Disambiguation and authentication of device users

• US 10,678,894 B2
• Filed: 08/23/2017
• Issued: 06/09/2020
• Est. Priority Date: 08/24/2016
• Status: Active Grant

• Current Assignee: Experian Information Solutions Incorporated (Experian plc)
• Original Assignee: Experian Information Solutions Incorporated (Experian plc)
• Inventors: Yin, Steven, Chen, Kevin, Haller, Eric, Tsang, Alan, Kerry, David, Toub, Derek, Yu, Jack, Wang, Zhixuan, Wheeler, Christopher, Upendran, Sreeram
• Primary Examiner(s): Lagor, Alexander
• Assistant Examiner(s): Louie, Howard H.
• Application Number:
  US15/684,590
• Publication Number:
  US 20180060546A1
• Time in Patent Office: 1,021 Days
• CPC Class Codes: G06F 16/90335 Query processing
• CPC Class Codes: G06F 21/31 User authentication
• CPC Class Codes: G06F 21/316 by observing the pattern of computer usage, e.g. typical user behaviour
• CPC Class Codes: G06F 2221/2111 Location-sensitive, e.g. geographical location, GPS
• CPC Class Codes: G06F 3/0481 based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof
• CPC Class Codes: H04L 63/08 for authentication of entities cryptographic mechanisms or cryptographic arrangements for entity authentication H04L9/32
• CPC Class Codes: H04L 63/108 when the policy decisions are valid for a limited amount of time
• CPC Class Codes: H04W 12/02 Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]
• CPC Class Codes: H04W 12/06 Authentication
• CPC Class Codes: H04W 12/08 Access security
• CPC Class Codes: H04W 4/021 Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 4/14 Short messaging services, e.g. short message services [SMS] or unstructured supplementary service data [USSD]
• CPC Class Codes: H04W 88/02 Terminal devices

Abstract

Features are described for efficiently and accurately identifying a user of an electronic device with limited user interaction. The features include receiving a mobile device identifier from the mobile device. The features include transmitting the mobile device identifier to a service provider associated with the mobile device. The features include receiving information identifying the user from the service provider. The features include identifying a set of candidates associated with at least a portion of the information. The features include generating a metric for the candidates included in the set of candidates. An individual metric indicates a degree of relatedness between a value for the user for the at least one data field and a value for a candidate for the at least one data field. The features include identifying the user as a specific candidate included in the set of candidates based on the metric corresponding to a threshold.

Claims

1. A computer-implemented method of identification of a user of a mobile device, the method comprising:

under control of one or more computing devices configured with specific computer-executable instructions,

receiving first information identifying a user of a mobile device proximate to a location from a service associated with the mobile device;

querying, based on a fuzzy matching of at least a portion of the first information, a database which indexes credit header files of credit files of a plurality of users for a set of candidate users having the largest quantity of identity data values that matches user information from the credit header files, wherein identity data types of the matching identity data values include at least first name, last name, and postal code;

determining a degree of difference between the identity data values of candidate users of the set of candidate users and the first information;

removing users from the set of candidate users based on the degree of difference;

for each user included in the set of candidate users, generating a metric indicating a degree of relatedness between the identity data values and the user information;

identifying a first candidate user and a second candidate user each having a generated metric exceeding a threshold;

identifying an identity data type associated with different identity data values for the first candidate user and the second candidate user, wherein the different identity data values distinguish between the first candidate user and the second candidate user;

transmitting instructions to be received at the mobile device to request an input identity value of the identified identity data type from the user;

receiving the input identity value in response to transmitting the instructions;

determining that the input identity value corresponds to the identity data value of the identified identity data type for the first candidate user; and

identifying the user as the first candidate user and determining the first candidate user qualifies for an offer for a line of credit that is based at least in part on the location.

2. The computer-implemented method of claim 1, wherein the service comprises at least one of:

an internet service, an application service, a cellular service, or a merchant service.

3. The computer-implemented method of claim 1, wherein generating the metric indicating the degree of relatedness between the identity data values and the user information comprises:

generating a count of identity data types included in identity data having corresponding identity data types included in the user information; and

for an identity data type having a corresponding identity data type included in the user information, generating a comparison value indicating how closely the identity data value matches a user information value for the corresponding identity type included in the user information,

wherein the metric is based at least in part on the count and the comparison value.

4. The computer-implemented method of claim 1, wherein generating the metric indicating the degree of relatedness between the identity data values and the user information comprises:

identifying a historical weight for the identity data value based on a difference between a current time and a time when identity data was collected; and

generating a comparison value indicating how closely the identity data value matches a user information value for the corresponding identity type included in the user information, wherein the comparison value is weighted based at least in part on the historical weight.

5. The computer-implemented method of claim 1, wherein the first information is included in a request for personalized content, and wherein the computer-implemented method further comprises:

transmitting;

(i) at least a portion of the user information, and (ii) at least one of the identity data values of the first candidate user to a personalized content service.

6. The computer-implemented method of claim 1, further comprising:

receiving second information identifying a second user of a second mobile device from a second service associated with the second mobile device;

requesting candidate user information from the database storing user information regarding the plurality of users using the second information;

receiving no candidate user information results from the database; and

in response to receiving no candidate user information results, transmitting instructions to the second mobile device to request input values to identify the second user.

