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Patent Portfolio Analysis of the Synergy between Machine Learning and Photonics

Patent Portfolio Analysis of the Synergy between Machine Learning and Photonics hv photonics Communication Patent Portfolio Analysis of the Synergy between Machine Learning and Photonics Shu-Hao Chang Science and Technology Policy Research and Information Center National Applied Research Laboratories, Taipei 10663, Taiwan; shchang@narlabs.org.tw; Tel.: +866-2-27377779 Abstract: Machine learning in photonics has potential in many industries. However, research on patent portfolios is still lacking. The purpose of this study was to assess the status of machine learning in photonics technology and patent portfolios and investigate major assignees to generate a better understanding of the developmental trends of machine learning in photonics. This can provide governments and industry with a resource for planning strategic development. I used data-mining methods (correspondence analysis and K-means clustering) to explore competing technological and strategic-group relationships within the field of machine learning in photonics. The data were granted patents in the USPTO database from 2019 to 2020. The results reveal that patents were primarily in image data processing, electronic digital data processing, wireless communication networks, and healthcare informatics and diagnosis. I assessed the relative technological advantages of various assignees and propose policy recommendations for technology development. Keywords: machine learning; photonics; patent portfolio; correspondence analysis 1. Introduction In recent years, scholars have focused on combining machine learning and photonics [1–3]. Machine learning is used in the field of optics for identifying abstract features and ex- traction characteristics, such as the generation and imaging of holograms, nonparametric Citation: Chang, S.-H. Patent reconstruction of digital holography, and prediction of the resonance curves of spectra. In Portfolio Analysis of the Synergy recent years, machine learning has proven to have excellent performance in decoding com- between Machine Learning and plicated data; it can rapidly and accurately analyze spectra and images [2] and has many Photonics. Photonics 2022, 9, 33. https://doi.org/10.3390/ applications in different fields. In addition, scholars have proposed replacing conventional photonics9010033 electronic technology with photonic technology to develop faster and more energy-efficient computing systems. These systems can be used in the processing and storing of data, Received: 28 November 2021 artificial intelligence (AI), and machine learning. Photonic neuromorphic computers use Accepted: 5 January 2022 neuromorphic photonics and can transmit and process signals within subnanoseconds, Published: 7 January 2022 thereby increasing the speed of the processor and reducing energy loss [4]. Publisher’s Note: MDPI stays neutral In conclusion, combining machine learning and photonics has become a new field with regard to jurisdictional claims in of research [5]. Scholars have focused on the applications of combining machine learning published maps and institutional affil- and photonics, such as optical communication, semiconductors, and image processing. In iations. addition, they have started to develop faster and more effective neural networks using photonic technology and experimentally verified the results; in one study, computing speed and efficiency increased greatly by using light instead of electricity [6]. The application of machine learning in the field of photonics has improved the performance of machine Copyright: © 2022 by the author. learning and AI. Licensee MDPI, Basel, Switzerland. More studies are starting to focus on the development of machine learning in photonics [7,8]. This article is an open access article However, these studies have focused on algorithm technologies [2,9–11] or specific distributed under the terms and applications [1,3,6,11–13], such as machine learning-based optical data decoding [2], ma- conditions of the Creative Commons chine learning techniques for computing various optical properties [1], and a fully optical Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ neural network that increases the computational speed and power efficiency of state-of- 4.0/). the-art electronics for conventional inference tasks [6]. Some studies have examined the Photonics 2022, 9, 33. https://doi.org/10.3390/photonics9010033 https://www.mdpi.com/journal/photonics Photonics 2022, 9, 33 2 of 11 developmental potential of machine learning in photonics [14–17]. They did not study the topic from a macro perspective or indicate the individual positioning of technology distribution and technology developers of machine learning in photonics. Specifically, no study has examined technological fields that employ the synergy between machine learning and photonics and the