Market Context — Why This Technology, Why Now

The increasing volatility of global weather patterns and the imperative for climate resilience are driving demand for hyper-local environmental intelligence. Industries from agriculture to urban planning and energy management require granular, real-time data to mitigate risks, optimize resource allocation, and adapt to rapid environmental shifts. This technology directly supports these efforts by providing a critical layer of predictive accuracy.

Key Competitive Advantages
01

Increases prediction accuracy by up to ~20% compared to conventional models.

02

Boosts data utilization efficiency by 1.5x through detailed historical data analysis.

03

Applies broadly across industries, from agriculture to smart cities and disaster risk assessment.

Market Opportunity
Smart Agriculture
$300M–$400M globally (AI est.)
Precise temperature forecasting enables optimized pest and disease management, irrigation, and harvest timing, leading to increased yields and improved crop quality, accelerating adoption.
Large-scale agricultural corporations AgTech solution providers Greenhouse and vertical farm operators
Smart Cities & Disaster Prevention
$150M–$250M globally (AI est.)
Advanced weather data utilization is crucial for mitigating urban heat island effects, predicting heatstroke risks, and forecasting localized heavy rainfall, enhancing citizen safety and comfort while maintaining urban functions.
Urban planning and development agencies Smart city technology integrators Public safety and disaster management solution providers
Energy Management
$400M–$500M globally (AI est.)
High-precision temperature data directly supports stabilizing power supply-demand balance and energy conservation, particularly in renewable energy output forecasting and optimizing HVAC systems for buildings and factories.
Energy utility companies Renewable energy developers Building management system providers
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects a robust temperature estimation method, device, and program. It covers a core algorithm that uses regression analysis of historical deviation data and radiative cooling strength (RCS) data to enhance local temperature prediction accuracy. The claims were strategically designed and successfully navigated a rigorous examination process, indicating strong patentability and broad protection for the technology's core and potential applications.

Competitive White Space

This patent primarily covers the temperature estimation algorithm. White space exists in developing novel sensor hardware for data input, integrating the predictions into autonomous control systems for specific applications, or creating user-facing platforms for data visualization and decision support.

Economic Impact
~$1.5M/year estimated revenue improvement per facility (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

For an agricultural enterprise with ~$66.5M (AI est.) in annual revenue, implementing this technology could reduce harvest losses by an average of 2% by enabling precise pest control, irrigation optimization, and optimal harvest timing. This could lead to an estimated annual revenue improvement of over ~$1.5M (AI est.) per facility.

Speed to Market
6× faster than in-house development
This technology's core temperature estimation algorithm is already established and patented, eliminating the need for licensees to conduct R&D from scratch. By focusing on data integration with existing weather data acquisition systems and IoT sensor networks, rapid prototype development and validation are achievable. The established algorithm allows licensees to concentrate resources on optimizing for their specific environments and integrating data, significantly accelerating time-to-market.
Competitive Positioning

X: Local Prediction Accuracy
Y: Cost Efficiency

Business Models & Applications
📝 Technology Licensing Model
License the core algorithm and estimation method to companies in specific industrial sectors. Licensees can integrate this technology into their products and services to gain a competitive advantage.
☁️ SaaS-based Prediction Service
Offer high-precision temperature prediction data via a cloud-based SaaS model. This can integrate with licensee systems through APIs, securing a continuous revenue stream.
📊 Data Analysis & Optimization Solutions
Combine the licensee's existing data with this technology to provide customized data analysis and optimization solutions tailored to specific problem-solving needs.
Adjacent Application Opportunities
👵 Elderly Care & Monitoring
Heatstroke Risk Prediction for Elderly Care
Predicts micro-climatic temperature variations in specific regions to provide localized heatstroke risk alerts for elderly residents. By integrating with care facilities and home care services, this could enhance preventive care, contributing to the health and safety of users in a ~$500M market (AI est.).
🏭 Manufacturing
Precision Temperature Control for Manufacturing Processes
Predicts micro-weather conditions inside and outside factories for manufacturing processes requiring strict temperature control, such as semiconductor fabrication or food processing. This could optimize temperature and humidity control on production lines, stabilizing product quality and reducing energy costs by up to 15%.
🚚 Logistics & Delivery
Cold Chain Quality Assurance for Perishables
Accurately forecasts temperature fluctuations along delivery routes for perishable goods and pharmaceuticals requiring refrigeration. This enables optimal transport planning, minimizing quality degradation risks and improving cold chain management efficiency by ~10% across a ~$1B global market (AI est.).
Integration Roadmap — Estimated 8-Month Deployment
Phase 1: Current State Analysis & Data Integration Design
Duration: 2 months
Evaluate the licensee's existing weather data, IoT sensor data, and operational systems. Design the necessary data interfaces and system architecture for integration with this technology.
Phase 2: Model Adjustment & Pilot Operation
Duration: 4 months
Adjust the prediction model to the licensee's regional characteristics and data. Initiate pilot operations in a limited environment, conducting initial effect measurements and model accuracy verification.
Phase 3: Full Deployment & Impact Verification
Duration: 2 months
Based on pilot results, fully deploy the system to the production environment. Maximize the concrete value this technology brings to the business through continuous impact measurement and improvement activities.
Technical Feasibility
This technology is centered on an algorithm that leverages existing weather data and data obtainable from IoT sensors, such as deviation indicators and radiative cooling strength. The regression analysis, calculation, and correction steps described in the patent claims can be easily integrated as software modules into existing weather prediction systems or data analysis platforms. Given its expected use of generic data formats for input/output, it could be implemented with high compatibility with existing IT infrastructure and sensor networks, without requiring significant capital investment.
Success Scenario
Implementing this technology could enable high-precision prediction of pest and disease outbreak risks in specific agricultural fields up to one week in advance. This may optimize pesticide application timing, potentially reducing usage by 20% while increasing yields by 5%. In urban planning, it could visualize heatstroke risk in specific areas in detail, allowing for early public warnings and countermeasures, thereby reducing health hazard risks.
Patent Record
APPLICATION NO.
特願2020-016417
REGISTRATION NO.
7373846
FILING DATE
2020/02/03
GRANT DATE
2023/10/26
EXPIRATION DATE
2040/02/03
PATENT HOLDER
国立研究開発法人農業・食品産業技術総合研究機構
Examination History
2022年11月22日
出願審査請求書
2023年08月08日
拒絶理由通知書
2023年09月22日
手続補正書（自発・内容）
2023年09月22日
意見書
2023年10月03日
特許査定