Systems and methods to identify users using an automated learning process

1. A method, comprising:

collecting first data from a first computer on which first software is installed, the first data including first characteristics associated with the first computer and adoption results of the first software on the first computer, wherein the first characteristics include hardware attributes of the first computer, and wherein the adoption results include information on whether the first software is used by a user for more than a predetermined time period;

correlating, via a data processing system, the first characteristics with the adoption results to generate a correlation result, wherein the correlation result is a model, and the correlating comprises providing the first characteristics and the adoption results as inputs to a machine learning algorithm to train the model, the first characteristics being parameters of the model and the adoption results being targets of the model;

prior to installation of the first software on a second computer, collecting second data that includes characteristics of the second computer, the characteristics of the second computer including hardware attributes of the second computer; and

determining whether to install the first software on the second computer based on at least the second data and the correlation result.

2. The method of claim 1 , wherein the correlating is performed using supervised machine learning.

3. The method of claim 1 , further comprising:

correlating second characteristics associated with additional computers with adoption results of the first software on the additional computers to provide an update of the correlation result;

prior to installation of the first software on a third computer, collecting third data associated with characteristics of the third computer; and

making another determination whether to install the first software on the third computer based on at least the third data and the update of the correlation result.

4. The method of claim 1 , wherein the first software is software that implements one or more of a profile builder and a profile presenter.

5. The method of claim 1 , wherein the collecting the second data is performed via an installer during installation of a second software on the second computer, the method further comprising in response to the determination, offering the first software as a bundle with the second software.

6. The method of claim 5 , wherein the second software includes one or more of: a web browser, a document viewer, and a media player.

7. The method of claim 1 , wherein the adoption results include a paid upgrade by a user from a first version to a second version of the first software.

8. The method of claim 1 , wherein the first characteristics further include one or more of: software attributes of the first computer, user preferences for users of the first computer, and usage patterns for the first computer.

9. The method of claim 1 , wherein the making the determination comprises predicting a likelihood of a particular adoption result, and determining whether the likelihood is above a threshold.

10. The method of claim 9 , wherein the threshold is a dynamic threshold based on at least a cost of distribution of the first software.

11. The method of claim 1 , wherein the making the determination comprises combining a likelihood of a plurality of adoption results to generate a value indicator to indicate a value for offering the first software.

12. A tangible non-transitory computer-readable storage medium storing computer-readable instructions for causing a data processing system to:

collect first data from a first computer on which first software is installed, the first data including first characteristics associated with the first computer and adoption results of the first software on the first computer, wherein the first characteristics include hardware attributes of the first computer, and wherein the adoption results include information on whether the first software is used by a user for more than a predetermined time period;

correlate, via the data processing system, the first characteristics with the adoption results to generate a correlation result, wherein the correlation result is a model, and the correlating comprises providing the first characteristics and the adoption results as inputs to a machine learning algorithm to train the model, the first characteristics being parameters of the model and the adoption results being targets of the model;

prior to installation of the first software on a second computer, collect second data that includes characteristics of the second computer, the characteristics of the second computer including hardware attributes of the second computer; and

determine whether to install the first software on the second computer based on at least the second data and the correlation result.

13. The storage medium of claim 12 , wherein the first characteristics further include one or more of: software attributes of the first computer, user preferences for users of the first computer, and usage patterns for the first computer.

14. The storage medium of claim 12 , wherein the making the determination comprises predicting a likelihood of a particular adoption result, and determining whether the likelihood is above a threshold.

15. A system, comprising:

a processor; and

a memory in communication with the processor and storing instructions that, when executed by the processor, cause the system to:

collect first data from a first computer on which first software is installed, the first data including first characteristics associated with the first computer and adoption results of the first software on the first computer, wherein the first characteristics include hardware attributes of the first computer, and wherein the adoption results include information on whether the first software is used by a user for more than a predetermined time period;

correlate the first characteristics with the adoption results to generate a correlation result, wherein the correlation result is a model, and the correlating comprises providing the first characteristics and the adoption results as inputs to a machine learning algorithm to train the model, the first characteristics being parameters of the model and the adoption results being targets of the model;

prior to installation of the first software on a second computer, collect second data that includes characteristics of the second computer, the characteristics of the second computer including hardware attributes of the second computer; and

determine whether to install the first software on the second computer based on at least the second data and the correlation result.

16. The system of claim 15 , wherein collecting the second data is performed via an installer during installation of a second software on the second computer, and wherein the memory further stores instructions for causing the system to offer, in response to the determination, the first software as a bundle with the second software.

17. The system of claim 15 , wherein the adoption results include one or more of: a paid upgrade by a user from a first version to a second version of the first software, and use of the first software by a user for more than a predetermined time period.