Market Context — Why This Technology, Why Now

The global shift towards smart agriculture, driven by demographic changes, climate concerns, and food security imperatives, is accelerating the adoption of autonomous farming solutions. This technology is critical for scaling these operations, offering a robust framework for managing complex multi-vehicle tasks. It enables agricultural enterprises to overcome labor constraints, optimize resource allocation, and enhance productivity, positioning them competitively in a market projected to reach $10B globally (AI est.) with a 12.5% CAGR.

Key Competitive Advantages
01

Safely and efficiently control multiple vehicles by optimally integrating instructions from multiple operators and autonomous control, minimizing collision risks.

02

Dynamically assign control authority between multiple instruction entities (human/AI) based on instruction priority, enabling flexible operation according to real-time field conditions.

03

Establish strong market superiority due to high uniqueness, evidenced by only three prior art documents cited by the examiner, allowing early market advantage for licensees.

Market Opportunity
Agricultural Machinery Manufacturers
$0.5B–$1B globally (AI est.)
The development competition for smart agricultural machinery is intensifying, making fleet control systems essential for product differentiation. There is high demand for integrating this technology into next-generation products.
Global agricultural equipment OEMs Specialized smart farming hardware developers Drone and robotics manufacturers for agriculture
Agricultural Service Providers
$300M–$400M globally (AI est.)
When expanding automated work-on-demand services for large-scale farms, this foundational technology ensures both efficiency and safety, establishing a competitive advantage.
Large-scale farm management companies Autonomous farming as a service (AFaaS) providers Agricultural drone service operators
Large-Scale Agricultural Corporations
$1B–$1.5B globally (AI est.)
These corporations are accelerating the adoption of autonomous vehicles on their farms to address labor shortages and improve cost efficiency, driving very high demand for highly efficient operational solutions.
Major corporate farms Agribusiness conglomerates Food production companies with integrated farming operations
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent successfully clarified its scope and secured patentability through prompt and precise amendments and arguments in response to examiner objections, resulting in a robust, low-invalidation-risk right. It is protected by 7 claims, offering licensees exclusive rights over a wide range of technical implementations, with clear superiority over prior art.

Competitive White Space

This patent focuses on dynamic control authority. White space exists in advanced sensor fusion for obstacle detection beyond basic collision avoidance, or in predictive maintenance algorithms for autonomous fleets.

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

Assuming 10 autonomous working vehicles are operated using this technology. Traditionally, if each vehicle required one operator, annual personnel costs would be ~$50K/person (AI est.) × 10 vehicles = ~$500K (AI est.). With this technology, if two supervisors can control 10 vehicles, personnel costs would be ~$50K/person (AI est.) × 2 supervisors = ~$100K (AI est.). The difference of ~$400K (AI est.), combined with a 20% reduction in fuel and material costs due to improved operational efficiency (~$350K (AI est.) annually × 20% = ~$70K (AI est.)), and increased revenue from higher utilization rates, is estimated to generate an annual economic impact of over $1M (AI est.).

Speed to Market
4× faster than in-house development
This technology's core algorithms for an autonomous working vehicle control system are already established and patented, providing a clear technical foundation. Specifically, the dynamic control authority assignment logic for multiple instruction entities has been meticulously designed. This allows licensees to significantly shorten development times compared to in-house development from scratch. Designed for integration with existing autonomous platforms and sensor systems, it enables rapid transition to the demonstration phase.
Competitive Positioning

X: Operational Efficiency & Safety
Y: System Scalability & Versatility

Business Models & Applications
💻 Software Licensing
Provide this control system as a software license for existing autonomous vehicles or robot platforms, enabling rapid market introduction.
🤝 Joint Development & OEM Supply
Collaborate with agricultural machinery manufacturers or robotics development companies to develop and produce next-generation autonomous working vehicles equipped with this technology, distributing them through an OEM supply model.
📊 Agricultural DX Solution Provision
Build an agricultural management platform centered on this technology, offering SaaS-based services such as operational data analysis, work optimization, and remote monitoring.
Adjacent Application Opportunities
📦 Logistics & Warehousing
Fleet Control for Autonomous Guided Vehicles (AGV/AMR)
Optimize collaborative work for multiple AGVs/AMRs in warehouses, controlling collision avoidance and efficient transport routes in real-time. Integrating human instructions with AI autonomous decisions could maximize productivity for picking and shelving operations, potentially increasing throughput by 25%.
🏗️ Construction & Civil Engineering
Coordinated Control of Multiple Construction Heavy Equipment
Applicable as a safe and highly efficient fleet control system for multiple construction heavy equipment, such as unmanned dump trucks and bulldozers, when performing earthmoving or grading tasks. It enables dynamic task assignment based on site conditions and smooth operator intervention during emergencies, potentially reducing project timelines by 15%.
🚨 Security & Surveillance
Optimized Patrols for Autonomous Security Robots
Applicable to systems where multiple autonomous security robots collaboratively execute efficient patrol routes in large facilities or factories, coordinating responses to anomalies. Integrating human instructions with robot autonomous decisions could enhance security quality and contribute to labor savings, reducing human patrol hours by up to 40%.
Integration Roadmap — Estimated 22-Month Deployment
Phase 1: Technical Feasibility & Basic Design
Duration: 4 months
Evaluate technical compatibility between this technology and the licensee's existing autonomous platform. Define basic design for system integration, including control interfaces and data linkage methods.
Phase 2: Prototype Development & Field Trials
Duration: 9 months
Develop a prototype system based on the basic design and conduct field trials in a controlled environment. Verify multi-vehicle coordination, effectiveness of instruction priority control, and safety, identifying any challenges.
Phase 3: Full System Deployment & Optimization
Duration: 9 months
Incorporate feedback from field trials to optimize the system for production environments. Customize the system to align with the licensee's operational structure, initiating full deployment and operation for large-scale autonomous vehicle fleets.
Technical Feasibility
This technology primarily consists of software logic and algorithms for vehicle control units. It can be integrated into existing autonomous vehicle hardware platforms through software updates or module additions. It exhibits high compatibility with general-purpose sensors and communication interfaces, making technical integration into existing systems relatively easy without requiring significant capital investment.
Success Scenario
Upon adopting this technology, the utilization rate of autonomous working vehicles on large-scale farms could increase from the current 70% to 90%. This could improve operational efficiency by up to 30%, allowing more land to be managed with fewer personnel throughout the year. Consequently, it is estimated to significantly reduce labor costs while stably increasing production output.
Patent Record
APPLICATION NO.
特願2021-051513
REGISTRATION NO.
7575063
FILING DATE
2021/03/25
GRANT DATE
2024/10/21
EXPIRATION DATE
2041/03/25
PATENT HOLDER
国立研究開発法人農業・食品産業技術総合研究機構
Examination History
2023年11月16日
出願審査請求書
2024年07月30日
拒絶理由通知書
2024年09月24日
意見書
2024年09月24日
手続補正書（自発・内容）
2024年10月01日
特許査定