# US 2026/0228769 A1 · Disengagement prevention incentive

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- Number: US 2026/0228769 A1
- Document ID: US20260228769A1
- Status: Published application
- Kind: A1
- Application number: 19/042,796
- Filed: 2025-01-31
- Published: 2026-08-06
- Earliest claimed: 2025-01-31
- Assignees: Walmart Apollo, LLC
- Applicants: Walmart Apollo, LLC
- Inventors: Lawrence David Lin; Faezeh Akhavizadegan; Chia-Yen Ho; Yokila Arora; Topojoy Biswas; Tanya Mendiratta; Sushant Kumar; Kannan Achan
- Web page: https://patentgrep.com/patent/US20260228769A1

## CPC

- G06Q 30/0218: {based on score}
- G06Q 10/0635: Risk analysis of enterprise or organisation activities
- G06Q 30/0239: {Online discounts or incentives}
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

Example implementations relate to systems and methods for detecting disengagement. In an example, a system determines, by a risk identifier, predetermined risk labels based on the user data. The system determines, using a risk evaluator that receives input features, a risk of disengagement for a user. The system determines, using a risk interpreter that receives the input features and the predetermined risk labels, a disengagement reason for the user. The system also determines, based on the risk of disengagement and the disengagement reason, a disengagement prevention incentive for the user. The system further presents, at a computing device associated with the user, a user interface element for interacting with the disengagement prevention incentive.

## Claims

### Claim 1

A system, comprising:
a processor; and
a non-transitory memory storing instructions, that when executed, cause the processor to:
obtain user data and input features;
determine, by a risk identifier, predetermined risk labels based on the user data, wherein the predetermined risk labels identify disengagement risks, and determining the predetermined risk labels includes:
for each user of the user data:
determining a type of disengagement description included in the user data;
in accordance with a determination that the user data includes a first type of disengagement description, determining an explicit risk label, and
in accordance with a determination that the user data includes a second type of disengagement description, distinct from the first type of disengagement description, determining an implicit risk label;
combining each of the explicit risk labels into a set of explicit risk labels, and each of the implicit risk labels into a set of implicit risk labels; and
combining the set of explicit risk labels and the set of implicit risk labels from the user data to form the predetermined risk labels;
determine, using a first trained machine learning model that receives the input features, a first score value characterizing a risk of disengagement for a user, wherein the first trained machine learning model is assigned to a first virtual machine that executes on a first processing resource;
determine, using a second trained machine learning model that receives the input features and the predetermined risk labels, a second score value characterizing a disengagement reason for the user, wherein the second trained machine learning model is assigned to a second virtual machine that executes on a second processing resource;
generate a risk score based on the first score value characterizing the risk of disengagement and the second score value characterizing the disengagement reason;
determine, based on the risk score, a disengagement prevention incentive for the user; and
transmit, to a computing device associated with the user, the disengagement prevention incentive, wherein the disengagement prevention incentive causes the computing device to display a user interface element for interacting with the disengagement prevention incentive.

### Claim 2 (depends on 1)

The system of claim 1, wherein determining the explicit risk label comprises:
determining whether the first type of disengagement description includes structured data;
in accordance with a determination that the first type of disengagement description includes structured data, determining a first risk label based on the structured data;
determining whether the first type of disengagement description includes unstructured data;
in accordance with a determination that the first type of disengagement description includes unstructured data, determining a second risk label based on the unstructured data;
determining whether the first risk label and the second risk label are the same; and
in accordance with a determination that the first risk label and the second risk label are the same, including the first risk label or the second risk label in the set of explicit risk labels.

### Claim 3 (depends on 2)

The system of claim 2, wherein determining the explicit risk label comprises:
determining whether the first risk label and the second risk label are different;
in accordance with a determination that the first risk label and the second risk label are different, determining respective scores for the first risk label and the second risk label; and
selecting a respective risk label with a score that satisfies a risk label threshold to include in the set of explicit risk labels.

### Claim 4 (depends on 2)

The system of claim 2, wherein determining the second risk label based on the unstructured data comprises:
determining a term frequency-inverse document frequency (TF-IDF) value for the unstructured data; and
in accordance with a determination that the TF-IDF value for the unstructured data satisfies a TF-IDF threshold for a predetermined segment, associating the predetermined segment to the second risk label.

