GENERATING NATURAL-LANGUAGE TEXT DESCRIPTIONS OF DATA VISUALIZATIONS

• US 20200134074A1
• Filed: 10/26/2018
• Published: 04/30/2020
• Est. Priority Date: 10/26/2018
• Status: Abandoned Application

• Current Assignee: CA, Inc. (d/b/a CA Technologies) (Broadcom, Inc.)
• Original Assignee: CA, Inc. (d/b/a CA Technologies) (Broadcom, Inc.)
• Inventors: Mankovskii, Serge, Greenspan, Steven, Velez-Rojas, Maria
• Application Number:
  US16/171,754
• Publication Number:
  US 20200134074A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/24573 using data annotations, e.g. user-defined metadata
• CPC Class Codes: G06F 16/26 Visual data mining; Browsing structured data
• CPC Class Codes: G06F 16/904 Browsing; Visualisation therefor for navigating the web G06F16/954; browsing optimisation for the web G06F16/957
• CPC Class Codes: G06F 40/56 Natural language generation

Abstract

Provided is a process, including: obtaining a set of candidate captions associated with one or more data visualizations; obtaining criteria designating whether candidate captions are descriptive of potential instances of the one or more data visualizations; producing a plurality of simulated instances of each of the one or more data visualizations; determining which of the captions apply to each of the simulated instances of each of the one or more data visualizations based on whether the simulated instances satisfy corresponding criteria; causing captions determined to be applicable to be presented; receiving feedback indicative of whether presented captions are perceived as descriptive of the corresponding simulated instances of data visualizations; and adjusting the criteria based on the feedback.

Claims

1. A method, comprising:

obtaining, with one or more processors, a set of candidate captions associated with one or more data visualizations, wherein;

the set of candidate captions includes at least some natural language captions, and

at least some of the one or more data visualizations are associated with a plurality of the candidate captions in the set;

obtaining, with one or more processors, criteria designating whether the candidate captions are descriptive of potential instances of the one or more data visualizations, wherein the potential instances are producible by applying data to be visualized to the one or more data visualizations;

generating, with one or more processors, data to be visualized;

producing, with one or more processors, a plurality of simulated instances of each of the one or more data visualizations;

determining, with one or more processors, which of the captions apply to each of the simulated instances of each of the one or more data visualizations based on whether the simulated instances satisfy corresponding criteria among the obtained criteria;

causing, with one or more processors, captions determined to be applicable to be presented by one or more user computing devices in visual association with corresponding simulated instances of data visualizations to which captions are determined to apply;

receiving, with one or more processors, feedback obtained via the one or more user computing devices indicative of whether presented captions are perceived as descriptive of the corresponding simulated instances of data visualizations;

adjusting, with one or more processors, the criteria based on the feedback; and

storing, with one or more processors, the adjusted criteria in memory.

2. The method of claim 1, wherein:

the at least some natural language captions include natural language text phrases;

the candidate captions are each associated with one of the one or more data visualizations in a one-to-one relationship;

at least some of the candidate captions are obtained before producing an instance of a corresponding data visualization;

at least some of the criteria specify visual features of data-visualization instances for which a corresponding one of the candidate captions is designated as descriptive;

each of the one or more data visualizations are configured to produce a plurality of different instances depending on data to be visualized;

generating data to be visualized comprises generating fictional data to simulate non-fictional data for which the one or more data visualizations are designed to depict;

determining which of the captions apply comprises comparing the criteria corresponding to a given data visualization to each of the instances of the given data visualization and forming a composite caption from a plurality of candidate captions determined to apply to at least one of the instances of the given data visualization;

causing captions to be presented comprises generated, with a server system, instructions that when executed by a web browser cause the web browser to render a user interface and sending the instructions to a web browser executing on at least one of the one or more user computing devices;

a sequence of operations comprising determining which of the captions apply, causing captions to be presented, and adjusting the criteria is preformed iteratively, in sequence, through a plurality of iterations until a stopping condition is detected; and

the method comprises, after storing the adjusted criteria in memory;

obtaining non-fictional data to be visualized;

producing a non-simulated instance of each of the one or more data visualizations by applying at least some of the non-fictional data;

determining, based on the adjusted criteria, which candidate captions apply to at least some non-simulated instances of the one or more data visualizations; and

causing the candidate captions determined to be applicable to the at least some non-simulated instances to be presented in visual association with the at least some non-simulated instances of the one or more data visualizations.

3. The method of claim 1, wherein at least some of the criteria are obtained by:

obtaining a record specifying the one or more data visualizations via a dashboard design application; and

determining the at least some of the criteria based on the record and natural language text of at least some of the captions.

4. The method of claim 3, wherein:

determining the at least some of the criteria comprises;

determining the at least some of the criteria with a supervised machine learning model trained on a training set that expressly or implicitly associates visual features of instances of data visualizations with captions designated as applicable.

5. The method of claim 1, wherein:

at least some of the criteria include relevance weightings; and

multiple candidate captions determined to apply to a given instances of the one or more data visualizations are ranked according to relevance weightings to select a subset of the candidate captions above a threshold rank to be presented.

6. The method of claim 1, wherein:

at least some of the criteria specify attributes of a relationship between two different data visualizations of a dashboard user-interface; and

candidate captions corresponding to the at least some of the criteria describe states of the relationship.

7. The method of claim 1, wherein:

causing captions to be presented comprises composing a composite caption from two different candidate captions determined to be applicable to a single instance of the one or more data visualizations.

