View source for 18183794. TECHNIQUES FOR RECOMMENDING REPLY STICKERS simplified abstract (Snap Inc.)

=TECHNIQUES FOR RECOMMENDING REPLY STICKERS=


==Organization Name==

[[:Category:Snap Inc.|Snap Inc.]]


[[Category:Snap Inc.]]

==Inventor(s)==

[[:Category:Roman Golobokov of London (GB)|Roman Golobokov of London (GB)]][[Category:Roman Golobokov of London (GB)]]

[[:Category:Sergey Smetanin of London (GB)|Sergey Smetanin of London (GB)]][[Category:Sergey Smetanin of London (GB)]]

[[:Category:Sofya Savinova of Lehi UT (US)|Sofya Savinova of Lehi UT (US)]][[Category:Sofya Savinova of Lehi UT (US)]]

[[:Category:Aleksandr Zakharov of Dubai (AE)|Aleksandr Zakharov of Dubai (AE)]][[Category:Aleksandr Zakharov of Dubai (AE)]]

==TECHNIQUES FOR RECOMMENDING REPLY STICKERS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18183794 titled 'TECHNIQUES FOR RECOMMENDING REPLY STICKERS

The abstract describes a technique for processing received media content items in a messaging application to generate a selection of recommended stickers for the user to reply with.

* The messaging application processes the received media content item to identify specific attributes and characteristics.
* A scoring model is used to analyze the identified attributes and characteristics to recommend a predetermined number of stickers for the user to choose from.
* The recommended stickers are presented in a user interface for the user to select for replying to the received media content item.

Potential Applications:
- Messaging applications
- Social media platforms
- Communication tools

Problems Solved:
- Enhances user engagement in messaging conversations
- Provides personalized sticker recommendations based on the content of the message

Benefits:
- Improves user experience in messaging applications
- Increases user interaction and response rates
- Enhances communication through visual elements

Commercial Applications:
Title: Enhanced User Engagement Tool for Messaging Applications
This technology can be utilized by messaging application developers to enhance user engagement and interaction, leading to increased user satisfaction and retention. It can also be integrated into social media platforms to improve user communication and response rates.

Prior Art:
Researchers can explore existing patents related to sticker recommendation systems in messaging applications to understand the evolution of this technology and identify areas for further innovation.

Frequently Updated Research:
Researchers can stay updated on advancements in machine learning algorithms for content analysis and recommendation systems to enhance the accuracy and efficiency of sticker recommendations in messaging applications.

Questions about Sticker Recommendation Systems:
1. How does the scoring model determine the recommended stickers for the user?
The scoring model analyzes the attributes and characteristics of the received media content item to identify stickers that are most relevant and suitable for the user to use in their reply.

2. What are the potential implications of integrating this technology into social media platforms?
Integrating this technology into social media platforms can enhance user engagement, improve communication dynamics, and increase user satisfaction with the platform.


==Original Abstract Submitted==

Described herein is a technique for processing a received media content item (e.g., a message), received at a messaging application of a first end-user of a messaging service, to generate a selection of some predetermined number of recommended stickers. The recommended stickers are then presented in a user interface to the first end-user, allowing the first end-user to select a sticker for use in replying to the received media content item. To generate the selection of recommended stickers, in response to receiving the media content item, the messaging application processes the media content item to identify specific attributes and characteristics (e.g., text included with the message, stickers used with the message, and other contextual metadata). The identified attributes and characteristics of the received message are then processed by a scoring model to identify the predetermined number of stickers for presenting in the reply interface as recommended reply stickers.



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