System and method for research report guided proactive news analytics for streaming news and social media

• US 9,753,913 B1
• Filed: 09/04/2015
• Issued: 09/05/2017
• Est. Priority Date: 06/25/2013
• Status: Active Grant

• Current Assignee: JP Morgan Chase Bank, N.A. (JP Morgan Chase & Co)
• Original Assignee: JP Morgan Chase Bank, N.A. (JP Morgan Chase & Co)
• Inventors: Kursun, Eren, Fernandez, Gene, Berson, Alex, Goodman, Brian
• Primary Examiner(s): Spooner, Lamont
• Application Number:
  US14/846,131
• Time in Patent Office: 732 Days
• Field of Search: 704 1, 704 9, 704 10
• CPC Class Codes: G06F 16/24568 Data stream processing; Continuous queries
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/367 Ontology
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 40/134 Hyperlinking
• CPC Class Codes: G06F 40/279 Recognition of textual entities
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/00 Computing arrangements using knowledge-based models

Abstract

Systems and methods may provide proactive news analytics based on integrated prediction statements. Users may extract and collect conditional statements from research reports. A compiled list based on the processing/linking of statements for signal generation may then be created. Similarly, a list of counter statements that are assigned a conflict rating that specifies how much agreement/disagreement on a specific topic exists for the counter statement and the statement itself may be created. The custom library may have semantic capabilities to justify conditional statements in order to capture meaning and identify supporting news related to the statement. When a relevant event is detected that relates to a conditional statement, expected conclusions are linked, and customized indexes are calculated to allow for analysis of the relevant event.

Claims

1. A computer implemented method, comprising:

extracting, by a computer processor, one or more statements from one or more unstructured sources wherein the extracted statements comprise at least one of predictive and conditional statements pertaining to an event in the future;

normalizing, by the computer processor, the extracted statements;

linking the extracted statements from the one or more unstructured sources based on one or more factors;

creating, by the computer processor, a list of custom keywords based on the extracted statements;

creating a custom keyword library from the list of custom keywords;

creating natural language processing structures for identifying analogous expressions for the custom keyword library;

linking the custom keyword library and the natural language processing structures;

prefetching data, based on the linked custom keyword library and natural language processing structures, from at least one of a stream of news, social media, and unstructured sources;

ranking the extracted statements based on a strength of agreement; and

creating an alternative conditional statement flow chart upon the extracted statements having a high degree of disagreement.

2. The method of claim 1, further comprising:

testing the extracted statements for consistency, wherein consistency is checked in a multi-dimensional space of time, causality, and hierarchy; and

calculating a confidence score based at least two of the extracted statements from different ones of the unstructured sources match.

3. The method of claim 1, wherein the unstructured sources comprise research reports.

4. The method of claim 1, wherein the one or more factors comprise one or more of:

the conditions or predictions that the extracted statements refer to, an expected timeline, an expected cause, an expected dependency, a keyword or keywords, and a list of tracked entities.

5. The method of claim 1, further comprising:

creating a linked and timeline based flow diagram based on the extracted statements and the event.

6. The method of claim 1, further comprising:

associating a sentiment position signal with each of the extracted statements,

wherein the linking of the custom keyword library and the natural language processing structures is based on a semantic analysis and

wherein the prefetching of data is based on potential matching of the data to items in the custom keyword library and the natural language processing structures, wherein the potential matching of the data to items in the custom keyword library and the natural language processing structures is measured at least in terms comprising one or more of;

entity matching, keyword matching, and timeline matching;

analyzing the prefetched data based on the custom keyword library and the natural language processing structures for matching items;

processing the matching items based upon sentiment and confidence scoring; and

implementing machine based learning for future prefetching based on an accuracy of the matching items and the event.

7. The method of claim 6, further comprising:

determining the accuracy of the matching items and the event by;

verifying the matching items using a set of cross-channel checks that comprise verifying each matching item against one or more other independent sources.

8. The method of claim 6, wherein the stream is a weighted stream with weights applied to each source in the stream.

9. The method of claim 6, further comprising:

applying a feedback mechanism that extracts information from the one or more unstructured sources pertaining to the occurrence of the event.

10. A system, comprising:

a processor; and

a memory comprising computer-readable instructions which when executed by the processor cause the processor to perform the steps comprising;

extracting one or more statements from one or more unstructured sources wherein the extracted statements comprise at least one of predictive and conditional statements pertaining to an event in the future;

normalizing the extracted statements;

linking the extracted statements from the one or more unstructured sources based on one or more factors;

creating a list of custom keywords based on the extracted statements;

creating a custom keyword library from the list of custom keywords;

creating natural language processing structures for identifying analogous expressions for the custom keyword library;

linking the custom keyword library and the natural language processing structures;

prefetching data, based on the custom keyword library, at least one of a stream of news, social media, and unstructured sources;

ranking the extracted statements based on a strength of agreement; and

creating an alternative conditional statement flow chart upon the extracted statements having a high degree of disagreement.

11. The system of claim 10, further comprising:

testing the extracted statements for consistency, wherein consistency is checked in a multi-dimensional space of time, causality, and hierarchy; and

calculating a confidence score based at least two of the extracted statements from different ones of the unstructured sources match.

12. The system of claim 10, wherein the unstructured sources comprise research reports.

13. The system of claim 10, wherein the one or more factors comprise one or more of:

the conditions or predictions that the extracted statements refer to, an expected timeline, an expected cause, an expected dependency, a keyword or keywords, and a list of tracked entities.

14. The system of claim 10, further comprising:

creating a linked and timeline based flow diagram for the extracted statements and the event.

15. The system of claim 10, further comprising:

associating a sentiment position signal with each of the extracted statements;

wherein the linking of the custom keyword library and the natural language processing structures is based on a semantic analysis and

wherein the prefetching of data is based on potential matching of the data to items in the custom keyword library and the natural language processing structures;

analyzing the prefetched data based on the custom keyword library and the natural language processing structures for matching items;

processing the matching items based upon sentiment and confidence scoring; and

implementing machine based learning for future prefetching based on an accuracy of the matching items and the event.

16. The system of claim 15, further comprising:

determining the accuracy of the matching items and the event by;

verifying the matching items using a set of cross-channel checks that comprise verifying each matching item against one or more other independent sources.

17. The system of claim 15, wherein the stream is a weighted stream with weights applied to each source in the stream.

18. The system of claim 15, further comprising:

applying a feedback mechanism that extracts information from the one or more unstructured sources pertaining to the occurrence of the event.