Market Context — Why This Technology, Why Now

The proliferation of AI across industries is driving an urgent need for efficient neural network (NN) deployment on increasingly diverse hardware, from specialized AI accelerators to low-power edge devices. Companies face intense pressure to rapidly innovate and customize AI solutions while grappling with a shortage of skilled AI engineers. This technology offers a critical advantage by streamlining NN adaptation, enabling faster iteration and reducing the high costs associated with manual optimization, thereby enhancing competitive agility in a dynamic global AI landscape.

Key Competitive Advantages
01

Automates machine learning parameter conversion between different neural network types, reducing design man-hours by up to 30% for new model development and optimization.

02

Estimates neural network scale and performance at the manufacturing stage based on converted parameters, reducing rework and shortening development lead times.

03

Establishes robust technical superiority, having been granted patentability after comparison with four prior art documents, indicating a stable and defensible IP foundation.

Market Opportunity
Semiconductor and AI Chip Development
$3.5B globally (AI est.)
Designing neural network circuits optimized for specific hardware in AI chip and accelerator development is highly complex. This technology streamlines the transition from software NN models to hardware implementation, reducing development time and costs.
AI chip manufacturers Semiconductor IP providers Custom ASIC design firms
Embedded Systems and Edge AI
$800M domestically (AI est.)
IoT devices and embedded systems require high-performance AI functions within limited resources, making efficient NN model conversion and optimization essential. This technology addresses this challenge, accelerating the adoption of edge AI.
IoT device manufacturers Automotive electronics suppliers Industrial control system developers
Cloud AI Services
$4.5B globally (AI est.)
Cloud-based AI services need to efficiently develop and deploy various NN models to meet diverse customer needs. This technology enhances development process flexibility and speed, boosting service competitiveness.
Cloud service providers AI platform developers Enterprise software vendors
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects an information processing apparatus and program for automated machine learning parameter conversion between different neural network types. Its broad claims and successful grant without rejection, despite four prior art citations, indicate strong novelty and inventiveness, providing a robust and defensible intellectual property foundation.

Competitive White Space

This patent primarily covers the conversion and manufacturing information generation for neural networks. It leaves white space in novel neural network architectures, advanced hardware fabrication processes, and post-deployment runtime optimization techniques for AI models.

Economic Impact
~$150K/year estimated cost reduction per facility (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

By enabling parameter conversion and optimization between different neural networks, this technology could significantly reduce man-hours for NN model redesign and retraining in AI chip and embedded system development. For example, if 15% of the man-hours spent by 10 design engineers on NN optimization (assuming an annual personnel cost of ~$0.5M (AI est.)) are reduced, an annual cost saving of ~$50K (AI est.) is expected. Including reduced opportunity loss from faster time-to-market, the total economic impact could exceed ~$150K/year (AI est.).

Speed to Market
6× faster than in-house development
This technology features established parameter conversion algorithms between different neural networks and is envisioned as a deployable software program. This eliminates the need for licensees to develop similar technology from scratch. It can be integrated relatively quickly as a software module into existing AI development environments or EDA tools. With demonstration data already prepared, post-integration validation periods are also shortened, significantly reducing overall development lead times.
Competitive Positioning

X: Cost-Effective Development Efficiency
Y: Hardware Optimization & Performance

Business Models & Applications
🚀 Platform-Integrated Licensing
Licensees could integrate this technology into existing AI development platforms or Electronic Design Automation (EDA) tools, offering it as a service to developers. This would streamline customer NN design and optimization processes, reducing development time and costs.
💡 Industry-Specific Solution Development
Leverage this technology to accelerate the development of AI chips and embedded NN solutions optimized for specific industries. This supports high-value hardware development, such as low-power NN designs for edge AI devices.
🔄 NN Conversion SaaS Provision
Offer the technology's parameter conversion capabilities as an independent service, generating subscription revenue from customers seeking AI model type conversion or hardware optimization. Its versatility across diverse NN models is a key strength.
Adjacent Application Opportunities
🚗 自動運転・ロボット
Efficient Design for Multimodal AI in Autonomous Systems
Autonomous driving and robotics rely on diverse neural networks for multi-sensor data processing and control. This technology could streamline parameter conversion and hardware optimization between different NN models (e.g., CNNs for image recognition and RNNs for path planning), potentially reducing development cycles by 20-30%. This accelerates the creation of safe, high-performance systems.
🧬 ライフサイエンス・医療AI
Accelerated Development of Complex Scientific AI Models
Life sciences, including drug discovery and genomics, demand complex NN models for vast datasets. This technology could accelerate knowledge transfer and hardware optimization between different NN types (e.g., protein structure prediction and drug response prediction models), potentially shortening R&D cycles by 15-25%. This contributes to faster drug development and personalized medicine.
🏭 スマートファクトリー
Real-time NN Optimization for Smart Factory Production Lines
Smart factories require flexible, high-performance NN models for high-mix, low-volume production and real-time anomaly detection. This technology enables efficient parameter conversion for NN models handling diverse sensor data and their integration into manufacturing equipment, potentially improving production efficiency by 10-20% and optimizing quality control.
Integration Roadmap — Estimated 12-Month Deployment
Phase 1: Analysis & System Design
Duration: 2 months
Analyze current NN development processes to identify optimal integration points for this technology. Define parameter conversion requirements and design interfaces with existing systems.
Phase 2: Implementation & Pilot Validation
Duration: 4 months
Implement the technology's program into existing development environments and conduct functional and performance validation with a small-scale pilot project. Verify converted NN model operation and evaluate manufacturing information accuracy.
Phase 3: Company-Wide Rollout & Optimization
Duration: 6 months
Based on pilot validation results, optimize the overall system and plan for company-wide deployment. Integrate this technology across all relevant NN development projects to maximize its impact.
Technical Feasibility
This technology functions as an information processing program centered on machine learning parameter conversion algorithms and manufacturing information generation logic. The patent claims suggest implementation on existing computing resources, not strong dependence on specific physical devices. Therefore, integration as a software module into existing AI development environments or EDA tools is technically straightforward, with high compatibility and no need for significant capital investment.
Success Scenario
Upon adoption, AI development teams could significantly reduce manual effort when optimizing neural network models for different hardware platforms. This could shorten development cycles, potentially accelerating new product market entry by 20%. Consequently, organizations could deploy a wider range of AI applications more rapidly, establishing a competitive advantage.
Patent Record
APPLICATION NO.
特願2021-138412
REGISTRATION NO.
7686273
FILING DATE
2021年08月26日
GRANT DATE
2025年05月23日
EXPIRATION DATE
2041年08月26日
PATENT HOLDER
国立大学法人　東京大学
Examination History
2024年08月23日
出願審査請求書
2025年04月15日
特許査定