Market Context — Why This Technology, Why Now

The global construction industry is undergoing a significant digital transformation, driven by demands for increased productivity, enhanced safety, and sustainability. Regulatory pressures for safer worksites and the imperative to reduce carbon footprints are pushing companies towards data-driven solutions. This technology enables firms to leverage existing equipment for advanced analytics, fostering a competitive edge in an increasingly automated and data-centric market.

Key Competitive Advantages
01

Enables Precise Operation Data Analysis: This technology links lever tilt, time, and operation mode, quantifying skilled operator know-how into precise data. This enables accurate improvements in work efficiency and skill transfer, unlike conventional vague operational data.

02

Facilitates Easy Integration with Existing Equipment: Designed to be attachable/detachable, this technology avoids major capital investment or machine modifications. It facilitates easy integration into existing construction equipment fleets, significantly reducing initial DX costs and enabling rapid data utilization.

03

Enhances Safety and Efficiency: Operation data identifies hazardous patterns and inefficient operations, allowing for operator feedback and potential application in automated control. This could reduce accident risks and shorten work cycle times by up to 20%.

Market Opportunity
Smart Construction Market
$60B–$70B globally (AI est.)
The market is projected for strong growth due to accelerating adoption of IoT devices and data analytics solutions, driven by digital transformation initiatives and increasing demand for labor-saving in construction sites.
Major construction equipment manufacturers Digital construction solution providers Large-scale infrastructure developers
Construction Equipment IoT Market
$1.5B–$2.5B globally (AI est.)
Demand for real-time collection and analysis of construction equipment operational data is increasing, making this technology crucial for efficient predictive maintenance and fleet management.
Telematics and IoT platform providers Heavy machinery rental companies Predictive maintenance software developers
Skill Transfer Solutions Market
$300M–$400M globally (AI est.)
As skilled workers retire, technology transfer to less experienced personnel is a critical challenge, leading to increased investment in data-driven education and training solutions.
Workforce training and development firms VR/AR simulation companies Construction management software vendors
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects a detection device that links lever operation, time information, and operation mode information for construction machinery. The claims are well-defined, having been granted after overcoming prior art and examiner objections through an accelerated examination process, indicating strong stability and enforceability.

Competitive White Space

This patent primarily covers the detection and output of operational data from construction machinery levers. White space exists in developing advanced AI models for predictive maintenance or fully autonomous control systems, and integrating with other sensor types (e.g., vision, lidar) for comprehensive environmental awareness.

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

Precision operation data analysis could reduce fuel consumption by 10% (~$15K/year (AI est.)), optimize maintenance frequency by 15% (~$10K/year (AI est.)), and improve work efficiency by 10%. This efficiency gain, based on 2,000 annual operating hours at ~$35/hour (AI est.) labor cost, could save ~$5K/year (AI est.) in labor equivalent. For a company operating 10 construction machines, this could lead to an estimated annual cost reduction of ~$300K (AI est.) (calculated as ($15K + $10K + $5K) × 10 units).

Speed to Market
6× faster than in-house development
This technology, structured as a detachable detection device for construction machinery, has already been patented. This allows adopting companies to bypass the initial R&D phase, significantly reducing the extensive time and cost associated with technical validation and fundamental algorithm development. Its detachable design facilitates relatively easy physical integration into existing construction machinery, enabling early proof-of-concept testing and field deployment. This significantly shortens time-to-market, establishing a competitive advantage.
Competitive Positioning

X: Data Analysis Precision
Y: Ease of Integration

Business Models & Applications
📊 Data Analytics Service Provision
Offer subscription-based services for analyzing collected operational lever, time, and mode data on a cloud platform, providing reports on work efficiency improvements and safety enhancement proposals.
⚙️ Detection Device Sales & Licensing
Sell the detection device itself to construction machinery manufacturers or rental companies, or license its manufacturing. This addresses retrofit demand for existing machines, lowering adoption barriers.
🤖 AI/Automation Integration Platform
Leverage high-precision operational data as a foundation for developing AI-driven autonomous driving algorithms for construction machinery and enhancing remote control system accuracy, deploying it as a platform.
Adjacent Application Opportunities
🏭 Manufacturing & Factories
Industrial Robot Operation Analysis
Apply this technology to detect and analyze operational lever (joystick, etc.) movements of industrial robots and manipulators in factories. Quantifying skilled worker's nuanced operational know-how could optimize robot programming and new employee training, potentially improving production efficiency and reducing defect rates.
🚜 Agricultural Machinery
Smart Agricultural Machine Operation Optimization
Detect and analyze operational lever information from agricultural machinery like tractors and combines, linking it with time and operation modes (tillage, sowing, harvesting). This could help learn efficient operating patterns from experienced farmers, contributing to improved autonomous driving accuracy, optimized fuel consumption, and maximized yields.
🚢 Logistics & Ports
Enhanced Safety for Crane & Forklift Operations
Collect and analyze real-time operational lever data from port cranes and warehouse forklifts. Detecting hazardous operating behaviors and providing feedback to operators could significantly reduce accident risks. It also contributes to efficient personnel training by objectively evaluating operational proficiency.
Integration Roadmap — Estimated 18-Month Deployment
Phase 1: Technical Validation and Requirements Definition
Duration: 3 months
Assess the technology's suitability for the licensee's construction machinery types and operating environments, design data linkage interfaces, and set specific data utilization goals.
Phase 2: Prototype Development and Field Validation
Duration: 6 months
Install prototype detection devices on selected construction machinery, build data collection systems, and conduct small-scale field validation experiments through actual construction site deployment to confirm technical effectiveness and stability.
Phase 3: Full-Scale Deployment and System Integration
Duration: 9 months
Based on validation results, formulate a full-scale deployment plan and proceed with integration into existing fleet management systems and AI analysis platforms. Establish and optimize operational structures for company-wide rollout.
Technical Feasibility
This technology is configured as a detachable detection device for construction machinery, making its physical integration into existing equipment highly feasible. Lever tilt detection can be achieved with general-purpose sensors, and time detection via GPS modules, eliminating the need for large-scale new capital investment. Its primary function is data detection and output, allowing for high technical feasibility of integration via a data linkage interface without significant modifications to existing construction machinery control systems.
Success Scenario
Upon adopting this technology, skilled operator patterns could be quantitatively visualized, potentially reducing skill transfer periods for less experienced operators by 20%. Furthermore, real-time detection of hazardous operations and subsequent alerts could reduce accident rates on construction sites by an estimated 15%. This would contribute to enhancing corporate brand value and optimizing insurance premiums, leading to sustainable construction site operations.
Patent Record
APPLICATION NO.
特願2021-200726
REGISTRATION NO.
7100924
FILING DATE
2021/12/10
GRANT DATE
2022/07/06
EXPIRATION DATE
2041/12/10
PATENT HOLDER
学校法人　関西大学
Examination History
2021年12月14日
早期審査に関する事情説明書
2021年12月14日
出願審査請求書
2022年01月07日
早期審査に関する通知書
2022年02月04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06月17日
特許査定