IP Library Granted Patent US 11,687,596
Granted Patent B2
US 11,687,596 · App. 17/207,924 · Granted Jun 27, 2023

Systems and methods for automatic generation of data visualizations

Inventors: Jericho McLeod (Arlington, VA); Niyati Shah (Falls Church, VA); Amar Gawade (Herndon, VA)
Assignee: MICROSTRATEGY INCORPORATED
G06F16/904G06F16/906G06F16/9535G06F16/9538
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Quick Facts
Patent No.
US 11,687,596
App. No.
17/207,924
Granted
Jun 27, 2023
Kind
B2
Abstract

A computer-implemented method for automatic generation of data visualizations may include: receiving, from a user, a request to open a document, receiving, from the user, a selection of data for visualization in the new document, determining whether the user has a trained visualization model, upon determining that the user has a trained visualization model, loading the user's trained visualization model, upon determining that the user does not have a trained visualization model, loading a default trained visualization model as the user's trained visualization model, using the user's trained visualization to generate one or more suggested visualizations of the selected data, and displaying the one or more suggested visualizations to the user.

Claims (61)

1. A computer-implemented method for automatic generation of data visualizations, the method comprising:

receiving, from a user, a request to open a document;

receiving, from the user, a selection of data for visualization in the document;

determining whether the user has a trained visualization model;

upon determining that the user has a trained visualization model, loading the user's trained visualization model;

upon determining that the user does not have a trained visualization model, loading a default trained visualization model as the user's trained visualization model;

using the user's trained visualization model to generate one or more suggested visualizations of the selected data; and

displaying the one or more suggested visualizations to the user;

receiving, from the user, a selection of a suggested visualization among the one or more suggested visualizations to add to the document;

training the user's trained visualization model;

wherein training the user's trained visualization model comprises:

determining whether the user is a high priority user or a low priority user;

upon determining that the user is a high priority user, loading an individual trained visualization model as the user's trained visualization model;

upon determining that the user is a low priority user, loading an aggregated trained visualization model for low priority users as the user's trained visualization model;

parameterizing the selected data for visualization to determine parameterized data; and

training the user's trained visualization model based on the parameterized data, the selected visualization among the one or more suggested visualizations and the selected data for visualization.

2. The computer-implemented method of claim 1 , wherein the default trained visualization model is the aggregated trained visualization model for one or more additional users, the aggregated trained visualization model comprising trained visualization models for the one or more additional users.

3. The computer-implemented method of claim 1 , wherein generating the one or more suggested visualizations of the selected data is further based on information about the user, including a role of the user within an organization.

4. The computer-implemented method of claim 1 , wherein parameterizing the selected data is based on data types of the selected data.

5. The computer-implemented method of claim 1 , wherein the one or more suggested visualizations are displayed to the user in a sorted order.

6. A system for automatic generation of data visualizations, the system comprising:

at least one data storage device storing instructions for automatic generation of data visualizations in an electronic storage medium; and

at least one processor configured to execute the instructions to perform operations including:

receiving, from a user, a request to open a document;

receiving, from the user, a selection of data for visualization in the document;

determining whether the user has a trained visualization model;

upon determining that the user has a trained visualization model, loading the user's trained visualization model;

upon determining that the user does not have a trained visualization model, loading a default trained visualization model as the user's trained visualization model;

using the user's trained visualization model to generate one or more suggested visualizations of the selected data; and

displaying the one or more suggested visualizations to the user;

receiving, from the user, a selection of a suggested visualization among the one or more suggested visualizations to add to the document;

training the user's trained visualization model;

wherein training the user's trained visualization model comprises:

determining whether the user is a high priority user or a low priority user;

upon determining that the user is a high priority user, loading an individual trained visualization model as the user's trained visualization model;

upon determining that the user is a low priority user, loading an aggregated trained visualization model for low priority users as the user's trained visualization model;

parameterizing the selected data for visualization to determine parameterized data; and

training the user's trained visualization model based on the parameterized data, the selected visualization among the one or more suggested visualizations and the selected data for visualization.

