New business development for new fields and technologies

Inspired by the "jobs-to-be-done" theory advocated by Professor Christensen, famous for his book "innovator's dilemma", we aim to discover unsolved issues in the market and develop solutions for them with the latest technologies. As a common language, we use the JOBS method developed by INDEE Japan, who actively spreads the theory in Japan.

We work on all the following phases from scratch to a business success:

● Market/technology research
● Market selection
● Hypothesis testing with prototyping
● Business development
● Product development/sales/support
● Business scaling

We have set up a satellite office at TOYO Tech LLC in Silicon Valley for incubation activities and formed a partnership with a start-up company Uila to aggressively address the global market.
Partner uila
On-going Projects
1. Security Attack Detection System
As faster network is resulting in enormous amount of log data, correlation analysis with a greater variety of logs is becoming impossible by human effort.
We have been developing a technology for automatically detecting attacks through correlation analysis on logs of various sources such as proxy servers, web servers and security devices.
With our architecture, agent software collects all the log data at the data server. Based on distinctions extracted from the collected logs, AI automatically identifies attacks and sends alerts.
Security attack detection systems
AI predicts attacks through the following two phases:
Security attack detection systems
2. Time Series Anomaly Detection System
Voluminous amounts of time series data have been produced with the growth of Internet-of-things (IoT) devices and Web social networks. Time Series Anomaly Detection System is developed to obtain valuable insight into large time series data. It provides time-stamp data collection, cloud storage and visualization functionalities, and exploits AI to detect change points and anomaly points automatically. With this platform, it is convenient to conduct time-series monitoring and alerting in manufacturing industry, and realize predictive maintenance, i.e., identify failures before they occur to avoid downtime and protect the bottom line.
Security attack detection systems
3. Change Point Detection and Classification for Large-scale Datasets
In manufacturing industry, durability assessment is essential for assuring the reliability and safety of materials and structural components, and usually produces voluminous datasets. For example, an accurate experimental evaluation of vehicle noise and vibration levels in both stationary conditions and urban driving conditions is conducted with a high sampling rate and lasts for a long time, and often generates datasets in large scale. AI is exploited to detect change points and conduct classification on large-scale datasets automatically, and it can achieve a faster, more accurate and more efficient performance than a human.
Security attack detection systems