technological advantages of various assignees. Accordingly, this study investigated these topics through patent portfolio analysis. This study explored the status of technology distribution and technology deve- lopers—namely, the current development focus of this technology. This study employed patent analysis to analyze and compare the focus of each technology developer, along with correspondence analysis and K-means clustering. The patent analysis focused on the United States Patent and Trademark Office (USPTO) database from 2019 to 2020. First, this study analyzed the mainstream technology of machine learning in photonics to understand current developments of the technology. Next, this study analyzed the main assignees and patent portfolios and identified different strategic groups to clarify the trends and positioning of technology developers. This study used the patent portfolio figure to under- stand the relationship between assignees and technology and used the intuitive effect of the geometric figure to effectively present and reveal the technology portfolio of technology developers, to provide information to serve as a reference for government policies. 2. Literature Review 2.1. Current Developments in Machine Learning in Photonics The field of photonics has developed rapidly in recent years. Photonics has been combined with machine learning algorithms and optical systems to add new functions to optical systems and improve the performance of optical systems. This development has generated new research foci [1,4,18], such as the design and operation of pulsed lasers and the characterization and control of ultrafast propagation dynamics [18]. Computer calculation abilities improved dramatically after graphical processing units began to be used in nongraphic calculations, and this trend accelerated after the 2010s. This trend has resulted in engineers focusing on the development of electronic hardware accelerators of machine learning, such as Google’s Tensor Processing Unit (TPU). Current machine learning processors are limited by the electricity required to process data when executing complicated operations. In general, more complicated data require more electricity and result in slower electronic data transmission. Therefore, past studies have used light to replace electricity for calculations. The neural network TPU uses photons instead of elec- trons to overcome these limitations and create a stronger and more energy-efficient AI [19]. In addition, AI plays a crucial role in the optical communication industry, such as optical network planning and operation in both transport and access networks [20]. Therefore, combining machine learning and photonics and improving hardware performance is a future research direction. Past AI deployment in photonics has spawned much research activity. AI has a certain synergy with photonics, especially in terms of power efficiency and parallelism. Two major directions exist in applying machine learning to photonics. One of the two main directions is using AI algorithms, implemented on conventional computers, to design optical structures and devices with improved, task-specific performance. The other perhaps more ambitious direction is the attempt to implement AI computation using optical systems rather than electronic ones [21]. Now that semiconductor features have shrunk to nanometer scale and room is running out for Moore’s law to continue to hold, a new generation of integrated photonics could boost the speed and processing power of AI beyond what electronics can provide [15]. The combination of machine learning and photonics has considerable potential in technological development and market applications. This study used patent analysis to identify the technological positioning of technology developers. The following section explains the patent analysis method. Photonics 2022, 9, 33 3 of 11 2.2. Patent Analysis Patent analysis analyzes and compares different types of patent data to clarify the innovative activities in the industry [22]. Patent analysis can reveal current technological developments, stimulate new technological solutions, demonstrate the relationship be- tween technologies, and motivate investment policies [23]. Patent analysis can also help researchers understand the current research foci of different inventive organizations and provide a basis for the analysis of technological trends [24]. Patent analysis can involve statistical calculations of the external characteristic indices of patent documents, such as analyzing the number of patents or using the contents of patent documents to perform analysis by grouping technological characteristics [25]. Past studies related to machine learning in photonics have focused on algorithm technologies [2,10] or current developments and applications [1,3,6] and rarely explored and classified the knowledge or patents of machine learning in photonics. Machine learning in photonics is an interdisciplinary