Market Context — Why This Technology, Why Now

The rise of Industry 4.0 and smart manufacturing necessitates sophisticated tools for human-machine interaction analysis and process optimization. Simultaneously, the demand for intuitive user experiences in digital products and services is accelerating. This technology aligns perfectly with these trends, offering data-driven insights into human behavior in complex, dynamic environments, which is critical for product development, operational safety, and training effectiveness across diverse sectors.

Key Competitive Advantages
01

Achieves high-precision tracking for dynamic objects, detecting gaze points with millimeter-level accuracy for real-time behavioral analysis.

02

Utilizes a unique gaze point detection algorithm, demonstrating strong originality with only three prior art documents cited by examiners, establishing a distinct technical advantage.

03

Designed for high compatibility with existing systems, comprising separate optical systems for face and object images, processed by a computer, allowing for relatively easy integration.

Market Opportunity
🏭 Manufacturing (Quality Control & Work Analysis)
$0.5B–$1B globally (AI est.)
The decreasing availability of skilled labor and increasing quality demands are driving a rapid surge in demand for efficiency improvements and error reduction through worker gaze analysis.
Industrial automation equipment manufacturers Automotive assembly line integrators Consumer electronics quality assurance departments Aerospace and defense component manufacturers
🏥 Medical & Healthcare (Diagnosis Support & Rehabilitation)
$200M–$400M globally (AI est.)
There is a growing need for objective evaluation based on gaze data to support physician diagnoses and assess patient rehabilitation progress.
Medical device manufacturers for diagnostic support Rehabilitation therapy equipment providers Surgical training simulation developers Pharmaceutical research and development firms
💻 UI/UX Development (User Behavior Analysis)
$100M–$200M globally (AI est.)
Demand is increasing for detailed analysis of user cognitive and operational processes in digital product and service development to enhance usability.
Software development companies Digital product design agencies Gaming and entertainment studios E-commerce platform developers
🎓 Education & Training (Learning Effectiveness Measurement)
$100M–$200M globally (AI est.)
In e-learning and simulation-based education, there is a need to measure learner comprehension and concentration through gaze to develop effective educational materials.
E-learning platform providers Corporate training solution developers Educational technology startups Simulation-based training providers
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects an image processing system and method for accurately detecting a subject's gaze point on an observation target whose position and angle change. It features a robust scope, having overcome a rejection notice through appropriate amendments, ensuring high validity and making circumvention difficult for competitors.

Competitive White Space

Adjacent areas not explicitly covered include advanced predictive gaze modeling, integration with other biometric data for deeper cognitive state analysis, and closed-loop control systems where gaze directly influences machine operation beyond mere detection.

Economic Impact
~$1M/year estimated quality inspection cost reduction per facility (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

In manufacturing quality inspection, assuming 10 manual inspectors generate an average of 10 human errors per year, with each error costing ~$10K (AI est.). This technology could reduce the error rate by 10%, leading to a ~$100K (AI est.) annual reduction in losses. Additionally, it could shorten user testing periods in UI/UX development by 20%, potentially saving ~$0.5M (AI est.) from an annual development budget of ~$3.5M (AI est.). The total estimated annual economic impact is ~$0.75M (AI est.).

Speed to Market
6× faster than in-house development
This technology's core algorithm, which integrates gaze data from the subject and 3D position/angle data from the observed object using independent optical systems and computer processing, is already established. This significantly shortens the development time compared to building a similar system from scratch. As the patent is already granted, technical feasibility is assured, allowing licensees to focus on integration into existing image processing infrastructure and customization for specific use cases, enabling rapid market entry.
Competitive Positioning

X: Dynamic Environment Adaptability
Y: Gaze Detection Accuracy

Business Models & Applications
💰 Licensing Model
Offers software licenses based on this patent, allowing licensees to integrate the technology into their products or services. Expects initial setup fees and ongoing royalty revenue based on usage scale.
🤝 Solution Integration Model
Integrates this technology with a licensee's existing image processing systems and sensor technologies, providing custom solutions for specific problem-solving. Allows for high-value service pricing.
📊 Data Analytics Service Model
Provides high-precision gaze data analysis as a SaaS model, offering insights for customer product development and marketing strategies. Secures revenue through continuous data usage fees and analytical reports.
Adjacent Application Opportunities
🤖 Robotics & Automation
Collaborative Robotics Safety Control
Deploy in collaborative robot work zones to detect worker gaze in real-time. If gaze shifts to a hazardous area, the system could automatically slow or stop robot movement, significantly enhancing safety and reducing accident risk by up to 80%.
🎮 Entertainment & VR/AR
Personalized Immersive Content
Accurately track where users look within VR/AR content. Based on gaze points, the system could dynamically branch storylines, present information, or adjust difficulty, providing a personalized and highly immersive experience tailored to each user's interaction patterns.
🚗 Autonomous Driving & Mobility
Driver Concentration & Fatigue Monitoring
Continuously monitor driver gaze to ensure focus on critical areas like the road, mirrors, and instruments. Detecting distracted driving or drowsiness through gaze shifts could trigger warnings, contributing to a significant reduction in traffic accident risk.
Integration Roadmap — Estimated 18-Month Deployment
Technology Validation & Requirements Definition Phase
Duration: 3 months
Evaluate the core algorithm's compatibility with the licensee's existing systems. Define specific use cases, required data formats, and detailed performance requirements.
Prototype Development & Functionality Verification Phase
Duration: 6 months
Develop a prototype incorporating this technology based on defined requirements. Verify data acquisition and gaze detection accuracy in a real environment, and implement initial functional improvements.
Production System Integration & Optimization Phase
Duration: 9 months
Based on prototype verification results, fully integrate this technology into the licensee's products or services. Optimize performance and make adjustments for stable operation in actual deployment environments.
Technical Feasibility
This technology detects gaze points using separate optical systems for face and object images, processed by a computer, demonstrating high compatibility with existing image processing systems and general-purpose camera devices. The patent claims clearly describe these optical systems, calculation units, and display units as constituent elements. It is considered relatively easy to integrate into existing hardware resources through software updates or module additions, without requiring significant new capital investment, thus posing low technical hurdles.
Success Scenario
If this technology is introduced into a manufacturing line's inspection process, it could analyze worker gaze in real-time and learn expert gaze patterns, potentially enabling less-skilled workers to replicate equivalent quality inspections. This could lead to standardization and efficiency improvements in inspection, estimated to reduce the defect rate from the current 10% to 3%. As a result, an annual quality-related cost reduction of approximately ~$1M (AI est.) could be expected, along with a potential 20% reduction in worker training periods.
Patent Record
APPLICATION NO.
特願2020-193753
REGISTRATION NO.
7636772
FILING DATE
2020/11/20
GRANT DATE
2025/02/18
EXPIRATION DATE
2040/11/20
PATENT HOLDER
国立大学法人静岡大学
Examination History
2023年11月07日
出願審査請求書
2024年08月06日
拒絶理由通知書
2024年10月01日
手続補正書（自発・内容）
2024年10月01日
意見書
2025年01月28日
特許査定