{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-9854285","patent":{"patent_number":"US-9854285","title":"Popular media items data set with exponential decay","assignee":null,"inventors":[],"filing_date":"2015-07-23T00:00:00.000Z","publication_date":"2017-12-26T00:00:00.000Z","cpc_codes":["H04N","G06F","G06F","G06F","G06F","H04N","H04N","H04N","H04N"],"num_claims":20,"abstract":"A processing device computes scores for a plurality of media items. A score for a media item is computed based on a plurality of positive user actions associated with the media item. The media items are ranked based on the scores. One or more of the media items that have not been featured by any source external to the content hosting platform are identified. A popular media item data set is then created, based on the ranking, with highest ranked media items that have been featured by at least one source external to the content hosting platform."},"analysis":{"summary":"The Popular Media Items Data Set with Exponential Decay patent introduces a novel method for ranking media items on content hosting platforms, prioritizing organically popular content and recent trends. This technology addresses the problem of content discovery, where users often struggle to find relevant and engaging media items due to the dominance of externally promoted content. The key technical approach involves computing scores for media items based on positive user actions, such as likes, shares, and views, and applying an exponential decay factor to prioritize recent trends. The system also identifies media items that have not been heavily featured by external sources, ensuring that organically popular content is surfaced. \n\nThe business value of this innovation lies in its ability to enhance user engagement, increase user retention, and foster a more diverse and engaging content ecosystem. By promoting organically popular content, the platform can cater to niche interests and foster a sense of community. This can lead to increased user satisfaction and loyalty. Furthermore, the system can help content creators gain visibility and recognition for their work, even if they don't have a large following or marketing budget. The market opportunity for this technology is significant, as content hosting platforms are constantly seeking ways to improve their content discovery and recommendation capabilities. The Popular Media Items Data Set with Exponential Decay offers a powerful tool for achieving this goal, and its adoption could lead to increased revenue and profitability for these platforms. This system provides a more equitable playing field for content creators and enriches the user experience through more relevant content recommendations.","layman_explanation":"The Popular Media Items Data Set with Exponential Decay patent addresses the problem of content overload and the difficulty of finding relevant media items on online platforms. Current recommendation systems often prioritize content that is heavily promoted or has been popular for a long time, which can lead to a homogenous and stale user experience. This patent offers a solution by introducing a system that ranks content based on recent user engagement and organic popularity.\n\nThis technology works by tracking how users interact with different media items, such as videos, articles, or songs. It then assigns a score to each item based on the number of likes, shares, views, and comments it receives. However, unlike traditional ranking systems, this technology also incorporates an 'exponential decay' factor. This means that the score of an item decreases over time, giving newer and trending content a better chance of being discovered. Imagine a popularity contest where the votes from yesterday count less than the votes from today. This ensures that the most relevant and engaging content is always at the top, even if it's not from a well-known source.\n\nThis innovation matters because it can significantly improve the user experience on content platforms. By surfacing organically popular content, it can help users discover new and interesting media items that they might otherwise miss. It also provides a more level playing field for content creators, allowing them to gain visibility even if they don't have a large marketing budget. The potential ROI for businesses lies in increased user engagement, retention, and monetization opportunities. A more engaging and relevant content feed leads to happier users who spend more time on the platform and are more likely to subscribe or make purchases.\n\nLooking ahead, this technology could be further developed to incorporate personalized recommendations based on individual user preferences. It could also be applied to other areas, such as e-commerce, to help users find the most relevant and popular products. The market adoption timeline will depend on how quickly content platforms recognize the value of this technology and integrate it into their existing systems. However, given the growing demand for better content discovery solutions, the future looks promising.","technical_analysis":"The Popular Media Items Data Set with Exponential Decay patent outlines a system designed to improve content discovery through a combination of user action scoring and an exponential decay model. The technical architecture involves several key components: a user action tracking module, a score computation engine, an exponential decay calculation module, a ranking algorithm, and a content recommendation engine. The user action