Systems and methods for enhancing user data derived from digital communications

• US 10,353,898 B2
• Filed: 10/12/2016
• Issued: 07/16/2019
• Est. Priority Date: 11/04/2012
• Status: Active Grant

• Current Assignee: Cay Baxis Holdings, LLC
• Original Assignee: Cay Baxis Holdings, LLC
• Inventors: Kawecki, III, Walter J.
• Primary Examiner(s): Liao, Jason G
• Application Number:
  US15/292,104
• Publication Number:
  US 20170031992A1
• Time in Patent Office: 1,007 Days
• Field of Search: None
• CPC Class Codes: G06F 16/2379 Updates performed during online database operations; commit processing
• CPC Class Codes: G06F 16/2455 Query execution
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0255 based on user history

Abstract

A computer-implemented method for enhancing and utilizing user data derived from digital interactions includes receiving user submission data comprising records generated by input into a client side application interface by a first user on a first computing device and transmitted from the first computing device to the aggregation point via at least one of a data transmission service and a data transmission provider. The method includes generating a first dataset associated with the first user, generating a data model based on data in the first dataset and one or more correlations based on attributes in the first dataset, and generating a modeled dataset based on the data model.

Claims

1. A computer-implemented method for enhancing and utilizing user data derived from digital interactions, at least a portion of the method being performed by a computing device comprising at least one processor, the method comprising:

delivering a viewable issue-specific communication to a first user, the issue-specific communication advertising information and a predefined character string associated with a first issue;

receiving, at an aggregation point comprising a database comprising at least one non-transitory computer-readable storage medium and configured to receive and aggregate user data from a plurality of sources, a user communication initiated via input by the first user into a graphical user interface of a client side application on a first computing device and transmitted from the first computing device to the aggregation point, the user communication including the predefined character string;

storing a record of the user communication at the database;

determining a path and attributes of the path utilized to transmit the submission;

determining attributes of the user communication, the first computing device, and the client side application;

identifying, based on the attributes of the user communication and the predefined character string contained in the body of the user communication, that the user communication is a response in action to the issue-specific communication delivered to the first user;

generating a first dataset associated with the first user by;

associating the record of the user communication with each of data indicating the path utilized to transmit the user communication, the attributes of the first computing device, and the attributes of the client side application;

associating the predefined issue-specific data element with the user communication;

storing the first dataset at the database;

generating an identity data model based on data in the first dataset and one or more data elements predictive of correlations based on attributes in the first dataset;

generating an identity dataset associated with the first user based on the identity data model by;

searching one or more datasets for additional data to be associated with data elements of the first dataset, the data elements including the attributes of the path utilized to transmit the user communication, the attributes of the first computing device, the attributes of the client side application, and attributes of the first issue, the additional data correlating to attributes predictive of similarities between the first dataset and the additional data;

analyzing the additional data from the one or more datasets based on at least one of characteristics and source of the additional data to determine correlations between the additional data from the one or more datasets and each of the attributes of the path utilized to transmit the user communication, the attributes of the first computing device, the attributes of the client side application, and the attributes of the first issue;

associating the additional data from the one or more datasets with the first dataset based on the correlations between the additional data from the one or more datasets and each of the attributes of the path utilized to transmit the user communication, the attributes of the first computing device, the attributes of the client side application, and the attributes of the first issue;

appending the additional data from the one or more datasets to the first dataset to generate the identity dataset;

storing the identity dataset at the database.

2. The method of claim 1, wherein:

the identity dataset comprises at least one of;

linked data stored in multiple databases including the database;

linked data stored in multiple storage devices;

linked data accessible by multiple computing devices including the computing device;

linked data accessible by a distributed computing system.

3. The method of claim 1, wherein a distributed computing system performs one of database functions and data model generation.

4. The method of claim 1, wherein at least a portion of the method is performed by a distributed computing system.

5. The method of claim 1, wherein input into a user interface is used to at least one of generate, manipulate, and adjust at least one of the identity data model and the identity dataset.

