Model-driven data insights for latent topic materiality

Patent metadata

Field	Value
Publication number	US-12423386-B2
Application number	US-202418418171-A
Country	US
Kind code	B2
Filing date	Jan 19, 2024
Priority date	Jan 21, 2023
Publication date	Sep 23, 2025
Grant date	Sep 23, 2025

Abstract

Described herein are machine learning methods and systems for locating and tracking performance of latent themes in changing data from disparate sources. Themes may be indirect goals or consequential impacts indicated by latent topics. Identifying performance indicators of latent themes in large changing data sets uncovers underlying trends or previously concealed behaviors that may be accelerating or undermining goals.

First claim

What is claimed is:

1. A method comprising:
ingesting data from a from disparate data sources to identify data portions of multiple data sets with information that relate to the one or more issues;
evaluating the information for each data set against an ensemble model to determine a set of topics for the one or more issues;
generate a topic materiality score for each data portion that indicate relevancy of the information to a particular topic in the set of topics;
training a time series aggregation model with the topic materiality scores to the one or more issues over time for the data portions of multiple data sets;
utilizing the time series aggregation model to generate insights analysis for a selected issue of the one or more issues;
applying the insight analysis to updated data from the disparate data sources to output a prediction or recommend for the selected issue;
executing one or more actions in response to the prediction or recommendation, wherein the one or more actions include assigning computing power to an initiative.

2. The method of claim 1 , wherein the data include at least one data point selected comprising a measurement metric related to the one or more topics of the set of topics, and a performance measure related to the one or more topics of the set of topics.

3. The method of claim 1 , wherein the time series aggregation model is configured to determine a respective performance score for each respective topic of the associated of topics, wherein the respective performance score indicates a contribution of the initiative to overall performance for the respective issue.

4. The method of claim 1 wherein ingesting data from a from disparate data sources uses canonical type declarations with metadata to normalize data portions from different data sources.

5. The method of claim 1 , wherein the disparate data sources include enterprise data sources of an organization, wherein the set of topics are latent, and wherein the one or more issues comprises Environmental, Social, and Governance (ESG) issues for the organization.

6. The method of claim 1 , wherein the initiative is designed to offset carbon emissions.

7. The method of claim 1 , wherein the one or more actions include instructing one or more artificial intelligence applications to generate action items for improving progress of the initiative.

8. A system comprising:
one or more processors; and
memory storing instructions that, when executed by the one or more processors, cause the system to perform:
ingesting data from a from disparate data sources to identify data portions of multiple data sets with information that relate to the one or more issues;
evaluating the information for each data set against an ensemble model to determine a set of topics for the one or more issues;
generate a topic materiality score for each data portion that indicate relevancy of the information to a particular topic in the set of topics;
training a time series aggregation model with the topic materiality scores to the one or more issues over time for the data portions of multiple data sets;
utilizing the time series aggregation model to generate insights analysis for a selected issue of the one or more issues;
applying the insight analysis to updated data from the disparate data sources to output a prediction or recommend for the selected issue;
executing one or more actions in response to the prediction or recommendation, wherein the one or more actions include assigning computing power to an initiative.

9. The system of claim 8 , wherein the data include at least one data point selected comprising a measurement metric related to the one or more topics of the set of topics, and a performance measure related to the one or more topics of the set of topics.

10. The system of claim 8 , wherein the time series aggregation model is configured to determine a respective performance score for each respective topic of the associated of topics, wherein the respective performance score indicates a contribution of the initiative to overall performance for the respective issue.

11. The system of claim 8 wherein ingesting data from a from disparate data sources uses canonical type declarations with metadata to normalize data portions from different data sources.

12. The system of claim 8 , wherein the disparate data sources include enterprise data sources of an organization, wherein the set of topics are latent, and wherein the one or more issues comprises Environmental, Social, and Governance (ESG) issues for the organization.

13. The system of claim 8 , wherein the initiative is designed to offset carbon emissions.

14. The system of claim 8 , wherein the one or more actions include instructing one or more artificial intelligence applications to generate action items for improving progress of the initiative.

15. A non-transitory computer readable medium comprising instructions that, when executed, cause one or more processors to perform:
ingesting data from a from disparate data sources to identify data portions of multiple data sets with information that relate to the one or more issues;
evaluating the information for each data set against an ensemble model to determine a set of topics for the one or more issues;
generate a topic materiality score for each data portion that indicate relevancy of the information to a particular topic in the set of topics;
training a time series aggregation model with the topic materiality scores to the one or more issues over time for the data portions of multiple data sets;
utilizing the time series aggregation model to generate insights analysis for a selected issue of the one or more issues;
applying the insight analysis to updated data from the disparate data sources to output a prediction or recommend for the selected issue;
executing one or more actions in response to the prediction or recommendation, wherein the one or more actions include assigning computing power to an initiative.

16. The non-transitory computer readable medium of claim 15 , wherein the data include at least one data point selected comprising a measurement metric related to the one or more topics of the set of topics, and a performance measure related to the one or more topics of the set of topics.

17. The non-transitory computer readable medium of claim 15 , wherein the time series aggregation model is configured to determine a respective performance score for each respective topic of the associated of topics, wherein the respective performance score indicates a contribution of the initiative to overall performance for the respective issue.

18. The non-transitory computer readable medium of claim 15 wherein ingesting data from a from disparate data sources uses canonical type declarations with metadata to normalize data portions from different data sources.

19. The non-transitory computer readable medium of claim 15 , wherein the disparate data sources include enterprise data sources of an organization, wherein the set of topics are latent, and wherein the one or more issues comprises Environmental, Social, and Governance (ESG) issues for the organization.

20. The non-transitory computer readable medium of claim 15 , wherein the one or more actions include instructing one or more artificial intelligence applications to generate action items for improving progress of the initiative.

Assignees

• C3 Ai Inc

Inventors

• Parham David William
• Nguyet Hang Le Thi
• Young Robert S
• Dutta Suvansh
• Niyomkarn Thanaspakorn

Classifications

• G06Q50/26

  Government or public services (business processes related to the transportation industry G06Q50/40) · CPC title

• G06F18/2415 Primary

  based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate · CPC title