System and method for evaluating text to support multiple insurance applications

• US 9,026,551 B2
• Filed: 06/25/2013
• Issued: 05/05/2015
• Est. Priority Date: 06/25/2013
• Status: Active Grant

• Current Assignee: Hartford Fire Insurance Company (The Hartford Financial Services Group, Inc.)
• Original Assignee: Hartford Fire Insurance Company (The Hartford Financial Services Group, Inc.)
• Inventors: Drennan, Arthur Paul III
• Primary Examiner(s): NGUYEN, CAM LINH T
• Application Number:
  US13/925,996
• Publication Number:
  US 20140379386A1
• Time in Patent Office: 679 Days
• Field of Search: 707/758, 707/776
• US Class Current: 707/776
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: G06F 16/26 Visual data mining; Browsing structured data
• CPC Class Codes: G06F 16/3334 Selection or weighting of terms from queries, including natural language queries
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 16/374 Thesaurus
• CPC Class Codes: G06F 40/00 Handling natural language data speech analysis or synthesis, speech recognition G10L
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

A system for evaluating text data to support multiple insurance applications is disclosed. In some embodiments, text input data is received from multiple sources. The text input data may then be aggregated and mapped to create composite text input data. A semantic event in the composite text input data may be automatically detected, such as by being triggered by a semantic rule and associated semantic tag. A text mining result database may be updated by adding an entry to the database identifying the detected semantic event and the triggering semantic rule. An indication associated with the text mining result database may then be transmitted to a plurality of insurance applications.

Claims

1. A system for evaluating text data to support multiple insurance applications, comprising:

a communication device to receive text input data from multiple sources;

a processor coupled to the communication device; and

a storage device in communication with said processor and storing instructions adapted to be executed by said processor to;

aggregate and map the received text input data to create composite text input data;

automatically detect a semantic event in the composite text input data triggered by a semantic rule and associated semantic tag,

flag the detected semantic event as meeting a pre-determined condition,

update a text mining result database, responsive to the flag, by adding an entry to the database identifying the detected semantic event and the triggering semantic rule, and

transmit an indication associated with the text mining result database to a plurality of insurance applications.

2. The system of claim 1, wherein the text input data is associated with at least one of:

(i) an insurance claim file, (ii) an insurance claim note, (iii) a medical report, (iv) a police report, (v) social network data, (vi) big data information, (vii) a loss description, (viii) an injury description, (ix) a final notice of loss statement, (x) telephone call transcript, (xi) optical character recognition data, (xii) third-party data, and (xiii) a governmental agency.

3. The system of claim 1, wherein the insurance applications are associated with at least two of:

(i) a workers'"'"' compensation claim, (ii) a personal insurance policy, (iii) a business insurance policy, (iv) an automobile insurance policy, (v) a home insurance policy, (vi) a sentiment analysis, (vii) insurance event detection, (viii) a cluster analysis, (ix) a predictive model, (x) a subrogation analysis, (xi) fraud detection, (xii) a recovery factor analysis, (xiii) large loss and volatile claim detection, (xiv) a premium evasion analysis, (xv) an insurance policy comparison, (xvi) an underwriting decision, and (xvii) indicator incidence rate trending.

4. The system of claim 1, wherein the transmitted indication is to:

(i) trigger an insurance application, or (ii) update an insurance application.

5. The system of claim 1, wherein the transmitted indication is associated with a variable or weighing factor of a predictive model.

6. The system of claim 1, wherein multiple versions of the semantic rule are maintained along with multiple versions of the text mining result database.

7. The system of claim 1, wherein the semantic event is associated with at least one of:

(i) a word, (ii) a phrase, (iii) a shorthand term, (iv) a prescription drug, and (v) an insurance carrier name.

8. The system of claim 1, wherein the triggering semantic rule is associated with at least one of:

(i) a noun, (ii) a verb, (iii) a definition, (iv) a semantic tree, (v) a named entity recognition rule, (vi) a root, (vii) a noun phrase, (viii) a prepositional phrase, and (ix) a verb phrase.

9. The system of claim 8, wherein the triggering semantic rule was defined by an administrator using a graphical user interface.