6. The method of claim 1, wherein the identity data model is generated based on a combination of any of:

cluster analysis;

attribute correlation analysis;

attribute weighting;

attribute co-occurrence analysis;

attribute frequency analysis;

identifying patterns with the first dataset;

identifying targeting criteria;

identifying attribute clusters;

identifying attribute correlations;

applying weighting to attribute data;

identifying attribute frequency patterns;

inclusion of additional data into a pre-existing data model;

predictive analysis;

iterative analysis;

iterative testing;

formatting data;

sorting stored;

filtering stored data;

selecting stored data;

data input by user interface;

data generated by iterative data model testing;

inclusion of new data to an existing data model.

7. The method of claim 1, wherein the identity data model comprises data stored at the database and associated with at least one of:

a voter targeting profile;

a consumer targeting profile;

a prospective purchaser targeting profile;

a behavioral targeting profile;

a contributor targeting profile;

a prospective donor targeting profile;

a predictive profile;

an issue specific targeting profile;

a transmission data targeting profile;

a transmission channel targeting profile;

a transmission path targeting profile;

an iteratively generated profile;

a contributor targeting profile;

an internet protocol address targeting profile;

a device identification targeting profile;

a social media targeting profile;

an issue specific invitation targeting profile;

a submission data attribute targeting profile;

formatted data;

sorted data;

filtered data;

selected data;

a formula for associating additional data generated by analysis of the first dataset;

a formula for associating additional data generated by analysis of one or one or more modeled datasets.

8. The method of claim 1, wherein the identity dataset comprises data stored at the database and associated with one or more of:

information input into a client side application interface;

user records that meet profiling criteria;

user records that meet clustering criteria;

user records that meet targeting criteria;

user records that share similarities between attributes of the first user and attributes of the additional users;

attribute data that meet profiling criteria;

attribute data that meet clustering criteria;

attribute data that meet targeting criteria;

data transformed by the method for targeting, profiling, clustering, or analysis;

correlated attribute data;

data grouped by attribute;

data grouped by attribute correlations;

data grouped by iterative analysis;

behavioral data;

consumer data;

linguistic data;

sentiment data;

voter data;

voting data;

political contribution data;

charitable contribution data;

political data;

personal data;

polling data;

survey data;

purchase data;

financial transaction data;

transmission data;

channel data;

path data;

user submission data;

digital submission data;

internet protocol address data;

device identification data;

user name data;

screen name data;

handle data;

membership number data;

account number data.

9. The method of claim 1, wherein at least a portion of the identity dataset is generated based on a response to an information request that is generated based on the first dataset.

10. The method of claim 1, wherein at least a portion of the identity dataset is iteratively generated based on a response to an information request that is generated based on the identity dataset.

11. The method of claim 1, wherein at least a portion of the identity data model is iteratively generated based on a response to an information request that is generated based on a data model.

12. The method of claim 1, further comprising generating, based on correlations within the identity data model, at least one of:

voter targeting criteria;

consumer targeting criteria;

contributor targeting criteria;

internet protocol address targeting criteria;

device identification targeting criteria;

message targeting criteria;

behavioral targeting criteria;

semantic targeting criteria

lingual targeting criteria;

sentiment targeting criteria;

social media targeting criteria.

13. The method of claim 1, further comprising analyzing at least a portion of one or more additional datasets in view of at least one of:

the identity dataset;

a data model associated with at least one other user of another computing device.

14. The method of claim 1, wherein the first computing device comprises at least one of:

a handheld computing device;

a mobile computing device;

a tablet computing device;

a wearable computing device;

a portable computing device;

a fixed computing device;

a physiologically embedded computing device;

a biologically integrated computing device;

a touchless data transmittal device.

