{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-9854310","patent":{"patent_number":"US-9854310","title":"Intelligent system and methods of recommending media content items based on user preferences","assignee":null,"inventors":[],"filing_date":"2013-05-14T00:00:00.000Z","publication_date":"2017-12-26T00:00:00.000Z","cpc_codes":["H04N","G06F","G06F","G11B","H04N","H04N","H04N","H04N","H04N","H04N","H04N","H04N","H04N","H04N","H04N","H04N","H04N","H04N","H04N","H04N","H04N","H04N","H04N","H04N","H04N","H04N","G11B","G11B","H04N","H04N"],"num_claims":18,"abstract":"A system and method for making program recommendations to users of a network-based video recording system utilizes expressed preferences as inputs to collaborative filtering and Bayesian predictive algorithms to rate television programs using a graphical rating system. The predictive algorithms are adaptive, improving in accuracy as more programs are rated."},"analysis":{"summary":"The Intelligent System and Methods of Recommending Media Content Items Based on User Preferences patent addresses the challenge of content overload in the modern media landscape. The core innovation lies in its adaptive recommendation engine, which utilizes user-expressed preferences as inputs to collaborative filtering and Bayesian predictive algorithms. This system solves the problem of users struggling to find relevant and engaging content among the vast array of options available on streaming services and other media platforms. The key technical approach involves combining collaborative filtering, which identifies users with similar tastes, with Bayesian predictive algorithms, which predict the likelihood that a user will enjoy a particular piece of content. The business value of this innovation lies in its ability to increase user engagement, reduce customer churn, and enhance the overall user experience on media platforms. By providing more accurate and personalized recommendations, the system can drive revenue growth and improve customer loyalty. The market opportunity is significant, as the demand for personalized content experiences continues to grow across all media formats. This patent represents a valuable asset for companies seeking to differentiate themselves in the competitive media landscape.","layman_explanation":"The Intelligent System and Methods of Recommending Media Content Items Based on User Preferences patent addresses the problem of information overload in the media landscape. With the proliferation of streaming services and on-demand content, consumers are faced with an overwhelming number of choices, making it difficult to find content that aligns with their individual preferences. Existing solutions often fall short because they rely on static data or broad generalizations, failing to adapt to individual viewing habits. \n\nThis patent presents a solution that works by learning your personal preferences. It does this by looking at what you've watched before and what you've rated highly. Then, it uses this information to predict what else you might like. Think of it like a really smart friend who knows your taste in movies and TV shows. It's not just about recommending popular content; it's about recommending content that you, specifically, will enjoy. \n\nThis matters because it can significantly improve your media consumption experience. It can save you time and frustration by helping you discover new content that you'll love. It also has significant market impact for companies like Netflix, Hulu, and Amazon Prime, because it can increase user engagement and retention. By providing more accurate and personalized recommendations, these companies can keep their customers coming back for more. \n\nLooking ahead, this technology could be further developed to incorporate other factors, such as social media activity and real-time viewing data. This could lead to even more personalized and accurate recommendations. The market adoption timeline is likely to be driven by the increasing demand for personalized content experiences and the growing recognition of the importance of user engagement and retention.","technical_analysis":"The Intelligent System and Methods of Recommending Media Content Items Based on User Preferences patent details a sophisticated system for personalized media recommendations. The technical architecture comprises a user preference database, collaborative filtering engine, and Bayesian predictive algorithm. The user preference database stores user-expressed preferences, such as ratings and reviews, for various media content items. The collaborative filtering engine analyzes this data to identify users with similar tastes. Implementation involves algorithms that identify user clusters based on rating patterns. The Bayesian predictive algorithm uses a probabilistic model to predict the likelihood that a user will enjoy a particular piece of content. Integration patterns involve APIs that allow media platforms to seamlessly integrate the recommendation engine into their existing infrastructure. Performance characteristics are optimized through caching and parallel processing techniques. The code-level implications involve the development of efficient algorithms for collaborative filtering and Bayesian inference. The system's adaptive nature ensures that recommendations become increasingly accurate as users rate