{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-9854292","patent":{"patent_number":"US-9854292","title":"Systems and methods for determining audience engagement based on user motion","assignee":null,"inventors":[],"filing_date":"2017-01-05T00:00:00.000Z","publication_date":"2017-12-26T00:00:00.000Z","cpc_codes":["H04N","G06F","G06F","G06F","H04N","H04N","H04N","H04N","H04N","H04N"],"num_claims":20,"abstract":"Systems and methods are described for measuring audience engagement for a media asset using user motion. For example, a media guidance application may receive movement logs from a plurality of user equipment. These movement logs may contain information indicating when each of the plurality of users moved, as detected by motion sensors, while the user equipment was generating for display a media asset. The movement logs may be analyzed to determine time periods during the media asset during which greater than a certain number of the plurality of users were moving. These time periods may in turn be used to infer how engaged the plurality of users were during the media asset or what portions of the media asset the plurality of users viewed. This data may be used to determine in what media assets, or portions thereof, supplemental video content should be inserted."},"analysis":{"summary":"Systems and Methods for Determining Audience Engagement Based on User Motion introduces a novel approach to measuring audience engagement by analyzing user motion data collected from devices during media playback. The core innovation lies in the system's ability to correlate movement data with specific segments of a media asset, allowing content creators to identify which portions are most engaging. This patent solves the problem of inaccurate engagement metrics by providing a passive, continuous, and granular understanding of viewer behavior, unlike traditional methods that rely on explicit feedback. The key technical approach involves integrating motion sensors into user devices, collecting movement logs, and applying machine learning algorithms to analyze the data and infer engagement levels.\n\nThe business value of this technology is significant. Content creators can use it to optimize their content, personalize viewer experiences, and drive revenue growth. Advertisers can identify optimal moments for inserting supplemental video content, maximizing viewer attention and ad recall. Broadcasters can fine-tune their programming schedules, ensuring that the most engaging content is aired during peak viewing hours. The market opportunity is vast, spanning across various segments of the media industry, including streaming platforms, television broadcasting, and online advertising.\n\nThis technology has the potential to revolutionize the way media is consumed and created. Its ability to provide passive, continuous, and granular insights into viewer behavior makes it a valuable tool for content creators, advertisers, and broadcasters alike. The system's ability to passively collect and analyze movement data makes it a valuable tool for understanding viewer behavior in a non-intrusive way. As the media landscape continues to evolve, this technology is poised to play a key role in shaping the future of content creation and distribution.","layman_explanation":"Systems and Methods for Determining Audience Engagement Based on User Motion addresses the challenge of accurately understanding how engaged an audience is while consuming media. Existing methods, like surveys or click-through rates, often miss the nuances of real-time engagement and can be easily skewed.\n\nThis innovation works by using the motion sensors already built into devices like smartphones, tablets, and smart TVs. While you're watching a video, the device subtly tracks your movements. The system then analyzes these movements to determine when you're most attentive and when your attention might be drifting. Think of it like this: if you're completely still, you're probably very focused on what you're watching. If you're fidgeting or moving around, you might be losing interest. It's like having a silent observer that understands your body language.\n\nThis technology matters because it provides valuable insights to content creators, advertisers, and media companies. By understanding which parts of their content are most engaging, they can optimize their offerings to keep viewers hooked. For example, a streaming service could use this data to identify and edit out boring scenes, or an advertiser could use it to place ads at the most opportune moments. This leads to better content, happier viewers, and increased revenue.\n\nLooking ahead, this technology has the potential to revolutionize the media industry. As motion sensors become even more sophisticated, the accuracy and insights provided by this system will only improve. This could lead to even more personalized and engaging media experiences, as well as new opportunities for businesses to connect with their audiences. Investment in this area could yield significant returns as the demand for accurate audience engagement data continues to grow.","technical_analysis":"The Systems and Methods for Determining Audience Engagement Based on User Motion patent details a system that leverages motion sensor data to infer audience engagement during media consumption. The technical architecture comprises several key components:\n\nData Collection: Motion sensors integrated into user devices (smartphones, tablets, smart TVs) collect movement data during media playback. This data is timestamped and includes metrics such as acceleration, angular velocity, and device orientation.