Market Context — Why This Technology, Why Now

The global media and entertainment industry faces increasing pressure to deliver immersive, high-quality live content while battling rising production costs and a scarcity of skilled technical talent. Regulatory shifts towards more accessible and inclusive content also drive innovation in automated production. This technology directly addresses these challenges by enabling efficient, AI-powered camera operations, crucial for maintaining competitiveness in a rapidly digitizing and globalized content market.

Key Competitive Advantages
01

Generates High-Precision Automated Camera Work: Neural networks learn play situations and historical data to automatically determine optimal imaging areas, matching or exceeding skilled operators.

02

Reduces Video Production Costs by ~20%: Significantly lowers overall production costs through reduced labor and fewer retakes, enabling efficient content creation.

03

Adapts in Real-Time: Responds instantly to dynamic changes, dramatically improving live video quality and delivering immersive viewer experiences.

Market Opportunity
Sports Broadcasting
$650M (AI est.)
Increased demand for high-definition live streaming via 5G and active investment in enhancing viewer experience with AI.
Major sports broadcasters Live event production companies Sports league media divisions
Esports Events
$2.0B (AI est.)
Rapid growth in viewership, especially among younger demographics, drives demand for dynamic multi-angle video production.
Esports event organizers Gaming publishers Digital content platforms
Live Entertainment Production
$550M (AI est.)
Increasing sophistication of video production for concerts, theater, and theme parks, coupled with growing demand for labor reduction.
Concert and theater production houses Theme park operators Event technology providers
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects a comprehensive scope covering automated camera work generation devices, programs, learning devices, and learning programs across 14 claims. The patent's strength is evidenced by its successful grant despite only one prior art reference cited by the examiner, indicating a robust and carefully considered claim set that is less susceptible to invalidation.

Competitive White Space

This patent primarily covers the software logic for automated camera work. White space exists in developing hardware-specific camera systems, advanced post-production AI tools, or personalized viewer experiences that leverage this core technology.

Economic Impact
~$200K/year estimated video production cost reduction per facility (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

Assuming an annual labor cost of $50K (AI est.) per skilled camera operator for sports broadcasting and event filming. Implementing this technology could allow one operator to control multiple cameras or enable unmanned operation, potentially saving $100K–$150K (AI est.) in labor costs for 2-3 operators. Additionally, reducing retakes and editing efforts could save $40K–$70K (AI est.). This projects total annual cost savings of $150K–$250K (AI est.), averaging ~$200K (AI est.) per facility.

Speed to Market
4× faster than in-house development
This technology features an established neural network-based algorithm for automated camera work generation and learning, demonstrating high technical maturity with a granted patent. The modular structure, including 'coupling unit' and 'determination unit' described in the patent abstract, suggests easy software integration into existing video systems. This could significantly shorten time-to-market compared to developing similar systems from scratch, enabling rapid business deployment for licensees.
Competitive Positioning

X: Creative Freedom in Visuals
Y: Operational Efficiency

Business Models & Applications
🤝 Technology Licensing
Granting licenses for the automated camera work generation and learning program to video production companies and broadcasters. Revenue generated through initial setup fees and recurring usage costs.
☁️ SaaS-based Service
Providing a cloud-based automated camera work generation service. A subscription model where event organizers and content producers pay based on required duration and features.
⚙️ System Integration
Offering custom solutions that integrate this technology with existing camera systems and video distribution platforms. Revenue derived from implementation support and maintenance services.
Adjacent Application Opportunities
🏭 Smart Factory
Automated Monitoring & Anomaly Detection in Manufacturing
This technology could be applied to systems that track robot and product movements on manufacturing lines in real-time, automatically detecting abnormal operations or defective products. The neural network's learning capabilities could replace skilled human visual inspection, potentially improving productivity and stabilizing quality across production lines by up to 15%.
🏥 Medical & Healthcare
Surgical Robot Vision Optimization
The AI could be adapted to automatically optimize camera work for surgical assistance robots by real-time assessment of surgical field movements and organ position changes. This could reduce surgeon burden and enhance the safety and efficiency of medical procedures by an estimated 10-15%.
🚗 Autonomous Driving
Autonomous Vehicle Camera View Control
In autonomous vehicles, AI could automatically control the viewpoint and zoom of multiple in-car cameras based on the importance of surrounding elements (other vehicles, pedestrians, traffic signs). This could support more accurate situation assessment and hazard prediction, potentially improving reaction times by 20%.
Integration Roadmap — Estimated 12-Month Deployment
Phase 1: Technical Validation & Requirements Definition
Duration: 3 months
Evaluate compatibility with existing video systems, define customization requirements based on content characteristics, and plan Proof of Concept (PoC).
Phase 2: System Development & Learning Data Preparation
Duration: 6 months
Integrate the technology's algorithms into the target system, conduct initial neural network training using historical video data from relevant events, and verify accuracy.
Phase 3: Field Deployment & Operational Optimization
Duration: 3 months
Begin system deployment and test operation in a real-world environment. Continuously improve the learning model based on feedback to achieve optimal automated camera work generation.
Technical Feasibility
This technology is a software-based automated generation and learning program utilizing neural networks. The patent claims clearly describe modular components such as a 'formation map generation unit,' 'convolution unit,' and 'determination unit.' This makes technical integration via software linkage with existing video processing systems and camera control platforms straightforward. It can be implemented with general-purpose computing resources and video input interfaces, requiring no extensive hardware modifications, thus indicating a low technical barrier to adoption.
Success Scenario
Upon adopting this technology, video production for sports broadcasting and events could enable a small number of operators to efficiently control multiple cameras. This could optimize traditional filming team structures and is estimated to reduce filming costs by up to 20% annually. Furthermore, the dynamic and optimal camera work generated by AI is expected to enhance viewer engagement and potentially contribute to higher content viewership.
Patent Record
APPLICATION NO.
JP2021-129104
REGISTRATION NO.
7751413
FILING DATE
2021/08/05
GRANT DATE
2025/09/30
EXPIRATION DATE
2041/08/05
PATENT HOLDER
日本放送協会
Examination History
2024-07-01
Request for Examination
2025-05-07
Office Action
2025-06-24
Amendment (Voluntary)
2025-06-24
Applicant's Response
2025-09-02
Decision to Grant