- Intellectual Property
Strategy to Support Fujitsu's
- Intellectual Property Portfolio
- Efforts to Utilize
- OSS Compliance
- Brands &
Trademarks / Design
& Design Rights
- Evaluations from
Intellectual Property Portfolio
In this section, we introduce our intellectual property portfolio of Key Technologies (Computing, Network, AI, Data & Security, and Converging Technologies), which support the Key Focus Areas.
Fujitsu has been in the computer business since the 1950s. While many Japanese companies have introduced technology through partnerships with foreign companies, Fujitsu develops its own technology.
Fujitsu will continue to develop quantum and other computing technologies to realize innovation in the ICT infrastructure for supporting digital society. According to the Annual Report on Patent Administration 2022 (Statistics and Materials), Fujitsu ranked first in Japan in the number of patent registrations for I-6 computer technologies in the statistical table of registered numbers by field. The "Fugaku" supercomputer that has been in development by RIKEN and Fujitsu has successfully retained the top spot for five consecutive terms in multiple major high-performance computer rankings including HPCG and Graph500 BFS (Breadth-First Search).
Case: Digital Annealer™, a solution for quickly solving optimization problems
Digital Annealer has been developed as a mechanism to solve combinatorial optimization problems at high speed. Since its 2018 commercialization, we have been working to increase the number of bits and to increase computation speed to handle larger combinatorial optimization problems. Now in its third generation, Digital Annealer can solve even larger combinatorial optimization problems than ever before, enabling it to demonstrate its capabilities to solve various social issues. At present, we provide total support for solving customer issues in three forms: cloud services, technical services, and on-premises. We have filed many patent applications mainly for the core technologies. In addition, we will strengthen our patent portfolio in the fields of application.
Fujitsu is committed to developing network software and cloud-native technologies based on the network technologies that we have cultivated over many years since the company's founding in 1935. Our goal is to make end-to-end virtualized cloud native networks available worldwide.
Fujitsu participates in and leads the activities of the O-RAN Alliance, an organization that promotes the expansion of next-generation wireless access networks, including 5G, by telecommunications carriers and equipment vendors, as well as the open deployment of such networks.
Fujitsu and NTT have embarked on a strategic alliance to realize the Sustainable Digital Society. Under this strategic alliance, the two companies will conduct joint research in fields that leverage their respective technological strengths, including optical technology, for which NTT and Fujitsu together boast of the world's highest number of patents, and other telecommunications technologies. Fujitsu is also working to strengthen its acquisition of standard-essential patents (SEPs) in order to promote uptake of our company's technologies in international markets. By participating in patent alliances and patent pools, we are also making efforts to collaborate with partners. By doing so, we aim to realize services that contribute to solving social issues by utilizing 5G and other technologies.
Fujitsu aims to contribute to the expansion of the AI business by enhancing research and development on "reliable AI," which facilitates advanced decision-making and human creativity.
As early as 1974, Fujitsu filed patent applications that incorporate the concepts of AI, and we have continued to file patent applications based on the results of our R&D activities. According to an October 2022 survey by the Japan Patent Office, Fujitsu ranked first in Japan in terms of the number of AI-related patent applications, and according to a January 2019 survey by the World Intellectual Property Organization (WIPO), Fujitsu ranked sixth in the world in terms of the number of AI-related patent applications.
In addition to AI-related technologies, Fujitsu also engages in a variety of other R&D activities, and the knowledge and results obtained through these activities is sometimes utilized in the development of AI technologies. In this article, we will focus on technologies developed by applying the results of other R&D to the development of AI technologies, and we will also introduce some of the AI technologies that Fujitsu is developing.
Case 1: Combining TDA (Topological Data Analysis) with AI
TDA is an abbreviation of Topological Data Analysis. At Fujitsu, research and development on TDA was originally begun separately from that of AI in order to consider geometric data analysis as an analysis method as part of an approach that differed from mainstream statistical data analysis methods. In the course of conducting research on AI and TDA, Fujitsu found that when using Deep Learning, an AI technology known to exhibit high performance in the field of image processing, to conduct analysis of time series data (for example, heart rate data, acceleration sensor data, etc.), analysis performance can be greatly improved by combining TDA with Deep Learning. We are now conducting research and development on AI technology by applying such TDA, and we are applying for patents as needed to use Fujitsu's own TDA.
As mentioned above, Fujitsu believes that use of TDA offers significant value, but when one examines the industry as a whole, use of TDA in actual solutions is not yet widespread. Therefore, Fujitsu engineers are contributing to the OSS community by providing source code that is helpful for using TDA and creating a foundation upon which more people can easily make use of TDA. Fujitsu personnel also enjoy the opportunity to serve as facilitators on the theme of TDA at workshops and other events hosted by AI-related academic societies. By making the most of these opportunities, in addition to actively utilizing TDA, we are working to increase Fujitsu's presence in the TDA field, including through the aforementioned patent applications.