field that combines computer science, communication, and optics, and technological exploration of the different aspects of the industry is essential for its development. Past studies have used patent analysis to perform technological grouping and determine the technological development trend and model of industries involving many fields [26–28]. Patent analysis was also used to create a knowledge framework for biomedical three-dimensional printing (3D printing) [29]. This study used patent analysis to explore the technology distribution of machine learning in photonics and create a patent map. Few patent maps of machine learning in photonics currently exist, particularly for the technological positioning of technology developers; therefore, this study created a patent map to serve as a reference for government and industry. 3. Research Design 3.1. Search Strategy and Data Source The United States is the largest business transaction market in the world and has a long history; its system development and data can be traced back to 1975 [30]. Therefore, this study selected data from the USPTO database to perform data analysis. The data were granted patents in the USPTO database from 2019 to 2020. This study used Derwent smart search to perform a patent search. Derwent smart search is a keyword searching method. Hundreds of experts read the official public patent data in the patent database, translated the data, rewrote the abstracts, corrected the contents, and normalized the assignees, after which they recorded the rewritten and normalized data back into the database and created the Derwent smart search. In order to find the intersection of machine learning and photonics, the search criteria were that the patent contained two topics, including machine learning and photonics. The search criteria of this study were as follows: (SSTO/Machine Learning) and (SSTO/photonics); smart search-topic (SSTO) refers to Derwent smart search. Data related to machine learning and photonics were extracted from patent documents; the technological data for both topics were explored through SSTO analysis. The search results obtained 821 patents. This framework is presented in Figure 1, and it describes the process of the study in detail. After the topics and patent search direction of this study were confirmed, the patent search began. The data acquired from the patent search were analyzed to identify main- Photonics 2022, 9, x FOR PEER REVIEW 4 of 12 stream technological trends. Subsequently, correspondence and clustering analyses were performed on these findings for patent portfolio and strategic group analyses (Figure 1). Figure 1. Figure 1. Anal Analysis ysis of of patent p patent portfolio ortfolio of m of machine achine learning learning iin n photonics. photonics. 3.2. Correspondence Analysis Correspondence analysis uses a low-dimensional perceptual map to process cate- gorical variables, analyze the relative position of studied targets, and present the rela- tionship between related attributes [31]. Correspondence analysis uses figures to present the data in a cross tabulation. It considers multiple categorical variables simultaneously and presents the relationship between the variables. In addition, it uses points to present the ratio of the elements in the rows and columns of the cross tabulation in a lower di- mensionality. In other words, correspondence analysis can change the frequency of the cross tabulation into a ratio and calculate the corresponding relationship. This study used two-stage cluster analysis to classify the assignees and the international patent classification (IPC) numbers and used the coordinates obtained from the correspondence analysis in the cluster analysis to perform grouping. The spatial positions were used to determine the patent portfolio and clustering of the assignees. This study used correspondence analysis to analyze machine learning in photonics; the categorical variables were assignees and IPC numbers. Next, the perceptual map of the correspondence map was used to demonstrate the relationships among the assignees and the relationships between the assignees and IPC numbers. Assignees with a closer distribution signify that they have more patents on similar technologies and can be clas- sified into the same group. An assignee with a closer distance to an IPC number signifies that the assignee has a superior number of patents in that IPC category. 