Autonomous configuration of conversion code to control display and functionality of webpage portions

• US 10,726,196 B2
• Filed: 03/02/2018
• Issued: 07/28/2020
• Est. Priority Date: 03/03/2017
• Status: Active Grant

• Current Assignee: Evolv Technology Solutions, Inc.
• Original Assignee: Evolv Technology Solutions, Inc.
• Inventors: Severn, Robert
• Primary Examiner(s): Khan, Shahid K
• Application Number:
  US15/911,050
• Publication Number:
  US 20180253408A1
• Time in Patent Office: 879 Days
• CPC Class Codes: G06F 16/9577 Optimising the visualization of content, e.g. distillation of HTML documents
• CPC Class Codes: G06F 3/00 Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
• CPC Class Codes: G06F 3/0481 based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• CPC Class Codes: G06F 3/04847 Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
• CPC Class Codes: G06F 40/154 Tree transformation for tree-structured or markup documents, e.g. XSLT, XSL-FO or stylesheets
• CPC Class Codes: G06F 40/16 Automatic learning of transformation rules, e.g. from examples
• CPC Class Codes: G06F 8/51 Source to source
• CPC Class Codes: G06F 9/45512 Command shells
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/063 using electronic means
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: H04L 41/16 using machine learning or artificial intelligence
• CPC Class Codes: H04L 41/22 comprising specially adapted graphical user interfaces [GUI]

Abstract

The technology disclosed is generally directed to massively multivariate testing, conversion rate optimization, and product recommendation and, in particular, directed to automatically and autonomously placing conversion code (e.g., scripts) in webpages of a host website without requiring any affirmative action on the part of the host. The conversion code modifies display and functionality of a particular portion of a host webpage without modifying other portions of the host webpage. The conversion code is placed by a website modification service which is limitedly authorized by the host to modify only the particular portion of the host webpage under a product recommendation and/or conversion rate optimization scheme.

Claims

1. A system for autonomous configuration of conversion code that controls display and functionality of a portion of a webpage, the portion having a plurality of elements, each element having a plurality of dimensions along which a rendering value can be specified, comprising:

a server having memory and a processor coupled to the memory, the processor configured to, in response to receiving a request from a client executing an embedded script for a configuration of a portion of the webpage, return toward the client an object that includes;

rendering values corresponding to dimensions of elements of the portion of the webpage and rendering algorithm parameters as selected by a first machine-learned configuration,

an identification of one or more additional scripts as conversion code required in order to generate an updated interface version of the portion of the webpage by applying the first machine-learned configuration to the portion of the webpage and to control functionality of the updated interface version, wherein the additional scripts apply the first machine-learned configuration to a presentation database to determine a subset of the rendering values corresponding to the dimensions of elements of the portion of the webpage as selected by the first machine-learned configuration, and

an identification of evaluation services to which to send data for performance measures that identify success of the updated interface version in promoting target online user behavior.

2. The system of claim 1, wherein the object further includes an identification of recommendation services from which to request recommendation data in response to an element of the updated interface version of the portion of the webpage being selected.

3. The system of claim 2, wherein the object further includes a load modulator that determines from which one of the recommendation services to request the recommendation data from based on at least one of:

processing load of the recommendation services,

pre-caching status of the recommendation services, and

recommendation algorithms used by the recommendation services.

4. The system of claim 1, wherein the object further includes a treatment identifier (ID) of the object that is persisted at the client for use in requesting further configurations of the portion of the webpage from the client.

5. The system of claim 1, wherein one of the additional scripts, when executed by the client, will:

render the updated interface version of the portion of the webpage according to the first machine-learned configuration as specified by the rendering values;

control the functionality of the updated interface version in response to an element of the updated interface version being selected;

send data for the performance measures toward the identified evaluation services; and

request the recommendation data from one of the recommendation services determined by the load modulator, and further render the recommendation data in the updated interface version.

6. The system of claim 5, wherein the additional script, when executed by the client, will request from the server a further configuration of the portion of the webpage.

