Location and social network data entity identification system

• US 10,147,142 B2
• Filed: 05/23/2016
• Issued: 12/04/2018
• Est. Priority Date: 12/01/2011
• 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: Peak, David F., Vasagiri, Anil
• Primary Examiner(s): Poinvil, Frantzy
• Application Number:
  US15/161,967
• Publication Number:
  US 20160267604A1
• Time in Patent Office: 925 Days
• Field of Search: 705 3- 44
• CPC Class Codes: G06K 19/06037 multi-dimensional coding
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 51/52 for supporting social networking services
• CPC Class Codes: H04L 67/52 specially adapted for the location of the user terminal

Abstract

According to some embodiments, historic transaction data and loss data may be received from a data storage device. Geographic data and dynamically updated social network data corresponding to the entity may also be received and automatically analyzed by an estimator device to determine an estimated number of employees employed by the entity and similar entities having at least one common characteristic with the entity. A data weighting device generates weighted data by applying weighting factors to the received data including a weighting factor based on a location of users providing social networking posts and a weighting factor based on whether the social network data includes image, video, or audio data. At least one risk-related parameter corresponding to the entity is determined by a comparison engine that applies predictive models to (i) the weighted data, (ii) the estimated number of employees, and (iii) the similar entities to the entity.

Claims

1. A computer system comprising:

a data storage device storing historic transaction data and loss data;

a communication device configured to receive data about an entity including geographic data corresponding to the entity and dynamically updated social network data from social networking sites corresponding to the entity, and to output the received data;

an estimator device configured to receive the received data from the communication device;

automatically determine, based on the received data, an estimated number of employees employed by the entity; and

automatically identify, based at least in part on the estimated number of employees employed by the entity, one or more other similar entities having at least one common characteristic with the entity;

a data weighting device configured to;

generate weighted data by applying weighting factors to the received data including a first weighting factor based on a location of users providing social networking posts corresponding to the social network data about the entity and a second weighting factor based on whether the social network data includes one or more of image data, video data, and audio data; and

a comparison engine configured to receive the weighted data from the data weighting device and apply one or more predictive models, trained on the historic transaction data and loss data, to (i) the weighted data, (ii) the estimated number of employees, and (iii) the identified one or mere similar entities to the entity, to determine at least one risk-related parameter corresponding to the entity;

wherein the communication device is further configured to output to a computer monitor the at least one determined risk-related parameter for the entity.

2. The system of claim 1, wherein the at least one common characteristic between the entity and the one or more other similar entities comprises one of number of employees, an industry type of the entity, and materials used by the entity.

3. The system of claim 1, wherein the determined risk-related parameter comprises one of an estimated risk value for the entity, a recommend coverage type for the entity, a premium value for the entity, historical loss values for the entity, average claim values for the entity, and a claim frequency value for the entity.

4. The system of claim 1, wherein the estimator device is further configured to estimate one or more of a sales value, a total payroll value, job titles associated with employees, benefits associated with the employees, a property value, or a type of industry of the entity based on the received data.

5. The system of claim 1, wherein the comparison engine is further configured to generate, for storage in a local database repository, a table including entity identifier data field, an agent identification (ID) data field, and a similar entity data field.

6. The system of claim 1, wherein the received data further includes mobile device data, from a mobile device, pertaining to the entity including one or more of a quick response (QR) code corresponding to the entity, a two-dimensional bar code corresponding to the entity, photographic data corresponding to the entity, and text data corresponding to the entity.

7. The system of claim 6, wherein the system further includes the mobile device, wherein the mobile device is configured to capture the mobile device data corresponding to the entity.

8. The system of claim 1, wherein the received data further includes customer data, received from customer devices of patrons of the entity.

9. A computer-implemented method for processing third party computer system data and geographic data, comprising:

receiving, by a computer processor, historic transaction data and loss data from a data storage device;

receiving, by a communication device in communication with the computer processor, data pertaining to an entity including geographic data corresponding to the entity and dynamically updated social network data from social networking sites corresponding to the entity;

receiving, by an estimator device from the communication device, the received data pertaining to the entity;

responsive to receipt of the received data pertaining to the entity;

automatically determining, by the estimator device based on the received data, an estimated number of employees employed by the entity; and

automatically identifying, by the estimator device based at least in part on the estimated number of employees employed by the entity, one or more other similar entities having at least one common characteristic with the entity;

generating, by a data weighting device, weighted data by applying weighting factors to the received data including a first weighting factor based on a location of users providing social networking posts about the entity and a second weighting factor based on whether the social network data includes one or more of image data, video data, and audio data; and

determining, by a coverage comparison engine applying one or more predictive models, trained on the historic transaction data and loss data, to (i) the weighted data, (ii) the estimated number of employees, and (iii) the identified one or more similar entities to the entity, at least one risk-related parameter corresponding to the entity; and

outputting, by the communication device, to a computer monitor the at least one determined risk-related parameter for the entity.

10. The computer-implemented method of claim 9, wherein the at least one common characteristic between the entity and the one or more other similar entities comprises one of number of employees, an industry type of the entity, and materials used by the entity.

11. The computer-implemented method of claim 9, wherein the determined risk-related parameter comprises one of an estimated risk value for the entity, a recommend coverage type for the entity, a premium value for the entity, historical loss values for the entity, average claim values for the entity, and a claim frequency value for the entity.

12. The computer-implemented method of claim 9, further comprising estimating, by the estimator device, one or more of a sales value, a total payroll value, job titles associated with employees, benefits associated with the employees, a property value, or a type of industry of the entity based on the received data.

13. The computer-implemented method of claim 9, further comprising generating, by the comparison engine, for storage in a local database repository, a table including entity identifier data field, an agent identification (ID) data field, and a similar entity data field.

14. The computer-implemented method of claim 9, wherein the received data further includes mobile device data, from a mobile device, pertaining to the entity including one or more of a quick response (QR) code corresponding to the entity, a two-dimensional bar code corresponding to the entity, photographic data corresponding to the entity, and text data corresponding to the entity.

15. The computer-implemented method of claim 9, wherein the received data further includes customer data, received from customer devices of patrons of the entity.

16. The computer-implemented method of claim 9, wherein generating weighted data further comprises applying a third weighting factor to the received data based on the entity having a reputation on the social networking sites of at least a threshold level and a fourth weighting factor to the received data based on the entity having a reputation on the social networking sites less than the threshold level, wherein the third weighting factor is higher than the fourth weighting factor.