10. The system of claim 1, wherein the storage device further stores instructions adapted to be executed by said processor to:

map typographical errors in the composite text input data to a word dictionary,

map abbreviations in the composite text input data to the word dictionary,

map phrases in the composite text input data to a phrase dictionary,

apply semantic tags to the mapped phrases,

match named entity recognition rules based on the applied semantic tags, and

output rule matches at a level representing at least one of;

(i) a note level, (ii) a sentence level, and (iii) a phrase level.

11. A computer-implemented method for evaluating text data to support multiple insurance applications, comprising:

receiving, at a text mining platform, text input data from multiple sources including a source internal to an insurance enterprise and a source external to the insurance enterprise;

aggregating and mapping the received text input data to create composite input text data;

automatically detecting, by a computer processor of the text mining platform, a semantic event in the composite text input data triggered by a semantic rule and associated semantic tag;

updating, by the computer processor, a text mining result database by adding an entry to the database, the entry including;

(i) a file note identifier, (ii) an insert date, (iii) a text source, (iv) a rule identifier, and (v) a line of business; and

transmitting, by the computer processor, an indication associated with the text mining result database to a plurality of insurance applications.

12. The method of claim 11, wherein the text input data is associated with at least one of:

(i) an insurance claim file, (ii) an insurance claim note, (iii) a medical file, (iv) a police report, (v) social network data, (vi) big data information, (vii) a loss description, (viii) an injury description, (ix) a final notice of loss statement, (x) telephone call transcript, (xi) optical character recognition data, (xii) third-party data, and (xiii) a governmental agency.

13. The method of claim 11, wherein the insurance applications are associated with at least two of:

(i) a workers'"'"' compensation claim, (ii) a personal insurance policy, (iii) a business insurance policy, (iv) an automobile insurance policy, (v) a home insurance policy, (vi) a sentiment analysis, (vii) insurance event detection, (viii) a cluster analysis, (ix) a predictive model, (x) a subrogation analysis, (xi) fraud detection, (xii) a recovery factor analysis, (xiii) large loss and volatile claim detection, (xiv) a premium evasion analysis, (xv) an insurance policy comparison, (xvi) an underwriting decision, and (xvii) indicator incidence rate trending.

14. The method of claim 11, further comprising:

mapping typographical errors in the text input data to a word dictionary;

mapping the composite text input data to the word dictionary;

mapping phrases in the composite text input data to a phrase dictionary;

applying semantic tags to the mapped phrases;

matching named entity recognition rules based on the applied semantic tags; and

outputting rule matches at a level representing at least one of;

(i) a note level, (ii) a sentence level, and (iii) a phrase level.

15. The method of claim 11, wherein said detecting is performed at least one of:

(i) on a periodic basis, (ii) upon a change in the text input data, (iii) on a daily basis, and (iv) responsive to a request.

16. A non-transitory computer-readable medium storing instructions adapted to be executed by a computer processor to perform a method, said method comprising:

receiving text input data from multiple sources;

aggregating and mapping the received text input data to create composite text input data

automatically detecting a semantic event in the composite text input data triggered by a semantic rule and associated semantic tag;

updating a text mining result database by adding an entry to the database identifying the detected semantic event and the triggering semantic rule; and

transmitting an indication associated with the text mining result database to a plurality of insurance applications.

17. The medium of claim 16, wherein the method further comprises:

mapping typographical errors in the composite text input data to a word dictionary;

mapping abbreviations in the composite text input data to the word dictionary;

mapping phrases in the composite text input data to a phrase dictionary;

applying semantic tags to the mapped phrases;

matching named entity recognition rules based on the applied semantic tags; and

outputting rule matches at a level representing at least one of;

(i) a note level, (ii) a sentence level, and (iii) a phrase level.

18. The medium of claim 17, wherein said detecting is performed at least one of:

(i) on a periodic basis, (ii) upon a change in the text input data, (iii) on a daily basis, and (iv) responsive to a request.

19. The medium of claim 18, wherein the triggering semantic rule is associated with at least one of:

(i) a noun, (ii) a verb, (iii) a definition, (iv) a semantic tree, (v) a named entity recognition rule, (vi) a root, (vii) a noun phrase, (viii) a prepositional phrase, and (ix) a verb phrase.