Methods of processing and segmenting web usage information

• US 9,965,764 B2
• Filed: 05/23/2007
• Issued: 05/08/2018
• Est. Priority Date: 05/23/2007
• Status: Expired due to Fees

• Current Assignee: R2 Solutions LLC (Acacia Research Corporation)
• Original Assignee: Excalibur IP, LLC (Acacia Research Corporation)
• Inventors: Jain, Ankur, Gupta, Abhinay, John, George H.
• Primary Examiner(s): MARCUS, LELAND R
• Application Number:
  US11/752,831
• Publication Number:
  US 20080294495A1
• Time in Patent Office: 4,003 Days
• Field of Search: 705 10
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

A current behavioral targeting system is first tested using a suite of test data. The output of the test is one or more performance metrics. Next, newly proposed behavioral targeting system created. The newly proposed behavioral targeting system is then evaluated using both the existing source data and a new source data. The evaluation of the newly proposed behavioral targeting system produces one or more performance metrics of the same type earlier calculated. Finally, the two sets of performance metrics are compared. The performance metric difference represents the impact of the new source data.

Claims

1. A method comprising:

creating, by a processor, a baseline behavioral targeting system using a machine learning system comprising;

retrieving a training data set comprising page view data, ad view data, and ad click data, and

training the machine learning system using the training data set;

calculating, by the processor, a baseline performance metric value for said baseline behavioral targeting system a test data set comprising;

retrieving existing source data and existing result data, the existing source data comprising webpage viewing habits and demographic information of users, the existing result data comprising information for indicating whether a user has clicked on advertisements on at least one webpage that were selected by using the existing source data and an amount of time spent at an associated advertiser'"'"'s web site after a click;

inputting the existing source data into the baseline behavioral targeting system,

calculating predicted result data using the baseline behavioral targeting system, and

comparing the predicted result data to the existing result data to calculate the baseline performance metric value;

creating, by the processor, a proposed behavioral targeting system using the machine learning system comprising;

retrieving a proposed training data set comprising the training data set and new search data, and

training the machine learning system using the proposed training data set;

calculating, by the processor, a second performance metric value for said proposed behavioral targeting system comprising;

retrieving the existing source data and the existing result data;

inputting the existing source data into the proposed behavioral targeting system,

calculating proposed predicted result data using the proposed behavioral targeting system, and

comparing the proposed predicted result data to the existing result data to calculate the second performance metric value;

determining, by the processor, a value of the new search data by comparing said second performance metric value to said baseline performance metric value, the value indicating whether the new search data improved the predicted result data of the proposed behavioral targeting system in comparison with the predicted result data of the baseline behavioral targeting system;

when said second performance metric value is greater than said baseline performance metric value, using said new search data for selecting advertisements for display;

when said baseline performance metric value is greater than said second performance metric value, using said existing source data for selecting advertisements for display; and

displaying, by the processor, the selected advertisements on a webpage.

2. The method as set forth in claim 1 wherein determining a value of new search data comprises calculating a percentage increase of said second performance metric value over said baseline performance metric value.

3. The method as set forth in claim 2 wherein determining a value of said new search data further comprises multiplying said percentage increase of said second performance metric value over said baseline performance metric value by a monetary value of said baseline behavioral targeting system.

4. The method as set forth in claim 1 wherein said method further comprises:

creating a third behavioral targeting system using said training data set and a third set of data;

calculating a third performance metric value for said proposed behavioral targeting system using said training data set and said third set of data;

determining a value of said third set of data by comparing said third performance metric value to said baseline performance metric value; and

comparing said value of said third set of data to said value of said new search data.

5. The method as set forth in claim 4 wherein said method further comprises:

allocating development resources dependent on said value of said new search data and said third sets of data.

6. A non-transitory computer-readable storage medium, said computer-readable storage medium comprising a set of instructions for evaluating behavioral targeting source data, said set of instructions implementing the steps of:

creating a baseline behavioral targeting system using a machine learning system comprising;

retrieving a training data set comprising page view data, ad view data, and ad click data, and

training the machine learning system using the training data set;

calculating a baseline performance metric value for said baseline behavioral targeting system comprising;

retrieving existing source data and existing result data, the existing source data comprising webpage viewing habits and demographic information of users, the existing result data comprising information for indicating whether a user has clicked on advertisements on at least one webpage that were selected by using the existing source data and an amount of time spent at an associated advertiser'"'"'s web site after a click;

inputting the existing source data into the baseline behavioral targeting system,

calculating predicted result data using the baseline behavioral targeting system, and

comparing the predicted result data to the existing result data to calculate the baseline performance metric value;

creating a proposed behavioral targeting system using the machine learning system comprising;