### Claim 5 (depends on 1)

The system of claim 1, wherein determining the explicit risk label comprises:
determining whether the first type of disengagement description includes structured data; and
in accordance with a determination that the first type of disengagement description includes structured data:
determining a first risk label based on the structured data, and
including the first risk label in the set of explicit risk labels.

### Claim 6 (depends on 1)

The system of claim 1, wherein determining the explicit risk label comprises:
determining whether the first type of disengagement description includes unstructured data; and
in accordance with a determination that the first type of disengagement description includes unstructured data:
determining a second risk label based on the unstructured data, and
including the second risk label in the set of explicit risk labels.

### Claim 7 (depends on 1)

The system of claim 1, wherein determining the implicit risk label comprises:
determining user profile data from the second type of disengagement description;
determining an implicit risk label based on the user profile data; and
including the implicit risk label in the set of implicit risk labels.

### Claim 8

A computer-implemented method, comprising:
obtaining user data and input features;
determining, by a risk identifier, predetermined risk labels based on the user data, wherein the predetermined risk labels identify disengagement risks and determining the predetermined risk labels includes:
for each user of the user data:
determining a type of disengagement description included in the user data;
in accordance with a determination that the user data includes a first type of disengagement description, determining an explicit risk label, and
in accordance with a determination that the user data includes a second type of disengagement description, distinct from the first type of disengagement description, determining an implicit risk label;
combining each of the explicit risk labels into a set of explicit risk labels, and each of the implicit risk labels into a set of implicit risk labels; and
combining the set of explicit risk labels and the set of implicit risk labels from the user data to form the predetermined risk labels;
determining, using a first trained machine learning model that receives the input features, a first score value characterizing a risk of disengagement for a user, wherein the first trained machine learning model is assigned to a first virtual machine that executes on a first processing resource;
determining, using a second trained machine learning model that receives the input features and the predetermined risk labels, a second score value characterizing a disengagement reason for the user, wherein the second trained machine learning model is assigned to a second virtual machine that executes on a second processing resource;
generating a risk score based on the first score value characterizing the risk of disengagement and the second score value characterizing the disengagement reason;
determining, based on the risk score a disengagement prevention incentive for the user; and
transmitting, to a computing device associated with the user, the disengagement prevention incentive, wherein the disengagement prevention incentive causes the computing device to display a user interface element for interacting with the disengagement prevention incentive.

### Claim 9 (depends on 8)

The computer-implemented method of claim 8, wherein determining the explicit risk label comprises:
determining whether the first type of disengagement description includes structured data;
in accordance with a determination that the first type of disengagement description includes structured data, determining a first risk label based on the structured data;
determining whether the first type of disengagement description includes unstructured data;
in accordance with a determination that the first type of disengagement description includes unstructured data, determining a second risk label based on the unstructured data;
determining whether the first risk label and the second risk label are the same; and
in accordance with a determination that the first risk label and the second risk label are the same, including the first risk label or the second risk label in the set of explicit risk labels.

### Claim 10 (depends on 9)

The computer-implemented method of claim 9, wherein determining the explicit risk label comprises:
determining whether the first risk label and the second risk label are different;
in accordance with a determination that the first risk label and the second risk label are different, determining respective scores for the first risk label and the second risk label; and
selecting a respective risk label with a score that satisfies a risk label threshold to include in the set of explicit risk labels.

### Claim 11 (depends on 9)

The computer-implemented method of claim 9, wherein determining the second risk label based on the unstructured data comprises:
determining a term frequency-inverse document frequency (TF-IDF) value for the unstructured data; and
in accordance with a determination that the TF-IDF value for the unstructured data satisfies a TF-IDF threshold for a predetermined segment, associating the predetermined segment to the second risk label.

### Claim 12 (depends on 8)

The computer-implemented method of claim 8, wherein determining the explicit risk label comprises:
determining whether the first type of disengagement description includes structured data; and
in accordance with a determination that the first type of disengagement description includes structured data:
determining a first risk label based on the structured data, and
including the first risk label in the set of explicit risk labels.

