View source for Walmart apollo, llc (20240256851). DEEP LEARNING-BASED MULTI-OBJECTIVE PACING SYSTEMS AND METHODS simplified abstract

=DEEP LEARNING-BASED MULTI-OBJECTIVE PACING SYSTEMS AND METHODS=


==Organization Name==

[[:Category:walmart apollo, llc|walmart apollo, llc]]


[[Category:walmart apollo, llc]]

==Inventor(s)==

[[:Category:Yue Ding of Mountain View CA (US)|Yue Ding of Mountain View CA (US)]][[Category:Yue Ding of Mountain View CA (US)]]

[[:Category:Changzheng Liu of Sunnyvale CA (US)|Changzheng Liu of Sunnyvale CA (US)]][[Category:Changzheng Liu of Sunnyvale CA (US)]]

[[:Category:Jixiang Huang of Pleasanton CA (US)|Jixiang Huang of Pleasanton CA (US)]][[Category:Jixiang Huang of Pleasanton CA (US)]]

[[:Category:Boning Zhang of Santa Clara CA (US)|Boning Zhang of Santa Clara CA (US)]][[Category:Boning Zhang of Santa Clara CA (US)]]

[[:Category:Georgios Rovatsos of San Francisco CA (US)|Georgios Rovatsos of San Francisco CA (US)]][[Category:Georgios Rovatsos of San Francisco CA (US)]]

[[:Category:Wei Shen of Pleasanton CA (US)|Wei Shen of Pleasanton CA (US)]][[Category:Wei Shen of Pleasanton CA (US)]]

[[:Category:Dongbo Zhang of Redwood City CA (US)|Dongbo Zhang of Redwood City CA (US)]][[Category:Dongbo Zhang of Redwood City CA (US)]]

==DEEP LEARNING-BASED MULTI-OBJECTIVE PACING SYSTEMS AND METHODS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240256851 titled 'DEEP LEARNING-BASED MULTI-OBJECTIVE PACING SYSTEMS AND METHODS

The patent application describes systems and methods for deep learning-based multi-objective pacing content deployment.

* A first set of input parameters is received and used to generate a first set of pacing parameters by a trained pacing model.
* The trained pacing model includes a k-nearest neighbor (KNN) portion and neural basis expansion analysis for time series (N-BEATS) portion.
* The pacing pipeline is modified to incorporate the set of pacing parameters, which are used to generate deployment parameters.
* Content is deployed to one or more content systems based on the deployment parameters, and feedback data is received.
* A second set of pacing parameters is generated based on the feedback data, using the trained pacing model.

Potential Applications:
- This technology can be used in digital marketing to optimize content deployment strategies.
- It can be applied in e-commerce platforms to improve product recommendations and customer engagement.

Problems Solved:
- Helps in automating and optimizing the deployment of content based on multiple objectives.
- Enhances the efficiency and effectiveness of content deployment processes.

Benefits:
- Improves the accuracy and relevance of content deployment.
- Increases user engagement and satisfaction.
- Saves time and resources by automating pacing and deployment processes.

Commercial Applications:
Title: "Deep Learning-Based Multi-Objective Pacing Content Deployment Technology for Digital Marketing"
This technology can be utilized by digital marketing agencies, e-commerce platforms, and content management systems to enhance their content deployment strategies, leading to increased customer engagement and improved conversion rates.

Questions about Deep Learning-Based Multi-Objective Pacing Content Deployment Technology:
1. How does this technology improve the efficiency of content deployment processes?
- This technology utilizes deep learning algorithms to analyze input parameters and generate pacing parameters, optimizing the deployment of content based on multiple objectives.

2. What are the key components of the trained pacing model used in this technology?
- The trained pacing model includes a k-nearest neighbor (KNN) portion and neural basis expansion analysis for time series (N-BEATS) portion, which work together to generate pacing parameters for content deployment.


==Original Abstract Submitted==

systems and methods of deep learning-based multi-objective pacing content deployment are disclosed. a first set of input parameters is received and a first set of pacing parameters are generated by a trained pacing model that receives the first set of input parameters. the trained pacing model includes a k-nearest neighbor (knn) portion and neural basis expansion analysis for time series (n-beats) portion. in response to generating the first set of pacing parameters, a pacing pipeline is modified to incorporate the set of pacing parameters. the pacing pipeline is configured to generate deployment parameters. content is deployed to one or more content systems based on the deployment parameters and feedback data representative of the deployed content is received. a second set of pacing parameters is generated by the trained pacing model. the trained pacing model receives a second set of input parameters that are based at least in part on the feedback data.



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[[Category:CPC_G06N3/08]]