Systems and methods for analyzing document coverage

• US 10,380,252 B2
• Filed: 01/06/2017
• Issued: 08/13/2019
• Est. Priority Date: 08/28/2014
• Status: Active Grant

• Current Assignee: Northern Light Group, LLC
• Original Assignee: Northern Light Group, LLC
• Inventors: Seuss, C. David
• Primary Examiner(s): Desir, Pierre Louis
• Assistant Examiner(s): Wang, Yi Sheng
• Application Number:
  US15/399,991
• Publication Number:
  US 20170116176A1
• Time in Patent Office: 949 Days
• CPC Class Codes: G06F 16/00 Information retrieval; Database structures therefor; File system structures therefor
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 40/10 Text processing natural language analysis G06F40/20; semantic analysis G06F40/30; processing or translation of natural language G06F40/40
• CPC Class Codes: G06F 40/211 Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
• CPC Class Codes: G06F 40/253 Grammatical analysis; Style critique
• CPC Class Codes: G06F 40/295 Named entity recognition
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06T 11/206 Drawing of charts or graphs
• CPC Class Codes: G06T 11/60 Editing figures and text; Combining figures or text

Abstract

A method includes storing a meaning taxonomy including meaning loaded entities and associations between the meaning loaded entities and a plurality of syntactic structures; receiving, from a first source, first content including one or more first syntactic structures; identifying, based at least in part on identification criteria, one or more meaning loaded entities that are linked to the one or more first syntactic structures by one or more first associations of the plurality of associations, the identification criteria including at least one of a type of the first content, a document size of the first content, a publication date of the first content, and an importance of the meaning load entities; calculating a first content summary indicating a level of coverage of the meaning loaded entities within the first content; and generating a first visual representation of the first content summary.

Claims

1. A system comprising:

memory storing a meaning taxonomy including;

a text analytics database;

a plurality of meaning loaded entities; and

a plurality of associations between the plurality of meaning loaded entities and a plurality of syntactic structures, each association of the plurality of associations linking at least one meaning loaded entity of the plurality of meaning loaded entities to at least one syntactic structure of the plurality of syntactic structures; and

at least one processor in data communication with the memory, the processor configured to;

receive first content from a first source, the first content including one or more first syntactic structures;

identify, based at least in part on identification criteria, one or more first meaning loaded entities of the plurality of meaning loaded entities that are linked to the one or more first syntactic structures by one or more first associations of the plurality of associations, the identification criteria including at least one of a type of the first content, a document size of the first content, a publication date of the first content, and an importance of the one or more first meaning load entities;

store, in the text analytics database, a mapping of the one or more first syntactic structures within the first content to the identified one or more first meaning loaded entities;

calculate, based on the mapping in the text analytics database, a first content summary indicating a first level of coverage of the one or more first meaning loaded entities within the first content; and

generate a first visual representation of the first content summary, the first visual representation comprising visual indicia that graphically represent a domain of the one or more first meaning loaded entities addressed in the first content and a level of coverage dedicated to each meaning loaded entity in the domain of meaning loaded entities addressed in the first content.

2. The system of claim 1, wherein the type of the first content is one of textual content, graphical content, and audio content.

3. The system of claim 1, wherein the first content includes a set of documents including the one or more first syntactic structures and the first content summary includes a depth of coverage of the one or more first meaning loaded entities within the set of documents.

4. The system of claim 3, wherein the set of documents includes at least one of white papers, presentations, news articles, press releases, market research reports, websites, and social media feeds.

5. The system of claim 3, wherein the first visual representation is a radar chart presenting the depth of coverage.

6. The system of claim 1, wherein the one or more components are further collectively configured to:

receive second content from a second source, the second content including one or more second syntactic structures;

identify one or more second meaning loaded entities of the plurality of meaning loaded entities that are linked to the one or more second syntactic structures by one or more second associations of the plurality of associations;

store, in the text analytics database, a second mapping of the one or more second syntactic structures within the second content to the identified one or more second meaning loaded entities;

calculate, based on the second mapping in the text analytics database, a second content summary indicating a second level of coverage of the one or more second meaning loaded entities within the second content; and

provide a comparative representation that combines the first visual representation and a second representation of the second content summary to the external entity.

7. The system of claim 6, wherein the first content summary and the second content summary indicate a level of coverage of at least one common meaning loaded entity extracted from the first content and the second content.

8. The system of claim 7, wherein the one or more first syntactic structures and the one or more second syntactic structures share no common syntactic structures.