### Claim 13 (depends on 8)

The computer-implemented method of claim 8, wherein determining the explicit risk label comprises:
determining whether the first type of disengagement description includes unstructured data; and
in accordance with a determination that the first type of disengagement description includes unstructured data:
determining a second risk label based on the unstructured data, and
including the second risk label in the set of explicit risk labels.

### Claim 14 (depends on 8)

The computer-implemented method of claim 8, wherein determining the implicit risk label comprises:
determining user profile data from the second type of disengagement description;
determining an implicit risk label based on the user profile data; and
including the implicit risk label in the set of implicit risk labels.

### Claim 15

A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations comprising:
obtaining user data and input features;
determining, by a risk identifier, predetermined risk labels based on the user data, wherein the predetermined risk labels identify disengagement risks and determining the predetermined risk labels includes:
for each user of the user data:
determining a type of disengagement description included in the user data;
in accordance with a determination that the user data includes a first type of disengagement description, determining an explicit risk label, and
in accordance with a determination that the user data includes a second type of disengagement description, distinct from the first type of disengagement description, determining an implicit risk label;
combining each of the explicit risk labels into a set of explicit risk labels, and each of the implicit risk labels into a set of implicit risk labels; and
combining the set of explicit risk labels and the set of implicit risk labels from the user data to form the predetermined risk labels;
determining, using a first trained machine learning model that receives the input features, a first score value characterizing a risk of disengagement for a user, wherein the first trained machine learning model is assigned to a first virtual machine that executes on a first processing resource;
determining, using a second trained machine learning model that receives the input features and the predetermined risk labels, a second score value characterizing a disengagement reason for the user, wherein the second trained machine learning model is assigned to a second virtual machine that executes on a second processing resource;
generating a risk score based on the first score value characterizing the risk of disengagement and the second score value characterizing the disengagement reason;
determining, based on the risk score a disengagement prevention incentive for the user; and
transmitting, to a computing device associated with the user, the disengagement prevention incentive, wherein the disengagement prevention incentive causes the computing device to display a user interface element for interacting with the disengagement prevention incentive.

### Claim 16 (depends on 15)

The non-transitory computer readable medium of claim 15, wherein determining the explicit risk label comprises:
determining whether the first type of disengagement description includes structured data;
in accordance with a determination that the first type of disengagement description includes structured data, determining a first risk label based on the structured data;
determining whether the first type of disengagement description includes unstructured data;
in accordance with a determination that the first type of disengagement description includes unstructured data, determining a second risk label based on the unstructured data;
determining whether the first risk label and the second risk label are the same; and
in accordance with a determination that the first risk label and the second risk label are the same, including the first risk label or the second risk label in the set of explicit risk labels.

### Claim 17 (depends on 16)

The non-transitory computer readable medium of claim 16, wherein determining the explicit risk label comprises:
determining whether the first risk label and the second risk label are different;
in accordance with a determination that the first risk label and the second risk label are different, determining respective scores for the first risk label and the second risk label; and
selecting a respective risk label with a score that satisfies a risk label threshold to include in the set of explicit risk labels.

### Claim 18 (depends on 16)

The non-transitory computer readable medium of claim 16, wherein determining the second risk label based on the unstructured data comprises:
determining a term frequency-inverse document frequency (TF-IDF) value for the unstructured data; and
in accordance with a determination that the TF-IDF value for the unstructured data satisfies a TF-IDF threshold for a predetermined segment, associating the predetermined segment to the second risk label.

### Claim 19 (depends on 15)

The non-transitory computer readable medium of claim 15, wherein determining the explicit risk label comprises:
determining whether the first type of disengagement description includes structured data; and
in accordance with a determination that the first type of disengagement description includes structured data:
determining a first risk label based on the structured data, and
including the first risk label in the set of explicit risk labels.

### Claim 20 (depends on 15)

The non-transitory computer readable medium of claim 15, wherein determining the implicit risk label comprises:
determining user profile data from the second type of disengagement description;
determining an implicit risk label based on the user profile data; and
including the implicit risk label in the set of implicit risk labels.