View source for Loop Now Technologies, Inc. (20240348849). DYNAMIC SHORT-FORM VIDEO TRAVERSAL WITH MACHINE LEARNING IN AN ECOMMERCE ENVIRONMENT simplified abstract

=DYNAMIC SHORT-FORM VIDEO TRAVERSAL WITH MACHINE LEARNING IN AN ECOMMERCE ENVIRONMENT=


==Organization Name==

[[:Category:Loop Now Technologies, Inc.|Loop Now Technologies, Inc.]]


[[Category:Loop Now Technologies, Inc.]]

==Inventor(s)==

[[:Category:Ziming Zhuang of Palo Alto CA (US)|Ziming Zhuang of Palo Alto CA (US)]][[Category:Ziming Zhuang of Palo Alto CA (US)]]

[[:Category:Vishal Arora of Walnut Creek CA (US)|Vishal Arora of Walnut Creek CA (US)]][[Category:Vishal Arora of Walnut Creek CA (US)]]

==DYNAMIC SHORT-FORM VIDEO TRAVERSAL WITH MACHINE LEARNING IN AN ECOMMERCE ENVIRONMENT - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240348849 titled 'DYNAMIC SHORT-FORM VIDEO TRAVERSAL WITH MACHINE LEARNING IN AN ECOMMERCE ENVIRONMENT

**Simplified Explanation:**
The patent application describes a method for dynamically recommending short-form videos to users in an ecommerce environment using machine learning.

**Key Features and Innovation:**
* Accessing and customizing a graph structure of short-form videos based on products for sale on a website.
* Rendering customized short-form videos to users with an interactive overlay and ecommerce environment.
* Collecting and analyzing video consumption behavior data using a machine learning model.
* Determining next short-form videos for users to view based on sales goals, behavior data, and user interaction.
* Synthesizing additional videos to enhance viewer engagement and product sales.

**Potential Applications:**
This technology can be applied in various ecommerce platforms to personalize video recommendations for users, increasing engagement and driving sales.

**Problems Solved:**
This technology addresses the challenge of providing relevant and engaging video content to users in an ecommerce setting, ultimately improving user experience and increasing sales.

**Benefits:**
* Personalized video recommendations enhance user engagement.
* Machine learning analysis improves the relevance of video suggestions.
* Increased sales through targeted video content.

**Commercial Applications:**
Dynamic video recommendation systems powered by machine learning can revolutionize the way ecommerce platforms engage with users, leading to higher conversion rates and customer satisfaction.

**Prior Art:**
Prior art related to this technology may include research on personalized content recommendation systems in ecommerce environments and machine learning algorithms for video analysis.

**Frequently Updated Research:**
Stay updated on advancements in machine learning algorithms for video analysis and personalized content recommendation systems to enhance the effectiveness of this technology.

**Questions about Dynamic Short-Form Video Traversal with Machine Learning:**
1. How does the machine learning model determine the next short-form videos for users to view?
2. What are the potential challenges in implementing this technology on a large scale?


==Original Abstract Submitted==

disclosed embodiments provide techniques for dynamic short-form video traversal with machine learning in an ecommerce environment. a graph structure associated with a library of short-form videos is accessed and customized in a back-end environment based on products for sale on a website. one or more of the customized short-form videos from the library are rendered to one or more users, along with an interactive overlay and an ecommerce environment. as the video is viewed, video consumption behavior data is collected and analyzed by a machine learning model. the machine learning model determines one or more next short-form videos from the graph structure for the user to view, based on sales goals, video consumption behavior data, and interaction with the user. the machine learning model can synthesize additional short-form videos and insert them into the graph structure in order to enhance viewer engagement and product sales.



[[Category:H04N21/25]]
[[Category:G06Q30/0241]]
[[Category:G06Q30/0601]]
[[Category:H04N21/478]]
[[Category:CPC_H04N21/25]]