View source for TapText llc (20240257096). SYSTEM AND METHOD FOR LINK-INITIATED USER ENGAGEMENT AND RETENTION UTILIZING GENERATIVE ARTIFICIAL INTELLIGENCE simplified abstract

=SYSTEM AND METHOD FOR LINK-INITIATED USER ENGAGEMENT AND RETENTION UTILIZING GENERATIVE ARTIFICIAL INTELLIGENCE=


==Organization Name==

[[:Category:TapText llc|TapText llc]]


[[Category:TapText llc]]

==Inventor(s)==

[[:Category:Steve Doumar of Fort Lauderdale FL (US)|Steve Doumar of Fort Lauderdale FL (US)]][[Category:Steve Doumar of Fort Lauderdale FL (US)]]

[[:Category:David Teodosio of Guilford CT (US)|David Teodosio of Guilford CT (US)]][[Category:David Teodosio of Guilford CT (US)]]

==SYSTEM AND METHOD FOR LINK-INITIATED USER ENGAGEMENT AND RETENTION UTILIZING GENERATIVE ARTIFICIAL INTELLIGENCE - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240257096 titled 'SYSTEM AND METHOD FOR LINK-INITIATED USER ENGAGEMENT AND RETENTION UTILIZING GENERATIVE ARTIFICIAL INTELLIGENCE

**Simplified Explanation:**
The patent application describes a system that uses generative artificial intelligence to create personalized messages for users within a messaging application on their mobile devices. By analyzing user profiles, interaction history, and deep link context, the system generates contextually relevant messages to enhance user engagement and retention.

**Key Features and Innovation:**
* Utilizes generative artificial intelligence to create personalized messages for users.
* Analyzes user profiles, interaction history, and deep link context to generate contextually relevant messages.
* Integrates with messaging applications through an API to provide timely and valuable interactions.
* Employs an LSTM-based model with attention mechanisms for timing and content prediction.
* Continuous monitoring and model updates ensure optimal performance and alignment with user preferences.

**Potential Applications:**
The technology can be applied in various industries such as e-commerce, social media, customer service, and marketing to enhance user engagement and retention.

**Problems Solved:**
The system addresses the challenge of creating personalized and contextually relevant messages for users within messaging applications to improve user engagement and retention.

**Benefits:**
* Enhances user experience by providing personalized and timely interactions.
* Increases user engagement and retention within messaging applications.
* Improves the effectiveness of follow-up messages accompanying deep links.

**Commercial Applications:**
The technology can be used in marketing automation platforms, customer relationship management systems, and mobile applications to drive user engagement and retention.

**Questions about the Technology:**
1. How does the system analyze user profiles and interaction history to generate personalized messages?
2. What are the potential limitations of using generative artificial intelligence for user engagement in messaging applications?


==Original Abstract Submitted==

a system and methods for dynamic-link initiated user engagement and retention utilizing generative artificial intelligence. the system integrates a generalized generative ai, personalized to each user's context, generating pre-filled message to be displayed within a messaging application on a mobile device. it utilizes user profiles, interaction history, and deep link context to dynamically generate contextually relevant pre-filled messages. the system may employ an lstm-based model with attention mechanisms for both timing and content prediction. it interfaces with the messaging app through an api, extracting deep link context and triggering ai-generated suggestions. this enables seamless, personalized follow-up messages accompanying deep links, fostering customer engagement and retention by providing timely and valuable interactions. continuous monitoring and model updates ensure optimal performance and alignment with user preferences, ultimately enhancing user experience and long-term app engagement.



[[Category:G06Q20/32]]
[[Category:G06Q20/40]]
[[Category:H04W4/80]]
[[Category:CPC_G06Q20/3276]]