retrieving a proposed training data set comprising the training data set and new search data, and

training the machine learning system using the proposed training data set;

calculating a second performance metric value for said proposed behavioral targeting system comprising;

retrieving the existing source data and the existing result data;

inputting the existing source data into the proposed behavioral targeting system,

calculating proposed predicted result data using the proposed behavioral targeting system, and

comparing the proposed predicted result data to the existing result data to calculate the second performance metric value;

determining a value of the new search data by comparing said second performance metric value to said baseline performance metric value, the value indicating whether the new search data improved the predicted result data of the proposed behavioral targeting system in comparison with the predicted result data of the baseline behavioral targeting system;

when said second performance metric value is greater than said baseline performance metric value, using said new search data to select a first new advertisement for display;

when said baseline performance metric value is greater than said second performance metric value, using said existing source data to select a second new advertisement for display;

displaying, by the processor, the selected advertisements on a webpage.

7. The non-transitory computer-readable storage medium as set forth in claim 6 wherein determining a value of said new search data comprises calculating a percentage increase of said second performance metric value over said baseline performance metric value.

8. The non-transitory computer-readable storage medium as set forth in claim 7 wherein determining a value of said new search data further comprises multiplying said percentage increase of said second performance metric value over said baseline performance metric value by a monetary value of said baseline behavioral targeting system.

9. The non-transitory computer-readable storage medium as set forth in claim 6 wherein performance metric values further comprises an amount of time that the user has spent on an advertiser web site after interacting with the advertisement selections.

10. The non-transitory computer-readable storage medium as set forth in claim 6 wherein said set of instructions further implement the steps of:

creating a third behavioral targeting system using said training data set and a third set of data;

calculating a third performance metric value for said proposed behavioral targeting system using said training data set and said third set of data;

determining a value of said additional third set of data by comparing said third performance metric value to said baseline performance metric value; and

comparing said value of said third set of data to said value of said new search data.

11. The non-transitory computer-readable storage medium of evaluating behavioral targeting source data as set forth in claim 10 wherein said set of instructions further implement the steps of:

allocating development resources dependent on said value of said new search data and said third sets of data.

12. A method comprising:

creating, by a processor, a baseline behavioral targeting system using a machine learning system comprising;

retrieving a training data set comprising page view data, ad view data, and ad click data, and

training the machine learning system using the training data set;

calculating, by a processor, a baseline performance metric value for said baseline behavioral targeting system comprising;

retrieving existing source data and existing result data, the existing source data comprising webpage viewing habits and demographic information of users, the existing result data comprising information for indicating whether a user has clicked on advertisements on at least one webpage that were selected by using the existing source data and an amount of time spent at an associated advertiser'"'"'s web site after a click;

inputting the existing source data into the baseline behavioral targeting system,

calculating predicted result data using the baseline behavioral targeting system, and

comparing the predicted result data to the existing result data to calculate the baseline performance metric value;

creating, by the processor, a proposed behavioral targeting system using the machine learning system comprising;

retrieving a proposed training data set comprising the training data set and new source data, the new source data comprising additional webpage viewing habits of users, geographic location information, and additional demographic information of users, and

training the machine learning system using the proposed training data set;

calculating, by the processor, a second performance metric value for said proposed behavioral targeting system comprising;

retrieving the existing source data and the existing result data;

inputting the source data into the proposed behavioral targeting system,

calculating proposed predicted result data using the proposed behavioral targeting system, and

comparing the proposed predicted result data to the existing result data to calculate the second performance metric value;

determining, by the processor, a value of said new source data by comparing said second performance metric value to said baseline performance metric value, the determination indicating whether the new source data improved the predicted result data of the proposed behavioral targeting system in comparison with the predicted result data of the baseline behavioral targeting system;

when said second performance metric value is greater than said baseline performance metric value, using said new source data to select a first new advertisement for display;

when said baseline performance metric value is greater than said second performance metric value, using said existing source data to select a second new advertisement for display;

displaying, by the processor, the selected advertisements on the at least one webpage.

13. The method as set forth in claim 12 wherein determining a value of said new source data comprises calculating a percentage increase of said second performance metric value over said baseline performance metric value.

14. The method as set forth in claim 12 wherein determining a value of said new source data comprises calculating a difference between said second performance metric value and said baseline performance metric value.

15. The method as set forth in claim 13 wherein determining a value of said new source data further comprises multiplying said percentage increase of said second performance metric value over said baseline performance metric value by a monetary value of said baseline behavioral targeting system.

16. The method as set forth in claim 12 wherein performance metric values further comprises an amount of time that the user has spent on an advertiser web site after interacting with the advertisement selections.