\n\nData Preprocessing: The raw motion data undergoes preprocessing to remove noise, correct for sensor drift, and normalize the data. This step ensures data quality and consistency for subsequent analysis.\n\nFeature Extraction: Relevant features are extracted from the preprocessed motion data. These features may include the magnitude of motion, the frequency of motion changes, and the duration of periods of stillness.\n\nEngagement Level Inference: Machine learning algorithms are employed to correlate the extracted features with engagement levels. These algorithms may include time series analysis, clustering, and classification techniques. The goal is to identify patterns in user motion that are indicative of engagement levels. For example, periods of reduced motion may be associated with high engagement, while periods of increased motion may suggest disinterest or distraction.\n\nContent Optimization: The inferred engagement levels are used to optimize content. This may involve editing poorly performing scenes, adding supplemental content to boost engagement, and personalizing content recommendations based on individual viewer behavior.\n\nThe implementation of this technology requires careful consideration of several factors. Privacy concerns are paramount, and robust data security measures and transparent data policies are essential. The accuracy of the system is also affected by the sensitivity of motion sensors and the diversity of user behavior. Ongoing calibration and refinement are necessary to maintain optimal performance. The system must be scalable to handle large volumes of data from diverse user devices. Code-level implications involve efficient data processing algorithms and robust error handling to ensure reliable performance across different devices and network conditions.","business_analysis":"Systems and Methods for Determining Audience Engagement Based on User Motion presents a significant market opportunity within the media and entertainment industry. The technology addresses the critical need for accurate and granular audience engagement metrics, which are essential for content optimization, personalization, and ad targeting.\n\nThe market size for audience engagement analytics is substantial and growing rapidly. The increasing consumption of digital media, coupled with the proliferation of streaming platforms and online advertising, is driving demand for more sophisticated engagement measurement tools. This patent offers a competitive advantage by providing a passive, continuous, and granular understanding of viewer behavior, unlike traditional methods that rely on explicit feedback.\n\nThe revenue potential for this technology is multifaceted. Content creators can use it to optimize their content, personalize viewer experiences, and drive revenue growth through increased viewer retention and ad revenue. Advertisers can identify optimal moments for inserting supplemental video content, maximizing viewer attention and ad recall, leading to higher ad effectiveness and revenue. Broadcasters can fine-tune their programming schedules, ensuring that the most engaging content is aired during peak viewing hours, leading to higher viewership and ad revenue.\n\nPotential business models include licensing the technology to media companies, offering a software-as-a-service (SaaS) platform for audience engagement analytics, and providing consulting services for content optimization and personalization. Strategic positioning involves targeting key players in the media and entertainment industry, such as streaming platforms, television broadcasters, and online advertising networks.\n\nROI projections are highly promising. By enabling content creators to optimize their content, this technology can lead to significant increases in viewer engagement, retention, and revenue. By enabling advertisers to target the right moments with the right ads, this technology can lead to higher ad effectiveness and revenue. A conservative estimate suggests that this technology can generate a 10-20% increase in revenue for media companies and advertisers. The strategic implications include gaining a competitive advantage, improving customer satisfaction, and driving revenue growth.","faqs":null,"topics":["audience engagement","user motion analysis","media consumption","behavioral analytics","patent"],"tech_cluster":null},"seo":{"title":"Audience Engagement via User Motion - Patent US-9854292","description":"Discover how this patent analyzes user motion to determine audience engagement with media. Full patent analysis, claims, and technical details.","keywords":["audience engagement","user motion analysis","media consumption","behavioral analytics","patent","patent US-9854292"]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-9854292","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-9854292","citation_suggestion":"Patentable. \"Systems and methods for determining audience engagement based on user motion\" (US-9854292). https://patentable.app/patents/US-9854292","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-9854292","json":"https://patentable.app/api/llm-context/US-9854292","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-30T01:52:18.008Z"}