Samsung Electronics Co., Ltd.

Honeywell International Inc.

Talent Unlimited Online Services Private Limited

Abhishek R Sharma

Jitendra Sitaram Chaurasia

Rahul Prasad

Manu Thomas

Mainak Choudhury

Vinay Kumar

Deepraj Prabhakar Patkar

Vipul Gupta

Ankit Prasad

Vijaya Kumar Tukka

Ashok Babu Channa

Amogha D Shanbhag

Vaibhav Negi

Sravana Kumar Karivedala

Vikalp Mullick

Aryan Jaiswal

Mudit Rastogi

The present disclosure provides a method for intelligent user localization in a metaverse, including: detecting movements of a wearable head gear configured to present virtual content to a user, and generating sensor data and visual data using an inertial sensor and a camera, respectively, mapping the visual data to a virtual world using an image associated with the visual data to localize the user in the virtual world; providing the visual data and the sensor data to a first Machine Learning (ML) model and a second ML model, respectively; extracting a plurality of key points from the visual data and distinguishing stable key points and dynamic key points; and removing visual impacts corresponding to the visual data having a relatively low weightage, and providing a relatively high weightage to the sensor data processed through the second ML model.

System and method for intelligent user localization in metaverse

A method for converting speech in one of a plurality of input languages into text using machine transliteration and transfer learning is disclosed. The method includes a training stage. The training stage includes receiving a training set of a plurality of audio files and an input text corresponding to the audio input in any input language using the speech recognition engine; transliterating the training set to transform the input text into transliterated text that includes characters of a base language and training acoustic model with the plurality of audio files and corresponding transliterated text using transfer learning. The method further includes an inference stage. The inference stage includes performing decoding on output of the trained acoustic model to generate text includes characters of the base language at inference and transliterating the generated text to output text includes characters in input language using reverse transliteration.

System and method for speech recognition using machine transliteration and transfer learning

The embodiments herein provide a system and method for personalized cartoon image generation. The method () comprises launching a keyboard interface (), capturing a digital picture (), face segmentation using neural network (), normalization of segmented face (), face cartoonification (), which generates bobble head, facial landmark extraction (), facial expression feature transfer () and customization of the generated plurality of cartoon images (). Hence, the embodiments herein helps in creation of personalized plurality of cartoon images to make the user part of the conversations and the graphics or content shared look similar to the user input face and more aesthetically pleasing instead of using any reference stickers to convey the messages.

System and method for personalized cartoon image generation

Devices, methods, and systems for monitoring captured images and/or video from one or more cameras are described herein. The systems may include one or more cameras, a video recorder, a mobile display device, and a location detection system. The location detection system may detect a proximity of the mobile display device relative to one or more cameras and determine when the proximity to a camera meets a predetermine proximity criteria. The mobile display device may display at least part of a recorded video stream that is captured by the camera when it is determined the proximity of the mobile display device to the camera meets the predetermined proximity criteria. In some cases, the displayed video may be displayed in a virtual reality or three dimensional format. Further, as the proximity of the mobile display device to the camera changes, a zoom level and/or direction of view of the displayed video may change.

System and method for auto selecting a video for display on a mobile device based on the proximity of the mobile device relative to the video source

Provided is a method, performed by an information processing device, of processing information by using an Internet of things (IoT) device, the method including: receiving, from a user, a web search query; fetching context information of at least one IoT device related to the web search query; automatically generating a synthetic web search query including the web search query and the context information of the at least one IoT device; and determining a control to be applied to the at least one IoT device by using a search result regarding the synthetic web search query.

Information processing method and device

A method and an apparatus for managing a service request in a blockchain network are provided. The method includes receiving, by a first device, a service request, identifying an intent from the service request, selecting one or more atomic contracts, each of which is related to the intent, wherein the atomic contracts are associated with a second device on the blockchain network and are verified in the blockchain network, generating a new contract including the atomic contracts, and broadcasting the new contract over the blockchain network.

Method and apparatus for managing a service request in a blockchain network

Provided is an apparatus for managing user authentication in a blockchain network and the apparatus comprises a processor configured to transmit, to a server, a request for a snapshot identifier (ID) with user data comprising at least one of one-time password, biometric data, context data, routine data, or device metadata, receive the snapshot ID generated based on the user data, initiate a transaction with the snapshot ID in the blockchain network comprising a blockchain server which authenticates the snapshot ID, and output blockchain transaction data associated with the transaction based on the authentication of the snapshot ID.

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Bengaluru, India

Abhishek R Sharma