View source for 18617050. METHODS AND SYSTEMS FOR GENERATING ONE OR MORE EMOTICONS FOR ONE OR MORE USERS simplified abstract (Samsung Electronics Co., Ltd.)

=METHODS AND SYSTEMS FOR GENERATING ONE OR MORE EMOTICONS FOR ONE OR MORE USERS=


==Organization Name==

[[:Category:Samsung Electronics Co., Ltd.|Samsung Electronics Co., Ltd.]]


[[Category:Samsung Electronics Co., Ltd.]]

==Inventor(s)==

[[:Category:Dimple Sharma of Noida (IN)|Dimple Sharma of Noida (IN)]][[Category:Dimple Sharma of Noida (IN)]]

[[:Category:Dewesh Deo Singh of Noida (IN)|Dewesh Deo Singh of Noida (IN)]][[Category:Dewesh Deo Singh of Noida (IN)]]

[[:Category:Sattdeepan Dutta of Noida (IN)|Sattdeepan Dutta of Noida (IN)]][[Category:Sattdeepan Dutta of Noida (IN)]]

==METHODS AND SYSTEMS FOR GENERATING ONE OR MORE EMOTICONS FOR ONE OR MORE USERS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18617050 titled 'METHODS AND SYSTEMS FOR GENERATING ONE OR MORE EMOTICONS FOR ONE OR MORE USERS

The method described in the patent application involves generating emoticons for users based on fictional characters using a Multiple Localized Discriminator Generative Adversarial Network (MLD GAN).

* The method starts by receiving an image generated by the MLD GAN based on features associated with the fictional characters.
* The output values associated with each discriminator are determined based on the features.
* A weight is calculated for each discriminator based on the distance between the discriminator and the features.
* An image info-graph is generated based on the received image.
* The relevance of each discriminator is calculated based on the image info-graph, features, and distance.
* A plurality of images representing emoticons associated with the fictional characters are generated based on multiple sets of features.

Potential Applications:
- Customized emoticons for users based on their favorite fictional characters.
- Personalized messaging platforms with unique emoticons.
- Enhanced user engagement in online communication.

Problems Solved:
- Providing users with a fun and creative way to express emotions.
- Tailoring emoticons to match individual preferences.
- Increasing user satisfaction and enjoyment in digital interactions.

Benefits:
- Enhanced user experience in messaging and social media platforms.
- Increased user engagement and interaction.
- Personalized content creation for users.

Commercial Applications:
Title: "Personalized Emoticon Generation Technology for Messaging Platforms"
This technology can be utilized by messaging app developers to offer users a unique and engaging way to express themselves through customized emoticons. The market implications include increased user retention, higher user satisfaction, and potential revenue growth through premium emoticon features.

Questions about the technology:
1. How does this technology improve user engagement in digital communication?
2. What sets this emoticon generation method apart from existing methods?


==Original Abstract Submitted==

A method for generating one or more emoticons for one or more users with respect to one or more fictional characters is provided. The method includes receiving a first image generated by a multiple localized discriminator (MLD) generative adversarial network (GAN) based on a set of features from multiple sets of features associated with the one or more fictional characters, resulting in generation of an output value associated with each of the plurality of discriminators, determining a weight associated with each of the plurality of discriminators based on a distance between each discriminator and the set of features, generating an image info-graph associated with the first image generated by the MLD GAN upon receiving the first image, calculating a relevance associated with each of the plurality of discriminators based on the image info-graph, the set of features and the distance, and generating a plurality of images representing a plurality of emoticons associated with the one or more fictional characters based on each of the multiple sets of features.



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[[Category:CPC_G06T11/00]]