Systems and methods of audit trailing of data incorporation

• US 10,313,476 B2
• Filed: 04/17/2017
• Issued: 06/04/2019
• Est. Priority Date: 03/08/2012
• Status: Active Grant

• Current Assignee: Salesforce.com, Inc.
• Original Assignee: Salesforce.com, Inc.
• Inventors: Isaacs, Charlie, Passemard, Antony, Peri, Harish, Kumar, Seema
• Primary Examiner(s): Chiang, Jason
• Application Number:
  US15/489,072
• Publication Number:
  US 20170223135A1
• Time in Patent Office: 778 Days
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 21/604 Tools and structures for managing or administering access control systems
• CPC Class Codes: G06F 21/62 Protecting access to data via a platform, e.g. using keys or access control rules
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04L 63/10 for controlling access to devices or network resources
• CPC Class Codes: H04L 63/102 Entity profiles
• CPC Class Codes: H04L 63/20 for managing network security; network security policies in general filtering policies H04L63/0227
• CPC Class Codes: H04L 67/306 User profiles

Abstract

The technology disclosed relates to creating an audit trail of data incorporation in user profiles. In particular, it relates to linking trust objects to fields of the user profiles.

The technology disclosed also relates to maintaining an opt trail that captures user opt-ins by recording the circumstances surrounding opt-in actions. In particular, it relates to linking trust objects to user profiles that connect users to an advertising campaign.

The technology disclosed further relates to tracking and measuring reputation of product models in consumer markets. In particular, it relates to assembling consumer feedback on the product models from online social networks and service records of the product models and applying sentiment analysis on the consumer feedback.

Claims

1. A method of controlling flow of information from computer to computer using a database server, including:

maintaining opt trail fields in a trust object linked to a user profile that connects a target user to an information source of an information about a product or service, wherein the opt trail fields include information that assists the target user to recall circumstances in which the target user opted into the information source of the information about the product or service including a time, a date, and a location of an opt-in action;

providing information included in the opt trail fields to the target user;

receiving instructions setting a preference of the target user to continue or discontinue receipt of the information about the product or service from the information source of the information about the product or service and updating the trust object according to the preference; and

using the trust object updated, controlling sending of information by the information source of the information about the product or service to the target user based upon the preference stored in the trust object updated.

2. The method of claim 1, further including:

appending reputation objects to data objects, wherein the data objects track product models after sales of products in consumer markets;

assembling consumer feedback on one or more of the product models, including receiving user generated content from online social networks that mentions the product models;

receiving service records of the product models;

automatically categorizing the consumer feedback based on sentiment analysis;

updating fields of the reputation objects based on the consumer feedback on the product models; and

using the fields updated of the reputation objects, providing the consumer feedback on the product models to target users that indicated in respective trust objects an interest in receiving information about the products.

3. The method of claim 2, further including:

calculating one or more reputation metrics for the product models based on the reputation objects.

4. The method of claim 2, wherein the sentiment analysis includes identifying tones and sentiments of consumers.

5. The method of claim 3, further including:

categorizing the product models into reputation hierarchies based on the reputation metrics, wherein the reputation hierarchies stratify the products models into product families and product lots.

6. The method of claim 5, further including:

automatically updating the reputation metrics of the product models in a reputation hierarchy responsive to changes in the reputation metrics of other product models in the reputation hierarchy.

7. The method of claim 1, wherein the opt trail fields, for a voice opt-in, include:

a time and a date of a dispatch of a call soliciting the voice opt-in; and

information related to a device from which the opt-in action took place.

8. The method of claim 7, wherein the opt trail fields, for the voice opt-in, further include a transcript of a conversation via which the voice opt-in was presented.

9. The method of claim 1, wherein the opt trail fields, for an SMS opt-in, include:

a time and a date of a dispatch of an SMS soliciting the SMS opt-in;

a text of a message body via which the SMS opt-in was presented; and

information related to a device from which the opt-in action took place.

