Market Context — Why This Technology, Why Now

The global surge in AI and machine learning adoption is creating unprecedented demand for specialized, high-quality training data, particularly for natural language processing and speech recognition. Companies face immense pressure to accelerate AI model development and deployment, yet manual data labeling processes are costly and slow. This technology offers a critical solution by automating data classification from broadcast media, enabling faster, more efficient, and more accurate data preparation, which is vital for maintaining a competitive edge in the rapidly evolving AI landscape.

Key Competitive Advantages
01

Reduces AI Training Data Collection Costs by ~66% by automating subtitle text classification using EPG information, eliminating manual data labeling and significantly reducing data collection and preparation costs.

02

Generates High-Accuracy Genre-Specific Text Data by leveraging broadcast EPG genre information to classify text with consistent standards, ensuring a stable supply of high-quality data for AI training.

03

Supports Diverse Content Genres by flexibly identifying both high-level and granular classifications, enabling effective collection and categorization of text data from a wide range of broadcast content, from news to dramas and documentaries.

Market Opportunity
AI Development & Data Science
$1B–$2B globally (AI est.)
High-quality training data is crucial for advanced AI models. This technology provides efficient data supply, accelerating development cycles and meeting growing demand.
AI model developers Data science platforms Machine learning research institutions
Media & Content Production
$3B–$5B globally (AI est.)
This technology could become a foundational asset for media companies driving data-driven strategies, including content analysis, recommendation systems, and automated summary generation.
Broadcast networks Streaming service providers Content analytics firms
Smart Devices & Home Appliances
$10B–$15B globally (AI est.)
Diverse genre-specific text data is vital for improving the accuracy of speech recognition and conversational AI systems in smart speakers and IoT devices, an area where this technology could significantly contribute.
Smart speaker manufacturers IoT appliance developers Voice AI solution providers
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects a genre-specific text collection device and program, covering the automated extraction and classification of subtitle text from digital broadcasts using EPG information. Its claims are robust, having overcome multiple prior art rejections, indicating a clear and stable scope of protection.

Competitive White Space

This patent primarily covers genre-specific text collection from broadcast media. Licensees could develop additional IP in real-time sentiment analysis, cross-platform data integration, or advanced semantic content generation beyond broadcast sources.

Economic Impact
~$150K/year estimated data collection cost reduction per facility (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

If a company outsources 100,000 hours of text data classification annually at an estimated $16.50/hour (AI est.), it incurs ~$1.5M/year (AI est.) in costs. This technology could reduce those costs by ~10%, leading to an estimated annual saving of ~$150K (AI est.), directly accelerating AI development and market entry.

Speed to Market
6× faster than in-house development
This technology's logic for extracting subtitles and EPG from digital broadcasts is already established and does not depend on specific document formats, eliminating the need for licensees to develop from scratch. Key modules such as broadcast reception, subtitle information extraction, EPG information extraction, program information identification, and text extraction are clearly defined in the patent claims, allowing for rapid integration as a functional addition to existing broadcast reception systems. This could significantly shorten the development period for AI training data collection infrastructure and accelerate time-to-market.
Competitive Positioning

X: Data Collection Efficiency
Y: AI Training Data Quality

Business Models & Applications
🤝 Licensing Model
Granting patent usage rights for this technology to AI development companies, media enterprises, and data service providers to generate royalty revenue.
💡 Joint Development & Solution Provision Model
Jointly developing and providing customized data collection and analysis solutions for specific industries, built upon this technology, to client companies.
📊 Data Provision Service Model
Providing genre-classified text data collected by this technology to AI research institutions and businesses in a SaaS model, generating recurring revenue.
Adjacent Application Opportunities
📺 Media Analytics
Automated Content Evaluation & Trend Analysis
Leveraging genre-specific text data collected by this technology, systems could be built to analyze broadcast content popularity and viewer reactions in real-time. This could optimize program production and maximize advertising effectiveness, supporting rapid, data-driven decision-making for media companies.
🤖 AI Conversational Systems
Develop Domain-Specific AI Assistants
Large volumes of genre-classified text data could be used to train high-accuracy AI conversational systems specialized in fields like finance, healthcare, or law. This could enable automated customer support, rapid retrieval of expert information, or personalized educational content features, improving efficiency by up to 40% in some applications.
📚 Education & Learning
Automated Generation of Personalized Learning Materials
Genre-classified text data could facilitate the automated generation of personalized learning materials tailored to a learner's interests and proficiency. Applying this to foreign language learning apps or knowledge acquisition platforms could dramatically improve learning efficiency by an estimated 25-30%.
Integration Roadmap — Estimated 12-Month Deployment
Phase 1: Technology Evaluation & Requirements Definition
Duration: 3 months
Define API specifications for this technology and integration requirements with existing systems. This phase involves validating implementation effectiveness and technical suitability through a Proof of Concept (PoC).
Phase 2: System Development & Integration
Duration: 6 months
Develop and integrate this technology into the licensee's systems based on defined requirements. This stage includes building data flows, conducting test operations, and performing initial performance evaluations.
Phase 3: Production Deployment & Optimization
Duration: 3 months
Transition the developed system to a production environment and commence live operations. Continuously monitor collected data quality and system performance, driving ongoing improvements and optimization.
Technical Feasibility
This technology leverages digital broadcast subtitle and EPG information, making it highly integrable as a functional addition to existing digital broadcast reception environments and data processing infrastructures. The patent claims clearly define the modular structure, including broadcast reception, subtitle information extraction, EPG information extraction, program information identification, and text extraction means, enabling integration via module linkage with existing systems. No special hardware investment is required, and implementation is primarily software-based, indicating a low technical barrier.
Success Scenario
Adopting this technology could reduce a licensee's personnel costs for AI training text data collection by ~20% annually. This could shorten the data collection to model training cycle by approximately 30%, significantly accelerating the speed of AI product market entry. Furthermore, high-quality, genre-specific data is estimated to improve AI model accuracy, contributing to service quality differentiation.
Patent Record
APPLICATION NO.
特願2020-204235
REGISTRATION NO.
7606866
FILING DATE
2020/12/09
GRANT DATE
2024/12/18
EXPIRATION DATE
2040/12/09
PATENT HOLDER
日本放送協会
Examination History
2023年11月02日
出願審査請求書
2024年10月01日
拒絶理由通知書
2024年11月05日
意見書
2024年11月05日
手続補正書（自発・内容）
2024年11月19日
特許査定