particularly, techniques are disclosed herein for providing an AI-driven standardized intake tool. In some embodiments, a computer-implemented method includes: accessing an intake request, the intake request corresponding to an application to be incorporated within an entity; processing the intake request using a plurality of computational models, wherein processing the intake request using the plurality of computational models causes the plurality of computational models to generate a set of results; using the set of results and a machine-learning model to predict a set of workflow actions for the intake request; and updating a set of tools for the entity based on the set of workflow actions, wherein updating the set of tools for the entity initiates a workflow for incorporating the application within the entity. In some embodiments, at least one computational model of the plurality of computational models comprises a rule-based model. In some embodiments, the processing the intake request using the plurality of computational models comprises updating the rule-based model with a rule derived from data and information associated with the intake request that is collected during the processing. In some embodiments, at least one computational model of the plurality of computational models comprises a machine-learning model. In some embodiments, the processing the intake request using the plurality of computational models comprises fine-tuning the machine-learning model with data and information associated with the intake request that is collected during the processing. In some embodiments, the method further includes using the set of results to generate one or more visualizations for the intake request. In some embodiments, the one or more visualizations comprises a Gantt chart depicting a workflow associated with the set of workflow actions. Some embodiments include a system that includes one or more processors and one or more computer-readable media storing instructions which, when executed by the one or more processors, cause the system to perform part or all of the operations and/or methods disclosed herein. Some embodiments include one or more non-transitory computer-readable media storing instructions which, when executed by one or more processors, cause a system to perform part or all of the operations and/or methods disclosed herein. The techniques described above and below may be implemented in a number of ways and in a number of contexts. Several example implementations and contexts are provided with reference to the following figures, as described below in more detail. However, the following implementations and contexts are but a few of many. BRIEF DESCRIPTION OF THE DRAWINGS Features, embodiments, and advantages of the present disclosure are better understood when the following Detailed Description is read with reference to the accompanying drawings. FIG. 1 is a simplified diagram of an example environment for providing an artificial intelligence (AI)-driven standardized intake tool, according to some embodiments. FIG. 2 is a simplified diagram of an example flow of the AI-driven standardized intake tool, according to