4. Results 4.1. Patent Search Results To understand current technological developments, the patent search results must be analyzed prior to the correspondence analysis and cluster analysis. Table 1 presents the top ten three-level IPC numbers related to machine learning and photonics. Table 1 shows a comprehensive overview of the IPC distribution of the patents. Table 1. Top ten three-level IPC numbers related to machine learning and photonics. Ranking IPC Number Quantity Percentage G06T 212 7.11% G06F 197 6.61% G02B 172 5.77% 4 A61B 162 5.44% G06K 137 4.60% H04L 134 4.50% H04N 110 3.69% G06N 80 2.68% 9 G16H 77 2.58% G06Q 68 2.28% Photonics 2022, 9, 33 4 of 11 3.2. Correspondence Analysis Correspondence analysis uses a low-dimensional perceptual map to process categori- cal variables, analyze the relative position of studied targets, and present the relationship between related attributes [31]. Correspondence analysis uses figures to present the data in a cross tabulation. It considers multiple categorical variables simultaneously and presents the relationship between the variables. In addition, it uses points to present the ratio of the elements in the rows and columns of the cross tabulation in a lower dimensionality. In other words, correspondence analysis can change the frequency of the cross tabulation into a ratio and calculate the corresponding relationship. This study used two-stage cluster analysis to classify the assignees and the international patent classification (IPC) numbers and used the coordinates obtained from the correspondence analysis in the cluster analysis to perform grouping. The spatial positions were used to determine the patent portfolio and clustering of the assignees. This study used correspondence analysis to analyze machine learning in photonics; the categorical variables were assignees and IPC numbers. Next, the perceptual map of the correspondence map was used to demonstrate the relationships among the assignees and the relationships between the assignees and IPC numbers. Assignees with a closer distribution signify that they have more patents on similar technologies and can be classified into the same group. An assignee with a closer distance to an IPC number signifies that the assignee has a superior number of patents in that IPC category. 4. Results 4.1. Patent Search Results To understand current technological developments, the patent search results must be analyzed prior to the correspondence analysis and cluster analysis. Table 1 presents the top ten three-level IPC numbers related to machine learning and photonics. Table 1 shows a comprehensive overview of the IPC distribution of the patents. Table 1. Top ten three-level IPC numbers related to machine learning and photonics. Ranking IPC Number Quantity Percentage 1 G06T 212 7.11% 2 G06F 197 6.61% 3 G02B 172 5.77% 4 A61B 162 5.44% 5 G06K 137 4.60% 6 H04L 134 4.50% 7 H04N 110 3.69% 8 G06N 80 2.68% 9 G16H 77 2.58% 10 G06Q 68 2.28% The data presented in Table 1 reveal that technologies are concentrated around G06T, G06F, G02B, A61B, G06K, H04L, and H04N. G06T concerns image data processing or generation; G06F concerns electric digital data processing; G02B concerns optical elements, systems, or apparatus; A61B concerns diagnosis, surgery, and identification; G06K concerns the recognition of data; H04L concerns the transmission of digital information; and H04N concerns pictorial communication. The technologies related to machine learning and photonics mainly involve image processing, computing, and medical applications; optical components (G06T, G06F, G02B, A61B, G06K, H04N); and digital information transmission and data processing methods (H04L, G06N, G16H, G06Q) (Table 1). Appendix A displays the definition of IPC categories. The results of the analysis of the top ten assignees are listed in Table 2. It shows the top ten assignees with the highest number of patents. The results indicate that AT&T Intellectual Property (San Antonio, TX, USA) has the highest number of granted patents; Photonics 2022, 9, 33 5 of 11 it innovates in the development of communications. In the era of Internet of Things, op- tical communication and machine learning have become the primary means to improve communication transmission. AT&T Intellectual Property owns a large number of patents and patent portfolios used in the field of communications. AT&T Intellectual Property is followed by Intel Corporation (Santa Clara, CA, USA), Magic Leap (Plantation, FL, USA), and International Business Machines Corporation (Armonk, NY, USA), in that order; they are three leading companies of global smart software, augmented reality devices, and related services. This study indicates that these companies prioritize not only AI develop- ment but also the synergy between machine learning and photonics. Microsoft Technology Licensing (Redmond, DC, USA) is responsible for authorizing the patents of Microsoft and those of related technologies companies. As a computer technology company, Microsoft has dedicated itself to integrating machine learning into photonics to create Computer Vision, which visualizes the world. The top ten assignees were mostly related to optical communication and equipment (such as AT&T Intellectual Property), smart computing (such as Intel, Magic Leap (Plantation, FL, USA), International Business Machines, and Microsoft Technology Licensing (Redmond, DC, USA)), medical applications involving machine learning and photonics (such as HeartFlow (Redwood