Dai Nippon Printing Co., Ltd, Yamato Sylphid, and others are beginning to
utilize Causal™, a causal AI developed by VELDT in their research on
women-specific health issues

In this research collaboration between DNP, Yamato Sylpheed, and Daiko, the focus is on visualizing the causal relationships within "condition data" - a combination of daily metrics such as the female-specific "menstrual cycle," vital signs like heart rate, respiration, and body temperature, as well as factors like sleep patterns and nutritional balance, along with performance data from training and matches. xCausal™ has been adopted as an analytical tool to estimate causal relationships and discover solutions.

Unlike typical AI systems that derive correlations, xCausal™ is a novel AI that incorporates an understanding of causality, answering questions like "why did this outcome occur?" and "what can be done to improve it?" This makes it more accessible for human understanding and application. Particularly in fields where immediate understanding of causality is crucial and beneficial, xCausal™ can quickly estimate causal relationships from data, even without acquiring evidence, thus proving effective in identifying causes promptly.

【Future Initiatives】

According to the Ministry of Economy, Trade, and Industry, it is estimated that the overall economic losses to society due to women-specific health issues amount to approximately 3.4 trillion yen annually. In addition to “xCausal™,” which can unravel causal relationships in data to derive solutions and actions, our company also offers the app “you’d™” that provides hints for conditioning while safeguarding privacy using individual health data. Through these technologies and professional services, we plan to explore collaborations with DNP’s future initiatives in condition management services for female athletes and working women.

※1 Ministry of Economy, Trade, and Industry, 20th Meeting of the Industrial Structure Council, Realization of a New Health Society: It is estimated that the economic losses to society due to women-specific health issues such as menstrual symptoms and menopausal symptoms amount to 3.4 trillion yen annually.
https://www.meti.go.jp/shingikai/sankoshin/shin_kijiku/pdf/020_04_00.pdf

■ Dai Nippon Printing Co., Ltd. AB Center Data Distribution Business Development Unit Personal Data Business Promotion Department 1st Group Project Leader: Kiyoshi Majima

DNP will undertake various initiatives to collect data from female athletes with the athlete’s consent and visualize the relationships between various data points. In this initiative, it is necessary to prioritize factors from each data set that may affect health status and calculate the relationships between multiple data sets. By utilizing “xCausal™” provided by VELDT, it is expected that it will be possible to more clearly estimate causality between events, leading to the elucidation of underlying factors that may have gone unnoticed by anyone.

■ What is “xCausal™” ?

Website: https://xcausal.com/

xCausal™ is a SaaS-based causal AI (causal relationship-based AI) platform that prioritizes ease of use, allowing users to simply upload data and get started. It provides unique features to quickly and easily perform causal structure computation processes called "causal exploration," which previously required supercomputers in some cases. Based on the premises of causal relationships, xCausal™ recommends data that is strongly related to the desired improvement results and models the causal structure in a matter of seconds by narrowing down to important variables and performing calculations (causal exploration). Additionally, it seamlessly allows users to virtually manipulate variables believed to be causes and easily simulate causal effects to see how the results change (causal inference). With the ability to confirm causal relationships in a short time and see the effects, users can engage in virtual trial and error multiple times, leading to understanding of causes and decision-making on actions. Through professional services, custom usage tailored to various purposes becomes possible.

■ xCausal™ Professional Services

Even with the use of “causal exploration” and “causal inference,” reaching a satisfactory solution may not always be possible depending on how they are utilized. Especially if the necessary data to identify potential causes is not available, the discovery of causal relationships may not be achieved. To address this, our data science team provides consulting and data analysis services with an accompaniment approach, offering ideas for consideration, alternative solutions in cases where necessary variables cannot be obtained, handling and modeling methods for organization-specific data, and implementation of custom functionalities. We also offer professional services conducted by a committee of advisors consisting of professors and associate professors from universities and research institutions who conduct cutting-edge research in the field of data science, ranging from causal inference, deep learning, and machine learning to statistical analysis and large-scale natural language modeling, ensuring continuous creation of new knowledge.

■ About VELDT Inc.

VELDT is a data science company that develops data analytics technology for the real world and society. While technological innovations such as the internet and AI have brought convenience, modern society faces new social and environmental issues as side effects. Since its inception, Veldt has made “Life Tech Rebalance” its mission, aiming to create a positive spiral in human society and the global environment by innovating not only technological advancements but also the ways in which technology is utilized.