PRIVACY SENSTIVE PERSONA MANAGEMENT TOOLS

• US 20190333112A1
• Filed: 07/08/2019
• Published: 10/31/2019
• Est. Priority Date: 05/16/2013
• Status: Abandoned Application

• Current Assignee: Rally AG LLC (Teledisc Corporation)
• Original Assignee: AutoGraph Incorporated
• Inventors: Roundtree, Brian
• Application Number:
  US16/505,575
• Publication Number:
  US 20190333112A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 21/10 Protecting distributed programs or content, e.g. vending or licensing of copyrighted material protection in video systems or pay television H04N7/16 ; Digital rights management [DRM]
• CPC Class Codes: G06F 21/604 Tools and structures for managing or administering access control systems
• CPC Class Codes: G06F 21/6218 to a system of files or objects, e.g. local or distributed file system or database
• CPC Class Codes: G06F 21/6263 during internet communication, e.g. revealing personal data from cookies
• CPC Class Codes: G06F 2221/2117 User registration
• CPC Class Codes: G06F 2221/2141 Access rights, e.g. capability lists, access control lists, access tables, access matrices
• CPC Class Codes: G06F 2221/2149 Restricted operating environment
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: H04L 63/0421 Anonymous communication, i.e. the party's identifiers are hidden from the other party or parties, e.g. using an anonymizer
• CPC Class Codes: H04L 63/102 Entity profiles

Abstract

The disclosed tools include enhanced and flexible tools to enable users who may be business competitors to share non-generic data in a substantially generic and in a substantially equitable manner. The resulting incentive to more freely share data between competitors will benefit users such as brand owners and enhance content delivered to their end users based on shared data.

Claims

1. A processor-based system, comprising:

memory for storing contents that are executable by processor electronics;

the processor electronics configured to execute the contents in order to;

create generic data by analyzing customer data and removing identifying customer data by;

determining identifying customer data by cross referencing data in at least a portion of the customer data with non-generic marketing data; and

substituting generic marketing data with substantially similar associated probabilities for the identifying customer data.

2. The system of claim 1, wherein the memory stores contents that further cause the processor electronics to:

determine if there would be an increase in the detail of the customer data if the customer data was integrated with different customer data stored in a partition separate from the customer data.

3. The system of claim 2, wherein the memory stores contents that cause the processor electronics to determine that there is an increase in the customer detail in the customer data if the different customer data includes:

additional data that is similar in value to that of the customer data;

or

data based on a larger number of customers in comparison to the number of customers represented by the customer data.

4. The system of claim 2, wherein the memory stores contents that cause the processor electronics to determine that there is an increase in the customer detail in the customer data if the second portion of the different customer data includes:

unrelated customer data to that in the customer data.

5. The system of claim 2, wherein the memory stores contents that cause the processor electronics to determine that there is an increase in the customer detail in the customer data if the different customer data includes:

a different data category than those in the customer data;

or

at least one category with a larger amount of data in comparison to the amount of data of the same category in the customer data.

6. The system of claim 2, wherein the memory stores contents that cause the processor electronics to calculate a new data partition based on the generic data and the different customer data.

7. The system of claim 1, wherein the memory stores contents that cause the processor electronics to create a relationship link between data associated with a tag in the customer data and data associated with a similar tag in the different customer data.

8. The system of claim 1, wherein the memory stores contents that cause the processor electronics to create a relationship link between data associated with a tag in the customer data and data associated with a similar tag in the generic data.

9. The system of claim 1, wherein the customer data is comprised of:

a user characteristic and a statistical probability associated with said user characteristic, said user characteristic and statistical probability based at least upon a user content affinity input.

10. The system of claim 1, wherein the customer data is comprised of:

a user characteristic and a statistical probability associated with said user characteristic.

11. A non-transitory computer readable medium having contents stored thereon that are executable by processor electronics in order to receive generic customer data by:

receiving the generic customer data transmitted by a remote computing system, wherein the generic customer data was determined by;

finding customer data in a first data partition and the customer data in another data partition that are in common between the partitions, wherein the common customer data are associated with probabilities;

combining the associated probabilities between common customer data from the partitions; and

selecting the customer data, that are in both the first partition and the other data partition and any associated combined probabilities as the generic customer data.

12. The non-transitory computer readable medium of claim 11, wherein the contents include contents to further cause the processor electronics to create a generic data partition comprised of the generic customer data.

13. The non-transitory computer readable medium of claim 11, wherein the customer data in the first data partition is comprised of:

a user characteristic and a statistical probability associated with said user characteristic, said user characteristic and statistical probability based at least upon a user input.

14. The non-transitory computer readable medium of claim 11, wherein the customer data in the first data partition is comprised of:

a user characteristic and a statistical probability associated with said user characteristic.

15. A non-transitory computer readable medium having contents stored thereon that are executable by processor electronics in order to:

receive customer data which was transmitted by a remote device, in which the customer data is comprised of tags and associated statistical probabilities, wherein the received customer data is stored on a private partition; and

receive instructions from the remote device to allow access to the customer data in the private partition to a user in response to the user offering offer data if the offer data is comprised of;

generic customer data from which identifying data has been removed.

16. The non-transitory computer readable medium of claim 15, herein the customer data in the private partition includes:

a tag representing an end user characteristic; and

a statistical probability associated with said end user characteristic, said end user characteristic and statistical probability based at least upon user input.

17. The non-transitory computer readable medium of claim 15, wherein the customer data in the private partition includes:

a tag representing an end user characteristic; and

a statistical probability associated with said end user characteristic.

18. The non-transitory computer readable medium of claim 15, wherein the customer data in the private partition further includes data based as least on customer communication messages.