Case 2: DeepTwin, an AI technology that accurately acquires essential features
DeepTwin is an AI technology developed by Fujitsu for the first time in the world that accurately acquires essential features such as distribution and probability of high-dimensional data.
DeepTwin is a technology that applies Rate-Distortion theory to AI, which is the basis of technology for compressing video data to the minimum amount of information. In this study, we developed a new theory that by learning high-dimensional data with AI so that the amount of information can be most compressed, essential features such as the distribution and probability of the data can be acquired accurately. The outline is as follows.
DeepTwin uses an automatic encoder, which is one of the AI algorithms. An auto-encoder is a neural network trained to output the same data as the input data. The input data is dimensionally reduced before being output, and the restored data after dimensionality reduction becomes the output data. The performance of an auto-encoder is said to be better when the input data is reduced in dimension and the difference (error) between the input data and the output data is small. This is also similar to the concept of data compression described above.
In the case of dimension reduction in neural networks, especially by reducing the dimensions of high-dimensional data, the characteristics of the distribution of data in the data before the dimension reduction are lost, which causes performance degradation. Inspired by the Rate-Distortion theory used in data compression, Fujitsu was able to develop AI technology that can accurately capture the characteristics of data distribution in pre-dimensionality reduction data even after dimensionality reduction. A patent application was filed in 2019, and this technology was further refined and published in 2020. In 2020, DeepTwin was presented at the International Conference on Machine Learning 2020 (International Conference on Machine Learning 2020).
In reality, there are many more complex high-dimensional data and more difficult problems, but DeepTwin is continuing to develop technologies that can address more complex problems. As for DeepTwin, we will continue to apply for patents and present at academic conferences after the aforementioned publication and presentation, aiming to realize more practical AI.
Case 3: Wide Learning™ Explainable AI Technology
Wide Learning concerns XAI (eXplainable AI). In Wide Learning, data items are combined, and hypotheses are extracted without omission as criteria within a realistic computation time. Combining multiple data items facilitates making judgments. Since it is possible to comprehensively extract judgment grounds that are easy for people to understand, the AI algorithm can not only be explained but also contributes to knowledge discovery. In addition, practical accuracy can be achieved even in applications where learning data is scarce; for example, it is possible to generate a practical model from about 100 learning data sets. Recently, Wide Learning was used to analyze the results of the 49th House of Representatives of Japan election. As one of the XAI technologies enabling collaboration between humans and AI, Fujitsu is also focusing on building a patent portfolio related to Wide Learning.
Data & Security
Aiming to achieve zero trust and further to realize a cross-industry world that secures trust across society, Fujitsu is focusing on research and development of data trusts, which are essential for cross-industry zero trust collaboration.
Example: IDYX™ (IDentitY eXchange) enables assessing the trustworthiness of online trading partners
IDYX is an identity distribution technology that enables you to assess the trustworthiness of online trading partners. In digital business, it is possible to conduct secure, reliable transactions by accurately conveying personal information (identity), such as the backgrounds and qualifications of service providers and users. Fujitsu is expanding its blockchain technology and building a patent portfolio related to the development of "IDYX," a technology for securely distributing personal information via a distributed ID system in a way that enables verification of the trustworthiness of business partners based on evaluations from users who actually conduct transactions and the statuses of transactions to date.
As the introduction of online pharmaceutical administration guidance in response to the spread of COVID-19 is advancing through the application of IDYX, we are working on a joint demonstration project consisting of an information distribution and usage platform that implements CyberAgent, MG-DX, and IDYX. Our aim is to provide a service that enables safe, secure coordination of pharmaceutical products information among the various stakeholders involved in pharmaceutical product sales and delivery. By the end of fiscal 2021, we plan to put this platform into practical use and develop new services based on the knowledge obtained. Through these and other efforts, we aim to provide services that enable safe, secure coordination of pharmaceutical products information among stakeholders involved in pharmaceutical product sales and distribution.
Because complex social issues cannot be solved by technology alone, Fujitsu focuses on the development of converging technologies that create new value for people and society through fusion of digital technology with the humanities and social sciences.
Example: Actlyzer™, an AI technology that recognizes various human actions from images
Actlyzer is an AI technology that recognizes various human actions from images.
Approximately 100 basic movements (e.g., walking, shaking the head, and reaching with the hand) have been learned in advance for recognition, and by combining these movements, complex human actions such as suspicious activity and purchasing behavior can be recognized.
We have filed a number of patent applications related to Actlyzer. In addition to the core motion recognition technology, we have also filed several patent applications targeting retail, manufacturing, crime prevention, and other applications, and we have constructed a patent portfolio related to motion recognition.
We have also filed an application for design registration for hand-washing behavior analysis and a trademark for Actlyzer as a technology brand. We are also strengthening our IP portfolio through our IP mix strategy. By combining this technology with knowledge from behavioral science, we are now aiming to further improve performance, such as predicting next actions based on how people are behaving.
Status of patent applications related to Actlyzer (2020-2021 comparison)
Status of patent applications related to Actlyzer (by category)