10. The method of claim 1, wherein the opt trail fields, for an email opt-in, include:

a time and a date of a dispatch of an email soliciting the email opt-in;

a text of an email body via which the email opt-in was presented; and

information related to a device from which the opt-in action took place.

11. The method of claim 1, wherein the opt trail fields, for a website opt-in, include:

a unified resource locator of a web site on which the web site opt-in occurred;

a history of activity of the target user on the website on which the website opt-in occurred;

a copy of a webpage via which the website opt-in was presented; and

information related to a device from which the opt-in action took place.

12. The method of claim 1, wherein the opt trail fields, for a paper opt-in, include:

information that identifies an event at which the paper opt-in occurred; and

a particular medium via which the paper opt-in was presented.

13. A non-transitory computer readable storage medium configured with instructions for controlling flow of information from computer to computer using a database server, which instructions, when executed by one or more processors, implement:

maintaining opt trail fields in a trust object linked to a user profile that connects a target user to an information source of an information about a product or service, wherein the opt trail fields include information that assists the target user to recall circumstances in which the target user opted into the information source of the information about the product or service including a time, a date, and a location of an opt-in action;

providing information included in the opt trail fields to the target user;

receiving instructions setting a preference of the target user to continue or discontinue receipt of the information about the product or service from the information source of the information about the product or service and updating the trust object according to the preference; and

using the trust object updated, controlling sending of information by the information source of the information about the product or service to the target user based upon the preference stored in the trust object updated.

14. The non-transitory computer readable storage medium of claim 13, further including instructions that implement:

appending reputation objects to data objects, wherein the data objects track product models after sales of products in consumer markets;

assembling consumer feedback on one or more of the product models, including receiving user generated content from online social networks that mentions the product models;

receiving service records of the product models;

automatically categorizing the consumer feedback based on sentiment analysis;

updating fields of the reputation objects based on the consumer feedback on the product models; and

using the fields updated of the reputation objects, providing the consumer feedback on the product models to target users that indicated in respective trust objects an interest in receiving information about the products.

15. The non-transitory computer readable storage medium of claim 14, further including instructions that implement:

calculating one or more reputation metrics for the product models based on the reputation objects.

16. The non-transitory computer readable storage medium of claim 14, wherein the sentiment analysis includes identifying tones and sentiments of consumers.

17. The non-transitory computer readable storage medium of claim 15, further including instructions that implement:

categorizing the product models into reputation hierarchies based on the reputation metrics, wherein the reputation hierarchies stratify the products models into product families and product lots.

18. The non-transitory computer readable storage medium of claim 17, further including instructions that implement:

automatically updating the reputation metrics of the product models in a reputation hierarchy responsive to changes in the reputation metrics of other product models in the reputation hierarchy.

19. A system for controlling information flow from computer-to-computer, comprising:

a processor coupled to a computer readable storage medium storing instructions, which instructions, when executed by the processor, cause the processor to perform;

maintaining opt trail fields in a trust object linked to a user profile that connects a target user to an information source of an information about a product or service, wherein the opt trail fields include information that assists the target user to recall circumstances in which the target user opted into the information source of the information about the product or service including a time, a date, and a location of an opt-in action;

providing information included in the opt trail fields to the target user;

receiving instructions setting a preference of the target user to continue or discontinue receipt of the information about the product or service from the information source of the information about the product or service and updating the trust object according to the preference; and

using the trust object updated, controlling sending of information by the information source of the information about the product or service to the target user based upon the preference stored in the trust object updated.

20. The system of claim 19, wherein the computer readable storage medium further includes instructions that implement:

appending reputation objects to data objects, wherein the data objects track product models after sales of products in consumer markets;

assembling consumer feedback on one or more of the product models, including receiving user generated content from online social networks that mentions the product models;

receiving service records of the product models;

automatically categorizing the consumer feedback based on sentiment analysis;

updating fields of the reputation objects based on the consumer feedback on the product models; and

using the fields updated of the reputation objects, providing the consumer feedback on the product models to target users that indicated in respective trust objects an interest in receiving information about the products.