{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-9854290","patent":{"patent_number":"US-9854290","title":"System and method for data-driven ad-insertion in online video streams","assignee":null,"inventors":[],"filing_date":"2015-11-30T00:00:00.000Z","publication_date":"2017-12-26T00:00:00.000Z","cpc_codes":["H04N","G06F","G06F","G06Q","G06Q","G06Q","H04N","H04N","H04N","H04N","H04N","H04N"],"num_claims":15,"abstract":"These teachings concern smart insertion of advertisements in a video, and specifically include selecting locations in a video to place such advertisements. For a specific video there is determined an integer number k of locations for advertisements when streaming the specific video to a specific user that requests the specific video. A set of advertisements is selected to deliver with the specific video, and user feedback is utilized to determine an integer number k of discrete locations within the specific video at which to place advertisements of the set. The specific video is electronically or optically delivered to the specific user, with the set of advertisements dispersed among the k discrete locations for rendering on a local display device."},"analysis":{"summary":"The System and Method for Data-driven Ad-insertion in Online Video Streams patent addresses the challenge of optimizing ad placement within online video streams to enhance user engagement and revenue generation. The core innovation lies in its ability to dynamically adjust ad insertion based on real-time user behavior and video content analysis. This approach solves the problem of intrusive and irrelevant ads that often lead to user frustration and decreased ad effectiveness.\n\nThe system works by determining the optimal number of ad locations for a specific video and user, based on factors such as viewing habits and content type. It then selects a set of advertisements and uses user feedback to identify discrete locations within the video where these ads should be placed. This feedback can include data on ad engagement, user drop-off rates, and explicit user preferences. Machine learning algorithms are used to analyze video content, predict user behavior, and optimize ad placement.\n\nThe business value of this technology is significant. By delivering more engaging and relevant advertising experiences, the system can increase ad click-through rates, improve user retention, and drive revenue for video providers. The market opportunity is substantial, as the online video advertising market continues to grow rapidly. This technology offers a competitive advantage by providing a more personalized and effective advertising solution.\n\nPotential applications include integration with existing video streaming platforms, development of new ad tech solutions, and licensing to video content providers. The system's adaptability allows it to continuously learn and improve its ad placement strategies based on ongoing user feedback, ensuring its long-term effectiveness. The System and Method for Data-driven Ad-insertion in Online Video Streams is poised to transform the online video advertising landscape by creating a win-win situation for both advertisers and consumers.","layman_explanation":"The System and Method for Data-driven Ad-insertion in Online Video Streams patent addresses a common frustration in the digital age: annoying and irrelevant advertisements interrupting our online video viewing experience. This explanation breaks down the problem, the solution, and why this innovation matters for businesses and consumers alike.\n\n**1. What Problem Does This Solve?**\n\nThe core problem is that current online video advertising often feels intrusive and irrelevant. Think about it: how many times have you been watching a video, only to be interrupted by an ad for something you have no interest in? This leads to a poor viewing experience and makes people less likely to engage with the ads. Existing solutions, like simply showing more ads, only exacerbate the problem.\n\n**2. How Does It Work?**\n\nInstead of randomly inserting ads, this patent uses data to understand your viewing habits and preferences. Imagine it like this: Netflix recommends shows based on what you've watched before. This system does the same for ads! It analyzes what kind of videos you watch, how long you watch them, and even your feedback on previous ads. Based on this information, it intelligently places ads in the video stream at points where they are least disruptive and most relevant to you. For example, if you're watching a cooking show, you might see an ad for kitchen gadgets or grocery delivery services.\n\n**3. Why Does This Matter?**\n\nThis approach matters because it benefits everyone involved. For video streaming companies, it means happier viewers who are more likely to keep watching and subscribe. For advertisers, it means their ads are more likely to be seen and clicked on by the right people, leading to better ROI. For viewers like you and me, it means a more enjoyable and less frustrating online video experience.\n\n**4. What's Next?