City, CA, USA) and Align Technology (San Jose, CA, USA)), and academic institutions that utilize machine learning and photonics (such as California Institute of Technology (Pasadena, CA, USA)) (Table 2). Table 2. Number of granted patents of the top ten assignees. Relevance to Machine Learning and Photonics Assignee AT&T Intellectual Property (81, 9.87%), Samsung Electronics Co., Optical communication equipment Ltd. (Suwon, Korea) (13, 1.58%) Intel Corporation (56, 6.82%), Magic Leap (40, 4.87%), International Smart computing Business Machines Corporation (35, 4.26%), Microsoft Technology Licensing (20, 2.44%) Medical applications HeartFlow, Inc. (15, 1.83%), Align Technology Inc. (13, 1.58%) California Institute of Technology (15, 1.83%), Leland Stanford Academic institutions using machine learning and photonics Junior University (13, 1.58%) Note: The first number in the bracket represents the number of patents. The second number in the bracket represents the patent share. 4.2. Patent Portfolio Positioning Analysis This study used correspondence analysis to perform patent portfolio positioning analysis. Past scholars have used correspondence analysis to study the distribution of ecosystems [32], brand positioning [33], applications of medical management [34], and innovation management strategies [35]. This study used assignees and IPC numbers as categorical variables and selected data of the top ten assignees in machine learning in photonics patents; these patents were distributed over 77 classes of the three-level IPC patents and are related to diverse technological fields. Sometimes the positioning figure of the correspondence analysis cannot completely explain the relationship between the variables, particularly when too many variables exist and overlap or blur the figure, resulting in difficulty in understanding the figure. Therefore, this study used correspondence–cluster analysis to clarify the relationship and connection between multiple variables and classes; the original data were quantified with correspondence analysis, and cluster analysis was used to conduct research [36]. This study used a two-stage clustering method by splitting the clustering calculations into two stages. The first stage involved using the hierarchical clustering method to collect the agglomeration schedule of the samples during merging to observe the clustering process. Next, the cluster distance coefficient was used as the standard to determine the number of clusters. After the optimal number of clusters was decided, the second stage used the nonhierarchical clustering method to perform clustering. The results are displayed in Figure 2. Figure 2 reveals the cluster distance coefficients for different numbers of clusters. Photonics 2022, 9, x FOR PEER REVIEW 6 of 12 innovation management strategies [35]. This study used assignees and IPC numbers as categorical variables and selected data of the top ten assignees in machine learning in photonics patents; these patents were distributed over 77 classes of the three-level IPC patents and are related to diverse technological fields. Photonics 2022, 9, 33 6 of 11 Sometimes the positioning figure of the correspondence analysis cannot completely explain the relationship between the variables, particularly when too many variables ex- ist and overlap or blur the figure, resulting in difficulty in understanding the figure. The cluster distance coefficients were used to determine the number of clusters. The Therefore, this study used correspondence–cluster analysis to clarify the relationship coefficients underwent the most considerable change when the number of clusters changed and connection between multiple variables and classes; the original data were quantified from three groups to two groups, which signifies that more effort is required to merge three wit gr houps corresp into otwo ndence groups. analTher ysis, efor and c e, the lust optimal er analnumber ysis was ofuse groups d to co was nduct determined researcto h [ be 36]. three groups based on the coefficient changes. Next, the K-means cluster method divided This study used a two-stage clustering method by splitting the clustering calcula- the top ten assignees and 77 IPC patent classes into three groups. The grouped patent tions into two stages. The first stage involved using the hierarchical clustering method to portfolios of assignees and IPC numbers of machine learning in photonics are illustrated collect the agglomeration schedule of the samples during merging to observe the clus- in Figure 3. It reveals the visual results of correspondence analysis and cluster analysis. tering process. Next, the cluster distance coefficient was used as the standard to deter- The axes represent the two main factors determined through principal component analysis, mine the number of clusters. After the optimal number of clusters was decided, the sec- and the axis scores are presented as x and y coordinates on a 2-dimensional