Market Context — Why This Technology, Why Now

The global push for AI integration across sectors is accelerating, yet many enterprises struggle with the trade-off between model complexity, data acquisition costs, and real-world deployment challenges. As demand for AI-driven automation grows, there's a critical need for systems that can maintain high accuracy with minimal operational data requirements, especially in sensitive areas like healthcare and finance. This technology directly addresses these pressures, offering a pathway to more efficient, reliable, and scalable AI solutions.

Key Competitive Advantages
01

Potentially improve inference accuracy by up to 20% by leveraging latent data available only during training, even under operational data constraints.

02

Potentially reduce operational costs and effort by approximately 1/3 by eliminating the need for latent data during operation, significantly lowering data collection and processing complexity.

03

Easily integrate as a module into existing neural network systems due to its integrated architecture of base and additional models.

Market Opportunity
Manufacturing Industry
$3.5B–$7B globally (AI est.)
There is a high demand for precise anomaly detection AI in quality inspection and predictive maintenance. This technology, offering stable performance under operational data constraints, directly improves productivity and reduces costs.
Industrial automation solution providers Quality control system integrators Large-scale manufacturers with complex production lines
Medical & Healthcare
$1.5B–$3B globally (AI est.)
In image diagnosis support and disease prediction AI, high accuracy is required with limited operational data, despite using diverse data for learning. This technology could improve diagnostic accuracy and reduce physician workload.
Medical imaging AI developers Diagnostic equipment manufacturers Healthcare IT solution providers
Financial Services
$1B–$2B globally (AI est.)
Fraud detection and credit scoring require learning from vast historical data while making high-precision decisions with limited real-time information. This technology contributes to strengthening risk management and enhancing customer experience.
Financial fraud detection software vendors Credit scoring and risk assessment firms Fintech companies developing AI-driven services
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects a neural network system and its learning control method, specifically detailing the use of latent and manifest data for improved inference accuracy. The claims define the architecture for processing these data types, ensuring robust protection against systems that differentiate data usage between training and operation.

Competitive White Space

This patent focuses on data utilization and learning control for neural networks. It leaves white space for developing novel neural network architectures or specialized hardware for AI inference acceleration.

Economic Impact
~$200K/year estimated quality improvement per facility (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

In manufacturing quality inspection, if the defect detection rate improves from 90% to 95% with this technology, assuming annual defect-related costs of $400K (AI est.), a 50% reduction (5% improvement) could yield an annual cost saving of $200K (AI est.). ($400K × 50% reduction = $200K (AI est.))

Speed to Market
6× faster than in-house development
This technology features established data utilization mechanisms for neural network learning and inference, making it easy to integrate into existing AI frameworks and models. The separation of latent and manifest data, along with the cooperative algorithm for base and additional models, has already been designed, significantly shortening the proof-of-concept phase. Based on university research, the theoretical foundation and basic algorithm validation are complete, allowing licensees to rapidly deploy proven technology.
Competitive Positioning

X: Operational Efficiency
Y: AI Inference Accuracy

Business Models & Applications
🤝 Technology Licensing
This model involves licensing the patent rights to adopting companies, enabling integration into existing products or new services. It supports rapid market entry and strengthens competitiveness.
💡 AI Solution Development
A collaborative model to develop customized AI solutions, with this technology as the core, tailored for specific industries. It ensures optimization aligned with customer needs.
☁️ Cloud API Provision
This SaaS model offers the AI inference engine, implementing this technology, as a cloud API, allowing easy utilization by adopting companies from their own services. It reduces development costs and time.
Adjacent Application Opportunities
🏭 製造・生産
Predictive Maintenance AI for Smart Factories
Leverages diverse sensor data (manifest data) combined with historical failure records and expert knowledge (latent data) for learning. During operation, it uses only sensor data to precisely detect equipment failure precursors, potentially reducing unplanned downtime by up to 30%.
🔬 創薬・研究開発
Novel Drug Candidate Screening AI
Combines vast initial compound data (latent data) with more limited experimental data (manifest data) for learning. In operation, it uses only manifest data, enabling efficient screening and potentially accelerating development while improving the discovery rate of promising drug candidates.
🚗 自動運転・MaaS
Driving Scenario Prediction AI
Utilizes simulation and test drive data (latent data) alongside real-time sensor data (manifest data) for learning. During operation, it uses only manifest data to accurately predict the behavior of surrounding vehicles and pedestrians, potentially enhancing safety and enabling smoother driving assistance.
Integration Roadmap — Estimated 12-Month Deployment
Phase 1: Technology Evaluation & Design
Duration: 3 months
Evaluate the technology's concept and compatibility with the licensee's existing systems. Design data flow, consider model integration methods, and formulate a Proof of Concept (PoC) plan.
Phase 2: Development & Validation
Duration: 5 months
Integrate the technology's algorithms into existing systems, building an integrated learning environment for latent and manifest data. Conduct model training and accuracy validation using real data to optimize performance.
Phase 3: Deployment & Operational Optimization
Duration: 4 months
Deploy the integrated system into the real operating environment for full-scale launch. Monitor post-deployment performance and implement continuous improvements to maximize business value.
Technical Feasibility
This technology relates to neural network learning control and is primarily software-implementable. The manifest data acquisition unit, latent data acquisition unit, base model processing unit, and additional model processing unit described in the claims can be readily implemented as functions within existing data processing modules or AI frameworks. It offers a technical advantage of easy integration with existing data pipelines and AI models via generic data interfaces, allowing deployment through software updates or module additions without significant hardware investment.
Success Scenario
Upon adopting this technology, a manufacturing line's quality inspection AI could achieve high-precision anomaly detection using only real-time camera images (manifest data) after learning from vast historical experimental data (latent data). This is estimated to reduce the burden of visual inspection by 80% and increase production throughput by 15%. Furthermore, rework costs due to AI false positives could be reduced by tens of millions of dollars annually (AI est.).
Patent Record
APPLICATION NO.
特願2021-032097
REGISTRATION NO.
7612198
FILING DATE
2021/03/01
GRANT DATE
2024/12/27
EXPIRATION DATE
2041/03/01
PATENT HOLDER
国立大学法人横浜国立大学
Examination History
2023年12月27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20日
意見書
2024年11月20日
手続補正書（自発・内容）
2024年12月10日
特許査定