Predicting application conversion using eye tracking

Patent metadata

Field	Value
Publication number	US-11222351-B2
Application number	US-202117207851-A
Country	US
Kind code	B2
Filing date	Mar 22, 2021
Priority date	Aug 3, 2017
Publication date	Jan 11, 2022
Grant date	Jan 11, 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 computer-implemented 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;
determining, by the computing device, based at least on the real-time eye tracking data and the baseline eye tracking data, a current user experience regarding the first page, wherein the current user experience comprises a level of interest with respect to at least a subset of the first page, and wherein the level of interest is determined based on a comparison between the real-time frequency of pupil dilations and the baseline frequency of pupil dilations;
predicting, by the computing device, based on evaluating the current user experience, a user action;
determining, by the computing device, based at least on the predicting, an intervention that changes a likelihood of the user action; and
interacting, by the computing device, with the user according to the intervention.

2. The computer-implemented 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 computer-implemented method of claim 1 , wherein the intervention is determined by using a model to evaluate the current user experience for the first page and the likelihood of the user action.

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

5. The computer-implemented 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 computer-implemented 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 computer-implemented method of claim 1 , wherein the current user experience relates to a particular item on the first page.

8. The computer-implemented method of claim 1 , further comprising:
determining at least one metric comprising one or more of:
a count of user clicks for the first page;
a total amount of time spent by the user on the first page;
an age of the user;
a gender of the user;
an occupation of the user; or
a location of the user; and

evaluating the at least one metric in addition to the current user experience, using a model, to determine the likelihood of the user action.

9. 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 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;
determine, by the computing device, based at least on the real-time eye tracking data and the baseline eye tracking data, a current user experience regarding the first page, wherein the current user experience comprises a level of interest with respect to at least a subset of the first page, and wherein the level of interest is determined based on a comparison between the real-time frequency of pupil dilations and the baseline frequency of pupil dilations;
predict, by the computing device, based on evaluating the current user experience, a user action;
determine, by the computing device, based at least on the user action that was predicted, an intervention that changes a likelihood of the user action; and
interact, by the computing device, with the user according to the intervention.

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

11. The system of claim 9 , wherein the intervention is determined by using a model to evaluate the current user experience for the first page and the likelihood of the user action.

12. The system of claim 9 , wherein the current user experience comprises one or more of: excitement, fixation, or fatigue.

13. The system of claim 9 , 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.

14. The system of claim 9 , 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.

15. The system of claim 9 , wherein the current user experience comprises relates to a particular item on the first page.

16. The system of claim 9 , wherein the instructions, when executed by the one or more processors, further cause the system to:
determine at least one metric comprising one or more of:
a count of user clicks for the first page;
a total amount of time spent by the user on the first page;
an age of the user;
a gender of the user;
an occupation of the user; or
a location of the user; and

evaluate the at least one metric in addition to the current user experience, using a model, to determine the likelihood of the user action.

17. A computer-implemented 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;
determining, by the computing device, based at least on the real-time eye tracking data and the baseline eye tracking data, a current user experience regarding the first page, wherein the current user experience comprises a numerical score indicating a level of interest with respect to at least a subset of the first page, and 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;
predicting, by the computing device, based on evaluating the current user experience, a user action;
determining, by the computing device, based at least on the predicting, a type of intervention that changes a likelihood of the user action; and
interacting, by the computing device, with the user according to the type of intervention.

18. The computer-implemented method of claim 17 , wherein the baseline eye tracking data further comprises one or more of: point of gaze; sa

Assignees

• Intuit Inc

Inventors

• Podgorny Igor A
• Indyk Benjamin
• Graves Michael J

Classifications

• 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

• 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