IP Library › Granted Patent US 12,373,498
Granted Patent B2
US 12,373,498 · App. 16/672,130 · Granted Jul 29, 2025

Providing data visualizations based on personalized recommendations

Inventors: Eric Roy Brochu (Vancouver, CA); Mya Rose Warren (Vancouver, CA); Yogesh Sood (Vancouver, CA); David John Mosimann (New Westminster, CA); Connie Hong-Ying Wong (Vancouver, CA); Kazem Jahanbakhsh (Vancouver, CA)
Assignee: Tableau Software, LLC
G06F16/904G06F11/3438G06N5/04G06N20/00
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Quick Facts
Patent No.
US 12,373,498
App. No.
16/672,130
Granted
Jul 29, 2025
Kind
B2
Abstract

Embodiments are directed to managing visualizations of data. Visualization models and a user profile may be provided such that the visualization models and the user profile may be associated with an organization. A complexity score for the organization may be provided based on one or more characteristics of the organization. A recommendation model may be provided based on the complexity score and a baseline model. The recommendation model may be employed to determine one or more recommended visualization models based on the user profile such that the recommendation model associates each recommended visualization model with a confidence score. The one or more recommended visualization models may be rank ordered based on each associated confidence score. A report that includes a rank ordered list of the one or more recommended visualization models may be provided to a user associated with the user profile.

Claims (69)

1. A method comprising:

at a computer system in communication with a display, the computer system having one or more processors and memory storing one or more programs, wherein the one or more programs are configured to be executed by the one or more processors:

for a selected user of a plurality of users stored in association with an organization of a plurality of organizations, displaying one or more recommended data visualizations that are generated using a recommendation model and a baseline model, wherein:

the baseline model is trained based on usage of data visualizations by users other than the plurality of users stored in association with the organization; and

the recommendation model corresponds to the baseline model additionally trained based on (i) usage of data visualizations by a selected user and (ii) a complexity score of the organization calculated based on at least a respective number of the plurality of users associated with the organization and data visualizations stored in association with the organization, wherein the recommendation model further comprises:

one or more first sub-models that generate the one or more recommended data visualizations based on a first popularity score of the one or more recommended data visualizations among the plurality of users stored in association with the organization and among the users other than the plurality of users stored in association with the organization;

one or more second sub-models that generate the one or more recommended data visualizations based on a second popularity score of the one or more recommended data visualizations among users having a role that corresponds to a same role of the selected user; and

one or more weight values applied to one or more outputs of the one or more first and second sub-models, wherein the one or more weight values determine how the one or more first and second sub-models are used to determine the one or more recommended data visualizations.

2. The method of claim 1 , wherein the recommendation model corresponding to the baseline model is further trained based on usage of data visualizations by the plurality of users.

3. The method of claim 1 , wherein the one or more recommended data visualizations are further selected by the recommendation model based on data visualizations explored by the selected user in a current workflow.

4. The method of claim 1 , wherein the recommendation model is further trained based on a role of the selected user.

5. The method of claim 1 , further comprising:

displaying the one or more recommended data visualizations in a ranked order based on a confidence score.

6. The method of claim 5 , wherein the confidence score is calculated based on a score provided by the selected user for respective data visualizations of the one or more recommended data visualizations, the method further comprising:

determining one or more data visualizations available to the selected user based on access permission information associated with the selected user;

determining a first subset of the one or more data visualizations that have previously been recommended to the selected user;

determining a second subset of the one or more data visualizations that have been previously accessed by the selected user; and

displaying the one or more recommended data visualizations without the first subset and the second subset.

7. The method of claim 5 , wherein displaying the one or more recommended data visualizations in the ranked order based on the confidence score further comprises:

determining one or more data visualization that have previously been recommended to a selected user;

determining a first subset of the one or more recommend data visualizations that have been previously accessed by the selected user; and

displaying the one or more recommended data visualizations without the first and second subset.

8. The method of claim 1 , wherein the complexity score of the organization is further calculated based on a classification of the organization based on one or more functions.

9. A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions that, when executed by a computer system in communication with a display, cause the computer system to:

for a selected user of a plurality of users stored in association with an organization of a plurality of organizations, display one or more recommended data visualizations that are generated using a recommendation model and a baseline model, wherein:

the baseline model is trained based on usage of data visualizations by users other than the plurality of users stored in association with the organization; and

the recommendation model corresponds to the baseline model additionally trained based on (i) usage of data visualizations by a selected user and (ii) a complexity score of the organization calculated based on at least a respective number of the plurality of users associated with the organization and data visualizations stored in association with the organization, wherein the recommendation model further comprises:

one or more first sub-models that generate the one or more recommended data visualizations based on a first popularity score of the one or more recommended data visualizations among the plurality of users stored in association with the organization and among the users other than the plurality of users stored in association with the organization;

one or more second sub-models that generate the one or more recommended data visualizations based on a second popularity score of the one or more recommended data visualizations among users having a role that corresponds to a same role of the selected user; and

one or more weight values applied to the one or more first and second sub-models, wherein the one or more weight values determine how the one or more first and second sub-models are used to determine the one or more recommended data visualizations.

