CONTENT EXPOSURE AND STYLING CONTROL FOR VISUALIZATION RENDERING AND NARRATION USING DATA DOMAIN RULES

• US 20200134090A1
• 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, Velez-Rojas, Maria, Greenspan, Steven
• Application Number:
  US16/171,795
• Publication Number:
  US 20200134090A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/345 Summarisation for human users
• CPC Class Codes: G06F 18/2178 based on feedback of a supervisor
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06F 18/2413 based on distances to training or reference patterns
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/006 based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/086 using evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06V 10/762 using clustering, e.g. of similar faces in social networks
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 10/7784 based on feedback from supervisors
• CPC Class Codes: G06V 10/82 using neural networks

Abstract

Provided is a process, including: obtaining a first identifier of a first user for whom a first presentation including a first natural language text summary of data is to be provided; selecting a first domain from among a plurality of domains based on the first identifier; selecting a first set of fields among a plurality of fields of the data based on the first domain; determining a first set of exposure-control rules based on the first set of fields of data; determining a first applicable subset of the first set exposure-control rules by comparing criteria of the first set of exposure-control rules to user attributes associated with the first identifier; generating with a trained captioning model, the first natural language text summary in the first domain of the data compliant with exposure permissions of the first applicable subset of the first set of exposure-control rules.

Claims

1. A method, comprising:

obtaining, with one or more processors, a first identifier of a first user for whom a first presentation including a first natural language text summary of data is to be provided;

selecting, with one or more processors, a first domain from among a plurality of domains based on the first identifier of the first user or based on input from the first user;

selecting, with one or more processors, a first set of fields among a plurality of fields of the data based on the first domain;

determining, with one or more processors, a first set of exposure-control rules based on the first set of fields of data;

determining, with one or more processors, a first applicable subset of the first set exposure-control rules by comparing criteria of the first set of exposure-control rules to user attributes associated with the first identifier;

generating, with one or more processors, with a trained captioning model, the first natural language text summary in the first domain of the data compliant with exposure permissions of the first applicable subset of the first set of exposure-control rules; and

causing, with one or more processors, the generated first natural language text summary to be presented to the first user.

2. The method of claim 1, wherein:

the data contains metrics of a monitored system;

the first domain is selected from among the plurality of domains based on the first identifier of the first user;

the first identifier uniquely identifies the first user among a plurality of users of a data visualization application;

at least some of the plurality of domains correspond to different selections of independent variables of data visualizations by which different perspectives on the monitored system are depicted;

the plurality of domains include at least three of the following types of domains;

time domain, geographic domain, actor domain, frequency domain, action domain, or boundary domain;

a first subset of the first set of fields are among independent variables of the first domain;

second subset of the first set of fields are among dependent variables of the first domain;

the first domain specifies, at least in part, pairings of members of the first subset with members of the second subset;

the pairings indicate relationships between dependent and independent variables depicted in a plurality of data visualizations of the first domain;

the exposure-control rules specify permissions to view values of fields of the data or summaries of values of fields of the data;

at least some of the exposure control rules specify permissions on a field-by-field basis; and

at least some of the exposure control rules specify permissions for a group of fields.

3. The method of claim 1, comprising:

accessing a set of data visualizations corresponding to the first domain in response to selecting the first domain;

generating instances of the set of data visualizations depicting values of the data in the first set of fields; and

causing the first set of instances to be presented to the user in association with the generated natural language text summary in a dashboard user interface.

4. The method of claim 1, wherein the plurality of domains include at least four of the following types of domains:

time domain, spatial domain, actor domain, metric domain, frequency domain, composition domain, tangible value domain, intangible value domain, abstraction domain, action domain, or boundary domain.

5. The method of claim 1, comprising:

obtaining a second identifier of a second user for whom a second presentation including a second natural language text summary of the data is to be provided;

selecting the first domain from among the plurality of domains based on the second identifier of the first user;

selecting a second applicable subset of the first set of exposure-control rules by comparing criteria of the first set of exposure-control rules to user attributes associated with the second identifier, the second applicable subset of exposure-control rules specifying at least some permissions that are different from permissions specified by the first applicable subset of exposure-control rules;

generating, with the trained captioning model, the second natural language text summary in the first domain of the data compliant with exposure permissions of the second applicable subset of the first set of exposure-control rules, wherein;

the second natural language text summary is different from the first natural language text summary, and

the second natural language text summary summarizes the same data as the first natural language text summary; and

causing the generated second natural language text summary to be presented to the second user.

