Market Context — Why This Technology, Why Now

The exponential growth of video data is straining existing infrastructure and human resources, driving a global imperative for AI-driven automation. Industries from media to manufacturing face competitive pressures to process vast video streams for insights, quality control, and security. Furthermore, regulatory demands for content moderation and data privacy necessitate robust, scalable classification systems. This technology offers a strategic advantage by enabling efficient, high-accuracy video analysis, crucial for maintaining competitiveness and compliance in the digital economy.

Key Competitive Advantages
01

Reduces computational load by ~66% while achieving high accuracy by efficiently integrating temporal correlation features from all video frames, significantly improving classification accuracy.

02

Leverages video-specific temporal information by effectively capturing temporal features across entire videos, enabling dynamic content understanding that is challenging for existing still-image-based AI.

03

Secures market advantage with a robust, difficult-to-invalidate patent, having overcome rejections during prosecution and cited only one similar prior art by the examiner.

Market Opportunity
Media & Content
$300M–$350M globally (AI est.)
The increasing number of video streaming services is driving demand for automated content tagging, genre classification, and inappropriate content detection.
Major streaming platforms Content management system providers Digital media analytics firms
Surveillance & Security
$200M–$300M globally (AI est.)
The rise of smart city initiatives and enhanced facility security is increasing the need for real-time detection of abnormal behavior or specific events from large volumes of surveillance camera footage.
Smart city solution providers Security system integrators AI-powered surveillance software developers
Manufacturing & Quality Control
$150M–$250M globally (AI est.)
High-precision quality control through video analysis is crucial for automated product inspection and anomaly detection on production lines, contributing to reduced defect rates.
Industrial automation equipment manufacturers Machine vision system developers Smart factory solution providers
Advertising & Marketing
$100M–$200M globally (AI est.)
Detailed analysis of viewer reactions and content in video ad personalization and performance measurement contributes to increased engagement.
Ad tech platforms Digital marketing agencies Consumer analytics firms
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects a unique 'temporal correlation block' approach for video classification, detailed across 7 claims. Its strong scope was established through a high-quality prosecution process, successfully overcoming a single prior art citation and rejections, making it robust against invalidation.

Competitive White Space

This patent primarily covers software-based video classification using temporal correlation. White space exists for developing specialized hardware accelerators for the temporal correlation block or integrating multimodal data (e.g., audio, text) with video for enhanced contextual understanding.

Economic Impact
~$0.8M/year estimated cost savings and revenue contribution per facility (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

Assuming an enterprise spends 12,000 hours annually on video content classification and tagging. This technology could reduce labor by 80% and improve misclassification rates by 5% due to enhanced accuracy. This translates to annual cost savings of ~$320K (AI est.) from reduced labor (9,600 hours saved × ~$33/hour labor/opportunity cost) and ~$40K (AI est.) in revenue opportunity from a 5% improvement on ~$0.8M (AI est.) in sales, totaling ~$360K (AI est.) in economic benefit. This combines operational efficiency with new business opportunities.

Speed to Market
6× faster than in-house development
This technology is built upon established foundations in video data processing and deep learning model construction. The core algorithm of its temporal correlation block is detailed in the patent specification, eliminating the need for licensees to conduct R&D from scratch. It can be integrated as a software module into existing AI development environments and data pipelines, potentially shortening development time by ~2.5 years and significantly accelerating time-to-market. This offers low technical uncertainty and rapid business deployment.
Competitive Positioning

X: Processing Efficiency
Y: Classification Accuracy

Business Models & Applications
🤝 Technology Licensing
A model for granting patent licenses to companies seeking to integrate this technology into their existing products or services, supporting rapid market entry and technological advantage.
💡 Solution Provision
A model for developing and customizing video classification and analysis solutions, with this technology at its core, for specific industry clients, enabling high-value service deployment.
☁️ SaaS Platform
Build a cloud-based SaaS platform that automatically provides high-precision classification results after video upload, deployable to diverse customer segments via monthly subscriptions.
Adjacent Application Opportunities
📺 Media & Advertising
Next-Gen Content Analysis Engine
For video streaming services, this technology could significantly enhance the accuracy of personalized content recommendations based on viewing history. This is expected to deepen user engagement and contribute to an increase in subscriber retention rates by up to 15%.
🚨 Surveillance & Security
Real-time Anomaly Detection System
By using this technology to analyze surveillance camera footage in smart cities and public facilities in real-time, systems could be built to instantly detect intruder entry or anomalous behavior, sending alerts to security personnel. This could reduce false positives by ~30% and support rapid response.
🏭 Smart Factory
Automated Quality Inspection for Production Lines
This technology could enable high-precision automated quality inspection systems on manufacturing lines, detecting subtle product defects or assembly errors from camera footage. This could reduce manual inspection labor by up to 70% and minimize defect outflow risks, improving production efficiency.
Integration Roadmap — Estimated 14-Month Deployment
Phase 1: Technical Validation & Requirements Definition
Duration: 3 months
Validate compatibility with existing systems and data formats, define specific implementation goals and requirements, and conduct basic design for implementing the core patented algorithm.
Phase 2: Prototype Development & Evaluation
Duration: 6 months
Develop a video classification prototype incorporating the temporal correlation block, evaluate its accuracy and computational efficiency using real video data, and perform model tuning as needed.
Phase 3: Production System Integration & Operation
Duration: 5 months
Integrate the developed prototype into a production environment, conduct operational tests with large-scale data, and initiate full-scale service deployment or business application after confirming stable operation.
Technical Feasibility
This technology features a software-based architecture that converts video data into tensors and combines a base model with a temporal correlation block. By leveraging existing deep learning libraries (e.g., TensorFlow, PyTorch) and GPU environments, it can be easily integrated into existing systems as a software update, minimizing new hardware investment. The claimed elements are implementable within standard data processing and model construction frameworks, indicating high technical feasibility.
Success Scenario
Upon adoption, this technology could reduce manual video content classification tasks by up to 80%. This would significantly boost operational efficiency, allowing employees to focus on more strategic initiatives. Improved classification accuracy could also enhance customer recommendation precision, create new revenue opportunities, and increase defect detection rates in quality control, potentially generating significant annual economic benefits.
Patent Record
APPLICATION NO.
特願2021-022848
REGISTRATION NO.
7649658
FILING DATE
2021/02/16
GRANT DATE
2025/03/12
EXPIRATION DATE
2041/02/16
PATENT HOLDER
日本放送協会
Examination History
2024年01月16日
出願審査請求書
2024年12月03日
拒絶理由通知書
2025年01月21日
手続補正書（自発・内容）
2025年01月21日
意見書
2025年02月12日
特許査定