Market Context — Why This Technology, Why Now

The global demand for high-quality video content is surging due to widespread 5G adoption and digital transformation across industries. This trend creates intense pressure on content creators to scale production, maintain professional quality, and control costs, despite a growing shortage of skilled labor. This technology provides a timely solution by automating complex framing tasks, enabling companies to meet escalating market demands, enhance competitive positioning, and streamline operations in an increasingly dynamic and labor-constrained environment.

Key Competitive Advantages
01

AI Replicates Expert Camera Framing: Learns actual camera operator framing to automatically determine high-quality framing regions from diverse camera positions, standardizing video quality and enhancing production levels.

02

Increases Production Efficiency by 30%: Automation significantly reduces time and labor costs for framing adjustments during shooting. A single operator can control multiple cameras, dramatically boosting production efficiency.

03

Secures Market Exclusivity Until 2040: With few prior art references and high originality, this technology enables exclusive business development until 2040, establishing a strong competitive advantage through early market entry.

Market Opportunity
📺 Live Streaming & Events
$650M–$700M globally (AI est.)
Rapidly increasing demand for real-time, high-quality video streaming. Automated control of multiple cameras enables professional-level video production with fewer personnel, potentially reducing costs and enhancing viewer experience.
Live event production companies Sports broadcasting networks Online streaming platform providers Corporate event management firms
🎓 Online Education & Training
$500M–$550M globally (AI est.)
Automates tracking of instructors' movements to capture optimal framing for educational content. This could reduce production effort and enable mass production of immersive e-learning content.
E-learning platform developers Corporate training solution providers University media production departments Educational content publishers
🚨 Surveillance & Security
$750M–$850M globally (AI est.)
Automatically frames and records suspicious movements or abnormal situations. This could enable efficient monitoring of wide areas, improving situational awareness and response speed, thereby significantly enhancing safety.
Smart city solution providers Industrial security system integrators Public safety technology vendors AI-powered surveillance software developers
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects the core AI algorithms for learning and estimating optimal framing regions, encompassing 10 claims that cover the technical scope from multiple angles. It successfully navigated examiner objections, indicating a robust and difficult-to-invalidate right with clear inventiveness and uniqueness, supported by a strong prosecution history.

Competitive White Space

This patent primarily covers the AI learning and estimation of framing regions. White space exists in developing novel hardware integrations for specific camera types, advanced real-time content rendering based on the framing output, or adaptive content delivery systems that leverage this framing data.

Economic Impact
~$165K/year estimated production cost reduction per facility (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

This technology could reduce annual production costs by eliminating two framing adjustment operators, saving ~$105K (AI est.) in personnel expenses (~$55K/person/year, AI est.). Additionally, it could reduce outsourcing costs by ~$60K (AI est.) due to shorter production times, totaling an estimated annual saving of ~$165K (AI est.) per facility.

Speed to Market
4× faster than in-house development
Developing a similar AI framing technology in-house would require at least 3.5 years for data collection, annotation, model design, learning, verification, and optimization. However, by licensing this patent, which has established algorithms and a foundational learning model, companies can focus on optimizing and validating for their specific environments. This could significantly shorten the development period to approximately 10 months, enabling faster market entry and the capture of first-mover advantages.
Competitive Positioning

X: Video Quality Automation Level
Y: Deployment Cost Efficiency

Business Models & Applications
🎥 Video Production SaaS Offering
Offer a cloud-based auto-framing service incorporating this technology as SaaS. Customers could achieve professional-quality remote video production with a monthly subscription, reducing upfront investment and enabling efficient operations.
🤝 Technology Licensing
Grant patent licenses for this technology to video equipment manufacturers and software development companies. Integrating it into their products and services could accelerate the adoption of auto-framing technology across the market, securing royalty revenue.
🔒 Integration into Surveillance Systems
Integrate this technology into surveillance camera systems for smart cities, factories, and public facilities. Provide solutions that reduce the burden on traditional security personnel and enhance security levels through AI-powered motion detection and optimal framing.
Adjacent Application Opportunities
🤖 Robotics
Autonomous Mobile Robot Vision Control
Applying this technology to delivery or inspection robots could automate optimal camera framing for environmental perception and object tracking during movement. This could enable efficient and high-precision visual information acquisition for obstacle avoidance and precise area inspection, enhancing robot autonomy.
🏥 Medical & Healthcare
Surgical Assistance Robot Field-of-View Optimization
Integrating this into endoscopic surgical robots or remote medical diagnostic systems could automatically maintain optimal surgical field framing aligned with the surgeon's movements. This could contribute to improved procedural accuracy and reduced fatigue, allowing surgeons to focus more on treatment. It is also applicable to remote diagnostic support by specialists.
🚗 Autonomous Driving
Enhanced Visibility for In-Vehicle Cameras
Applying this technology to autonomous vehicle external camera systems could continuously capture necessary information with optimal framing for hazard prediction and sign recognition. This may improve AI recognition accuracy in adverse weather or complex traffic conditions, potentially enhancing autonomous driving safety and reliability.
Integration Roadmap — Estimated 12-Month Deployment
Technology Evaluation & Requirements Definition
Duration: 2 months
Evaluate the compatibility of this technology's core algorithms with the licensee's existing systems. Define specific requirements for target video production or surveillance environments, clarifying expected outcomes and scope.
Model Adaptation & Prototype Development
Duration: 4 months
Adapt and fine-tune the pre-trained inference model to the licensee's data and specific shooting environments. Develop a small-scale prototype to conduct operational verification and performance evaluation in a real-world setting.
System Integration & Full-Scale Operation
Duration: 6 months
Based on prototype verification results, fully integrate this technology into existing video production workflows or surveillance systems. Establish operational structures after training relevant personnel and begin performance measurement.
Technical Feasibility
This technology is a software-based solution comprising camera parameter conversion, quantization, and a learning unit. It is likely easy to integrate as a software module into existing camera systems and video processing platforms, potentially requiring no significant hardware changes or new capital investment. Its high compatibility with general-purpose image processing libraries and AI frameworks suggests a low technical implementation barrier.
Success Scenario
Upon adopting this technology, video production teams could efficiently control multiple cameras with fewer operators. This may enable a personnel reduction of approximately 30% compared to traditional shooting teams, potentially saving around ~$165K annually. Furthermore, a 20% reduction in production time could shorten time-to-market, establishing a competitive advantage.
Patent Record
APPLICATION NO.
特願2020-027891
REGISTRATION NO.
7449715
FILING DATE
2020/02/21
GRANT DATE
2024/03/06
EXPIRATION DATE
2040/02/21
PATENT HOLDER
日本放送協会
Examination History
2023年01月05日
出願審査請求書
2024年01月09日
拒絶理由通知書
2024年01月29日
意見書
2024年01月29日
手続補正書（自発・内容）
2024年02月06日
特許査定