Video Border Steganalysis Markers for Real-Time Personalized Data
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Solution Overview
Problem
Traditional search utilities relying on text or voice inputs are inefficient, cumbersome, and lack real-time personalization, leading to lost interaction touch points and non-optimal computing resources, latency issues, and unavailability of user-specific recommendations due to the lack of real-time identification and processing engines.
Innovation Solution
A system utilizing steganalysis detection of metadata in video streams, combined with narrowband Internet-of-Things (NB-IoT) and computational memory computing, to embed encrypted codes in video borders, allowing user devices to scan and decode these codes for real-time data and recommendations without interrupting device memory or processing, using a decentralized network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional search utilities using text or voice inputs are used, then users can perform searches, but the search process is inefficient, cumbersome, and time consuming without real-time personalization
Solution Approach 1:
The system performs preliminary actions by pre-processing video streams to extract and embed metadata and encrypted codes into the video borders before playback. This allows the search and identification functions to be executed in real-time during video consumption without requiring separate pre-search operations, thereby improving search efficiency and reducing search time.
Solution Approach 2:
The patent replaces traditional mechanical search processes (text/voice input requiring manual typing or speaking) with automated visual recognition systems that detect markers and metadata directly from video streams using image processing and steganography techniques, enabling instant identification without manual search inputs.
2Adaptability or versatility
If traditional search utilities are used, then users can find information, but real-time personalization and user-specific recommendations are not available
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring user interactions with the video stream and adjusting recommendations in real-time. User preferences, viewing history, and interaction patterns are fed back into the system to dynamically generate personalized recommendations, ensuring real-time adaptability and maintaining user-specific information throughout the viewing experience.
Solution Approach 2:
The system performs self-service by automatically collecting, processing, and utilizing user data without requiring explicit user input for personalization. The system autonomously analyzes viewing behavior, extracts relevant information from video metadata, and generates personalized recommendations independently, thereby maintaining personalization capability while reducing information loss.
3Power
If centralized servers are used for data processing, then processing power is available, but latency and connectivity challenges occur
Solution Approach 1:
The patent extracts critical processing functions from centralized servers by embedding encrypted codes and metadata directly into the video stream at the source. This allows identification and processing operations to be performed locally on user devices using the extracted data, eliminating the need for continuous connection to centralized servers and thereby reducing latency while maintaining processing capability.
Solution Approach 2:
The system segments the processing task by dividing data processing into two parts: encoding and embedding at the source (where processing power is available) and decoding and identification at the user device (where speed is critical). This segmentation allows each component to operate independently, reducing dependency on centralized servers and improving data transmission speed.
4Loss of time
If video streams are processed in real-time, then immediate data is available, but computational resources and processing power are consumed
Solution Approach 1:
The system performs preliminary computational actions by pre-extracting and pre-encoding metadata and embedding encrypted codes into the video stream during the encoding phase. This shifts the computational burden from real-time processing during playback to the initial encoding phase, enabling real-time data availability while reducing computational energy consumption during the critical viewing period.
Data Source
AI summary
Systems, computer program products, and methods are described herein for steganalysis detection of metadata in a video stream for providing real-time data. An encrypted steganalysis marker is positioned within a boarder of a video stream and, upon user device scanning and identification provides a communication channel for real-time data display on the user device. The data displayed on the user device is transmitted via narrowband internet-of-things communication linkage displaying product or service data associated with the video stream. Using a combination of NB-IoT and computational memory computing creates a unique, in user device computing structure with various variable inputs to provide specified data and recommendations to the user.