more programs.","business_analysis":"The Intelligent System and Methods of Recommending Media Content Items Based on User Preferences patent presents a significant business opportunity in the rapidly evolving media landscape. The market opportunity size is substantial, as the demand for personalized content experiences continues to grow across all media formats. Streaming services, cable providers, and other media platforms are increasingly seeking ways to enhance user engagement and reduce customer churn. The system's adaptive algorithms and user-centric approach offer a compelling competitive advantage. Revenue potential lies in increased subscription rates, advertising revenue, and content sales. Business models can include licensing the technology to media platforms or offering a subscription-based recommendation service. Strategic positioning involves partnering with content providers to offer exclusive personalized recommendations. ROI projections are based on increased user engagement, reduced customer churn, and enhanced revenue generation.","faqs":[{"answer":"Intelligent System and Methods of Recommending Media Content Items Based on User Preferences is a patented system and method designed to provide personalized media recommendations to users of a network-based video recording system. It leverages user-expressed preferences as inputs to collaborative filtering and Bayesian predictive algorithms to rate television programs using a graphical rating system. This innovative approach aims to enhance the user experience by providing more accurate and relevant content suggestions, thereby addressing the challenge of information overload in the modern media landscape. The system is designed to be adaptive, continuously learning from user interactions to improve the quality of its recommendations over time. \n\nThe core of this technology lies in its ability to combine collaborative filtering, which identifies users with similar tastes, with Bayesian predictive algorithms, which predict the likelihood that a user will enjoy a particular piece of content. This combination allows the system to provide highly personalized recommendations that are tailored to individual viewing habits and preferences. The graphical rating system provides a user-friendly interface for users to express their preferences, making it easy for the system to gather the data it needs to provide accurate recommendations. \n\nThis patent represents a significant advancement in the field of personalized media recommendations, offering a compelling solution for both consumers and content providers. As the media landscape continues to evolve, this technology has the potential to play a crucial role in shaping the future of content discovery and consumption. Key features include personalized recommendations, adaptive algorithms, collaborative filtering, and Bayesian prediction. The system is designed for media consumption, user engagement, and content discovery.","question":"What is Intelligent System and Methods of Recommending Media Content Items Based on User Preferences?"},{"answer":"Intelligent System and Methods of Recommending Media Content Items Based on User Preferences operates by gathering user-expressed preferences and processing them through collaborative filtering and Bayesian predictive algorithms. The system first collects data on user viewing habits, such as ratings, reviews, and viewing history. This data is then used to identify users with similar tastes and to build a probabilistic model that predicts the likelihood that a user will enjoy a particular piece of content. The collaborative filtering component identifies users with similar preferences, while the Bayesian predictive algorithms use a probabilistic model to predict user preferences. \n\nThe system then uses this information to generate personalized recommendations, which are presented to the user through a graphical rating system. The graphical rating system allows users to easily express their preferences, providing valuable feedback that the system uses to improve its recommendations over time. The system is designed","question":"How does Intelligent System and Methods of Recommending Media Content Items Based on User Preferences work?"}],"topics":["media recommendation","personalized content","collaborative filtering","Bayesian algorithms","streaming services","intelligent","system","methods"],"tech_cluster":null},"seo":{"title":"Intelligent Media Recommendations - Patent US-9854310","description":"Discover the Intelligent System and Methods of Recommending Media Content Items Based on User Preferences. Personalized TV program ratings using collaborative filtering and Bayesian algorithms.","keywords":["media recommendation","personalized content","collaborative filtering","Bayesian algorithms","streaming services","patent","patent US-9854310"]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-9854310","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-9854310","citation_suggestion":"Patentable. \"Intelligent system and methods of recommending media content items based on user preferences\" (US-9854310). https://patentable.app/patents/US-9854310","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-9854310","json":"https://patentable.app/api/llm-context/US-9854310","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-31T19:15:47.291Z"}