Market Context — Why This Technology, Why Now

Global trends in smart building development, stringent indoor air quality regulations, and corporate sustainability goals are driving demand for advanced HVAC and ventilation solutions. Companies are under pressure to create healthier, more energy-efficient spaces while managing rising operational costs. This technology directly addresses these needs by providing a rapid, accurate, and cost-effective method for optimizing airflow, enabling compliance and enhancing occupant well-being across diverse commercial and industrial settings.

Key Competitive Advantages
01

Accelerates Airflow Prediction & Reduces Costs by ~66% compared to conventional CFD analysis, enabling rapid predictions without specialized expertise.

02

Enables High-Precision Spatial Design by generating high-resolution prediction images from low-resolution inputs, accurately reflecting furniture and human placement to maximize ventilation efficiency.

03

Provides a Stable IP Foundation, having secured patentability against 5 prior art documents, offering licensees a robust and reliable technology for confident deployment.

Market Opportunity
Smart Buildings and Offices
$300M–$400M globally (AI est.)
Increased focus on employee health, productivity, and energy efficiency is accelerating investment in advanced HVAC and ventilation systems.
Smart building solution providers Commercial real estate developers Large corporate facility managers HVAC system integrators
Medical Facilities and Cleanrooms
$150M–$250M globally (AI est.)
Reducing infection risks and maintaining precise environmental control are critical, driving demand for fine-tuned airflow management technologies.
Hospital system operators Pharmaceutical cleanroom builders Medical device manufacturers Specialized HVAC providers for healthcare
Factories and Data Centers
$200M–$300M globally (AI est.)
Efficient airflow prediction is essential for optimizing equipment thermal management, reducing cooling costs, and ensuring stable operations.
Industrial plant operators Data center infrastructure providers Semiconductor manufacturing equipment OEMs Cooling system specialists
Commercial and Educational Facilities
$150M–$250M globally (AI est.)
Ensuring comfort and safety through optimized ventilation in high-occupancy spaces is a growing priority, driving widespread adoption.
Retail chain developers University and school facility managers Entertainment venue operators Public sector building management
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects a deep learning-based airflow prediction system, specifically covering the interconnected modules for input parameter acquisition, image processing, prediction, and output processing. Its early grant without rejections, despite 5 prior art documents, indicates a robust and stable intellectual property foundation, offering strong defense against imitation.

Competitive White Space

While the patent covers the core AI prediction system, licensees could build additional IP around real-time sensor integration for adaptive HVAC control or specialized hardware interfaces for specific building management systems.

Economic Impact
~$500K/year estimated ventilation design and operational cost reduction per facility (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

For companies managing 20 HVAC design projects annually, traditional outsourcing or CFD software costs ~$20K per project, totaling ~$400K/year (AI est.). This technology enables rapid in-house prediction, reducing costs to ~$3.5K per project, for direct savings of ~$350K/year (AI est.). Additionally, a 10% reduction in HVAC power consumption through airflow optimization could yield ~$200K/year in energy savings (AI est.), totaling ~$500K/year in economic benefits (AI est.).

Speed to Market
6× faster than in-house development
This technology benefits from an already established deep learning model and clearly defined functional modules for input parameter acquisition, image processing, prediction, and output processing. This significantly reduces the time required for licensees to develop algorithms from scratch. The pre-trained model, particularly for 2D images, integrates easily with existing architectural CAD data and floor plans, enabling rapid system construction. Performance validation based on empirical data is also straightforward, potentially shortening time-to-market by approximately 2.5 years.
Competitive Positioning

X: Prediction Accuracy and Reproducibility
Y: Deployment and Operational Cost Efficiency

Business Models & Applications
💻 Software License Sales
License the software incorporating this technology to enterprises, enabling their design and facility management departments to perform in-house airflow predictions.
☁️ SaaS Subscription Model
Offer airflow prediction as a cloud-based Software-as-a-Service (SaaS), allowing users to access high-precision simulation results on demand with minimal upfront investment.
🤝 Consulting & Design Support
Provide specialized consulting services and design support to architectural firms and general contractors, leveraging this technology for ventilation optimization and HVAC design.
⚙️ OEM Integration for Existing Systems
Offer this technology as an OEM module to HVAC equipment manufacturers and smart building solution providers, enhancing their product competitiveness.
Adjacent Application Opportunities
🚗 自動車・モビリティ
In-Cabin HVAC Optimization System
This technology could optimize in-cabin airflow and temperature distribution to maximize passenger comfort while minimizing HVAC energy consumption. This is particularly relevant for electric vehicles (EVs) to improve battery range and overall efficiency.
🏠 住宅・建設
High-Performance Building Ventilation Design
The technology could serve as a design support tool for high-performance, airtight homes, optimizing ventilation efficiency to prevent condensation, maintain indoor air quality, and achieve energy savings. This contributes to healthier and more comfortable living spaces.
🏙️ 都市開発・環境
Urban Airflow & Microclimate Simulation
This system could predict wind flow in dense urban areas with high-rise buildings, contributing to urban planning for mitigating 'building wind' effects and reducing urban heat island phenomena. This would enhance comfort for outdoor activities in urban environments.
Integration Roadmap — Estimated 19-Month Deployment
Phase 1: PoC & Data Integration Design
Duration: 5 months
Define integration specifications with existing licensee data (CAD drawings, HVAC info, sensor data) and prepare input data for the prediction model. Conduct a small-scale Proof of Concept (PoC) to validate prediction accuracy and effectiveness.
Phase 2: System Development & Model Adaptation
Duration: 9 months
Based on PoC results, develop the system and adapt/optimize the model to meet specific licensee requirements. Customize the user interface and output formats, progressing towards practical implementation.
Phase 3: Pilot & Full Deployment
Duration: 5 months
Conduct trial operations of the developed system in a real environment to evaluate performance and stability. Make final adjustments based on feedback and initiate full-scale operation, establishing a continuous improvement cycle.
Technical Feasibility
This technology exhibits high compatibility with existing architectural design software and facility management systems. Its input parameter acquisition module supports generic sensor data and existing HVAC information, while the input image processing module handles common 2D images like CAD data and floor plans. The deep learning model is already established, allowing licensees to adapt the model to their specific environments through re-training, enabling relatively rapid system construction. No specialized additional hardware is required, as implementation can be entirely software-based.
Success Scenario
Upon adopting this technology, architectural design teams could rapidly validate multiple optimal ventilation designs, considering furniture placement and human traffic patterns, potentially reducing simulation effort for design changes by approximately 70% and shortening overall project lead times. Post-deployment, integration with real-time data could enable optimal HVAC control based on season and occupancy, with an estimated 20% annual reduction in energy costs.
Patent Record
APPLICATION NO.
特願2021-165222
REGISTRATION NO.
7142974
FILING DATE
2021/10/07
GRANT DATE
2022/09/16
EXPIRATION DATE
2041/10/07
PATENT HOLDER
株式会社　ＳＡＩ
Examination History
2021年10月07日
出願審査請求書
2022年08月30日
特許査定