US-20260127394-A1 - DYNAMICALLY CUSTOMIZING A USER INTERFACE OF AN ELECTRONIC PLATFORM VIA MACHINE LEARNING

US20260127394A1US 20260127394 A1US20260127394 A1US 20260127394A1US-20260127394-A1

Abstract

Via one or more electronic communication channels of an electronic platform, a request is detected from a user to interact with the electronic platform. Via a Natural Language Processing (NLP) model, an intent of the user behind the request to interact with the electronic platform is predicted. Via an Explainable Artificial Intelligence (XAI) model, one or more features associated with the user that contributed to the predicted intent are determined. Via a Large Language Model (LLM), a personalized message is generated for the user. The personalize message refers to the intent predicted by the NLP model or the one or more features associated with the user determined by the XAI model that contributed to the predicted intent. The personalized message is provided to the user via the one or more electronic communication channels.

Inventors

Xiaoying Han

Assignees

PAYPAL, INC.

Dates

Publication Date: 20260507
Application Date: 20241106

Claims (20)

1 . A method, comprising: detecting, via one or more electronic communication channels of an electronic platform, a request from a user to interact with the electronic platform; predicting, at least in part via a Natural Language Processing (NLP) model, an intent of the user behind the request to interact with the electronic platform; determining, at least in part via an Explainable Artificial Intelligence (XAI) model, one or more features associated with the user that contributed to the predicted intent; generating, at least in part via a Large Language Model (LLM), a personalized message for the user, wherein the personalized message refers to the intent predicted by the NLP model or the one or more features associated with the user determined by the XAI model that contributed to the predicted intent; and providing the personalized message to the user via the one or more electronic communication channels.
2 . The method of claim 1 , further comprising: detecting a user action after the personalized message has been provided to the user; updating, at least in part based on the detected user action and at least in part via one or more of the NLP model, the XAI model, or the LLM, the personalized message for the user; and providing the updated personalized message to the user via the one or more electronic communication channels.
3 . The method of claim 2 , wherein: the personalized message is provided to the user via a first electronic communication channel of the one or more electronic communication channels; and the updated personalized message is provided to the user via a second electronic communication channel of the one or more electronic communication channels.
4 . The method of claim 1 , wherein the personalized message contains an issue that pertains to the predicted intent and a recommended action for resolving the issue.
5 . The method of claim 1 , wherein the one or more electronic communication channels comprise a webpage, an Interactive Voice Response (IVR), a computer chatbot, or an email.
6 . The method of claim 1 , further comprising: determining, at least in via the XAI model, one or more attribution scores associated with the one or more features, respectively, wherein each of the one or more attribution scores indicates a degree of contribution of the feature associated therewith to the predicted intent; ranking the one or more features based on their respective attribution scores; and identifying a top feature of the one or more features based on the top feature having a highest attribution score, wherein the personalized message refers to the top feature.
7 . A system, comprising: one or more processors; and a non-transitory computer-readable medium having stored thereon instructions that are executable by the one or more processors to cause a machine to perform operations comprising: receiving a request from a user to interact with an electronic platform; accessing one or more machine learning models that are trained based at least in part on user data associated with one or more user activities of the user on the electronic platform; determining, via the one or more machine learning models, a user intent associated with the request; generating, via the one or more machine learning models and based on the determined user intent, an experience that is personalized for the user; and providing the experience to the user via a user interface of a user device of the user, wherein the user interface is associated with the electronic platform.
8 . The system of claim 7 , wherein the experience is generated at least in part by including a reference to a first user activity of the one or more user activities.
9 . The system of claim 7 , wherein: the one or more machine learning models comprise a Natural Language Processing (NLP) model and an Explainable Artificial Intelligence (XAI) model; the user intent is determined at least in part via the NLP model; and the experience is determined at least in part via the XAI model.
10 . The system of claim 7 , wherein the experience is a first experience, and wherein the operations further comprises: receiving, from the user, a response to the first experience; generating, via the one or more machine learning models and based on the response, a second experience that is personalized to the user; and providing the second experience to the user via the user interface.
11 . The system of claim 10 , wherein: the one or more machine learning models comprise a Large Language Model (LLM); and the first experience or the second experience is generated at least in part via the LLM.
12 . The system of claim 10 , wherein: the first experience comprises a message pertaining to the determined user intent; and the response comprises a confirmation or a rejection from the user with respect to the determined user intent.
13 . The system of claim 7 , wherein the experience comprises a textual message, a voice message, or a list of menu options.
14 . The system of claim 7 , wherein the experience is provided at least in part by reconfiguring at least one portion of the user interface.
15 . A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause performance of operations comprising: accessing an interaction between a user and an electronic platform; predicting, based on one or more machine learning models, an intent of the user in association with the interaction, wherein the one or more machine learning models have been trained based at least in part on historical interactions of the user with the electronic platform; generating, based on the predicted intent of the user and via the one or more machine learning models, an experience that is customized to the user; and communicating the experience to the user via one or more communication channels of the electronic platform.
16 . The non-transitory machine-readable medium of claim 15 , wherein: the interaction between the user and the electronic platform is conducted via a first communication channel of the one or more communication channels; and the first communication channel comprises a webpage, an Interactive Voice Response (IVR) system, or an electronic chat.
17 . The non-transitory machine-readable medium of claim 15 , wherein the experience is communicated at least in part by prompting the user to confirm whether the predicted intent is accurate.
18 . The non-transitory machine-readable medium of claim 15 , wherein the experience contains a reference to one or more of the historical interactions of the user with the electronic platform.
19 . The non-transitory machine-readable medium of claim 15 , wherein: the intent of the user is predicted at least in part via a Natural Language Processing (NLP) model of the one or more machine learning models; and the experience is generated at least in part via an Explainable Artificial Intelligence (XAI) model or a Large Language Model (LLM) of the one or more machine learning models.
20 . The non-transitory machine-readable medium of claim 15 , wherein the operations further comprise: detecting a user action in response to the experience that has been communicated to the user; updating, based on the detected user action and via the one or more machine learning models, the experience that is customized to the user; and communicating, to the user, the updated experience via the one or more communication channels of the electronic platform.

