US20260170705
2026-06-18
Physics
G06T11/00
The patent describes a sophisticated platform designed to generate personalized text-to-video messages for social media. Users can input text and select different types of multimedia content through a user-friendly interface. A pre-trained large language model (LLM) processes these inputs, utilizing a Knowledge Base with data from various reliable sources such as news outlets and academic publications. This system allows users to craft draft messages based on chosen topics or existing content, like news articles, with options for further customization. The platform combines text-to-audio conversion, image search, and video generation capabilities to create shareable video or audio files suitable for social media distribution.
This invention focuses on leveraging social media to distribute automated, fact-checked text-to-video messages. It aims to educate and inform users about topics of personal and local interest. The system automates the production of text-to-narrated audio-to-video content, displaying relevant quotes and graphics in sync with the narration. This approach enhances viewer comprehension and retention, using personal connections between smartphone users and local businesses to boost consumer interest and sales, improve public education, and foster confidence in governmental communications.
The shift from traditional media to video content consumption has transformed how information is accessed, especially among younger generations. Social media platforms are now central to communication, but they are also rife with misinformation. This change necessitates new methods for reliable information dissemination. The invention addresses the decline in traditional news consumption and the need for fact-checked content to engage younger audiences on platforms like TikTok and YouTube. It also seeks to combat misinformation by providing verified news in a format that is both engaging and educational.
AI and machine learning offer significant opportunities for innovation in media and education. The invention proposes an AI-managed Video Clip Library Descriptor Code to analyze and categorize video content with high precision. This system enhances the usability of video libraries, allowing users to find relevant clips quickly and accurately. Additionally, the invention aims to improve reading comprehension and knowledge retention by integrating visual and auditory information, leveraging dual-coding theory to reinforce learning and memory retention.
The invention seeks to address issues of declining readership and rising video viewership by converting written information into narrated videos. It aims to automate the incorporation of visual content from a Knowledge Base into text-to-audio and text-to-image processes. This automation facilitates the creation of engaging, issue-oriented documentary-style videos that align with the themes and tones of the text-based messages. These videos are designed to be visually appealing and intellectually stimulating, enhancing the communication efforts of corporations, educational institutions, and government bodies.