Samsung Electronics Co., Ltd.

Mritunjai Chandra

Barath Raj Kandur Raja

Vibhav Agarwal

Hojun Jaygarl

Pilsik Choi

Vanraj Vala

Sriram Shashank

Seungseok Kang

Naresh Purre

Yashwant Singh Saini

Sri Lakshmi Punuru

Ankur Agarwal

Himanshu Arora

Sanguk Jeon

Srinivasa Rao Siddi

Rishabh Kumar

Aayush Yadav

Jaehoon Park

Aditi Anil Kagane

Ankita Bhardwaj

Sudeep Kumar Kodali

Dwakara Bhamidipati Sreevatsa

Hemant Tiwari

Bharath C

Likhith Amarvaj

Harshavardhana

Vipin Rao

Muthu Kumaran

Ishan Vaid

Shweta Ratanpura

Manjunath Bhimappa Ujjinakoppa

Kranti Chalamalasetti

Mansoor Variyathapara Mohammed

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

Methods and systems for predicting keystrokes using a neural network analyzing cumulative effects of a plurality of factors impacting the typing behavior of a user. The factors may include typing pattern, previous keystrokes, specifics of keyboard used for typing, and contextual parameters pertaining to a device displaying the keyboard and the user. A plurality of features may be extracted and fused to obtain a plurality of feature vectors. The plurality of feature vectors can be optimized and processed by the neural network to identify known features and learn unknown features that are impacting the typing behavior. Thereby, the neural network predicts keystrokes using the known and unknown features.

Methods and systems for predicting keystrokes using a unified neural network

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Bengaluru, India

Mritunjai Chandra