MUMBAI, India, Sept. 28 -- Intellectual Property India has published a patent application (202641112921 A) filed by Srinivas University Institute Of Engineering And Technology on September 21, 2026, for Design And Developing Of Full-Stack Real-Time Collaborative Platform.
Inventors include Mrs. Mamatha Sj; Prajwal; Pramod; Raghavendra; Rakshith Pai; Chidanand M; Skanda Ms; Honnesh Mm; and J Shreyas Shetty.
The application for the patent was published on September 25, 2026, under issue no. 39/2026.
Abstract: Design and Developing of Full-Stack Real-Time Collaborative Platform Real-time collaborative platform development, leveraging modern web technologies including WebRTC, Socket.IO, and TensorFlow.js, has emerged as a powerful approach to enabling distributed teams, educators, and remote workers to communicate and co- create within a single unified digital workspace. Traditional collaboration methods typically rely on multiple disconnected tools, but productive virtual teamwork demands simultaneous communication, content creation, and decision-making across shared environments. By integrating diverse interaction modalities including video conferencing, document editing, interactive whiteboarding, and AI-based gesture recognition, the proposed platform aims to achieve more seamless, productive, and context-rich collaboration experiences. Techniques such as peer-to-peer media transmission, operational transformation for conflict-free concurrent editing, client-side hand landmark extraction, and real-time event synchronization are discussed. Additionally, we examine various applications of the platform including enterprise team collaboration, online education, and remote work environments. Despite its promising potential, challenges related to WebRTC mesh scalability, concurrent edit conflict resolution, gesture recognition accuracy under varied lighting conditions, and mobile browser compatibility are also considered. Finally, we highlight future directions including SFU-based media routing for large meetings, native mobile applications, AI-generated meeting summaries, and expanded gesture vocabularies to enhance the platform's accessibility, scalability, and collaborative capability. recognition systems that rely on a single type of data (e.g., only voice or facial expressions), multimodal systems aim to capture the complexity and richness of human emotions, which are often conveyed through multiple channels simultaneously. The field has gained significant attention due to its potential to provide more productive, context-aware, and unified collaboration experiences across diverse real-world remote working scenarios. This paper provides an overview of various approaches in real-time collaborative platform development, beginning with the system architecture and technology stack tailored to each platform module. For media transmission, WebRTC peer-to-peer streaming with adaptive bitrate control is employed, while collaborative content synchronization is achieved through Socket.IO event propagation and Operational Transformation algorithms. AI-based gesture interaction focuses on client-side hand landmark detection using TensorFlow.js and MediaPipe Hands, often employing confidence threshold filtering and temporal smoothing to ensure accurate and responsive gesture classification.
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