Utilizing machine learning models to generate interactive digital text threads with personalized digital text reply options

Patent metadata

Field	Value
Publication number	US-2024007421-A1
Application number	US-202217809765-A
Country	US
Kind code	A1
Filing date	Jun 29, 2022
Priority date	Jun 29, 2022
Publication date	Jan 4, 2024
Grant date	—

Abstract

The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a machine learning model to determine predicted client intent classifications and generate personalized digital text reply options within an automated interactive digital text thread. For example, disclosed systems utilize the machine learning model to generate predicted client intent classifications and corresponding intent classification probabilities. The disclosed systems utilize the predicted client disposition classifications and the disposition classification probabilities to generate personalized digital text reply options. Moreover, the disclosed systems can provide personalized digital text reply options to a client device within an automated interactive digital text thread, bypassing the inefficiency of menu options or protocols utilized to guide clients to terminal information.

First claim

What is claimed is:

1 . A computer-implemented method comprising:
extracting client features corresponding to a client device participating in an automated interactive digital text thread; generating, utilizing a machine learning model, a plurality of predicted client intent classifications and a plurality of intent classification probabilities from the client features; selecting at least two predicted client intent classifications from the plurality of predicted client intent classifications utilizing the plurality of intent classification probabilities; and providing, for display via the client device, at least two personalized digital text reply options corresponding to the at least two predicted client intent classifications via the automated interactive digital text thread.

2 . The computer-implemented method of claim 1 , further comprising: in response to user interaction with a personalized digital text reply option of the at least two personalized digital text reply options:
generating a digital text response corresponding to the personalized digital text reply option; and adding the digital text response to the automated interactive digital text thread.

3 . The computer-implemented method of claim 2 , further comprising: in response to user interaction with the personalized digital text reply option:
generating an additional plurality of digital text reply options corresponding to the digital text response; and providing, for display, the additional plurality of digital text reply options via the automated interactive digital text thread.

4 . The computer-implemented method of claim 1 , further comprising training the machine learning model by:
monitoring client interaction with the automated interactive digital text thread to determine a ground truth client intent; and modifying parameters of the machine learning model by comparing the plurality of predicted client intent classifications and the ground truth client intent.

5 . The computer-implemented method of claim 1 , wherein utilizing the machine learning model comprises generating the plurality of predicted client intent classifications and the plurality of intent classification probabilities utilizing one or more of a random forest model or gradient boosted decision tree model.

6 . The computer-implemented method of claim 1 , further comprising:
extracting the client features by determining a previous intent from a previous interactive text thread corresponding to the client device; and generating the plurality of predicted client intent classifications and the plurality of intent classification probabilities from the previous intent utilizing the machine learning model.

7 . The computer-implemented method of claim 1 , wherein extracting client features comprises one or more of determining a digital account value, a direct deposit status of a digital account, or application device activity on the digital account.

8 . The computer-implemented method of claim 1 , further comprising:
identifying a hierarchical intent architecture comprising a plurality of intent classifications organized in a plurality of hierarchical layers; and selecting a first predicted client intent classification from a first hierarchical layer of the plurality of hierarchical layers and a second predicted client intent classification from a second hierarchical layer of the plurality of hierarchical layers.

9 . A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause a computer system to:
extract client features corresponding to a client device participating in an automated interactive digital text thread; generate, utilizing a machine learning model, a plurality of predicted client intent classifications and a plurality of intent classification probabilities from the client features; select at least two predicted client intent classifications from the plurality of predicted client intent classifications utilizing the plurality of intent classification probabilities; and provide, for display via the client device, at least two personalized digital text reply options corresponding to the at least two predicted client intent classifications via the automated interactive digital text thread.

10 . The non-transitory computer-readable medium of claim 9 , wherein the instructions, when executed by the at least one processor, further cause the computer system to, in response to user interaction with a personalized digital text reply option of the at least two personalized digital text reply options:
generate a digital text response corresponding to the personalized digital text reply option; and add the digital text response to the automated interactive digital text thread.

11 . The non-transitory computer-readable medium of claim 10 , wherein the instructions, when executed by the at least one processor, further cause the computer system to, in response to user interaction with the personalized digital text reply option:
generate an additional plurality of digital text reply options corresponding to the digital text response; and provide, for display, the additional plurality of digital text reply options via the automated interactive digital text thread.

12 . The non-transitory computer-readable medium of claim 9 , wherein the instructions, when executed by the at least one processor, further cause the computer system to train the machine learning model by:
monitoring client interaction with the automated interactive digital text thread to determine a ground truth client intent; and modifying parameters of the machine learning model by comparing the plurality of predicted client intent classifications and the ground truth client intent.

13 . The non-transitory computer-readable medium of claim 9 , wherein the instructions, when executed by the at least one processor, further cause the computer system to: generate the plurality of predicted client intent classifications and the plurality of intent classification probabilities utilizing one or more of a random forest model or gradient boosted decision tree model.

14 . The non-transitory computer-readable medium of claim 9 , wherein the instructions, when executed by the at least one processor, further cause the computer system to:
identify a hierarchical intent architecture comprising a plurality of intent classifications organized in a plurality of hierarchical layers; and select a first predicted client intent classification from a first hierarchical layer of the plurality of hierarchical layers and a second predicted client intent classification from a second hierarchical layer of the plurality of hierarchical layers.

15 . A system comprising:
at least one processor; and at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to: extract client features corresponding to a client device participating in an automated interactive digital text thread; generate, utilizing a machine learning model, a plurality of predicted client intent classifications and a plurality of intent classification probabilities from the client features; select at least two predicted client intent classifications from the plurality of predicted client intent classifications utilizing the plurality of intent classification probabilities; and provide, for display via the client device, at least two personalized digital text reply options corresponding to the at least two predicted client intent classifications via the automated interactive digit

Assignees

• Chime Financial Inc

Inventors

• Mehta Jigar
• Chaver Abbey
• Sharma Abhi
• Guntury Sashidhar

Classifications

• H04L51/02 Primary

  using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages · CPC title

• H04L51/046

  Interoperability with other network applications or services · CPC title

• H04L41/026

  using e-messaging for transporting management information, e.g. email, instant messaging or chat · CPC title

• H04L41/5093

  wherein the managed service relates to messaging or chat services · CPC title

• G06F16/355 Primary

  Creation or modification of classes or clusters · CPC title