Market Context — Why This Technology, Why Now

The proliferation of IoT devices is generating unprecedented data volumes, straining existing network infrastructures and increasing operational costs. Simultaneously, the imperative for sustainability and operational efficiency drives demand for smarter, more resource-efficient solutions. This technology aligns perfectly with these trends by offering a method to extract critical insights from vast sensor networks without the overhead of constant, full-scale data transmission, enabling leaner, more responsive industrial and urban ecosystems.

Key Competitive Advantages
01

Reduces sensor installation and operational costs by up to ~60%

02

Increases data collection efficiency by up to ~3x

03

Enables real-time anomaly detection and rapid response

Market Opportunity
Smart Factories
$1.5B–$2.0B globally (AI est.)
There is a high demand for efficient data collection in monitoring production equipment, predictive maintenance, and quality control. This technology's cost reduction and real-time capabilities directly enhance productivity in these areas.
Large-scale manufacturing corporations Industrial automation solution providers Predictive maintenance software vendors
Smart Cities
$1.0B–$1.5B globally (AI est.)
In environments with numerous sensors spread across wide areas, such as traffic monitoring, environmental monitoring, and aging infrastructure surveillance, efficient data collection is crucial for optimizing urban operations.
Urban infrastructure developers Public utility operators Smart city platform providers
Agricultural IoT
$500M–$550M globally (AI est.)
For multi-point sensing across vast agricultural lands, including greenhouse temperature/humidity control, soil moisture, and crop growth, this technology enables precise environmental control while minimizing operational costs.
Agricultural technology companies Large-scale farm operators Greenhouse automation specialists
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects an exploratory information collection method within IoT sensor networks, specifically covering the process of identifying threshold-exceeding sensors and then selectively collecting data from proximate sensors. The claims are robust, having successfully navigated examiner objections, ensuring high stability and a broad scope of protection for this unique data optimization approach.

Competitive White Space

While the patent covers the method of selective data collection, white space exists in developing specialized hardware for ultra-low power sensor nodes, integrating advanced AI for predictive analytics on the collected data, or creating novel user interfaces for visualizing the localized anomaly information.

Economic Impact
~$1.0M/year estimated economic impact per facility (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

For large-scale IoT sensor network operators, assuming a ~20% reduction in sensor installations (from 10,000 to 8,000 units) and a ~40% reduction in data communication volume. With an estimated annual operational cost of ~$35/sensor unit (AI est.) and ~$0.5M/year (AI est.) for data communication, direct savings could be (~2,000 units reduced × ~$35/unit (AI est.)) + (~$0.5M/year (AI est.) × 40% reduction) = ~$70K (AI est.) + ~$200K (AI est.) = ~$270K/year (AI est.). Additionally, a 2% productivity improvement (e.g., ~$0.65M/year (AI est.) for a company with ~$35M/year (AI est.) revenue) could lead to a total economic impact of ~$1.0M/year (AI est.).

Speed to Market
6× faster than in-house development
This technology is designed for easy integration into existing sensor network infrastructures, significantly accelerating time-to-market. The core processes, such as receiving notifications from sensors exceeding a first threshold, are established software algorithms. These can be relatively easily embedded into existing IoT gateways, edge devices, or cloud platforms. This allows licensees to bypass extensive R&D, moving quickly from Proof of Concept (PoC) to full-scale deployment and establishing early market leadership.
Competitive Positioning

X: Cost Efficiency
Y: Data Collection Efficiency

Business Models & Applications
💡 IoT Solution Platform Provision
Develop sensor modules or gateways incorporating this technology, offering comprehensive IoT solutions for smart factories and smart cities.
📊 Data Collection & Analytics Service
Provide a SaaS-based service for analyzing and visualizing optimized data collected using this technology, supporting efficient asset management and environmental monitoring.
🤝 Technology Licensing
License this intellectual property to domestic and international IoT device manufacturers and system integrators, driving revenue through enhanced data collection efficiency across various industries.
Adjacent Application Opportunities
👵 介護・見守り
Anomaly Detection for Elderly Monitoring
Multiple sensors in a living space detect anomalies like falls or prolonged inactivity, triggering focused data collection from nearby sensors. This enables rapid safety checks and emergency responses while respecting privacy, potentially reducing response times by ~30% in elderly care scenarios.
👷 建設・土木
Structural Monitoring & Predictive Maintenance
Strain or vibration sensors on bridges, tunnels, or large structures detect abnormal values, initiating concentrated data collection from surrounding sensors. This could enable early detection of structural degradation, efficient inspection planning, and pre-emptive disaster warnings, potentially extending asset lifespan by ~15%.
📦 物流・倉庫
Smart Warehouse Environmental & Inventory Management
Temperature, humidity, or inventory sensors in a warehouse detect anomalies, prompting detailed data collection only from the affected area. This could enable early detection of product quality risks and precise inventory location, leading to a ~20% reduction in warehouse operational costs and improved management accuracy.
Integration Roadmap — Estimated 8-Month Deployment
Phase 1: Requirements Definition & System Design
Duration: 2 months
Define integration requirements with existing systems, sensor network scale, data collection items, and threshold settings. Develop a detailed system design for incorporating this technology.
Phase 2: Prototype Development & Validation
Duration: 4 months
Develop a prototype implementing the core algorithms based on the design. Conduct a Proof of Concept (PoC) in a small-scale environment to validate data collection efficiency, real-time performance, and system stability.
Phase 3: Production Deployment & Optimization
Duration: 2 months
Optimize the system based on validation results and proceed with deployment into the production environment. Implement continuous data monitoring and threshold adjustments post-deployment to fine-tune operations.
Technical Feasibility
This technology, an exploratory information collection method based on physical quantity threshold detection and proximity sensor notifications, is fundamentally a software algorithm applicable to existing communication protocols. The processes outlined in the patent claims can be implemented by adding functionality to existing IoT gateways, edge device firmware, or cloud-based data processing platforms. High compatibility with general-purpose sensors is expected, minimizing the need for extensive hardware changes or new capital investment, making smooth integration into existing IoT infrastructure technically feasible.
Success Scenario
Upon adoption, this technology could reduce communication costs and data processing load for extensive sensor networks by up to ~50%. This would enhance overall system responsiveness, estimated to shorten anomaly detection to response times by ~20%. Consequently, it could lead to fewer unexpected manufacturing line stoppages and an estimated ~10% reduction in annual equipment maintenance costs, strengthening business continuity and improving profitability.
Patent Record
APPLICATION NO.
特願2020-185792
REGISTRATION NO.
7540705
FILING DATE
2020/11/06
GRANT DATE
2024/08/19
EXPIRATION DATE
2040/11/06
PATENT HOLDER
国立大学法人信州大学
Examination History
2023年07月19日
出願審査請求書
2024年06月11日
拒絶理由通知書
2024年07月22日
手続補正書（自発・内容）
2024年07月22日
意見書
2024年08月06日
特許査定