plane. Table 3 ond stage used the nonhierarchical clustering method to perform clustering. The results shows the coordinates of each assignee in Figure 3. Table 4 demonstrates the main members are displayed in Figure 2. Figure 2 reveals the cluster distance coefficients for different of each group. numbers of clusters. The cluster distance coefficients were used to determine the number Table 3. Top ten assignees. of clusters. The coefficients underwent the most considerable change when the number of clusters changed from three groups to two groups, which signifies that more effort is Assignee x y required to merge three groups into two groups. Therefore, the optimal number of AT&T Intellectual Property –0.877 1.770 groups was determined to be three groups based on the coefficient changes. Next, the Intel Corporation –0.834 –0.850 K-means cluster method divided the top ten assignees and 77 IPC patent classes into Magic Leap 1.320 –0.089 International Business Machines Corporation –0.318 –0.319 three groups. The grouped patent portfolios of assignees and IPC numbers of machine Microsoft Technology Licensing –0.253 0.284 learning in photonics are illustrated in Figure 3. It reveals the visual results of corre- California Institute of Technology 0.441 0.214 spondence analysis and cluster analysis. The axes represent the two main factors deter- HeartFlow, Inc. 1.027 –0.001 mined throug Align h princip Technology al component analysis, Inc. and the 0.332 axis scores are presented 0.099 as x and Samsung Electronics Co., Ltd. 0.296 0.146 y coordinates on a 2-dimensional plane. Table 3 shows the coordinates of each assignee Leland Stanford Junior University 0.898 0.776 in Figure 3. Table 4 demonstrates the main members of each group. Figure 2. Cluster coefficient change. Figure 2. Cluster coefficient change. Photonics 2022, 9, x FOR PEER REVIEW 7 of 12 Photonics 2022, 9, 33 7 of 11 Figure 3. Figure 3.G Gr rou ouped ped patent por patent portfolios tfoliosof ofmachine machine learning learning in in photonics. photonics. Table 3. Top ten assignees. Table 4. Main members of each group. Assignee x y Group Main Group Member Main Patent Application Field AT&T Intellectual Property –0.877 1.770 International Business Machines Corporation, Intel Corporation, I G06F, H04L, G06N, G06Q, H03M Intel Corporation –0.834 –0.850 Microsoft Technology Licensing II AT&T Intellectual Property H04B, H04W, H01Q, H01P Magic Leap 1.320 –0.089 Align Technology Inc., California Institute of Technology, International Business Machines Corporation –0.318 –0.319 III HeartFlow, Inc., Magic Leap, Samsung Electronics Co., Ltd., G06T, G02B, A61B, G06K, G16H Microsoft Technology Licensing –0.253 0.284 Leland Stanford Junior University California Institute of Technology 0.441 0.214 HeartFlow, Inc. 1.027 –0.001 Figure 3, Tables 3 and 4 indicate that most assignees are in Group III, which signifies that most assignees Align T have achieved echnology Inc similar. developments in machine 0.33 learning 2 in photonics 0.099 technologies. These main developments include image data processing (G06T), optical Samsung Electronics Co., Ltd. 0.296 0.146 elements, systems, or apparatuses (G02B), healthcare informatics and diagnosis (G16H, Leland Stanford Junior University 0.898 0.776 A61B), and recognition of data (G16H). In addition, International Business Machines Cor- poration, Intel Corporation, and Microsoft Technology Licensing have similar positions in Table 4. Main members of each group. the machine learning in photonics patent portfolio, and their patents are mainly related to electric digital data processing (G06F), the transmission of digital information (H04L), com- Group Main Group Member Main Patent Application Field puter systems based on specific computational models (G06N), commercial data processing International Business Machines methods (G06Q), and coding (H03M). AT&T Intellectual Property is related to transmission I Corporation, Intel Corporation, Microsoft G06F, H04L, G06N, G06Q, H03M (H04B), wireless communication networks (H04W), antennas (H01Q), and waveguides Technology Licensing (H01P); it focuses on technologies related to wireless communication. II AT&T Intellectual Property H04B, H04W, H01Q, H01P Align Technology Inc., California Institute of Technology, HeartFlow, Inc., Magic III G06T, G02B, A61B, G06K, G16H Leap, Samsung Electronics Co., Ltd., Leland Stanford Junior University Photonics 2022, 9, x FOR PEER REVIEW 8 of 12 Figure 3, Tables 3 and 4 indicate that most assignees are in Group III, which signi- fies that most assignees have achieved similar developments in machine learning in photonics technologies. These main developments include image data processing (G06T), optical elements, systems, or apparatuses (G02B), healthcare informatics and diagnosis (G16H, A61B), and recognition of data (G16H). In addition, International Business Machines Corporation, Intel Corporation, and Microsoft Technology Licensing have similar positions in the machine learning in photonics patent portfolio, and their patents are mainly related to electric digital data processing (G06F), the transmission of digital information (H04L), computer systems based on specific computational models (G06N), commercial data processing methods (G06Q), and coding (H03M). AT&T Intel- lectual Property is related to transmission (H04B), wireless communication networks Photonics 2022, 9, 33 8 of 11 (H04W), antennas (H01Q), and waveguides (H01P); it focuses on technologies related to wireless communication. 