Predicting application conversion using eye tracking

Patent metadata

Field	Value
Publication number	US-11514467-B2
Application number	US-202117456907-A
Country	US
Kind code	B2
Filing date	Nov 30, 2021
Priority date	Aug 3, 2017
Publication date	Nov 29, 2022
Grant date	Nov 29, 2022

Abstract

Techniques are disclosed for determining application experience of a user. One embodiment presented herein includes a computer-implemented method, which includes receiving, at a computing device, eye tracking data of a user interacting with at least a first page of an application. The computer-implemented method further includes determining, based at least on the eye tracking data, at least a current user experience regarding the first page. The computer-implemented method further includes predicting, based on evaluating the current user experience, that the user is likely to discontinue use of the application. The computer-implemented method further includes determining, based at least on the prediction, an intervention that reduces a likelihood of the user discontinuing use of the application, and interacting with the user according to the intervention.

First claim

What is claimed is:

1. A method for determining an application experience, comprising:
determining, by a computing device, baseline eye tracking data of a user interacting with an application, the baseline eye tracking data comprising a baseline frequency of pupil dilations of the user;
receiving, at the computing device, real-time eye tracking data of the user interacting with the application, the real-time eye tracking data comprising a real-time frequency of pupil dilations of the user;
predicting, by the computing device, based at least on the real-time eye tracking data and the baseline eye tracking data, a user action, wherein the user action is predicted based on a comparison between the real-time frequency of pupil dilations and the baseline frequency of pupil dilations;
determining, by the computing device, based at least on the predicting, an intervention that changes a likelihood of the user action, wherein the intervention is determined by using a model to evaluate a metric, a current user experience relating to a page of the application, and the likelihood of the user action;
storing, by the computing device, the current user experience relating to the page of the application; and
interacting, by the computing device, with the user according to the intervention.

2. The method of claim 1 , wherein the baseline eye tracking data further comprises one or more of: point of gaze; saccadic eye movement duration; or saccadic eye movement patterns.

3. The method of claim 1 , wherein the current user experience comprises one or more of: excitement, fixation, or fatigue.

4. The method of claim 1 , wherein the current user experience relates to a particular item on the page.

5. The method of claim 1 , wherein the intervention comprises at least one of: offering a discount, offering assisted support, offering self-support content, or providing a list of content items.

6. The method of claim 1 , wherein interacting with the user according to the intervention comprises at least one of: a real-time intervention, an off-line intervention, presenting content items on an interface of the user, or altering at least one content item of the interface of the user.

7. The method of claim 1 , wherein the metric comprises:
a count of user clicks for the page of the application;
a total amount of time spent by the user on the page;
an age of the user;
a gender of the user;
an occupation of the user; or
a location of the user.

8. A system for determining an application experience, comprising:
one or more processors; and
a memory comprising instructions that, when executed by the one or more processors, cause the system to:
determine, by a computing device, baseline eye tracking data of a user interacting with an application, the baseline eye tracking data comprising a baseline frequency of pupil dilations of the user;
receive, at the computing device, real-time eye tracking data of the user interacting with the application, the real-time eye tracking data comprising a real-time frequency of pupil dilations of the user;
predict, by the computing device, based at least on the real-time eye tracking data and the baseline eye tracking data, a user action, wherein the user action is predicted based on a comparison between the real-time frequency of pupil dilations and the baseline frequency of pupil dilations;
determine, by the computing device, based at least on the predicting, an intervention that changes a likelihood of the user action, wherein the intervention is determined by using a model to evaluate a metric, a current user experience relating to a page of the application, and the likelihood of the user action;
store, by the computing device, the current user experience relating to the page of the application; and
interact, by the computing device, with the user according to the intervention.

9. The system of claim 8 , wherein the baseline eye tracking data further comprises one or more of: point of gaze; saccadic eye movement duration; or saccadic eye movement patterns.

10. The system of claim 8 , wherein the current user experience comprises one or more of: excitement, fixation, or fatigue.

11. The system of claim 8 , wherein the current user experience relates to a particular item on the page.

12. The system of claim 8 , wherein the intervention comprises at least one of: offering a discount, offering assisted support, offering self-support content, or providing a list of content items.

13. The system of claim 8 , wherein interacting with the user according to the intervention comprises at least one of: a real-time intervention, an off-line intervention, presenting content items on an interface of the user, or altering at least one content item of the interface of the user.

14. The system of claim 8 , wherein the metric comprises:
a count of user clicks for the page of the application;
a total amount of time spent by the user on the page;
an age of the user;
a gender of the user;
an occupation of the user; or
a location of the user.

15. A method for determining an application experience, comprising:
determining, by a computing device, baseline eye tracking data of a user interacting with an application, the baseline eye tracking data comprising a baseline frequency of pupil dilations of the user;
receiving, at the computing device, real-time eye tracking data of the user interacting with at least a first page of the application, the real-time eye tracking data comprising a real-time frequency of pupil dilations of the user;
predicting, by the computing device, based on a numerical score determined using the real-time eye tracking data and the baseline eye tracking data, a user action, wherein the numerical score is determined based on a comparison between the real-time frequency of pupil dilations and the baseline frequency of pupil dilations;
determining, by the computing device, based at least on the predicting, a type of intervention that changes a likelihood of the user action, wherein the type of intervention is determined by using a model to evaluate a metric, a current user experience relating to a page of the application, and the likelihood of the user action;
storing, by the computing device, the current user experience relating to the page of the application; and
interacting, by the computing device, with the user according to the type of intervention.

16. The method of claim 15 , wherein the baseline eye tracking data further comprises one or more of: point of gaze; saccadic eye movement duration; or saccadic eye movement patterns.

17. The method of claim 15 , wherein the current user experience comprises one or more of: excitement, fixation, or fatigue.

18. The method of claim 15 , wherein the current user experience relates to a particular item on the page.

19. The method of claim 15 , wherein the type of intervention comprises at least one of: offering a discount, offering assisted support, offering self-support content, or providing a list of content items.

20. The method of claim 15 , wherein interacting with the user according to the type of intervention comprises at least one of: a real-time intervention, an off-line intervention, presenting content items on an interface of the user, or altering at least one content item of the interface of the user.

Assignees

• Intuit Inc

Inventors

• Podgorny Igor A
• Indyk Benjamin
• Graves Michael

Classifications

• G06Q30/0218 Primary

  based on score · CPC title

• G06V40/174

  Facial expression recognition · CPC title

• G06F2203/011

  Emotion or mood input determined on the basis of sensed human body parameters such as pulse, heart rate or beat, temperature of skin, facial expressions, iris, voice pitch, brain activity patterns · CPC title

• H04L67/535

  Tracking the activity of the user (network monitoring arrangements H04L43/00; recording of computer activity G06F11/34) · CPC title

• G06F3/013

  Eye tracking input arrangements (G06F3/015 takes precedence) · CPC title