View source for 17948678. AI Driven Page navigation based on user BEHAVIOR simplified abstract (Salesforce, Inc.)

=AI Driven Page navigation based on user BEHAVIOR=


==Organization Name==

[[:Category:Salesforce, Inc.|Salesforce, Inc.]]


[[Category:Salesforce, Inc.]]

==Inventor(s)==

[[:Category:Andrew Mangano of Sunnyvale CA (US)|Andrew Mangano of Sunnyvale CA (US)]][[Category:Andrew Mangano of Sunnyvale CA (US)]]

[[:Category:Saket Agarwal of San Mateo CA (US)|Saket Agarwal of San Mateo CA (US)]][[Category:Saket Agarwal of San Mateo CA (US)]]

[[:Category:Umesh Prabhakar Zope of Fremont CA (US)|Umesh Prabhakar Zope of Fremont CA (US)]][[Category:Umesh Prabhakar Zope of Fremont CA (US)]]

[[:Category:Saurabh S. Davala of Saratoga CA (US)|Saurabh S. Davala of Saratoga CA (US)]][[Category:Saurabh S. Davala of Saratoga CA (US)]]

[[:Category:Stephen Goldberg of Frisco TX (US)|Stephen Goldberg of Frisco TX (US)]][[Category:Stephen Goldberg of Frisco TX (US)]]

==AI Driven Page navigation based on user BEHAVIOR - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17948678 titled 'AI Driven Page navigation based on user BEHAVIOR

==Simplified Explanation==

The patent application abstract describes a system for implementing AI-driven application navigation recommendations based on user behavior. The system generates a trained machine learning model using historical navigation logs, compresses the model, and provides navigation recommendations based on encoded navigation breadcrumb data.

* Trained machine learning model generated using historical navigation logs
* Compressed machine learning model deployed within the web application
* Navigation recommendations provided based on encoded navigation breadcrumb data

== Potential Applications ==
This technology could be applied in various industries such as e-commerce, social media platforms, and online learning platforms to enhance user experience and engagement.

== Problems Solved ==
This technology solves the problem of inefficient navigation within web applications by providing personalized recommendations based on user behavior, ultimately improving user satisfaction and retention.

== Benefits ==
The benefits of this technology include increased user engagement, improved user experience, and potentially higher conversion rates for businesses utilizing the system.

== Potential Commercial Applications ==
A potential commercial application of this technology could be in the field of online retail, where personalized navigation recommendations could lead to increased sales and customer loyalty.

== Possible Prior Art ==
One possible prior art for this technology could be personalized recommendation systems used in e-commerce websites, social media platforms, and streaming services to suggest content based on user behavior.

=== Unanswered Questions ===

=== How does the system handle user privacy and data security concerns? ===
The article does not address how the system ensures the protection of user data and privacy while collecting and analyzing navigation logs for generating recommendations.

=== What is the scalability of the system for large web applications with high traffic volume? ===
The article does not mention how the system can scale to accommodate large web applications with a high volume of user traffic while maintaining the accuracy and efficiency of navigation recommendations.


==Original Abstract Submitted==

Disclosed herein are system, method, and computer program product embodiments for implementing AI driven application navigation recommendations based on user behavior. An embodiment operates by generating a trained machine learning model using training data obtained based on historical navigation logs corresponding to the web application. The embodiment deploys a reduced machine learning model within an instance of the web application, and the reduced machine learning model is generated by compressing the trained machine learning model. The embodiment then generates the page navigation recommendation using the reduced machine learning model based on an encoded navigation breadcrumb data corresponding to the instance of the web application.



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