Market Context — Why This Technology, Why Now

The global media landscape is undergoing a profound transformation, driven by the proliferation of streaming platforms and the demand for personalized, on-demand content. This necessitates rapid content repurposing and efficient archive monetization. Beyond media, enterprises are increasingly relying on video for internal communications, training, and knowledge management, creating an urgent need for automated tools to manage these growing digital assets. This technology directly addresses these pressures by enabling scalable, precise video segmentation.

Key Competitive Advantages
01

Automatically segments news programs by content item with high precision, significantly improving content searchability.

02

Reduces content production and editing time by an estimated ~66% by automating manual indexing and re-editing tasks.

03

Secures market differentiation with patent protection until ~2042, validated against 7 prior art documents.

Market Opportunity
Broadcast and Media Industry 📺
$400M–$500M globally (AI est.)
The accelerating need for efficient management and re-editing of vast video assets makes AI automation indispensable. There is high demand for rapid content generation and archive utilization.
Major broadcasting networks Streaming service providers Digital content platforms Media asset management software vendors
Education and Training Content 🎓
$100M–$150M globally (AI est.)
With the spread of online learning, the structuring and searchability of educational content are becoming increasingly important. There is growing demand to segment long lecture videos by topic to enhance learning efficiency.
E-learning platform providers Corporate training solution developers University media departments Educational content publishers
Enterprise Knowledge Management 🏢
$50M–$75M globally (AI est.)
As corporate DX initiatives advance, video-based knowledge sharing is increasing. There is a need to efficiently manage video recordings of meetings and presentations to quickly retrieve necessary information.
Enterprise collaboration software vendors Internal communications platforms Corporate IT departments Document management system providers
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects an AI-driven video segmentation apparatus and program, specifically covering the use of CNNs and image discrimination models to automatically identify and segment video content by item. It establishes a strong, robust scope, having overcome seven prior art documents during examination, demonstrating clear novelty and inventive step.

Competitive White Space

This patent primarily covers visual-based video segmentation. White space exists in advanced semantic content understanding, cross-modal analysis incorporating complex audio cues, or real-time live stream segmentation for immediate content delivery.

Economic Impact
~$150K/year estimated economic impact per facility (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

Assuming annual personnel costs of ~$200K (AI est.) for video archive management and editing, this technology could reduce workload by 50%, yielding ~$100K/year (AI est.) in cost savings. Additionally, improved content searchability could generate ~$50K/year (AI est.) in new revenue from existing assets, totaling an estimated ~$150K/year (AI est.) economic impact.

Speed to Market
6× faster than in-house development
This technology is designed to run CNN and image discrimination models on existing deep learning frameworks, with core algorithms already established and validated. The entire processing flow, from cut-point detection to item segment identification, has been verified with empirical data, confirming high-precision operation. This significantly reduces R&D investment and model training periods, enabling rapid system implementation and market entry within months, offering a substantial first-mover advantage.
Competitive Positioning

X: Video Analysis Automation Level
Y: Content Utilization Efficiency

Business Models & Applications
💻 Software License Provision
This model involves integrating the technology as a software module into the licensee's existing video management and editing platforms, with license fees collected. It offers flexible deployment and operation.
☁️ Cloud API Service
Provide this technology as a video data analysis API, adopting a pay-per-use model based on processed video length or data volume. This encourages widespread adoption by reducing development costs and enabling rapid implementation.
🤝 Content Solution Provision
Offer customized content archiving and re-editing solutions based on this technology for specific industries. This enables the creation of high-value proposals with end-to-end service, from implementation to operation.
Adjacent Application Opportunities
Surveillance & Security 📹
Automated Summarization of Long Surveillance Footage
This technology could be adapted to automatically detect specific movements or anomalous behavior patterns from vast amounts of surveillance camera footage, extracting only relevant sections. This dramatically reduces the effort of reviewing hours of video, significantly enhancing security operation efficiency. The video segmentation and feature extraction capabilities are crucial for identifying critical event intervals.
Autonomous Driving & MaaS 🚗
Critical Scene Identification in In-Vehicle Camera Footage
From in-vehicle camera footage collected by autonomous vehicles, this technology could automatically detect and log critical scenes such as dangerous driving behavior, pre-accident situations, or specific traffic changes. This would contribute to improved development efficiency and enhanced safety assistance systems. The CNN-based image feature extraction is effective for diverse scene discrimination, promising high-precision identification.
Medical & Healthcare 🏥
Automated Indexing of Surgical Videos
This AI could automatically identify and index key procedural phases or notable events (e.g., start of specific device use, complication occurrence) from surgical videos recorded in medical settings. This would significantly streamline training for residents and data analysis for surgical procedure improvement, contributing to enhanced medical quality. Advanced image feature extraction ensures detailed situational awareness.
Integration Roadmap — Estimated 8-Month Deployment
Phase 1: Requirements Definition & Data Preparation
Duration: 2 months
Define detailed requirements for the licensee's video data format and integration with existing systems. Proceed with collecting and annotating data for AI model training.
Phase 2: Model Adaptation & System Integration Development
Duration: 4 months
Retrain and tune the AI model to the licensee's data for optimal accuracy. Develop API integration modules for existing video management and editing systems.
Phase 3: Pilot Operation & Production Deployment
Duration: 2 months
Conduct pilot operations in a small-scale environment to evaluate system accuracy and stability. Incorporate feedback and deploy to the production environment after final adjustments.
Technical Feasibility
This technology is designed to operate on general-purpose CNN libraries and image processing frameworks, allowing for relatively easy integration as a software module into existing video processing systems or cloud infrastructure. The patent claims define cut-point detection, image feature calculation, cut-score calculation, and segment identification as distinct functions, enabling implementation through software updates or API integration without significant hardware changes.
Success Scenario
Implementing this technology could reduce the time required for manual item segmentation in news program editing and archiving by approximately ~66%. This would allow editing resources to focus on more creative content production, potentially leading to an estimated ~$150K/year (AI est.) in cost savings. Additionally, improved searchability of existing archives could foster the creation of new content initiatives leveraging past video assets.
Patent Record
APPLICATION NO.
特願2021-088944
REGISTRATION NO.
7737819
FILING DATE
2021年05月27日
GRANT DATE
2025年09月03日
EXPIRATION DATE
2041年05月27日
PATENT HOLDER
日本放送協会
Examination History
2024年04月04日
出願審査請求書
2025年05月07日
拒絶理由通知書
2025年07月03日
手続補正書（自発・内容）
2025年07月03日
意見書
2025年08月05日
特許査定