Apple inc. (20240103998). SYSTEMS AND METHODS FOR VARIANT TESTING AT SCALE simplified abstract

SYSTEMS AND METHODS FOR VARIANT TESTING AT SCALE

Organization Name

apple inc.

Inventor(s)

Jae Hyeon Bae of San Jose CA (US)

Kurt M. Fredericks of San Francisco CA (US)

Nicholas Kistner of Cupertino CA (US)

Andrew T. Maher of London (GB)

Nihar Ranjan Hati of Santa Clara CA (US)

Mahesh Molakalapalli of Santa Clara CA (US)

Srivas Chennu of Rochester (GB)

Jamie J. Martin of London (GB)

SYSTEMS AND METHODS FOR VARIANT TESTING AT SCALE - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240103998 titled 'SYSTEMS AND METHODS FOR VARIANT TESTING AT SCALE

Simplified Explanation

The patent application describes techniques for variant testing at scale, specifically focusing on testing visual aspects of different variants of data associated with an application available through a software application store. The method involves using conversion data to compute conversion metrics for control and variant objects, and then applying statistical hypothesis testing to generate a performance measurement.

• Systems and methods for testing visual aspects of variants of data on a large-scale software application store
• Using conversion data to compute conversion metrics for control and variant objects
• Applying statistical hypothesis testing to generate performance measurements

Potential Applications

This technology could be applied in e-commerce platforms to test different visual elements of product listings, in social media platforms to test different layouts or designs, and in mobile applications to optimize user interfaces.

Problems Solved

This technology helps in identifying the most effective visual elements or designs for an application, leading to improved user engagement and conversion rates. It also streamlines the testing process for large-scale software applications.

Benefits

The benefits of this technology include improved user experience, increased conversion rates, and more efficient testing processes. It allows for data-driven decision-making in optimizing visual aspects of software applications.

Potential Commercial Applications

One potential commercial application of this technology could be in digital marketing agencies that specialize in optimizing conversion rates for their clients. Another application could be in software development companies looking to enhance the user experience of their applications.

Possible Prior Art

One possible prior art for this technology could be A/B testing tools commonly used in digital marketing to test different versions of web pages or advertisements.

What are the specific statistical hypothesis testing functions used in this method?

The specific statistical hypothesis testing functions used in this method are not explicitly mentioned in the abstract. Further details on the specific functions and methodologies employed would provide a clearer understanding of the statistical analysis involved in the testing process.

How does this method handle potential biases in the subset of conversion data used for computing conversion metrics?

The abstract does not provide information on how potential biases in the subset of conversion data are addressed in the method. Exploring how the method mitigates biases and ensures the accuracy and reliability of the conversion metrics would be crucial for understanding the robustness of the testing process.

Original Abstract Submitted

this application sets forth techniques for variant testing at scale. in particular, the embodiments set forth provide systems and methods for testing, on a large-scale software application store, visual aspects of one or more variants of representative data associated with an application available through the software application store. according to some embodiments, a method may include using a subset of conversion data associated with a control object and a subset of the conversion data associated with at least one variant object to compute at least one conversion metric for the control object and at least one conversion metric for the at least one variant object. the method may also include generating a performance measurement by applying at least one statistical hypothesis testing function to the at least one conversion metric for the control object and the at least one conversion metric for the at least one variant object.