EP-4740412-A2 - SYSTEM AND METHOD FOR AUTONOMOUS CUSTOMER SUPPORT CHATBOT AGENT WITH NATURAL LANGUAGE WORKFLOW POLICIES

Abstract

An autonomous customer support Chatbot Agent utilizes a large language model to aid in implementing a workflow to solve a customer issue. A natural language workflow policy may be selected by an admin, along with tools such as API calls. The large language model determines the implementation details for the workflow based on the workflow policy and the selected tools.

Inventors

• GHOCHE, SAMI
• Man, James
• Kong, Sunny
• Xing, Weitian
• Tosh, Zachary
• LU, ALAN
• Nicholas, Deon
• Hajal, Samy
• Chamoun, Jad
• Nasr, Antoine
• Lyubinets, Volodymyr
• Liao, EJ
• SHARMA, DEV
• LU, YI

Assignees

• Forethought Technologies, Inc.

Dates

Publication Date

20260513

Application Date

20240702

Claims (20)

1. 1. A method for responding to a customer service ticket comprising: prompting a large language model with a natural language text description of a workflow policy, wherein the workflow policy is selected based on a topic of a customer support ticket to solve a specific customer issue; prompting the large language model with conversation information regarding a conversation between a customer and an autonomous Al chatbot agent, wherein the conversation is associated with the customer support ticket for the specific customer issue; prompting the large language model with information describing applicable actions to implement the workflow, including any applicable API calls; and prompting the large language model with at least one text message for the workflow; the large language model observing the results of actions of the workflow, making decisions on information to request from the customer, and making decisions on actions to take to implement the workflow.
2. 2. An apparatus for responding to a customer service ticket comprising: an autonomous Al chatbot agent utilizing a large language model to implement a workflow to solve a customer service support ticket; the large language model being prompted with a natural language workflow policy and a description of available tools to generate an interactive workflow for interacting with a customer to solve the customer service support ticket.
3. 3. The apparatus of claim 2, further comprising a software entity to determine the intent of the customer service support ticket and select a workflow policy for the determined intent.
4. 4. The apparatus of claim 2, wherein the natural language workflow further comprises at least one action having an associated software tool.
5. 5. The apparatus of claim 4, wherein the associated software tool includes an API call.
6. 6. The apparatus of claim 2, wherein the natural language workflow includes at least one specified text message to respond to a particular instance of the w orkflow.
7. 7. The apparatus of claim 2, wherein the autonomous Al chatbot agent interacts with the large language model.
8. 8. The apparatus of claim 2, wherein autonomous the Al chatbot agent includes the large language model.
9. 9. The apparatus of claim 2, wherein the workflow policy comprises at least one natural language sentence.
10. 10. The apparatus of claim 2, wherein the large language model is prompted with conversation information associated with a customer ticket, prompted with the workflow policy, prompted with information on applicable software tools for the workflow policy.
11. 11. The apparatus of claim 10. wherein the large language model is further prompted with guard rail prompts.
12. 12. A method for responding to a customer service ticket comprising: receiving a natural language text description of a workflow policy for a particular customer support topic; determining a topic of a conversation between an autonomous Al chatbot agent respond to a customer service support ticket of a human being; utilizing a large language model to implement a workflow to solve a customer service support ticket; and prompting the large language model with the workflow policy and a description of available tools to generate an interactive workflow for interacting with the customer to solve the customer service support ticket.
13. 13. The method of claim 12, further comprising utilizing a taxonomy generated for historic customer support tickets to generate a list of customer support tickets.
14. 14. The method of claim 12, wherein the natural language workflow further comprises at least one action having an associated software tool.
15. 15. The method of claim 14, wherein the associated software tool includes an API call.
16. 16. The apparatus of claim 12, wherein the natural language workflow includes at least one specified text message to respond to a particular instance of the workflow.
17. 17. The method of claim 12, wherein the autonomous Al chatbot agent interacts with the large language model.
18. 18. The method of claim 12, wherein autonomous Al chatbot agent includes the large language model.
19. 19. The method of claim 12, wherein the workflow policy comprises at least one natural language sentence.
20. 20. The method of claim 12, wherein the large language model is prompted with conversation information associated with a customer ticket, prompted with the workflow policy, prompted with information on applicable software tools for the workflow policy.