7. The computer-implemented method of claim 1, further comprising:

transmitting, in response to receiving the first information, a consent request to be received at the mobile device; and

receiving a consent response in response to transmitting the consent request, wherein said querying is based in part on the consent response.

8. A computer-implemented method of identification of a user of a mobile device, the method comprising:

under control of one or more computing devices configured with specific computer-executable instructions,

receiving a mobile device identifier associated with the mobile device that is proximate to a location;

transmitting the mobile device identifier to a service provider associated with the mobile device;

receiving first information identifying the user from the service provider;

identifying, using fuzzy matching, a set of candidates associated with at least a portion of the first information from a credit header database, wherein each candidate includes second information identifying the respective candidate, wherein the second information includes at least one data field included in the first information;

determining a degree of difference between the second information of each respective candidate and the first information;

removing candidates from the set of candidates based on the degree of difference;

generating an individual metric for each of the candidates included in the set of candidates, wherein the individual metric indicates a degree of relatedness between a value for the user for the at least one data field and a value for a candidate for the at least one data field;

identifying the user as a specific candidate included in the set of candidates based on the associated individual metric corresponding to a threshold; and

determining the specific candidate qualifies for an offer for a line of credit that is based at least in part on the location.

9. The computer-implemented method of claim 8, wherein the service provider comprises at least one of:

an internet service provider, an application service provider, a cellular service provider, or a merchant service provider.

10. The computer-implemented method of claim 8, further comprising:

identifying the specific candidate and a second candidate having metrics corresponding to the threshold;

identifying a differentiation data field, wherein a value for the differentiation data field for the specific candidate is different from a value for the differentiation data field for the second candidate;

receiving input via the mobile device for the differentiation data field; and

determining the input corresponds to the value of the differentiation data field for the specific candidate.

11. The computer-implemented method of claim 8, wherein generating the individual metric comprises:

generating a count of data fields included in the first information having corresponding data fields included in the second information; and

for a data field having a corresponding data field included in the second information, generating a comparison value indicating how closely the value for the data field in the first information matches the value for the corresponding data field included in the second information,

wherein the metric is based at least in part on the count and the comparison value.

12. The computer-implemented method of claim 8, wherein generating the individual metric indicating the degree of relatedness comprises:

identifying a historical weight for the identity data value based on a difference between a current time and a time when the second information was collected; and

generating a comparison value indicating how closely the value for the data field in the first information matches the value for the data field included in the second information, wherein the comparison value is weighted based at least in part on the historical weight.

13. The computer-implemented method of claim 8, wherein the mobile identifier is included in a request for personalized content, and wherein the computer-implemented method further comprises:

transmitting;

(i) at least a portion of the mobile identifier, and (ii) at least one value of the specific candidate to a personalized content service.

14. The computer-implemented method of claim 8, further comprising:

determining the set of candidates includes no candidate users;

in response to receiving no candidate user information results, transmitting instructions to be received at the mobile device to request input values;

generating a custom candidate user based on the input values; and

adding the custom candidate user to the set of candidates.

15. The computer-implemented method of claim 8, further comprising:

transmitting, in response to receiving the mobile identifier, a consent request to be received at the mobile device; and

receiving a consent response in response to transmitting the consent request, wherein said identifying of the set of candidates is based in part on the consent response.

16. The computer-implemented method of claim 8, wherein identifying the set of candidates comprises querying a database storing information regarding a plurality of users, wherein the set of candidates include users having the largest quantity of identity data values matching the first information.

17. A system comprising:

a computer-readable memory storing executable instructions; and

one or more computer processors in communication with the computer-readable memory, wherein the one or more computer processors are configured to execute the executable instructions to at least;

receive a communication device identifier associated with a device that is associated with a user, the device being proximate to a location;

transmit the device identifier to a service provider associated with the device;

receive first information identifying the user from the service provider;

identify, using fuzzy matching, a set of candidates associated with at least a portion of the first information from a credit header database, wherein each candidate includes second information identifying the respective candidate, wherein the second information includes at least one data field included in the first information;

remove candidates from the set of candidates based on a degree of difference between the second information of each respective candidate and the first information;

generate an individual metric for each of the candidates included in the set of candidates, wherein the individual metric indicates a degree of relatedness between a value for the user for the at least one data field and a value for a candidate for the at least one data field;

identify the user as a specific candidate included in the set of candidates based on the associated individual metric corresponding to a threshold; and

determine the specific candidate qualifies for an offer for a line of credit that is based at least in part on the location.

18. The system of claim 17, wherein the one or more computer processors are further configured to:

identify the specific candidate and a second candidate having metrics corresponding to the threshold;

identify a differentiation data field, wherein a value for the differentiation data field for the specific candidate is different from a value for the differentiation data field for the second candidate;

receive input from the device for the differentiation data field; and

determine the input corresponds to the value of the differentiation data field for the specific candidate.

19. The system of claim 17, wherein the device identifier is included in a request for personalized content, and wherein the one or more computer processors are further configured to:

transmit;

(i) at least a portion of the device identifier, and (ii) at least one of the values of the specific candidate to a personalized content service.

20. The system of claim 17, wherein identifying the set of candidates comprises querying a database storing information regarding a plurality of users, wherein the set of candidates include users having the largest quantity of identity data values matching the first information.