8. The method of claim 7, wherein:

receiving feedback comprises receiving two-or-more dimensional feedback responsive to the composite caption, the two-or-more dimensional feedback including dimensions independently corresponding to each of the two different candidate captions determined to be applicable; and

adjusting the criteria comprises adjusting criteria corresponding to the two different candidate captions based on different values of the different dimensions.

9. The method of claim 1, wherein:

the candidate captions are obtained by a dashboard design application during a dashboard design session in which the one or more data visualizations are designed.

10. The method of claim 1, wherein adjusting comprises:

making a first criterion more stringent than prior to adjusting in response to feedback indicating a false positive determination that a first candidate caption associated with the first criterion is applicable; and

making a second criterion less stringent than prior to adjusting in response to feedback indicating a false negative determination that a second candidate caption associated with the second criterion is not applicable.

11. The method of claim 1, wherein the candidate captions include at least one of the following:

an indication that a limit or target mark is predicted to be attained by a displayed metric within a threshold duration of time;

an indication that a comparison of multiple metrics indicates a designated amount of difference or correlation between the multiple metrics;

a graphical attribute indicating a displayed metric exceeds a threshold, the graphical attribute being independent of a visualization schema of a corresponding data visualization, and the graphical attribute not being natural language text;

an alarm mark placed on an instance of a data visualization;

a context visual element indicative of a comparisons between multiple values of a single displayed metric;

a description of convergence or divergence of two displayed metrics;

a description of change in displayed metrics over time;

a ranking of multiple displayed metrics or a single displayed metric over time;

a comparison between a displayed metric and a pre-attentive visual feature of a data visualization;

a result of a what-if analysis;

a suggested responsive action to adjust a system characterized by a displayed metric;

or

an indication of correspondence to a statistical distribution.

12. The method of claim 1, wherein the candidate captions include at least six of the following:

an indication that a limit or target mark is predicted to be attained by a displayed metric within a threshold duration of time;

an indication that a comparison of multiple metrics indicates a designated amount of difference or correlation between the multiple metrics;

a graphical attribute indicating a displayed metric exceeds a threshold, the graphical attribute being independent of a visualization schema of a corresponding data visualization, and the graphical attribute not being natural language text;

an alarm mark placed on an instance of a data visualization;

a context visual element indicative of a comparisons between multiple values of a single displayed metric;

a description of convergence or divergence of two displayed metrics;

a description of change in displayed metrics over time;

a ranking of multiple displayed metrics or a single displayed metric over time;

a comparison between a displayed metric and a pre-attentive visual feature of a data visualization;

a result of a what-if analysis;

a suggested responsive action to adjust a system characterized by a displayed metric;

or

an indication of correspondence to a statistical distribution.

13. The method of claim 1, comprising:

steps for generating a narrative description of an instance of a data visualization.

14. The method of claim 1, comprising:

causing instances of a dashboard including instances of the one or more data visualizations to be presented on other user computing devices, the instances of dashboards including captions from among the set of candidate captions selected based on the criteria saved in memory.

15. The method of claim 1, wherein:

at least some attributes of the one or more data visualizations are at least partially specified when the candidate captions are obtained;

generating data to be visualized comprises generating fictional data or sampling historical non-fictional data; and

at least some criteria specify visual attributes of instances of data visualizations or attributes of data to be visualized without regard to visual attributes of instances of data visualizations.

16. A tangible, non-transitory, machine-readable medium storing instructions that when executed by one or more processors effectuate operations comprising:

obtaining, with one or more processors, a set of candidate captions associated with one or more data visualizations, wherein;

the set of candidate captions includes at least some natural language captions, and

at least some of the one or more data visualizations are associated with a plurality of the candidate captions in the set;

obtaining, with one or more processors, criteria designating whether the candidate captions are descriptive of potential instances of the one or more data visualizations, wherein the potential instances are producible by applying data to be visualized to the one or more data visualizations;

generating, with one or more processors, data to be visualized;

producing, with one or more processors, a plurality of simulated instances of each of the one or more data visualizations;

determining, with one or more processors, which of the captions apply to each of the simulated instances of each of the one or more data visualizations based on whether the simulated instances satisfy corresponding criteria among the obtained criteria;

causing, with one or more processors, captions determined to be applicable to be presented by one or more user computing devices in visual association with corresponding simulated instances of data visualizations to which captions are determined to apply;

receiving, with one or more processors, feedback obtained via the one or more user computing devices indicative of whether presented captions are perceived as descriptive of the corresponding simulated instances of data visualizations;

adjusting, with one or more processors, the criteria based on the feedback; and

storing, with one or more processors, the adjusted criteria in memory.

17. The medium of claim 17, wherein at least some of the criteria are obtained by:

obtaining a record specifying the one or more data visualizations via a dashboard design application; and

determining the at least some of the criteria based on the record and natural language text of at least some of the captions.

18. The medium of claim 17, wherein:

determining the at least some of the criteria comprises;

determining the at least some of the criteria with a supervised machine learning model trained on a training set that expressly or implicitly associates visual features of instances of data visualizations with captions designated as applicable.

19. The medium of claim 17, wherein:

causing captions to be presented comprises composing a composite caption from two different candidate captions determined to be applicable to a single instance of the one or more data visualizations;

receiving feedback comprises receiving two-or-more dimensional feedback responsive to the composite caption, the two-or-more dimensional feedback including dimensions independently corresponding to each of the two different candidate captions determined to be applicable; and

adjusting the criteria comprises adjusting criteria corresponding to the two different candidate captions based on different values of the different dimensions.

20. The medium of claim 17, wherein:

the candidate captions are obtained by a dashboard design application during a dashboard design session in which the one or more data visualizations are designed.