Systems and methods for customized data parsing and paraphrasing

• US 10,191,970 B2
• Filed: 08/19/2015
• Issued: 01/29/2019
• Est. Priority Date: 08/19/2015
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Hewitt, Trudy L., Satira, Zachary, Stewart, Nathan, Velez, Wilson, Williams, Heidi D.
• Primary Examiner(s): Bromell, Alexandria Y
• Application Number:
  US14/829,782
• Publication Number:
  US 20170053021A1
• Time in Patent Office: 1,259 Days
• Field of Search: 707708, 707755, 707771, 707732, 707733, 707734, 707780, 706 8, 706 12, 706 25
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: G06F 16/338 Presentation of query results
• CPC Class Codes: G06F 16/345 Summarisation for human users
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

Methods and apparatus, including computer program products, implementing and using techniques for customized data parsing and paraphrasing. A communications module receives content from several resources. An analytics engine parses the content based on a user query for content. An artificial intelligence engine determines a confidence ranking for the parsed content and determines a set of prioritized parsed content from the parsed content, based on the confidence ranking for the parsed content. A natural language engine converts, using a natural language processing technique, the set of prioritized parsed content into a format for user interface. A user interface presents a summarized output including the converted set of prioritized parsed content based on information associated with the user query.

Claims

1. A system comprising:

a communications module configured to receive a plurality of content from a plurality of resources;

an analytics engine comprising at least one processor and a memory configured to receive a user query associated with a user and parse the plurality of content received from the communications module based on at least one of an administration rule, a user profile corresponding to the user, and historical data corresponding to the user;

an artificial intelligence engine comprising the at least one processor and the memory and configured to;

determine a confidence ranking for each of the plurality of parsed content; and

select a set of prioritized parsed content from the plurality of parsed content based on the confidence ranking for each of the plurality of parsed content;

a natural language engine comprising the at least one processor and the memory and configured to;

convert, using a natural language processing technique, the set of prioritized parsed content into a format suitable for a user interface, wherein the format is based on a writing style of the user and a skill level of the user;

identify a learning style of the user based on information associated with the user query, wherein the learning style is selected from a group consisting of a visual learning style, an auditory learning style, a reading-writing learning style, and a tactile learning style;

filter the converted set of prioritized parsed content based on the learning style of the user; and

combine the filtered set of prioritized parsed content into a summarized output; and

a user interface configured to present the summarized output to the user based on the information associated with the user query.

2. The system of claim 1, wherein the plurality of resources comprises a content repository.

3. The system of claim 1, wherein the analytics engine is configured to parse the plurality of content based on an administrative rule comprising at least one of system level criteria and a preference setting of the user associated with the user query.

4. The system of claim 1, wherein the analytics engine is configured to parse the plurality of content based on historical data comprising at least one of a browsing history, electronic mail messages, and prior search history results associated with the user associated with the user query.

5. The system of claim 1, wherein the artificial intelligence engine is further configured to determine the confidence ranking for each of the plurality of parsed content by:

for each of the plurality of parsed content;

determine a type of resource associated with the parsed content;

determine a popularity metric associated with the parsed content; and

assign the confidence ranking for the parsed content based on the determined type of resource and the determined popularity metric.

6. The system of claim 5, wherein the type of resource comprises at least one of an Internet resource type, technical document type, discussion forum type, social media type, and a multimedia type.

7. The system of claim 5, wherein the popularity metric comprises a number of click-throughs associated with the parsed content.

8. The system of claim 5, wherein the popularity metric comprises a number of recommendations associated with the parsed content.

9. The system of claim 5, wherein the popularity metric comprises a number of downloads associated with the parsed content.

10. The system of claim 5, wherein the popularity metric comprises a number of shares associated with the parsed content.

11. The system of claim 1, wherein the artificial intelligence engine is further configured to:

compare the confidence ranking for each of the plurality of parsed content to each of the other plurality of parsed content;

sort the plurality of parsed content based on the comparison of the confidence rankings for each of the plurality of parsed content; and

select the set of prioritized parsed content from the sorted plurality of parsed content, for determining the set of prioritized parsed content from the plurality of parsed content.

12. The system of claim 11, wherein the artificial intelligence engine is further configured to sort the plurality of parsed content from at least one of the plurality of parsed content having a high confidence ranking to at least one other of the plurality of parsed content having a low confidence ranking.

13. The system of claim 12, wherein the artificial intelligence engine is further configured to select the set of prioritized parsed content from the plurality of parsed content having a high confidence ranking.

14. The system of claim 1, wherein the natural language engine is further configured to filter the converted set of prioritized content based on the information associated with the user query.

15. The system of claim 14, wherein the information associated with the user query comprises at least one of a user profile, an administrative rule, and historical data of the user associated with the user query.

16. A method comprising:

receiving a user query at a computing device comprising at least one processor and a memory;

receiving a plurality of content from a plurality of resources at a communications module;

parsing the plurality of content using an analytics engine based on at least one of an administration rule, a user profile corresponding to a user, and historical data corresponding to the user;

parsing the plurality of content based on a user query associated with the user;

determining a confidence ranking for each of the plurality of parsed content using an artificial intelligence engine;

selecting a set of prioritized parsed content from the plurality of parsed content based on the confidence ranking for each of the plurality of parsed content;

converting, using a natural language processing technique, the set of prioritized parsed content into a format suitable for a user interface, wherein the format is based on at least a writing style of the user and a skill level of the user;

identifying a learning style of the user based on information associated with the user query, wherein the learning style is selected from a group consisting of a visual learning style, an auditory learning style, a reading-writing learning style, and a tactile learning style;

filtering the converted set of prioritized parsed content based on the learning style of the user;

combining the filtered set of prioritized parsed content into a summarized output; and

presenting the summarized output to the user.