tracking module collects data on various user interactions with media items, such as likes, shares, views, and comments. Each action is assigned a weight, and the score computation engine calculates a weighted sum of these actions for each media item. The exponential decay calculation module applies an exponential decay factor to the score of each media item. This factor prioritizes recent trends and ensures that older content does not dominate the ranking. The ranking algorithm uses the decayed scores to rank the media items. The content recommendation engine then uses the ranking to recommend content to users. \n\nThe implementation details involve several considerations. The system must be able to handle a high volume of user actions and update the scores in real-time. This requires a scalable and efficient data processing architecture. The algorithm for calculating the exponential decay factor is also crucial. The decay rate must be carefully tuned to ensure that recent trends are prioritized without completely ignoring older content. The integration patterns involve connecting the system to the content hosting platform's existing data infrastructure. This requires careful consideration of data formats, APIs, and security protocols. The performance characteristics of the system depend on the efficiency of the data processing architecture and the ranking algorithm. The system must be able to generate recommendations in a timely manner, even for a large number of media items. The code-level implications involve implementing the various modules in a programming language that supports efficient data processing and numerical computation. The system may also require the use of specialized libraries for machine learning and data analysis.","business_analysis":"The Popular Media Items Data Set with Exponential Decay patent presents a significant market opportunity for content hosting platforms. The core value proposition is improved content discovery, which can lead to increased user engagement, retention, and monetization. The market opportunity size is substantial, as content hosting platforms are constantly seeking ways to improve their content discovery and recommendation capabilities. The competitive advantages of this technology include its focus on organically popular content and its use of exponential decay to prioritize recent trends. This allows the platform to surface hidden gems that might otherwise be overlooked. \n\nThe revenue potential of this technology is significant. By increasing user engagement and retention, the platform can generate more revenue from advertising, subscriptions, and other sources. The business models that can be supported by this technology include advertising-based models, subscription-based models, and freemium models. The strategic positioning of this technology is as a key enabler of content discovery and recommendation. It can be integrated into existing content hosting platforms to improve their performance and competitiveness. The ROI projections for this technology are favorable, as the benefits of improved content discovery can be quantified in terms of increased user engagement, retention, and revenue. The technology fosters a more democratic content environment, potentially attracting a broader user base and increasing overall platform value. This innovation can be a key differentiator in a crowded content market, enhancing brand loyalty and attracting new users seeking authentic and engaging content experiences.","faqs":[{"answer":"Popular Media Items Data Set with Exponential Decay is a patented technology designed to improve content discovery on online platforms. It focuses on ranking media items based on user engagement and organic popularity, rather than relying solely on external promotion or historical data. This approach helps to surface more relevant and engaging content to users, while also providing a more level playing field for content creators.\n\nThe system works by tracking user actions such as likes, shares, views, and comments, and assigning a score to each media item based on these interactions. However, unlike traditional ranking systems, Popular Media Items Data Set with Exponential Decay incorporates an exponential decay factor, which reduces the weight of older interactions over time. This ensures that recent trends and emerging content are given greater prominence in the ranking.\n\nBy prioritizing","question":"What is Popular Media Items Data Set with Exponential Decay?"}],"topics":["content ranking","media recommendation","exponential decay","user engagement","content discovery","popular","media","items"],"tech_cluster":null},"seo":{"title":"Popular Media Items Data Set with Exponential Decay - Patent US-9854285","description":"Discover the Popular Media Items Data Set with Exponential Decay patent for ranking media based on user actions and exponential decay. Full analysis, claims, and details.","keywords":["content ranking","media recommendation","exponential decay","user engagement","content discovery","patent","patent US-9854285"]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-9854285","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-9854285","citation_suggestion":"Patentable. \"Popular media items data set with exponential decay\" (US-9854285). https://patentable.app/patents/US-9854285","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-9854285","json":"https://patentable.app/api/llm-context/US-9854285","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-30T01:53:13.611Z"}