7. The system of claim 6 , wherein the default trained visualization model is the aggregated trained visualization model for one or more additional users, the aggregated trained visualization model comprising trained visualization models for the one or more additional users.

8. The system of claim 6 , wherein generating the one or more suggested visualizations of the selected data is further based on information about the user, including a role of the user within an organization.

9. The system of claim 6 , wherein parameterizing the selected data is based on data types of the selected data.

10. The system of claim 6 , wherein the one or more suggested visualizations are displayed to the user in a sorted order.

11. A non-transitory machine-readable medium storing instructions that, when executed by a computing system, causes the computing system to perform operations for automatic generation of data visualizations, the operations comprising:

receiving, from a user, a request to open a document;

receiving, from the user, a selection of data for visualization in the document;

determining whether the user has a trained visualization model;

upon determining that the user has a trained visualization model, loading the user's trained visualization model;

upon determining that the user does not have a trained visualization model, loading a default trained visualization model as the user's trained visualization model;

using the user's trained visualization model to generate one or more suggested visualizations of the selected data; and

displaying the one or more suggested visualizations to the user;

receiving, from the user, a selection of a suggested visualization among the one or more suggested visualizations to add to the document;

training the user's trained visualization model;

wherein training the user's trained visualization model comprises:

determining whether the user is a high priority user or a low priority user;

upon determining that the user is a high priority user, loading an individual trained visualization model as the user's trained visualization model;

upon determining that the user is a low priority user, loading an aggregated trained visualization model for low priority users as the user's trained visualization model;

parameterizing the selected data for visualization to determine parameterized data; and

training the user's trained visualization model based on the parameterized data, the selected visualization among the one or more suggested visualizations and the selected data for visualization.

12. The non-transitory machine-readable medium of claim 11 , wherein the default trained visualization model is the aggregated trained visualization model for one or more additional users, the aggregated trained visualization model comprising trained visualization models for the one or more additional users.

13. The non-transitory machine-readable medium of claim 11 , wherein generating the one or more suggested visualizations of the selected data is further based on information about the user, including a role of the user within an organization.

14. The non-transitory machine-readable medium of claim 11 , wherein parameterizing the selected data is based on data types of the selected data.

Assignments (4)
CHANGE OF NAME Recorded Aug 19, 2025
From: MICROSTRATEGY INCOPORATED
To: STRATEGY INC
Reel/Frame 072513/0437 →
TERMINATION AND RELEASE OF PATENT SECURITY AGREEMENT AT REEL/FRAME: 056647/0687, REEL/FRAME: 057435/0023, REEL/FRAME: 059256/0247, REEL/FRAME: 062794/0255 AND REEL/FRAME: 066663/0713 Recorded Sep 26, 2024
From: U.S. BANK TRUST COMPANY, NATIONAL ASSOCIATION, AS SUCCESSOR IN INTEREST TO U.S. BANK NATIONAL ASSOCIATION, IN ITS CAPACITY AS COLLATERAL AGENT FOR THE SECURED PARTIES
To: MICROSTRATEGY INCORPORATED; MICROSTRATEGY SERVICES CORPORATION
Reel/Frame 069065/0539 →
SECURITY INTEREST Recorded Jun 22, 2021
From: MICROSTRATEGY INCORPORATED
To: U.S. BANK NATIONAL ASSOCIATION, AS COLLATERAL AGENT
Reel/Frame 056647/0687 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 22, 2021
From: MCLEOD, JERICHO; SHAH, NIYATI; GAWADE, AMAR
To: MICROSTRATEGY INCORPORATED
Reel/Frame 055672/0451 →
Continuity (2)
Provisional Application 63002392 · Mar 31, 2020
Related Publication 20210303625A1 · Sep 30, 2021