Market Context — Why This Technology, Why Now

The global push for digital transformation (DX) and automation is intensifying across all sectors, fueled by the demand for greater operational efficiency and resilience against economic fluctuations. Companies are seeking integrated AI solutions that can adapt to dynamic business environments without requiring extensive, costly, and time-consuming custom development for each new task. This technology directly supports this trend by offering a unified, high-performance AI framework.

Key Competitive Advantages
01

Reduces AI development costs by ~30% by processing multiple tasks with a single learning model, significantly lowering development and operational expenses for individual AI models.

02

Improves task processing speed by 2x through a mechanism that determines and selects optimal output data based on the input task, supporting rapid decision-making in complex scenarios.

03

Establishes high uniqueness and market superiority with only two prior art documents, indicating strong technical advantage and potential for a unique market position difficult for competitors to follow.

Market Opportunity
Manufacturing Industry
$650M–$1.0B globally (AI est.)
The manufacturing sector is increasingly adopting AI for smart factories, automated quality inspection, and predictive maintenance. Multi-task AI is highly sought after to enhance productivity in these areas.
Smart factory solution providers Industrial automation equipment manufacturers Quality inspection system developers
Information & Communication
$1.0B–$1.5B globally (AI est.)
There is growing demand for AI solutions that efficiently process diverse tasks such as automated call center responses, data analysis, and network monitoring in the information and communication sector.
Telecommunications service providers Data analytics platform developers Network monitoring solution vendors
Financial Industry
$350M–$650M globally (AI est.)
The financial sector can benefit from AI that simultaneously handles multiple complex tasks like fraud detection, customer behavior analysis, and portfolio optimization, improving operational efficiency and risk management.
Financial fraud detection software companies Customer behavior analytics firms Investment portfolio optimization platforms
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects an information processing apparatus and method utilizing a recursive neural network for multi-task learning, specifically covering the process of acquiring, determining, and outputting data based on input tasks. The claims are robust, having successfully overcome examiner objections, confirming the patent's validity and strong differentiation from prior art.

Competitive White Space

This patent focuses on the core multi-task learning algorithm for recursive neural networks. White space exists in integrating this technology with novel hardware accelerators or specialized data pre-processing techniques for specific industry verticals, allowing for further IP development without conflict.

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

Assume developing and operating three distinct AI models conventionally incurs an estimated annual personnel and maintenance cost of ~$0.35M (AI est.) per model. This technology consolidates these into a single model, reducing overall development, operation, and maintenance costs by approximately two-thirds. This results in a direct annual cost reduction of ~$0.7M (AI est.) (calculated as (~$0.35M × 3) - ~$0.35M). Including reduced opportunity costs from faster development, the total economic impact is projected to exceed ~$1.0M (AI est.) annually.

Speed to Market
4× faster than in-house development
This technology is built upon established recursive neural network principles, with its multi-task learning algorithm already patented. This significantly shortens the R&D period for licensees. The patent's abstract and detailed description clearly outline the information processing apparatus's configuration and method, facilitating relatively easy system design. Integration into existing AI frameworks and hardware resources is anticipated, minimizing technical verification and implementation time for rapid market entry.
Competitive Positioning

X: AI Model Development Efficiency
Y: Multifunctionality & Task Adaptability

Business Models & Applications
🤝 Technology Licensing
Grant licenses for the technology's algorithms and implementation to companies, facilitating integration into their existing products and services for revenue generation.
💡 Solution Provision
Develop AI solutions centered on this technology, customizing them for specific industries or enterprises to create high-value business opportunities.
🔬 Joint Research & Development
Collaborate with universities and research institutions to further evolve and commercialize this technology in specific application fields, opening new markets.
Adjacent Application Opportunities
🏥 Healthcare & Medical
Streamlined Diagnostic AI Support
This technology could enable the construction of a diagnostic support system that integrally processes multiple medical image analyses (X-ray, MRI, CT) and information extraction from electronic health records using a single AI model. This has the potential to accelerate physician diagnostic processes and contribute to reducing misdiagnosis risks by up to 20%.
🚗 Autonomous Driving
Integrated Environmental Perception Systems
This multi-task learning AI could be adapted for autonomous driving systems to efficiently process multiple recognition tasks, such as integrating diverse sensor data (camera, LiDAR, radar), identifying pedestrians, vehicles, and signals, and monitoring driver status. This could lead to a 15% improvement in real-time environmental awareness, contributing to safer and more reliable autonomous driving.
👨‍💻 Software Development
Automated Code Generation & Review
This technology could form the core of a system that executes multiple software development tasks—including automated code generation from requirements, bug detection, security vulnerability analysis, and code quality assessment—with a single AI model. This has the potential to boost development process efficiency by ~25% and significantly enhance software quality.
Integration Roadmap — Estimated 18-Month Deployment
Phase 1: PoC & Requirements Definition
Duration: 3 months
Identify specific challenges and goals for the adopting company, and verify the applicability of this technology. Establish a Proof-of-Concept (PoC) environment and detail technical requirements.
Phase 2: Prototype Development & Validation
Duration: 6 months
Develop a prototype incorporating this technology based on identified requirements. Conduct functional verification and performance evaluation using real-world data.
Phase 3: System Integration & Production
Duration: 9 months
Proceed with API integration and data pipeline construction with existing systems, initiating operation in a production environment. Continuously improve and optimize the system.
Technical Feasibility
This technology, patented as an information processing method using recursive neural networks, can be integrated as a software module into existing information processing systems. The patent claims specifically describe the data acquisition, determination, and output flow through the collaboration of a processing unit and a determination unit, which are technical elements implementable with standard programming languages and AI frameworks (e.g., TensorFlow, PyTorch). It does not require the introduction of new dedicated hardware, making deployment on existing IT infrastructure or cloud environments straightforward, thus indicating low technical barriers to adoption.
Success Scenario
Upon adopting this technology, companies could efficiently manage and execute multiple distinct AI tasks with a single model. For example, it could simultaneously and accurately automate various inspection items (e.g., appearance, dimensions, function) on a manufacturing line. This is estimated to reduce operational costs by ~30% annually compared to conventional individual AI systems, accelerating quality control processes and optimizing resource allocation, thereby significantly enhancing market competitiveness.
Patent Record
APPLICATION NO.
特願2021-007422
REGISTRATION NO.
7551113
FILING DATE
2021/01/20
GRANT DATE
2024/09/06
EXPIRATION DATE
2041/01/20
PATENT HOLDER
国立大学法人九州工業大学
Examination History
2021年02月15日
手続補正書（自発・内容）
2023年09月22日
出願審査請求書
2024年04月23日
拒絶理由通知書
2024年05月24日
意見書
2024年05月24日
手続補正書（自発・内容）
2024年08月13日
特許査定