Chat Resonac can analyze handwritten documents, which is a useful attribute in analog-intensive Japan.
Chat Resonac also enables all users to access materials known only to the elder generation but unknown to and unused by the younger generation.
Japanese electronic materials giant Resonac Corporation has developed an in-house generative AI system dubbed Chat Resonac that can interactively utilize accumulated data and documents of its preceding companies Showa Denko and Hitachi Chemical. Chat Resonac enables all users to access materials known only to the elder generation but unknown to and unused by the younger generation. Thus, Chat Resonac makes it possible for the employees of the Resonac Group to realize smooth handing down of knowledge and know-how from the elder generation to the current and future generations.
In addition, Chat Resonac enables users to access technical information accumulated in each department beyond departmental walls in an interactive manner. Therefore, it can help engineers in developing new semiconductor materials by fusing technologies of the former Showa Denko and former Hitachi Chemical entities.
Each of former Showa Denko and former Hitachi Chemical, which were predecessors of Resonac Holdings Corporation and Resonac Corporation, had a history of about 100 years and had accumulated more than fifty thousand materials concerning development and manufacturing of materials throughout its history. Veteran employees can search files of paper and databases for necessary materials and utilize them. However, there are many cases in which young employees cannot access necessary materials because they don’t know existence of those materials. Moreover, there is a risk that those materials are not being put to use after veteran employees’ retirement.
The Chat Resonac application program loads files of in-house materials into data storage servers free from information leakage to outside of the company, and receives data and feedback about generated answers from veteran employees, thereby improving the accuracy of generated answers. When the system reads handwritten documents and digitalizes them with optical character recognition (OCR), the system corrects omissions and errors by utilizing generative AI technologies. The system can also utilize data stocked on digital laboratory notebooks, which have already been put to practical use in the company.
Chat Resonac not only mediates between different generations but also enables each employee to communicate and utilize information beyond walls between departments to which each employee belongs or formerly belonged. Each employee can search the database of Chat Resonac for necessary information including knowledge about composition of materials and data on analysis results and contact and communicate with other employees who have such information.
Mr. Soichiro Takeshita, who is an engineer belonging to Materials Analysis Center, Institute for Polymer Technology, outlines his expectation about active use of Chat Resonac and his vision of the future: “I am absolutely confident that Chat Resonac will help the company to strengthen its technology development capability by enabling employees to share and utilize personal knowledge and know-how accumulated through past experiences.”
Resonac has developed two types of Chat Resonac application programs, namely general-purpose Chat Resonac and specialized Chat Resonac. The former handles information which may be shared among all employees, while the latter handles information which may be shared among employees belonging to specified departments. Further, there are more than 20 application programs under development as specialized Chat Resonac.
In addition, Resonac plans to apply AI-based Chat Resonac to various fields such as support tools for document creation, business efficiency improvement, and career development. Mr. Yoshinari Okuno, General Manager of Research Center for Computational Science and Informatics, said: “We have entered a new era in which human beings can challenge more difficult issues while entrusting jobs which can be handled by AI-based systems to AI-based systems.”