15. The method of claim 1, further comprising routing the user communication through a messaging service, the messaging service comprising at least one of:

a short message service;

a multimedia messaging service;

a smart messaging service;

an extended message service;

an instant messaging service;

an email service;

a message transmittal application;

a message response capture application;

a social media application programming interface;

a share-to-donate application programming interface;

a post-to-donate application programming interface;

a tweet-to-donate application programming interface;

a social media stream capture application;

a user post application;

a user response application;

a user sharing application;

a touchless data transmittal application.

16. The method of claim 1, wherein the user communication is transmitted from the first computing device to the aggregation point via at least one of a wireless data transmission service and a wireless data transmission provider.

17. The method of claim 1, wherein the user communication is transmitted from the first computing device to the aggregation point via an application programming interface.

18. The method of claim 1, wherein the user communication is transmitted via digital communication data and comprises at least one of:

a text-to-give transaction;

a text-to-donate transaction;

a text-to-purchase transaction;

a text-to-contribute transaction;

a text-to-sign-up transaction;

a text-to-purchase transaction;

a text-to-vote transaction;

a text-to-be-billed transaction;

a text-to-join transaction;

a text-to-opt-in transaction;

a text-to-respond transaction;

a share-to-donate transaction;

a post-to-donate transaction;

a tweet-to-donate transaction;

a respond-to-donate transaction;

a respond-to-purchase transaction;

a tweet-to-purchase transaction;

an online post;

an online message;

a social media post;

a social media message;

a direct message;

an application to application message;

a tweet-to-donate post;

an input interface;

an online posting application;

an online messaging application;

a social media posting application;

a social media messaging application;

a direct messaging application;

an application to application messaging application;

a post-to-donate application;

a tweet-to-donate post application;

an input interface application.

19. The method of claim 1, wherein the issue-specific communication is transmitted via digital communication data and comprises at least one of a television advertisement, a radio advertisement, an on-line advertisement, an email advertisement, a mobile messaging advertisement, a print advertisement, a newspaper advertisement, a mail advertisement, a billboard advertisement, a vendor product display, and a media display.

20. The method of claim 1, wherein the issue-specific communication is transmitted via digital communication data and advertises information concerning at least one of an issue, a cause, a topic, an individual, an entity, an event, a product, an organization, a good, and a service.

21. The method of claim 1, further comprising:

obtaining behavioral data for the first user;

determining that the first user belongs to a target group of individuals based on the behavioral data.

22. The method of claim 21, further comprising sending, via digital communication data, a targeted communication directed to the target group of individuals to the first user, wherein the targeted communication comprises one or more of:

an advertisement;

an email message;

a text message;

a multimedia messaging service message;

a short messaging service message;

a social media message;

a social media post;

a video message;

a request for a political contribution;

a request for a charitable donation.

23. The method of claim 21, further comprising modeling data of the target group of individuals, including the behavioral data of the first user, to identify at least one of:

additional correlations between one or more user attributes;

preferences of individuals in the target groups of individuals;

additional issues potentially of interest to individuals in the target groups of individuals.

24. A system for enhancing and utilizing user data derived from digital interactions, the system comprising:

at least one computing device that delivers a viewable issue-specific communication to a first user, the issue-specific communication advertising information and a predefined character string associated with a first issue;

a receiving module that receives, at an aggregation point configured to receive and aggregate user data from a plurality of sources, a user communication initiated via input by the first user into a graphical user interface of a client side application on a first computing device and transmitted from the first computing device to the aggregation point, the user communication including the predefined character string;

a database comprising at least one non-transitory computer-readable storage medium that stores a record of the user communication;

a determining module that determines;

a path and attributes of the path utilized to transmit the submission;

attributes of the user communication, the first computing device, and the client side application;

an identifying module that identifies, based on the attributes of the user communication and the predefined character string contained in the body of the user communication, that the user communication is a response in action to the issue-specific communication delivered to the first user;

a generating module that;

generates a first dataset associated with the first user by;

associating the record of the user communication with each of data indicating the path utilized to transmit the user communication, the attributes of the first computing device, and the attributes of the client side application;

