Market Context — Why This Technology, Why Now

Industries worldwide are grappling with the increasing complexity of data generated by advanced sensors and IoT devices. The demand for real-time, high-precision analysis of non-stationary signals is escalating, driven by the need for proactive anomaly detection in manufacturing, enhanced diagnostic capabilities in healthcare, and stringent quality control in process industries. This technology directly addresses these challenges by providing a robust, model-free solution that reduces reliance on expert interpretation and accelerates data-driven decision-making across critical sectors.

Key Competitive Advantages
01

Accurately quantifies time-varying frequency components from complex signals.

02

Enables detailed analysis from a single signal channel, simplifying data acquisition.

03

Delivers high-precision, model-free analysis for diverse non-stationary signals.

Market Opportunity
Medical and Healthcare Diagnostics
$200M–$650M globally (AI est.)
This technology contributes to early diagnosis, preventive medicine, and remote monitoring by enabling non-invasive, high-precision analysis of biosignals (e.g., ECG, EEG, EMG). It could help reduce healthcare burdens and improve quality of life in aging societies.
Medical device manufacturers Digital health platform providers Diagnostic imaging companies Wearable health tech developers
Industrial Predictive Maintenance
$350M–$1.5B globally (AI est.)
By providing detailed analysis of vibration and acoustic signals from industrial equipment like motors and bearings, this technology could enable early detection of machine anomalies. This directly reduces unplanned downtime, improves productivity, and optimizes maintenance costs.
Industrial IoT solution providers Manufacturing equipment OEMs Condition monitoring system developers Smart factory integrators
Food and Chemical Plant Quality Control
$150M–$550M globally (AI est.)
This technology could monitor and analyze subtle physical and chemical signal changes in real-time during manufacturing processes, enabling early detection of product quality anomalies. This contributes to improved production efficiency and product yield.
Process control system vendors Food processing equipment manufacturers Chemical plant automation providers Quality assurance solution developers
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects a signal waveform analysis system and program capable of quantitatively extracting frequency components from complex, time-varying signals using wavelet analysis. With 8 claims, the patent demonstrates robust scope, having overcome two office actions, suggesting strong validity and low invalidation risk. Its limited prior art references underscore its high originality and market technical advantage.

Competitive White Space

This patent primarily covers the core wavelet analysis algorithm and system. White space exists in developing specialized sensor hardware for data acquisition, integrating the analysis output with advanced AI/ML models for autonomous decision-making, or creating novel user interfaces for data visualization and interpretation.

Economic Impact
~$1.0M/year estimated diagnostic cost reduction and efficiency improvement per facility (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

Introducing this technology into medical diagnostics could reduce complex biosignal analysis time by an average of 30%. For a hospital performing approximately 2000 diagnoses annually, shortening diagnosis time per case (e.g., from 10 minutes to 7 minutes) could increase the number of diagnoses per physician by about 600 cases annually. Assuming an annual physician salary of ~$100K (AI est.), a 30% improvement in diagnostic efficiency corresponds to ~$30K/year in cost savings (AI est.). Additionally, factoring in increased equipment utilization and reduced re-examination costs from lower misdiagnosis rates, an overall economic impact of ~$1.0M annually is anticipated (AI est.).

Speed to Market
3× faster than in-house development
This technology is an established signal waveform analysis algorithm based on wavelet analysis, primarily implemented in software. This allows for significantly faster deployment compared to in-house development. Since the core algorithm is academically proven, licensees can focus on integrating it into existing systems and optimizing for specific applications, potentially reducing time-to-market by approximately 2 years. This enables early competitive advantage and maximized market opportunity.
Competitive Positioning

X: Analysis Accuracy and Real-time Capability
Y: Deployment Cost Efficiency

Business Models & Applications
💻 Software License Provision
Offers a license for the core analysis software or library to be integrated into a licensee's existing systems or products, enabling flexible implementation.
🧩 Analysis Module Integration Service
Develops and provides custom analysis modules implementing this technology, tailored to specific licensee needs and optimized for integration with existing sensor data collection systems.
📊 Data Analysis Platform Development
Jointly develops a cloud-based data analysis platform leveraging this technology, offering advanced signal analysis services to a diverse range of industry clients.
Adjacent Application Opportunities
👵 Elderly Care & Monitoring
Non-Invasive Biosignal Monitoring System
Continuously monitors subtle biosignals (e.g., breathing sounds, heart sounds, micro-vibrations from movement) in the elderly. This technology could analyze these signals to detect early signs of falls or sudden health changes, enabling non-contact monitoring that enhances safety without compromising quality of life.
🚜 Smart Agriculture
Plant Growth & Soil Environment Analysis System
Captures subtle growth vibrations from plants and weak signals related to soil moisture/nutrient fluctuations using sensors. This technology could analyze these to predict growth anomalies, diseases, or optimal watering times, contributing to increased yields and optimized resource utilization.
🚗 Autonomous Driving
Vehicle Sensor Anomaly Detection System
Analyzes subtle anomalies in real-time from sensor data (LiDAR, radar, cameras) in autonomous vehicles. This could include changes in road conditions, unexpected behavior of other vehicles, or signs of sensor malfunction, enhancing safety and enabling predictive maintenance for vehicle components.
Integration Roadmap — Estimated 18-Month Deployment
Technology Evaluation and PoC
Duration: 3 months
Validates the core analytical capabilities of this technology using existing licensee data or simulated data, confirming suitability for specific applications. Involves technical requirements definition and initial design.
Prototype Development and System Integration
Duration: 6 months
Develops a prototype for integrating this technology into the licensee's system, based on PoC results. Establishes connectivity with existing data acquisition interfaces and conducts functional testing.
Pilot Deployment and Operational Optimization
Duration: 9 months
Deploys the developed prototype in a real-world environment for performance evaluation and continuous data analysis. Optimizes the system based on operational feedback, preparing for full-scale deployment.
Technical Feasibility
This technology is a software solution centered on a wavelet analysis algorithm, making it relatively easy to integrate into existing signal processing systems. The patent claims feature a processing means that performs wavelet analysis on signal waveforms and calculates frequency components based on wavelet coefficient characteristics, implementable with general-purpose processors and software environments. As no special hardware or significant capital investment is required, licensees could leverage existing data collection infrastructure and add advanced analytical capabilities through software updates alone.
Success Scenario
Implementing this technology could significantly enhance the accuracy of non-stationary signal analysis in manufacturing lines and medical settings. For instance, in manufacturing anomaly detection, it could capture subtle vibration changes previously difficult to detect in real-time, potentially improving predictive maintenance accuracy by 20% and avoiding tens of millions of dollars (AI est.) in annual unexpected downtime losses. In medical diagnostics, it could lead to faster diagnosis times and improved accuracy, enabling quicker patient response and optimized healthcare costs.
Patent Record
APPLICATION NO.
特願2020-215284
REGISTRATION NO.
7549879
FILING DATE
2020/12/24
GRANT DATE
2024/09/04
EXPIRATION DATE
2040/12/24
PATENT HOLDER
国立大学法人九州工業大学
Examination History
2023年10月16日
出願審査請求書
2024年05月21日
拒絶理由通知書
2024年07月09日
手続補正書（自発・内容）
2024年07月09日
意見書
2024年07月30日
拒絶理由通知書
2024年08月02日
手続補正書（自発・内容）
2024年08月02日
意見書
2024年08月13日
特許査定