6. The method of claim 1, comprising:

obtaining a second identifier of a second user requesting a second presentation including a second natural language text summary of data;

selecting a second domain from among the plurality of domains;

determining a second set of fields among the plurality of fields of the data based on the second domain, the second set of fields being at least partially different from the first set of fields;

determining a second set of exposure-control rules based on the second set of fields of data;

selecting a second applicable subset of the second set of exposure-control rules by comparing criteria of the second set of exposure-control rules to user attributes associated with the second identifier;

generating, with the trained captioning model, the second natural language text summary in the second domain of the data compliant with exposure permissions of the second applicable subset of the second set of exposure-control rules, wherein;

the second natural language text summary is different from the first natural language text summary, and

the second natural language text summary summarizes at least some of the same data as the first natural language text summary; and

causing the generated second natural language text summary to be presented to the second user.

7. The method of claim 1, wherein:

the trained captioning model is configured to generate the first natural language summary based on a subset of text phrases input to a data visualization design application while designing a data visualization of the first domain and rules associated with the text phrases indicating states of the data visualization for which the text phrases are designated as descriptive.

8. The method of claim 7, wherein:

the trained captioning model is trained by adjusting criteria of the rules based on feedback from a human reviewer or model thereof.

9. The method of claim 7, wherein:

the subset of text phrases comprises a plurality of text phrases;

the trained captioning model comprises a natural-language processing summarization sub-model configured to generate a summary of the subset of text phrases; and

the first natural language text summary is based on the summary of the subset of text phrases.

10. The method of claim 1, wherein:

input features of the trained captioning model include values indicative of exposure permissions of at least some of the first applicable subset of the first set of exposure-control rules; and

the trained captioning model is configured to generate text based on both visual features of instances of data visualizations in the first domain and the values.

11. The method of claim 1, wherein:

the trained captioning model is configured to generate a plurality of candidate natural language text summaries of varying entropy and filter the plurality of candidate natural language text summaries based on exposure permissions.

12. The method of claim 1, wherein:

the trained captioning model comprises a plurality of sub-models each trained to output summaries of a different amount of generality;

the trained captioning model is configured to select among the sub-models based on exposure permissions; and

the first natural language text summary is based on output of a first sub-model among the plurality of sub-models selected based on the first applicable subset of exposure-control rules.

13. The method of claim 1, wherein:

at least some of the exposure-control rules comprise topics for which exposure is prohibited for users satisfying one or more criteria or failing to satisfy one or more criteria of the at least some of the exposure control rules; and

the trained captioning model is configured to classify topics of candidate natural language text summaries with latent Dirichlet allocation and filter out those with topics for which exposure is prohibited by the at least some of the exposure-control rules.

14. The method of claim 1, comprising:

steps for controlling styling of data visualizations or text summaries of data visualizations.

15. The method of claim 1, comprising:

steps for controlling exposure to data in data visualizations or text summaries of data visualizations.

16. The method of claim 1, wherein:

the first identifier is a class of users to which a set of exposure permissions apply; and

the exposure permissions include positive permissions indicative of users for whom exposure is permitted or negative permissions indicative of users for whom exposure is prohibited.

17. 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 first identifier of a first user for whom a first presentation including a first natural language text summary of data is to be provided;

selecting, with one or more processors, a first domain from among a plurality of domains based on the first identifier of the first user or based on input from the first user;

selecting, with one or more processors, a first set of fields among a plurality of fields of the data based on the first domain;

determining, with one or more processors, a first set of exposure-control rules based on the first set of fields of data;

determining, with one or more processors, a first applicable subset of the first set exposure-control rules by comparing criteria of the first set of exposure-control rules to user attributes associated with the first identifier;

generating, with one or more processors, with a trained captioning model, the first natural language text summary in the first domain of the data compliant with exposure permissions of the first applicable subset of the first set of exposure-control rules; and

causing, with one or more processors, the generated first natural language text summary to be presented to the first user.

18. The medium of claim 17, wherein:

the trained captioning model is configured to generate the first natural language summary based on a subset of text phrases input to a data visualization design application while designing a data visualization of the first domain and rules associated with the text phrases indicating states of the data visualization for which the text phrases are designated as descriptive.

19. The medium of claim 17, wherein:

the trained captioning model is configured to generate the first natural language summary based on a subset of text phrases input to a data visualization design application while designing a data visualization of the first domain and rules associated with the text phrases indicating states of the data visualization for which the text phrases are designated as descriptive;

the subset of text phrases comprises a plurality of text phrases;

the trained captioning model comprises a natural-language processing summarization sub-model configured to generate a summary of the subset of text phrases; and

the first natural language text summary is based on the summary of the subset of text phrases.

20. The medium of claim 17, wherein:

input features of the trained captioning model include values indicative of exposure permissions of at least some of the first applicable subset of the first set of exposure-control rules; and

the trained captioning model is configured to generate text based on both visual features of instances of data visualizations in the first domain and the values.