Google llc (20240248825). CONTRIBUTION INCREMENTALITY MACHINE LEARNING MODELS simplified abstract

CONTRIBUTION INCREMENTALITY MACHINE LEARNING MODELS

Organization Name

google llc

Inventor(s)

Xinlong Bao of Los Altos CA (US)

Ali Nasiri Amini of Redwood City CA (US)

Jing Wang of Mountain View CA (US)

Mert Dikmen of Belmont CA (US)

Amy Richardson of Santa Cruz CA (US)

Dinah Shender of Mountain View CA (US)

Junji Takagi of Sunnyvale CA (US)

Sen Li of Mountain View CA (US)

Ruoyi Jiang of Sunnyvale CA (US)

Yang Jiao of San Mateo CA (US)

Yang Zhang of Sunnyvale CA (US)

Zhuo Zhang of Santa Clara CA (US)

CONTRIBUTION INCREMENTALITY MACHINE LEARNING MODELS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240248825 titled 'CONTRIBUTION INCREMENTALITY MACHINE LEARNING MODELS

The patent application describes methods, systems, and computer programs for training and using machine learning models. These methods involve creating a model that represents relationships between user attributes, content exposures, and performance levels for a target action using organic exposure data and third-party exposure data. The model is used to determine incremental performance levels attributable to third-party exposures at the time when the target action is performed by the user. Based on this information, transmission criteria for digital components to which the user was exposed are modified to improve performance.

• Creation of a model representing relationships between user attributes, content exposures, and performance levels for a target action.
• Use of organic exposure data and third-party exposure data to train the model.
• Determination of incremental performance levels attributable to third-party exposures at the time of the target action.
• Modification of transmission criteria for digital components based on the incremental performance levels.
• Improving user performance by adjusting exposure to digital components.

Potential Applications

This technology can be applied in digital marketing, personalized recommendations, and user behavior analysis.

Problems Solved

This technology addresses the challenge of optimizing user performance by adjusting exposure to digital components based on their impact on performance levels.

Benefits

The benefits of this technology include improved user performance, personalized user experiences, and more effective digital marketing strategies.

Commercial Applications

Title: Enhanced User Performance Optimization Technology This technology can be utilized in digital advertising platforms, e-commerce websites, and social media platforms to enhance user engagement and drive conversions.

Prior Art

Readers can explore prior research on machine learning models for user behavior analysis and personalized recommendations to understand the evolution of this technology.

Frequently Updated Research

Stay updated on the latest advancements in machine learning models for user behavior analysis and personalized recommendations to enhance your understanding of this technology.

Questions about Enhanced User Performance Optimization Technology

How does this technology impact digital marketing strategies?

This technology can significantly improve the effectiveness of digital marketing strategies by optimizing user performance through personalized recommendations and tailored content exposure.

What are the key factors influencing the performance levels determined by the model?

The performance levels determined by the model are influenced by user attributes, content exposures, and third-party exposures experienced by the user.

Original Abstract Submitted

methods, systems, and computer programs encoded on a computer storage medium, for training and using machine learning models are disclosed. methods include creating a model that represents relationships between user attributes, content exposures, and performance levels for a target action using organic exposure data specifying one or more organic exposures experienced by a particular user over a specified time prior to performance of a target action by the particular user and third party exposure data specifying third party exposures of a specified type of digital component to the particular user over the specified time period. using the model, an incremental performance level attributable to each of the third party exposures at an action time when the target action was performed by the particular user is determined. transmission criteria for at least some digital components to which the particular user was exposed are modified based on the incremental performance.