Description

SYSTEM AND METHOD FOR AUTONOMOUS CUSTOMER SUPPORT CHATBOT AGENT WITH NATURAL LANGUAGE WORKFLOW POLICIES FIELD OF THE INVENTION [0001] The present disclosure generally relates to servicing customer support issues, such as responding to questions or complaints during a lifecycle of a customer support issue. BACKGROUND [0002] Customer support service is an important aspect of many businesses. For example, there are a variety of customer support applications to address customer service support issues. As one illustration, a customer service helpdesk may have a set of human agents who use text messages to service customer support issues. There are a variety of Customer Relationship Management (CRM) and helpdesk-related software tools, such as SalesForce® or Zendesk®. [0003] Customer support issues may be assigned a ticket that is served by available human agents over the lifecycle of the ticket. The lifecycle of resolving the customer support issue(s) associated with a ticket may include one or more customer questions and one or more answers made by an agent in response to customer question(s). To address common support questions, the human agents may have available to them macros and templates in SalesForce® or templates in Zendesk® as examples. Macros and templates work well for generating information to respond to routine requests for information, such as if a customer asks, “Do you offer refunds?” However, there are some types of more complicated or nonroutine questions for which there may be no macro or template. [0004] Human agents may have available to them other data sources spread across an organization (e.g., Confluence®, WordPress®, Nanorep®, Readmeio®, JIRA®, Guru®, Knowledge Bases, etc.). However, while an institution may have a lot of institutional knowledge to aid human agents, there may be practical difficulties in training agents to use all the institutional knowledge that is potentially available to aid in responding to tickets. For example, conventionally, a human agent may end up doing a manual search of the institutional knowledge. However, an agent may waste time in unproductive searches of the institutional knowledge. [0005] Typically, a human expert makes decisions on how to label and route tickets, which is a resource intensive task. There is also a delay associated with this process because incoming tickets have to wait in a queue for a human expert to make labeling and routing decisions. [0006] However, there are substantial training and labor costs to have a large pool of highly trained human agents available to service customer issues. There are also labor costs associated with having human experts making decisions about how to label and route tickets. But in addition to labor costs, there are other issues in terms of the frustration customers experience if there is a long delay in responding to their queries. [0007] In addition to other issues, it has often been impractical in conventional techniques to have more than a small number of customer issue topics as categories. That is, conventionally tickets are categorized into a small number of categories (e.g., 15) for handling by agents. BRIEF DESCRIPTION OF THE DRAWINGS [0008] The present disclosure is illustrated by way of example, and not by way of limitation in the figures of the accompanying drawings in which like reference numerals are used to refer to similar elements. [0009] Fig. 1 is a block diagram illustrating a customer service support environment in accordance with an implementation. [0010] Fig. 2A is a block diagram illustrating a module for using Al to augment customer support agents in accordance with an implementation. [0011] Fig. 2B is a block diagram of a server-based implementation. [0012] Fig. 3 is a block diagram of a portion of a ML system in accordance with an implementation. [0013] Fig. 4 is a flow chart of a method of servicing tickets in accordance with an implementation. [0014] Fig. 5 is a flow chart of a method of automatically generating a templet answer to an incoming question in accordance with an implementation. [0015] Fig. 6 illustrates an example of ML pipeline in accordance with an implementation. [0016] Fig. 7 illustrates aspects of using supervised learning to solve macros in accordance with an implementation. [0017] Fig. 8 is a flow chart of a method of generating macro template answer codes in accordance with an implementation. [0018] Fig. 9 illustrates an example of a method of identifying macro template answers and also initiating a workflow task in accordance with an implementation. [0019] Fig. 10 illustrates an example of a method of training a ML model for triaging the routing of tickets in accordance with an implementation. [0020] Fig. 1 1 illustrates a method of performing triaging in the routing of tickets in accordance with an implementation. [0021] Fig. 12 illustrates a method of identifying knowledge based articles to respond to a question in accordance with an implementation. [0