7. The system of claim 6, wherein the additional script further will send the treatment ID of the object toward the server in conjunction with the request for the further configuration of the portion of the webpage.

8. The system of claim 1, wherein the updated interface version of the portion of the webpage is autonomously configured by a website modification service without affirmative action from a host of the webpage, and

wherein the website modification service controls only of the portion of the webpage.

9. The system of claim 1, wherein the object is returned to selective test clients based on a client state variable that identifies a test client and performance measures for the object are developed based on evaluation by the test clients during a testing period.

10. The system of claim 9, wherein the client state variable is at least one of Internet Protocol (abbreviated IP) address parameter, browser cookie parameter, and unified resource locator (abbreviated URL) parameter.

11. The system of claim 1, wherein the client is at least one of browser-based application running on an endpoint and a native application running on an endpoint.

12. The system of claim 1, wherein the object is a JavaScript Object Notation (JSON) object.

13. The system of claim 1, wherein the server iteratively returns toward the client successive objects in response to receiving requests from the client.

14. A method of autonomously configuring conversion code that controls display and functionality of a portion of a webpage, the portion having a plurality of elements, each element having a plurality of dimensions along which a rendering value can be specified, including:

in response to receiving a request from a client executing an embedded script for a configuration of a portion of the webpage, return toward the client an object that includes;

rendering values corresponding to dimensions of elements of the portion of the webpage and rendering algorithm parameters as selected by a first machine-learned configuration,

an identification of one or more additional scripts as conversion code required in order to generate an updated interface version of the portion of the webpage by applying the first machine-learned configuration to the portion of the webpage and to control functionality of the updated interface version, wherein the additional scripts apply the first machine-learned configuration to a presentation database to determine a subset of the rendering values corresponding to the dimensions of elements of the portion of the webpage as selected by the first machine-learned configuration, and

an identification of evaluation services to which to send data for performance measures that identify success of the updated interface version in promoting target online user behavior.

15. The method of claim 14, wherein the object further includes an identification of recommendation services from which to request recommendation data in response to an element of the updated interface version of the portion of the webpage being selected.

16. The method of claim 15, wherein the object further includes a load modulator that determines from which one of the recommendation services to request the recommendation data from based on at least one of:

processing load of the recommendation services,

pre-caching status of the recommendation services, and

recommendation algorithms used by the recommendation services.

17. The method of claim 14, wherein the object further includes a treatment identifier (ID) of the object that is persisted at the client for use in requesting further configurations of the portion of the webpage from the client.

18. The method of claim 14, wherein one of the additional scripts, when executed by the client, will:

render the updated interface version of the portion of the webpage according to the first machine-learned configuration as specified by the rendering values;

control the functionality of the updated interface version in response to an element of the updated interface version being selected;

send data for the performance measures toward the identified evaluation services; and

request the recommendation data from one of the recommendation services determined by the load modulator, and further render the recommendation data in the updated interface version.

19. The method of claim 18, wherein the additional script, when executed by the client, will request from the server a further configuration of the portion of the webpage.

20. A non-transitory computer readable storage medium impressed with computer program instructions for autonomous configuration of conversion code that controls display and functionality of a portion of a webpage, the portion having a plurality of elements, each element having a plurality of dimensions along which a rendering value can be specified, the instructions, when executed on a processor, implement a method comprising:

in response to receiving a request from a client executing an embedded script for a configuration of a portion of the webpage, return toward the client an object that includes;

rendering values corresponding to dimensions of elements of the portion of the webpage and rendering algorithm parameters as selected by a first machine-learned configuration,

an identification of one or more additional scripts as conversion code required in order to generate an updated interface version of the portion of the webpage by applying the first machine-learned configuration to the portion of the webpage and to control functionality of the updated interface version, wherein the additional scripts apply the first machine-learned configuration to a presentation database to determine a subset of the rendering values corresponding to the dimensions of elements of the portion of the webpage as selected by the first machine-learned configuration, and

an identification of evaluation services to which to send data for performance measures that identify success of the updated interface version in promoting target online user behavior.