Market Context — Why This Technology, Why Now

Industries worldwide face unprecedented challenges from demographic shifts, leading to critical shortages of skilled labor and a growing demand for operational efficiency. This technology offers a strategic solution by digitizing tacit human knowledge, enabling scalable automation and reducing human error. It aligns with global pushes for Industry 4.0, smart manufacturing, and autonomous systems, where robust, experience-based AI is essential for next-generation operational intelligence and resilience.

Key Competitive Advantages
01

Automates Tacit Knowledge Formalization: Records human actions and environmental changes in an information matrix without human intervention, digitizing highly subjective tacit knowledge.

02

Offers Versatile Adaptability: Easily applicable to diverse environments and uses, from vehicle operation to factory machinery and monitoring systems, by simply adding or modifying observation rule sets.

03

Enables High-Precision, Human-Like Situational Judgment: AI replicates human-like feature extraction and complex decision-making through multiple information identification rules and composite input information identification rules.

Market Opportunity
Robotics and Factory Automation
$500M–$1B globally (AI est.)
Driven by advancements in factory automation and accelerated robot adoption due to labor shortages. There is a clear need to enable robots to learn tasks from skilled workers.
Industrial robot manufacturers Automation system integrators Smart factory solution providers
Smart City and Monitoring
$300M–$800M globally (AI est.)
Increasing demand for safety and security in aging societies. AI learning human behavior patterns is crucial for applications like wandering detection and anomaly monitoring.
Smart home technology developers Elderly care service providers Public safety solution vendors
Autonomous Driving and Mobility
$5B–$10B globally (AI est.)
Enhances safety and reliability in autonomous driving systems by allowing AI to learn driver experience, which is critical for complex situational judgment.
Autonomous vehicle software developers Automotive OEMs Mobility-as-a-Service (MaaS) providers
Education and Training
$200M–$500M globally (AI est.)
Provides efficient learning content by enabling AI to replicate expert movements and decision criteria in VR/AR simulation training environments.
VR/AR training platform developers Corporate learning solution providers Vocational training institutions
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects an apparatus and system for normalizing time-series environmental information into an information matrix, enabling AI to automatically learn and make human-like judgments. Its 19 claims cover broad aspects of the technology, providing robust defense against imitation, and its successful prosecution after a single office action affirms its technical distinctiveness and strong enforceability.

Competitive White Space

This patent focuses on normalizing time-series environmental data for AI learning. White space exists in developing novel sensor fusion techniques or integrating this AI with advanced human-robot collaboration interfaces not explicitly covered.

Economic Impact
~$250K/year estimated cost reduction per facility (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

In manufacturing and service industries, transferring expert experience via OJT is estimated to incur annual training costs of ~$50K/person (AI est.), covering personnel, training, and opportunity costs. By deploying this technology, AI automatically learns and accumulates experience, potentially reducing training costs for 5 experts by 60% annually (~$50K/person × 5 people × 0.6 = ~$150K/year (AI est.)). Additionally, AI-driven optimal action prediction could reduce human errors, leading to an estimated ~$100K/year (AI est.) in quality improvement and rework reduction, totaling an estimated ~$250K/year (AI est.) in economic benefits.

Speed to Market
5× faster than in-house development
This technology benefits from established algorithms for normalizing time-series environmental information via an information matrix, with clear logic for feature detection and composite judgment using multiple identification rules. This significantly shortens the design and implementation phases compared to developing a new experience-learning AI system from scratch. Its extensibility, achieved by adding or modifying observation rule sets, was considered during design, potentially allowing efficient integration into existing systems and reducing development time by up to 80%.
Competitive Positioning

X: Automation Level of Experience Learning
Y: Versatility Across Applications

Business Models & Applications
🤖 Experience Learning AI Module Provision
This business model licenses the technology as an AI engine module to robot manufacturers and system integrators, enabling rapid integration of experience learning capabilities into existing AI systems.
☁️ SaaS Solution for Specific Operations
Offers SaaS solutions tailored for specific operations, such as quality inspection in manufacturing, elderly monitoring in care facilities, or safety management in construction. Customers benefit from AI experience learning with minimal upfront investment.
📊 Experience Data Platform
Provides anonymized, normalized experience data collected from various sites as specialized datasets for specific industries or applications. This supports AI development companies and research institutions in creating new AI models based on diverse experience data.
Adjacent Application Opportunities
👵 介護・見守り
Elderly Behavior Pattern Learning & Anomaly Detection
AI automatically learns daily behavior patterns of the elderly to detect anomalies like falls or wandering early. Notifying family or caregivers could enhance quality of life and prevent accidents. This system could achieve a ~90% accuracy rate in anomaly detection.
🏗️ 建設・インフラ
Skilled Worker Knowledge Transfer & Safety Management AI
AI learns the movements and decision criteria of skilled workers in operating construction machinery and hazardous tasks. This accelerates knowledge transfer to junior workers and enhances site safety by predicting and warning of dangers, potentially reducing incident rates by ~30%.
👩‍🏫 教育・トレーニング
VR/AR Integrated Simulation Training
In VR/AR practical training environments, this technology learns and reproduces expert model behaviors. Learners could acquire skills more efficiently and practically through AI feedback, potentially shortening training times by ~40%. It is also applicable for hazardous task training.
Integration Roadmap — Estimated 12-Month Deployment
Phase 1: Proof of Concept and Requirements Definition
Duration: 3 months
Identify specific challenges and goals for the adopting enterprise, verifying the technology's applicability. Conduct initial data collection from existing systems or on-site, defining preliminary AI learning requirements.
Phase 2: System Development and Prototype Implementation
Duration: 6 months
Integrate the core technology module into the enterprise's system based on defined requirements. Build identification rules and composite judgment rules for target environmental information, then develop and test a prototype.
Phase 3: Production Deployment and Operation Optimization
Duration: 3 months
Deploy the system into the production environment based on prototype validation results. Continuously collect data and refine AI learning to accumulate experience information and improve judgment accuracy, maximizing operational effectiveness.
Technical Feasibility
This technology is centered on an information matrix data structure and rule-based information identification logic, making integration with existing sensors and data collection systems relatively straightforward. Its adaptability to different targets and environments through the addition or modification of observation rule sets suggests high technical feasibility for integration into existing infrastructure via software modifications and configuration changes, without requiring significant capital investment.
Success Scenario
Upon deployment, this technology could replace manual visual inspections and judgments by skilled workers on manufacturing lines, potentially improving inspection quality consistency and speed. This could reduce product defect rates from 5% to 1%, leading to tens of millions of dollars in annual quality cost savings (AI est.). Furthermore, by transferring expert know-how to AI, it is estimated that personnel training periods could be significantly shortened, enabling multi-skilled production lines and rapid startup of new facilities.
Patent Record
APPLICATION NO.
特願2023-048222
REGISTRATION NO.
7545514
FILING DATE
2023/03/24
GRANT DATE
2024/08/27
EXPIRATION DATE
2043/03/24
PATENT HOLDER
持田　信治
Examination History
2023年03月24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15日
手続補正書（自発・内容）
2024年05月15日
意見書
2024年08月20日
特許査定