Market Context — Why This Technology, Why Now

The global push for digital transformation in R&D, coupled with increasing demand for sustainable and high-performance materials, is driving significant investment in advanced discovery platforms. Companies are seeking solutions to manage vast datasets, accelerate innovation cycles, and reduce the environmental footprint of material development. This technology provides a crucial tool for navigating complex material landscapes, enabling faster iteration and more targeted development, essential for meeting both market demands and regulatory pressures worldwide.

Key Competitive Advantages
01

Accelerates discovery efficiency by 3x compared to traditional trial-and-error methods

02

Reduces R&D costs by ~20% through optimized experimentation and simulation

03

Standardizes knowledge transfer by formalizing expert tacit knowledge into a searchable graph

Market Opportunity
Material Development & Manufacturing
$3.0B–$4.0B globally (AI est.)
The increasing complexity of new material development and prolonged R&D periods are significant challenges, driving a surge in demand for efficient exploration systems.
Large-scale chemical manufacturers Advanced materials R&D divisions Automotive and aerospace material suppliers
Chemical & Pharmaceutical
$1.5B–$2.5B globally (AI est.)
In new drug development and chemical process optimization, extracting insights from vast experimental data and efficient exploration are directly linked to maintaining competitiveness.
Pharmaceutical R&D firms Specialty chemical producers Biotech companies focused on drug discovery
Data Science & R&D Platforms
$1.0B–$1.5B globally (AI est.)
Demand for platform services that support relationship analysis and exploration among complex data is increasing, making this technology a valuable foundational asset.
Enterprise AI/ML platform providers Scientific data analytics companies Contract research organizations (CROs) offering informatics services
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent, comprising 6 claims, protects a search system and method for exploring relationships within material property graphs. It was granted after successfully overcoming two office actions, demonstrating clear inventive step over prior art and establishing a robust, stable right for licensees.

Competitive White Space

This patent focuses on the graph construction and search methodology. It leaves white space for licensees to develop proprietary data acquisition methods, advanced visualization tools for complex graph analysis, or specialized integration with automated lab equipment.

Economic Impact
~$1.5M/year estimated R&D cost reduction per facility (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

Assuming this technology shortens new material development search periods by an average of 20%. If an annual R&D budget of ~$6.5M (AI est.) includes ~$3.5M (AI est.) for material search personnel and experimental costs, a 20% reduction yields ~$0.5M/year (AI est.) in direct cost savings. Additionally, ~$1.0M/year (AI est.) in opportunity loss reduction from earlier market entry could be realized, totaling ~$1.5M/year (AI est.) in potential economic impact.

Speed to Market
6× faster than in-house development
This technology is a foundational system developed by a national research institution, with established methods for constructing material property relationship graphs and graph search algorithms. This significantly shortens development time compared to companies building similar systems from scratch. Focusing on integration with existing data infrastructure and interface development could enable rapid market deployment.
Competitive Positioning

X: R&D Efficiency
Y: Novel Discovery Potential

Business Models & Applications
💻 Software Licensing
Develop a search system based on this technology and offer it as a software license to material development companies and research institutions.
☁️ SaaS Platform
Offer a cloud-based material property relationship graph search service. A subscription model provides easy access to advanced exploration features.
🤝 Joint Research & Consulting
Provide consulting services for data analysis and algorithm customization using this technology, tailored to specific R&D challenges of licensee companies.
Adjacent Application Opportunities
👩‍🔬 研究開発
AI Drug Discovery Target Identification
Graphing relationships between drug candidates and biomolecules could significantly enhance target discovery efficiency in AI drug development, potentially accelerating lead compound identification by 2-3x.
⚙️ 製造業
Manufacturing Process Optimization
Modeling causal relationships between process parameters and product quality as a graph could optimize production conditions and identify defect causes, potentially improving manufacturing yield by 10-15%.
📊 データ分析
Customer Behavior Path Analysis
Graphing customer behavior and purchase data to explore paths to specific patterns could enhance marketing strategy and personalized recommendations, potentially increasing conversion rates by 5-10%.
Integration Roadmap — Estimated 18-Month Deployment
Requirements Definition & Data Integration
Duration: 3 months
Define integration specifications with the licensee's existing data (material property data, experimental data, etc.) and build a data infrastructure for conversion and storage into a graph structure.
System Development & Prototype
Duration: 6 months
Develop a user interface and search condition extraction logic tailored to the licensee's needs, based on this technology's graph search engine, and implement a prototype.
Operation, Verification & Scale-up
Duration: 9 months
Verify effectiveness through pilot operation with the prototype, optimize the system based on feedback. Then, expand to full-scale R&D processes and scale up.
Technical Feasibility
This technology is a graph-structured data exploration system with high compatibility with existing database technologies and data processing pipelines. The graph search unit and search condition extraction unit described in the patent claims can be implemented as software modules, allowing for relatively easy integration into existing R&D systems or data analysis platforms via API. Since it can leverage general-purpose computing resources, the need for large-scale new capital investment is considered low.
Success Scenario
Upon adopting this technology, researchers could potentially reduce the time spent on traditional trial-and-error in new material and process exploration by up to 66%. This could enable the efficient extraction of dozens of new material candidates annually, accelerating the development pipeline. Furthermore, formalizing expert knowledge could elevate the organization's overall R&D capabilities, accelerating future innovation.
Patent Record
APPLICATION NO.
特願2020-533412
REGISTRATION NO.
7169685
FILING DATE
2019/07/18
GRANT DATE
2022/11/02
EXPIRATION DATE
2039/07/18
PATENT HOLDER
国立研究開発法人物質・材料研究機構
Examination History
2021年01月19日
出願審査請求書
2022年02月18日
拒絶理由通知書
2022年04月04日
意見書
2022年04月04日
手続補正書（自発・内容）
2022年08月01日
拒絶理由通知書
2022年09月28日
手続補正書（自発・内容）
2022年09月28日
意見書
2022年10月07日
特許査定