9. A method implemented using a computer system including memory and at least one processor coupled to the memory, the method comprising:

storing, in the memory, a meaning taxonomy including;

a plurality of meaning loaded entities; and

a plurality of associations between the plurality of meaning loaded entities and a plurality of syntactic structures, each association of the plurality of associations linking at least one meaning loaded entity of the plurality of meaning loaded entities to at least one syntactic structure of the plurality of syntactic structures;

receiving first content from a first source, the first content including one or more first syntactic structures;

identifying, based at least in part on identification criteria, one or more first meaning loaded entities of the plurality of meaning loaded entities that are linked to the one or more first syntactic structures by one or more first associations of the plurality of associations, the identification criteria including at least one of a type of the first content, a document size of the first content, a publication date of the first content, and an importance of the one or more first meaning load entities;

storing, in a text analytics database, a mapping of the one or more first syntactic structures within the first content to the identified one or more first meaning loaded entities;

calculating, based on the mapping in the text analytics database, a first content summary indicating a first level of coverage of the one or more first meaning loaded entities within the first content; and

generating, by the at least one processor, a first visual representation of the first content summary, the first visual representation comprising visual indicia that graphically represent a domain of the one or more meaning loaded entities addressed in the first content and a level of coverage dedicated to each meaning loaded entity.

10. The method of claim 9, wherein the type of the first content is one of textual content, graphical content, and audio content.

11. The method of claim 9, wherein receiving the first content includes receiving a set of documents including the one or more syntactic structures and calculating the first content summary includes calculating a depth of coverage of the one or more first meaning loaded entities within the set of documents.

12. The method of claim 11, wherein receiving the set of documents includes receiving at least one of white papers, presentations, news articles, press releases, market research reports, websites, and social media feeds.

13. The method of claim 9, wherein the first visual representation is a radar chart presenting the depth of coverage.

14. The method of claim 9, further comprising:

receiving second content from a second source, the second content including one or more second syntactic structures;

identifying one or more second meaning loaded entities of the plurality of meaning loaded entities that are linked to the one or more second syntactic structures by one or more second associations of the plurality of associations;

storing, in the text analytics database, a second mapping of the one or more second syntactic structures within the second content to the identified one or more second meaning loaded entities;

calculating, based on the second mapping in the text analytics database, a second content summary indicating a second level of coverage of the one or more second meaning loaded entities within the second content; and

providing a comparative representation that combines the first visual representation and a second representation of the second content summary to the external entity.

15. The method of claim 14, wherein calculating the second content summary includes calculating a second content summary that indicates a level of coverage of at least one common meaning loaded entity extracted from the first content and the second content.

16. The method of claim 15, wherein identifying the one or more second meaning loaded entities includes identifying one or more second meaning loaded entities that are linked to one or more second syntactic structures that share no common syntactic structures with the one or more first syntactic structures.

17. A non-transitory computer readable medium storing sequences of instruction for analyzing coverage of concepts within content, the sequences of instruction including computer executable instructions that instruct at least one processor to:

store, in a memory in data communication with the at least one processor, a meaning taxonomy including;

a plurality of meaning loaded entities; and

a plurality of associations between the plurality of meaning loaded entities and a plurality of syntactic structures, each association of the plurality of associations linking at least one meaning loaded entity of the plurality of meaning loaded entities to at least one syntactic structure of the plurality of syntactic structures;

receive first content from a first source, the first content including one or more first syntactic structures;

identify, based at least in part on identification criteria, one or more first meaning loaded entities of the plurality of meaning loaded entities that are linked to the one or more first syntactic structures by one or more first associations of the plurality of associations, the identification criteria including at least one of a type of the first content, a document size of the first content, a publication date of the first content, and an importance of the one or more first meaning load entities;

store, in a text analytics database, a mapping of the one or more first syntactic structures within the first content to the identified one or more first meaning loaded entities;

calculate, based on the mapping in the text analytics database, a first content summary indicating a first level of coverage of the one or more first meaning loaded entities within the first content; and

generate a first visual representation of the first content summary, the first visual representation comprising visual indicia that graphically represent a domain of the first one or more meaning loaded entities in the first content and a level of coverage dedicated to each meaning loaded entity.

18. The computer readable medium of claim 17, wherein the first content includes a set of documents including the one or more syntactic structures and the first content summary includes a depth of coverage of the one or more first meaning loaded entities within the set of documents.

19. The computer readable medium of claim 18, wherein the set of documents includes at least one of white papers, presentations, news articles, press releases, market research reports, websites, and social media feeds.

20. The computer readable medium of claim 17, wherein the instructions further instruct the at least one processor to:

receive second content from a second source, the second content including one or more second syntactic structures;

identify one or more second meaning loaded entities of the plurality of meaning loaded entities that are linked to the one or more second syntactic structures by one or more second associations of the plurality of associations;

store, in the text analytics database, a second mapping of the one or more second syntactic structures within the second content to the identified one or more second meaning loaded entities;

calculate, based on the second mapping in the text analytics database, a second content summary indicating a second level of coverage of the one or more second meaning loaded entities within the second content; and

provide a comparative representation that combines the first visual representation and a second representation of the second content summary to the external entity.