Samsung Electronics Co., Ltd.

Yashwant Singh Saini

Barath Raj Kandur Raja

Vibhav Agarwal

Srinivasa Rao Siddi

Himanshu Arora

Hojun Jaygarl

Sreevatsa Dwaraka Bhamidipati

Vanraj Vala

Raju Suresh Dixit

Sriram Shashank

Pilsik Choi

Naresh Purre

Seungseok Kang

Sri Lakshmi Punuru

Sourabh Vasant Gothe

Rishabh Kumar

Hemant Tiwari

Jayesh Rajkumar Vachhani

Mritunjai Chandra

Sanguk Jeon

Kalyan Kakani

Balaji Vijayanagaram Ramalingam

Aayush Yadav

Mansoor Variyathapara Mohammed

Manjunath Bhimappa Ujjinakoppa

Muthu Kumaran

Vipin Rao

Harshavardhana Poojari

Likhith Amarvaj

Rakshith S

Dwakara Bhamidipati Sreevatsa

Sudeep Kumar Kodali

Ankita Bhardwaj

Aditi Anil Kagane

Jaehoon Park

Rishabh Khurana

Example systems and methods provide input suggestions to a user to improve user experience on user devices. The input suggestions can be fill information from another app on device to the present app being used by user, information for performing a search (without the user having to copy-paste data or entering the data manually), responses to a message/notification received by the user, information/content/data to be shared between apps (without switching between apps), and emojis/GIFs that can be used by the user. The method includes analyzing one or more content of one or more screen displayed on device, generating at least one of a logical tree structure and a data mashup model of the one or more analyzed content for each screen, and providing a recommendation to a user. The recommendation can be a connected action or an input suggestion.

Systems and methods for smart capture to provide input and action suggestions

Embodiments herein disclose a method and electronic device for predicting multi-modal drawings. The method includes: receiving, by the electronic device, at least one of a text input and strokes of a drawing and determining, by the electronic device, features associated with the text input and features associated with the strokes of the drawing. The method includes classifying, by the electronic device, the features associated with the text input and the features associated with the strokes of the drawing into one of a dominant feature and a non-dominant feature and performing, by the electronic device, early concatenation or late concatenation of the features based on the classification; classifying, by the electronic device, the strokes of the drawing based on the concatenation into a category using a deep neural network (DNN) model; and predicting, by the electronic device, primary drawings corresponding to the category.

Method and electronic device for predicting plurality of multi-modal drawings

An electronic device and a method for predicting a response are provided. The electronic device includes a display and a processor configured to receive at least one message, identify at least one contextual category of the at least one message, predict at least one response for the at least one message from a language model based on the at least one contextual category, and control the display to display the at least one predicted response.

Method and electronic device for predicting a response from context with a language model

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Karnataka, India

Yashwant Singh Saini