associating the predefined issue-specific data element with the user communication;

generates an identity data model based on data in the first dataset and one or more data elements predictive of correlations based on attributes in the first dataset;

generates an identity dataset associated with the first user based on the identity data model by;

searching one or more datasets for additional data to be associated with data elements of the first dataset, the data elements including the attributes of the path utilized to transmit the user communication, the attributes of the first computing device, the attributes of the client side application, and attributes of the first issue, the additional data correlating to attributes predictive of similarities between the first dataset and the additional data;

analyzing the additional data from the one or more datasets based on at least one of characteristics and source of the additional data to determine correlations between the additional data from the one or more datasets and each of the attributes of the path utilized to transmit the user communication, the attributes of the first computing device, the attributes of the client side application, and the attributes of the first issue;

associating the additional data from the one or more datasets with the first dataset based on the correlations between the additional data from the one or more datasets and each of the attributes of the path utilized to transmit the user communication, the attributes of the first computing device, the attributes of the client side application, and the attributes of the first issue;

appending the additional data from the one or more datasets to the first dataset to generate the identity dataset;

at least one physical processor configured to execute the determining module, the identifying module, and the generating module;

wherein the database;

stores the first dataset;

stores the identity dataset.

25. The system of claim 24, wherein the determining module, the identifying module, and the generating module are executed using distributed computing architecture.

26. The system of claim 24, wherein instructions for the determining module, the identifying module, and the generating module are executed by separate computing systems and transmitted via application programming interface.

27. The system of claim 24, wherein at least one of the first dataset, the identity data model, and the identity dataset is exported to an external computing device that is external to the system.

28. The system of claim 24, wherein at least one of the first dataset, the identity data model, and the identity dataset is accessed or manipulated via a user interface.

29. A non-transitory computer-readable-storage medium comprising one or more computer-readable instructions that, when executed by at least one processor of a computing device, cause the computing device to:

deliver a viewable issue-specific communication to a first user, the issue-specific communication advertising information and a predefined character string associated with a first issue;

receive, at an aggregation point comprising a database comprising at least one non-transitory computer-readable storage medium and configured to receive and aggregate user data from a plurality of sources, a user communication initiated via input by the first user into a graphical user interface of a client side application on a first computing device and transmitted from the first computing device to the aggregation point, the user communication including the predefined character string;

store a record of the user communication at the database;

determine a path and attributes of the path utilized to transmit the submission;

determine attributes of the user communication, the first computing device, and the client side application;

identify, based on the attributes of the user communication and the predefined character string contained in the body of the user communication, that the user communication is a response in action to the issue-specific communication delivered to the first user;

generate a first dataset associated with the first user by;

associating the record of the user communication with each of data indicating the path utilized to transmit the user communication, the attributes of the first computing device, and the attributes of the client side application;

associating the predefined issue-specific data element with the user communication;

store the first dataset at the database;

generate an identity data model based on data in the first dataset and one or more data elements predictive of correlations based on attributes in the first dataset;

generate an identity dataset associated with the first user based on the identity data model by;

searching one or more datasets for additional data to be associated with data elements of the first dataset, the data elements including the attributes of the path utilized to transmit the user communication, the attributes of the first computing device, the attributes of the client side application, and attributes of the first issue, the additional data correlating to attributes predictive of similarities between the first dataset and the additional data;

analyzing the additional data from the one or more datasets based on at least one of characteristics and source of the additional data to determine correlations between the additional data from the one or more datasets and each of the attributes of the path utilized to transmit the user communication, the attributes of the first computing device, the attributes of the client side application, and the attributes of the first issue;

associating the additional data from the one or more datasets with the first dataset based on the correlations between the additional data from the one or more datasets and each of the attributes of the path utilized to transmit the user communication, the attributes of the first computing device, the attributes of the client side application, and the attributes of the first issue;

appending the additional data from the one or more datasets to the first dataset to generate the identity dataset;

store the identity dataset at the database.