10.3.23-Showcase-Findability-Sciences

ANNOUNCER: Thank you, Rosen, for the introduction. So for the next session, please let me briefly introduce and presenter Matsugaya and his company, Findability Science. Takeda has invited them as our collaborator in development of the AI best technology to support pharmaceutical development.

And briefly Isao Matsugaya is currently serving as a vice president and the head of the AI division at SB Telecom America, a 100% subsidiary of the SoftBank. And since 2017, he played a significant role in the SoftBank's investment in Findability Sciences, and he has been an integral part of the Findability Sciences team. And with Takeda, we started the collaboration with Findability from 2021 to co-develop the AI best document automation platform where we can support the document automation for the quality document for the pharmaceutical companies.

So as you know, we need to submit a lot of documents to the health authority to ensure our product safety and quality. So in there, there are many time consuming process to check the document and prepare the document. So we tried to pursue some new technologies to support such kind of document preparation and checking. So we already have some successful result between the collaboration.

So today I'm really excited to invite them to this conference. And we will enjoy Matsugaya's presentation. So over to you, Isao.

ISAO MATSUGAYA: Thank you very much, Karashima. So before going to my presentation, I used to work in Osaka, 1998 to 2011. And I was sales account here and account for the Takeda, so I came here every day. So I feel like I'm home. So I think I'm going to bump up like some of the people I used to work here, but not so far, but anyways-- So my name is Isao Matsugaya. And I'm responsible for the AI businesses in the United States. And it's as a board member and Strategy Head in Findability Sciences.

So the Findability Sciences is established in 2010, founder name Anand Mahurkar. And we invested in this company in 2017. And we met because of the IBM Watson strategic partnership with IBM. They introduced us this company as an ecosystem partner. And we kind of learned how their data analytics platform is more than just IBM Watson.

So we started, go to market together in the United States. And we create a couple projects. And we invested in it. And also we have a joint venture in Japan. So the SoftBank and Findability Sciences work closely together in Japan as well.

So what we do-- so we kind of discover knowledge intelligence from documents, data image, and videos and finding what will happen and what to do in order to do the business process automation.

So these are like our solutions and the business challenges. The first one is intelligent document processing which Karashima mentioned about it. So I have a little bit of talk about the use cases after and the data analytics predictions. And GRC I'm going to pass to my colleague, Sagar, to explain about what is the GRC together with some of the videos.

And number four is AI/DX consultation. So a lot of-- not only the Japanese corporations, but even the American or whatever the corporation, they sometimes misunderstanding what is the AI and a DX is. We consulted with those peoples and then bring the more better idea about the AI and DX. Taha, you want to come here.

SPEAKER 1: Thank you, Isao. So today I'm going to present a tool that we recently released, and this is a compilation of all of our experiences, governance, risk, and compliance for artificial intelligence.

The tool is called Examine. Measure. And Respond. So with this tool, the first thing you're able to see is all the risks in the enterprise in a single view. You're able to dive into details and measure all of the tasks and the completions that have occurred SO far. And at the same time, you're able to prepare your response and priority both in revenue and compliance.

Additionally, we have the ability to integrate generative AI. Executives have the ability to use natural language query, human like questions, and just simply ask the risk database whatever they want to learn about. Additionally, we have intelligent document processing which Karashima described just now where, you're able to upload a variety of different documents and then create reports on the risk and also see any compliance issues that may be there and highlight them.

With this, appreciate your interest in some of our AI offerings. The Examine, Measure, Response tool brings together 10 plus years of experience in discriminative and generative AI and allows compliance with FDA SEC regulations for finance, healthcare, pharma, and manufacturing industries. Back to you, Isao.

ISAO MATSUGAYA: OK. So how we approach enterprise AI? We use machine learning, NLP, and computer vision. So because of the complexity in the business challenges, we have to combine those three technologies, so only one technology is not solving like business challenges most of the time. But we have the platform as a base, so we don't create from the scratch every time because saving time and effort.

Just again, we are very emphasizing on the concept of the wide data, not only the internal data, but also using external data, not structured data only, but unstructured data also. So making wide data and putting a discriminator AI and also we introduce generative AI in order to kind of, you know, better user experience with data.

And these are customers and partners. And we are cross-industry because data, everybody has it. So not focus only one segment, but almost a lot of industries we have.

And Japan's strategy-- because of the joint venture with the SoftBank, the SoftBank has four key solution segments. One is, of course, communication, automation, marketing, and security. So we are-- most of the time like we support automation in the marketing with the SoftBank team.

So this is the use case Karashima talk about, we started automate the quality control process, comparing-- so extracting data from a specification and certificate of analysis. So those two different documents, we have to extract the data and then compare if there's any mistake or any errors. So together with the Karashima's team, we create this solution. And now like, we try to migrate to the other pharmaceutical companies.

And this is another use case Daikin. Daikin is a HVAC company. They are headquartered in Osaka. But actually we working in the Daikin team in Texas. So they acquired a company named Goodman. Goodman is a very big name in the HVAC field.

So what their problem is they have 250 locations in the United States and over 1,000 SKU. So they have to predict precisely about how many sales quantities. But because of the diversify of the climate, economy, and you know, sensors, so they couldn't really do the 250 over 1,000 prediction in every month. So we support them with our Findability Predictive Platform, which make them realize like very deep analytics with those huge data.

And this is our initiative for small businesses. My standard AI, we create this for small limited item restaurants in order to help them. We are dealing with a big corporations, but we also want to help the small businesses as well. So connecting their POS, bring the data together with preset data or by weather and calendars, we can predict how many orders coming in tomorrow or 30 days or 90 days at once. So with this, like even the small companies, they can enjoy the newest technologies in lowest investment. So that's our next kind of project.

And lastly, thank you for giving me a chance to talk about our businesses. And we are open to any discussion together with the client or possible partners. Thank you.