Description

BACKGROUND Field of the Invention The present application generally relates to machine learning. More particularly, the present application involves using Natural Language Processing (NLP), Explainable Artificial Intelligence (XAI), and Large Language Models (LLMs) to generate a dynamically changing user interface of an electronic platform. Related Art Over the past several decades, rapid advances in Integrated Circuit fabrication and wired/wireless telecommunications technologies have brought about the arrival of the information age, in which electronic communications or interactions between various entities are becoming increasingly more common. For example, a user may interact with an entity (e.g., an electronic platform) through a user interface of the electronic platform in various situations. Unfortunately, conventional methods and systems have not been able to address the changing needs of the users, which may be different from user to user, and/or may change from time to time even for the same user. For example, an electronic platform may provide a static user interface that displays generic answers and/or prompts, which may not adequately address the user's questions and/or concerns and may therefore leave the user frustrated. This may also result in the user engaging more with the electronic platform in an attempt to get the desired content, which may then lead to additional time and computer processing by both the user device and the electronic platform. What is needed is a user interface that can be dynamically updated for a specific user based on information associated with that user, such that the user's needs can be anticipated and accurately addressed. BRIEF DESCRIPTION OF THE FIGURES FIG. 1 is a block diagram of a networked system according to various aspects of the present disclosure. FIGS. 2-4 illustrate a simplified process flows for providing a personalized experience to a user according to embodiments of the present disclosure. FIGS. 5A-5F illustrate example user interfaces for providing a personalized experience to a user according to various aspects of the present disclosure. FIG. 6 illustrates an example artificial neural network according to various aspects of the present disclosure. FIG. 7 is a simplified example of a cloud-based computing architecture according to various aspects of the present disclosure. FIGS. 8-9 are flowcharts illustrating methods of providing a personalized experience to a user according to various aspects of the present disclosure. FIG. 10 illustrates a computer system according to various aspects of the present disclosure. Embodiments of the present disclosure and their advantages are best understood by referring to the detailed description that follows. It should be appreciated that like reference numerals are used to identify like elements illustrated in one or more of the figures, wherein showings therein are for purposes of illustrating embodiments of the present disclosure and not for purposes of limiting the same. DETAILED DESCRIPTION It is to be understood that the following disclosure provides many different embodiments, or examples, for implementing different features of the present disclosure. Specific examples of components and arrangements are described below to simplify the present disclosure. These are, of course, merely examples and are not intended to be limiting. Various features may be arbitrarily drawn in different scales for simplicity and clarity. The present disclosure pertains to using machine learning models, such as Natural Language Processing (NLP), Explainable Artificial Intelligence (XAI), and Large Language Models (LLMs), to generate a dynamically changing user interface of an electronic platform. Conventionally, a user may interact with an electronic platform via one or more communication channels, for example, via a webpage, an online chat, a telephone call, an email, etc. Often times, the user is engaging in such an interaction because the user needs to have one or more issues resolved (e.g., getting a refund, changing a password, submitting a dispute, etc.). However, existing systems and methods have not been able to provide a personalized experience for the user. Instead, existing systems and methods typically provide a generic and/or static experience for their users. For example, an electronic platform may have a Frequently Asked Questions (FAQ) page that does not change from user to user, and it typically contains a static (e.g., unchanging) list of questions and answers that may or may not apply to all the users as a whole. Unfortunately, such a list may not be particularly relevant to any given user, who may have a specific issue in mind, but that issue is not included in the FAQ. As another example, electronic platforms may deploy computer chatbots to chat with users. However, such chatbots often greet the users with generic messages, and the ensuing messages (after the greeting) may also not be targeted to any parti