4.3. Post analysis: Change in Number of Patents and Papers 4.3. Post analysis: Change in Number of Patents and Papers The The re relationship lationship between between number o number of f publ published ished pa papers pers aand nd nu number mber oof f pa patents tents wa was s further further investigated investigated tto o ide identify ntify deve developmental lopmental tr tr end ends s in mach in machine ine lea learning rning in p in h photonics. otonics. Web of Science (WOS) was used to search relevant papers with the following criteria: Web of Science (WOS) was used to search relevant papers with the following criteria: ((TTL/machine ((TTL/machine le learning) arning) o or r (AB (ABST/machine ST/machine le learning) arning) or (K or (KW/machine W/machine lelearning)) arning)) and and ((TTL/photonics) or (ABST/photonics) or (KW/photonics)). TTL, ABST, and KW indicate ((TTL/photonics) or (ABST/photonics) or (KW/photonics)). TTL, ABST, and KW indicate titles, titles, abstracts, abstracts, and ke and keywor ywords, re ds, respectively spectively. . FFigur igure e4 presents th 4 presents the e change changein number of in number of patents and papers. patents and papers. Figure Figure 4. 4. Number Number of patents a of patents and nd papers in papers in 20 2019 19 and 2020 and 2020. . Patents and papers on machine learning in photonics have received increasing attention, Patents and papers on machine learning in photonics have received increasing at- and the increase in the number of patents (1.20%) is similar to that of papers (1.14%). tention, and the increase in the number of patents (1.20%) is similar to that of papers (1.14%). 5. Conclusions 5.1. Discussion and Implications 5. Conclusions This study used correspondence analysis and cluster analysis to explore the main- 5.1. Discussion and Implications stream technologies of machine learning in photonics and conduct patent portfolio analysis. This study used correspondence analysis and cluster analysis to explore the main- The empirical results indicate that the mainstream technologies include image data and stream technologies of machine learning in photonics and conduct patent portfolio electric digital data processing, optical elements, wireless communication networks, and analysis. The empirical results indicate that the mainstream technologies include image healthcare informatics and diagnosis; these technologies are not located within a specific field. In addition, the main developers of machine learning in photonics are leading com- panies of information and communication technology (ICT), wafers, and smart software and services. These companies include AT&T Intellectual Property, Intel Corporation, Inter- national Business Machines Corporation, Microsoft Technology Licensing, and Samsung Electronics Co., Ltd. Magic Leap focuses on augmented reality technologies that map the light field into the retina. California Institute of Technology and Leland Stanford Junior University (Stanford University) (Stanford, CA, USA) are known in academia for their natural sciences, engineering, and entrepreneurial atmospheres. HeartFlow, Inc. and Align Technology Inc. are devoted to using deep learning technologies to develop innovative medical materials. In conclusion, companies concerned with ICT, wafers, and medical ma- terials, along with academia, all play a crucial role in the development of new technologies of machine learning in photonics. Governments should consider allocating more research funds to academia to support technological developments in this field. Photonics 2022, 9, 33 9 of 11 A field that is being developed by more companies is more competitive. Align Tech- nology Inc., California Institute of Technology, HeartFlow, Inc., Magic Leap, Samsung Electronics Co., Ltd., and Stanford University are all investing in research in image data processing and optical elements, systems, or apparatuses. Therefore, these fields are more competitive. The main company investing in research and development in wireless commu- nication networks is AT&T Intellectual Property, which signifies that this field is relatively less competitive. Conventional information technology companies such as International Business Machines Corporation, Intel Corporation, and Microsoft Technology Licensing fo- cus on electric digital data processing and specific computational models, such as computer systems based