**\n\nThe future of this technology involves even more sophisticated data analysis and personalization. We can expect to see ads that are even more relevant and seamlessly integrated into the video content. As the online video market continues to grow, this technology will become increasingly important for creating a sustainable and enjoyable advertising ecosystem.","technical_analysis":"The System and Method for Data-driven Ad-insertion in Online Video Streams patent describes a system designed to optimize the placement of advertisements in online video streams, improving both user experience and advertising revenue. The technical architecture of the system comprises several key modules working in concert. The Video Analysis Module analyzes the video content to identify potential ad insertion points, considering factors such as scene changes, dialogue pauses, and overall video structure. This module often employs computer vision techniques and natural language processing to understand the content at a granular level. \n\nThe User Feedback Module is crucial for gathering data on user viewing habits and ad engagement. This module tracks metrics like ad click-through rates (CTR), user drop-off rates (the point at which a user stops watching the video), and explicit user preferences (e.g., through surveys or feedback forms). Real-time data processing is essential for this module to function effectively, enabling the system to adapt quickly to changing user behavior.\n\nThe Ad Selection Module is responsible for choosing the most relevant advertisements for a specific video and user. This involves considering factors such as user demographics, viewing history, and ad content. Machine learning algorithms are often used to predict the likelihood of a user engaging with a particular ad, ensuring that the most relevant ads are selected. \n\nThe Ad Insertion Engine is the core component that dynamically inserts the selected advertisements into the video stream at the optimal locations. This engine uses real-time data analysis and user feedback to adjust ad placement, ensuring minimal disruption to the viewing experience. Adaptive streaming technologies, such as HLS (HTTP Live Streaming) and DASH (Dynamic Adaptive Streaming over HTTP), are often used to facilitate seamless ad insertion. \n\nImplementing this system presents several technical challenges. Scalability is a major concern, as the system must be able to handle large volumes of video content and user data. Low latency is also critical, as the system must insert advertisements in real-time without introducing significant delays. Accuracy is paramount, as the system must accurately predict user behavior and optimize ad placement to maximize engagement and minimize disruption. Code-level implications include the need for efficient data structures, optimized algorithms, and robust error handling to ensure the system operates reliably under heavy load. Integration with existing video streaming platforms also requires careful consideration of APIs and data formats.","business_analysis":"The System and Method for Data-driven Ad-insertion in Online Video Streams patent presents a significant business opportunity within the rapidly expanding online video advertising market. The core value proposition lies in its ability to enhance the effectiveness of video advertising while simultaneously improving the user experience. This is achieved through data-driven ad placement, ensuring that ads are relevant and non-intrusive, leading to higher engagement and reduced churn. \n\nThe market opportunity is substantial. The global online video advertising market is projected to reach hundreds of billions of dollars in the coming years, driven by the increasing consumption of video content across various platforms. This technology offers a competitive advantage by addressing the growing concern of ad fatigue and the need for more personalized advertising experiences. \n\nThe revenue potential is multifaceted. Video streaming platforms can generate increased ad revenue through higher click-through rates and improved ad recall. Advertisers benefit from more targeted and effective campaigns, leading to better ROI. Users enjoy a more seamless and enjoyable viewing experience, fostering greater loyalty and engagement. \n\nSeveral business models can be pursued. Licensing the technology to video streaming platforms and ad tech companies is one option. Another is to develop a proprietary ad insertion platform and offer it as a service. A third is to integrate the technology into an existing ad network to enhance its capabilities. \n\nThe strategic positioning of this technology is strong. It aligns with the industry trend towards data-driven advertising and personalized content delivery. It addresses a critical pain point for both video providers and consumers, making it a valuable asset in the competitive landscape. \n\nROI projections are promising. By increasing ad revenue, reducing churn, and improving user engagement, this technology can deliver a significant return on investment for video streaming platforms and advertisers. The potential for long-term growth is high, as the online video market continues to evolve and expand.","faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"System and method for data-driven ad-insertion in online video streams","description":"These teachings concern smart insertion of advertisements in a video, and specifically include selecting locations in a video to place such advertisements. For a specific video there is determined an ","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-9854290","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-9854290","citation_suggestion":"Patentable. \"System and method for data-driven ad-insertion in online video streams\" (US-9854290). https://patentable.app/patents/US-9854290","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-9854290","json":"https://patentable.app/api/llm-context/US-9854290","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-30T04:32:08.853Z"}