10. The non-transitory computer readable storage medium of claim 9 , wherein the recommendation model corresponding to the baseline model is further trained based on usage of data visualizations by the plurality of users.

11. The non-transitory computer readable storage medium of claim 9 , wherein the one or more recommended data visualizations are further selected by the recommendation model based on data visualizations explored by the selected user in a current workflow.

12. The non-transitory computer readable storage medium of claim 9 , wherein the recommendation model is further trained based on a role of the selected user.

13. The non-transitory computer readable storage medium of claim 9 , wherein the one or more programs include instructions that when executed by the computer system cause the computer system to:

display the one or more recommended data visualizations in a ranked order based on a confidence score.

14. The non-transitory computer readable storage medium of claim 13 , wherein the confidence score is calculated based on a score provided by the selected user for respective data visualizations of the one or more recommended data visualizations, and the instructions, when executed by the computer system, further cause the computer system to:

determine one or more data visualizations available to the selected user based on access permission information associated with the selected user;

determine a first subset of the one or more data visualizations that have previously been recommended to the selected user;

determine a second subset of the one or more data visualizations that have been previously accessed by the selected user; and

display the one or more recommended data visualizations without the first subset and the second subset.

15. The non-transitory computer readable storage medium of claim 13 , wherein displaying the one or more recommended data visualizations in the ranked order based on the confidence score further comprises:

determining one or more data visualization that have previously been recommended to a selected user;

determining a first subset of the one or more recommend data visualizations that have been previously accessed by the selected user; and

and second subset.

16. The non-transitory computer readable storage medium of claim 9 , wherein the complexity score of the organization is further calculated based on a classification of the organization based on one or more functions and market participation by the organization.

17. A computer system comprising:

one or more processors; and

a memory storing one or more programs, wherein the one or more programs are configured to be executed by the one or more processors, the one or more programs including instructions for:

for a selected user of a plurality of users stored in association with an organization of a plurality of organizations, displaying one or more recommended data visualizations that are generated using a recommendation model and a baseline model, wherein:

the baseline model is trained based on usage of data visualizations by users other than the plurality of users stored in association with the organization; and

the recommendation model corresponds to the baseline model additionally trained based on (i) usage of data visualizations by a selected user and (ii) a complexity score of the organization calculated based on at least a respective number of the plurality of users associated with the organization and data visualizations stored in association with the organization, wherein the recommendation model further comprises:

one or more first sub-models that generate the one or more recommended data visualizations based on a first popularity score of the one or more recommended data visualizations among the plurality of users stored in association with the organization and among the users other than the plurality of users stored in association with the organization;

one or more second sub-models that generate the one or more recommended data visualizations based on a second popularity score of the one or more recommended data visualizations among users having a role that corresponds to a same role of the selected user; and

one or more weight values applied to the one or more first and second sub-models, wherein the one or more weight values determine how the one or more first and second sub-models are used to determine the one or more recommended data visualizations.

18. The computer system of claim 17 , wherein the recommendation model corresponding to the baseline model is further trained based on usage of data visualizations by the plurality of users.

19. The computer system of claim 17 , wherein the one or more recommended data visualizations are further selected by the recommendation model based on data visualizations explored by the selected user in a current workflow.

20. The computer system of claim 17 , wherein the recommendation model is further trained based on a role of the selected user.

21. The computer system of claim 17 , the one or more programs including instructions for:

displaying the one or more recommended data visualizations in a ranked order based on a confidence score.

22. The computer system of claim 21 , wherein the confidence score is calculated based on a score provided by the selected user for respective data visualizations of the one or more recommended data visualizations, and the one or more programs including instructions for:

determining one or more data visualizations available to the selected user based on access permission information associated with the selected user;

determining a first subset of the one or more data visualizations that have previously been recommended to the selected user;

determining a second subset of the one or more data visualizations that have been previously accessed by the selected user; and

displaying one or more recommended data visualizations without the first subset and the second subset.

23. The computer system of claim 21 , wherein the instructions for displaying the one or more recommended data visualizations in the ranked order based on the confidence score further include instructions for:

determining one or more data visualization that have previously been recommended to a selected user;

determining a first subset of the one or more recommend data visualizations that have been previously accessed by the selected user; and

displaying the one or more recommended data visualizations without the first and second subset.

24. The computer system of claim 17 , wherein the complexity score of the organization is further calculated based on a classification of the organization based on one or more functions and market participation by the organization.

Assignments (2)
CHANGE OF NAME Recorded Feb 22, 2021
From: TABLEAU SOFTWARE, INC.
To: TABLEAU SOFTWARE, LLC
Reel/Frame 055361/0073 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 21, 2019
From: BROCHU, ERIC ROY; WARREN, MYA ROSE; SOOD, YOGESH; MOSIMANN, DAVID JOHN; WONG, CONNIE HONG-YING; JAHANBAKHSH, KAZEM
To: TABLEAU SOFTWARE, INC.
Reel/Frame 051078/0093 →
Continuity (1)
Related Publication 20210133239A1 · May 6, 2021
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