Market Context — Why This Technology, Why Now

Enterprises worldwide are undergoing rapid digital transformation, driving an insatiable demand for AI-powered solutions to extract actionable insights from vast, often multilingual, datasets. The ability to accurately classify time expressions is fundamental for advanced analytics, predictive modeling, and efficient content management. Furthermore, a persistent global shortage of skilled data annotators and NLP specialists makes automated, high-efficiency data preparation technologies like this indispensable for maintaining competitive advantage and accelerating AI development cycles.

Key Competitive Advantages
01

Achieves High-Precision Time Expression Recognition by cross-checking target and translated language data, eliminating ambiguity and surpassing single-language systems.

02

Boosts Learning Data Generation Efficiency by 50% by automatically creating high-quality datasets through multilingual data integration, significantly cutting annotation costs.

03

Enables Easy Multilingual Adaptation with a unique architecture that leverages translated texts, addressing language complexities and supporting global information processing initiatives.

Market Opportunity
Information & Communication
$5B–$6B globally (AI est.)
The increasing demand for automated text classification and analysis makes highly accurate time expression extraction crucial for enhancing content value and searchability.
Media content providers Digital publishing platforms AI-driven search engine developers Telecommunications data analytics firms
Machinery & Parts Manufacturing
$4B–$5B globally (AI est.)
Extracting specific time information from vast text data like manufacturing histories, fault logs, and work instructions can significantly improve predictive maintenance and production efficiency.
Industrial equipment manufacturers Automotive component suppliers Aerospace and defense contractors Smart factory solution providers
Customer Support & Service
$3B–$4B globally (AI est.)
Time expressions are frequently used in customer inquiries and feedback, making their automated classification essential for streamlining customer service operations and improving quality.
Customer relationship management (CRM) software vendors Call center technology providers E-commerce platforms Service desk automation companies
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects a natural language processing apparatus, classification apparatus, and program, specifically covering a unique method for high-accuracy time expression classification by cross-referencing original and translated texts. The robust claim set and successful prosecution against prior art indicate a strong, defensible intellectual property asset.

Competitive White Space

This patent primarily covers time expression classification via multilingual cross-validation. White space exists in broader semantic understanding, sentiment analysis, or domain-specific entity recognition beyond temporal data, allowing for complementary IP development.

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

For typical NLP model development, if 5 annotators (at $20/hour, AI est.) are needed for 200 hours/month for time expression annotation, annual labor costs are ~$240K (AI est.). This technology's 50% efficiency gain in learning data generation could reduce annual costs by ~$120K (AI est.). Including reduced opportunity costs from faster model development and fewer reworks from misclassification, the total economic impact could reach ~$150K (AI est.) annually.

Speed to Market
4× faster than in-house development
This technology builds upon established NLP algorithms for time expression extraction and classification, combined with existing machine translation techniques. Its integration into current language processing infrastructures is relatively straightforward. The core logic for time expression extraction and label estimation is clearly defined by the patent, significantly shortening basic research and Proof-of-Concept (PoC) phases. The multilingual cross-check mechanism, utilizing translated texts, is particularly amenable to rapid PoC and quick development cycles, potentially reducing development time by approximately 3.0 years compared to in-house efforts.
Competitive Positioning

X: Time Expression Recognition Accuracy
Y: Learning Data Generation Efficiency

Business Models & Applications
🤖 Time Expression AI Solution
Provide AI services that automatically and accurately classify time expressions in diverse text data, from news articles to customer inquiries and meeting minutes, enhancing information searchability and analytical precision.
📊 Learning Data Generation Platform
Offer a service that automatically generates time-expression-annotated learning data, addressing a common bottleneck in NLP model development. Multilingual support meets global dataset demands.
🔗 API-Driven Feature Integration
Provide automatic time expression classification functionality via API for integration into existing content management systems and business applications, streamlining multilingual content organization and reporting.
Adjacent Application Opportunities
📺 メディア
Smart News & Content Curation
This technology could enhance news content classification and search engines by precisely identifying temporal expressions (e.g., 'next week,' 'last summer') within articles, beyond just publication dates. This enables faster, more accurate information delivery to users and boosts the value of media companies' information assets by extracting critical time data from diverse formats of press materials, potentially improving content discoverability by 25%.
🏢 企業向けSaaS
Intelligent Meeting & Deal Record Analysis
Automate the extraction of temporal information—schedules, deadlines, past events—from meeting minutes and business records for enhanced summarization and task management. For multinational corporations with multilingual meetings, cross-referencing translated texts ensures higher reliability of extracted time data, potentially improving document processing efficiency by 30%.
📞 カスタマーサポート
Customer Inquiry Trend Analysis
Automatically classify and analyze critical temporal information—such as campaign durations or service outage times—from customer inquiry histories and social media feedback. This enables rapid identification of customer behavior patterns and issue trends, leading to product improvements and optimized support systems, potentially reducing incident resolution times by 15%.
Integration Roadmap — Estimated 12-Month Deployment
Phase 1: Requirements Definition & Technical Validation
Duration: 3 months
Evaluate the technology's core performance, assess technical integration feasibility with existing systems, and define specific implementation requirements.
Phase 2: System Design & Prototype Development
Duration: 6 months
Based on validation results, design the system architecture and develop/implement the core technology as a prototype. Conduct performance evaluation and iterative improvements using test data.
Phase 3: Production Deployment & Operation Optimization
Duration: 3 months
Utilize prototype insights for production deployment, conducting tests and optimization with live operational data to ensure stable performance and maximize value.
Technical Feasibility
This technology, defined as a natural language processing apparatus, classification apparatus, and program, is designed as a software component easily integrable into existing IT infrastructure. The patent claims outline an architecture that allows for technical collaboration with existing text analysis pipelines and machine translation services by providing interfaces for input text data and its translated counterparts. It is expected to operate on general-purpose server or cloud environments, requiring no significant new hardware investment, thus enabling rapid deployment.
Success Scenario
Upon implementation, this technology could enable automatic classification of time expressions within text data in enterprise document management and customer support systems. This is estimated to reduce information retrieval time by 20% and significantly boost operational efficiency by instantly identifying past incident dates or accurately grasping date/time information in customer inquiries.
Patent Record
APPLICATION NO.
特願2021-085397
REGISTRATION NO.
7716885
FILING DATE
2021年05月20日
GRANT DATE
2025年07月24日
EXPIRATION DATE
2041年05月20日
PATENT HOLDER
日本放送協会
Examination History
2024年04月19日
出願審査請求書
2025年01月28日
拒絶理由通知書
2025年03月13日
意見書
2025年03月13日
手続補正書（自発・内容）
2025年06月24日
特許査定