Fronteo
Next Generation Innovation Enabler

FRONTEO was founded in 2003 as a provider of e-discovery and international litigation support services. These services include the preservation, investigation and analysis of evidence materials contained in electronic data, and computer forensic investigation. FRONTEO provides e-discovery and litigation support by making full use of its data analysis platform, “Lit i View”, and its Predictive Coding technology adapted to Asian languages.

FRONTEO supports the analysis of big data based on behavior informatics by utilizing its technology, “KIBIT”. FRONTEO’s KIBIT technology is driven by FRONTEO artificial intelligence based on knowledge acquired through its litigation support services. KIBIT incorporates experts’ tacit knowledge, including their experiences and intuitions, and utilizes that knowledge for big data analysis. FRONTEO continues to expand its business operations by applying KIBIT to new fields such as healthcare and marketing.

FRONTEO uses the in-house developed AI engine “KIBIT” and “concept encoder” specializing in natural language processing to extract meaningful and important information from a huge amount of text data to support corporate business. A data analysis company. Since its establishment in August 2003, it has been expanding globally to Japan, the United States, South Korea, and Taiwan, ocusing on legal tech businesses such as “e-discovery (electronic discovery)” and “digital forensic investigation” that support international litigation of companies. Has been deployed. Based on the AI technology cultivated in the legal tech business, we expanded the business field to the life science field, business intelligence field, and economic security from 2014, and by using AI to “turn text data into knowledge”, Drug discovery support, dementia diagnosis support, financial/personnel/sales support, etc., contributing to solving various corporate issues.

The company provides AI solutions that utilize the originally developed artificial intelligence engine “KIBIT” in the medical field using the natural language analysis AI engine “Concept Encoder” Medical device field, Medical intelligence field, Drug discovery support field, Medical information field, Regulatory response support field. Government offices (law enforcement agencies, various monitoring committees), domestic and foreign law firms, private companies (automobiles, machinery, precision equipment, electrical machinery, pharmaceuticals, petrochemicals, information and communications, trading companies, etc.), medical institutions use the company’s Artificial intelligence and digital forensic technology for International litigation e-discovery (electronic discovery) support service utilizing AI, fraud detection forensic investigation, provision of solutions for government agencies and law enforcement agencies.

By utilizing the state-of-the-art technology that we have cultivated by utilizing the knowledge, experience and judgment of experts, we provide the optimum solution to not miss the risks and opportunities buried in the record, so that legal, medical, financial, etc. It is the value brought about by the realization of a fair world where you can find necessary and appropriate information in the fields of intellectual property, education, personnel, etc.

The company’s CEO and Chairman of the Board, Masahiro Morimoto, said, “Since our founding in 2003, we have been active in the legal tech field with the philosophy of “protecting the pride of Japanese companies”.

We have expanded our business from the legal tech field to various fields such as finance, intellectual property, personnel, and medical fields, steadily accumulated achievements, and received the support of many people. We want to protect companies that are in trouble because they cannot find the information necessary for proceedings due to the immaturity of data analysis technology, and we want to deliver information that suits each patient from the medical information that overf lows around the world. We are refining our intelligence technology and services. Through such activities, the “Bright Value” that we want to realize is “to realize the fairness of the information society without overlooking the risks and opportunities buried in the records” by the data analysis know-how and advanced ||15SEPTEMBER - 2021artificial intelligence technology that we have cultivated so far. We have redefined it as “to do”. Going forward, we will continue to meet the growing expectations of technology and the diversifying needs of society toward the realization of our “Bright Value” and the realization of a future in which the necessary information is provided to the people who need it.”

FRONTEO Inc. (Headquarters: Minato-ku, Tokyo, President: Masahiro Morimoto, hereafter FRONTEO) is using the AI review tool “KIBIT Automator” equipped with the AI “KIBIT” developed in-house. We are announcing that we will enhance the usability of performance prediction and performance verification and release it as “KIBIT Automator 5.12”.

The amount of data managed by companies is increasing year by year. In discovery (discovery procedure) and digital forensics (fraud investigation), which collect data that can reach several TB per evidence holder (castdian), documents that lead to evidence are selected from a huge amount of documents. It had to be found within a limited period of time, and time, quality of investigation, and cost were major issues for the legal department and lawyers involved in the litigation. Among them, the process called “review (browsing)” is said to occupy 70% to 80% of the time and cost in the discovery and digital forensics processes, and reviews using technology (computer and AI) (TAR: Technology). The need for Assisted Review) is increasing.

In 2019, FRONTEO developed the AI review tool “KIBIT Automator” to meet this need. KIBIT Automator contains multiple algorithms that make up KIBIT. For example, in addition to the basic algorithm “Landscaping” that demonstrates learning ability even with a small amount of teacher data, which has been improved since its release in 2012, the original ensemble learning “Illumination” that is particularly effective when the ratio of teacher data is large. KIBIT includes “Forest” (released in January 2020) and “Illumination Forest + Meta” (released in October 2020) that can improve accuracy by considering the creator/sender of mails and files and date information.

This is an example of the algorithm used in. These are used properly according to the nature of the text data handled in fraud investigation/litigation cases and the purpose of search, and the Illumination Forest system algorithm has been widely used in cases in Japan and the United States since its release last year.

In addition, the selection of these algorithms requires knowledge of natural language processing and statistical analysis, but at FRONTEO, a data scientist dedicated to the legal tech field has built a system to support the selection of algorithms by lawyers. This usability-improved feature gives KBIT Automator’s core features of recall rate 1 , precision rate 2 , and cutoff point 3 predictions the f lexibility of data selection, especially algorithms like Illumination Forest. It makes verification easier than before.

In forensics and discovery, the concentration of evidence documents varies greatly from case to case. In addition, in a large-scale survey project, it is necessary to create multiple trained models for multiple text data groups with significantly different characteristics. By selecting the type of algorithm according to the difference in density and the characteristics of the text data and mastering multiple algorithms, more precise analysis that leads to cost reduction and accuracy improvement becomes possible.
As a digital forensic/discovery vendor that will continue to provide its own tools, FRONTEO will continue to work on improving algorithms and improving application functions and usability to master the algorithms, and by enhancing AI support capabilities by data scientists in the future. In addition to responding to the increase in the volume of surveyed data, which is expected to continue, we will contribute to improving the accuracy of Asian language processing.
1 Recall rate: Percentage of all surveyed data that includes data that is evaluated as “relevant as evidence”.

2 Precision rate: Percentage of all data scored by AI as “relevant” that actually includes data evaluated as relevant.

3 Cut-off point: A number that shows what percentage of the data that AI is highly relevant as evidence to the top percentage of the arranged data that humans can check to avoid viewing the rest of the data.


“KIBIT Automator” improves the efficiency of document review work in electronic discovery (e-discovery), reduces the burden on workers, and reduces costs, among other discoveries required in the trial procedure of US civil lawsuits. An AI tool developed for the purpose, released in March 2019. Applying the research method used in discovery, AI is used to examine and analyze large-capacity e-mails and electronic files that are evidence materials. In recent years, it is expected to respond to the short-term information disclosure required of companies.