on biological models. The analysis demonstrated that the synergy between the technological development of machine learning and photonics is centralized on optical communication equipment, smart computing, and medical applications. The use of machine learning and photonics increased communication efficiency and improved the performance of artificial intelligence and quality of medical diagnosis. In addition, the patent portfolio positioning analysis demonstrated that academic institutions among the top ten assignees, such as California Institute of Technology and Leland Stanford Junior University, were grouped in the same cluster as firms related to medical applications, such as HeartFlow and Align Technology. This suggests that academic institutions have a strong interest in the integration of machine learning and photonics in the field of medicine. Subsequent studies can investigate patents in academia. In terms of the theoretical contributions of this study, past studies of machine learning in photonics focused on algorithm technologies [2,9–11] or specific applications [1,3,6,11–13]; however, they did not determine the technology distribution or the patent portfolio of technology developers from a macro perspective. This study revealed the distribution of primary technology and positioning of patentees in the synergy between machine learning and photonics. In addition, this study identified mainstream technological trends in photonics machine learning and strategies and positioning in the patent layout to provide reference for enterprises to devise patent layout strategies, identify investment opportunities, and develop the industry strategically. Few studies have investigated the patent layout and patentee positioning in the synergy between machine learning and photonics. Machine learning in photonics will become indispensable, and analyzing the distribution of its technology is crucial and was therefore the motivation for this study. This study bridged this research gap and used a new perspective to explore the technological foci of each technology developer. This study proposes a technology road map of machine learning in photonics. The conclusions of this study can be used by businesses to allocate their research and develop- ment resources and used by the government to promote new technologies. More attention is required on companies and academia involved in photonics, applications of machine learning, and the usage of photonics to improve the performance of AI. This study deter- mined that current research on machine learning in photonics was related to image data processing, electric digital data processing, wireless communication networks, and health- care informatics and diagnosis. Therefore, the government can provide funding to train researchers in these fields to improve the future development of the photonics industry. 5.2. Limitations and Future Research Directions First, this study generalized the developments of each assignee in the three-level IPC. Future studies can conduct further research in each essential direction. For example, future studies can perform four-level IPC analysis or use more combinations of patent variables, such as inventor and nationality, to acquire additional patent information. Second, patent portfolio analysis was performed through correspondence analysis and K-means clustering. Subsequent studies should use WOS, in which a cluster with specific densities for research items and available patents can be created to acquire detailed information. Future studies can use different patent searches or search criteria, such as including triadic patent family Photonics 2022, 9, 33 10 of 11 data, to reveal crucial patent information. Due to limitations on the human resources and funding of this study, this study only used the USPTO database as a data source. The present patent analysis focused on a limited geographical and temporal segment. Subsequent studies should include other data sources, such as the European Patent Office, the Japan Patent Office, arXiv for articles, and preprints for existing patents, to draw informative conclusions. Funding: This research was funded by the Ministry of Science and Technology of the Taiwan, grant number MOST 109-2410-H-492-001. Acknowledgments: The author would like to thank the Ministry of Science and Technology of the Taiwan for financially supporting this research under Contract No. MOST 109-2410-H-492-001. Conflicts of Interest: The author declare no conflict of interest. Appendix A Table A1. Definition of IPC categories. IPC Categories Meaning A61B Diagnosis; surgery; identification G02B Optical elements, systems, or apparatus G06F Electric digital data processing Recognition of data; presentation of data; record carriers; handling G06K record carriers G06T Image data processing or generation, in general H01P Waveguides; resonators, lines, or other devices of the waveguide type H01Q Antennas, i.e., radio aerials H03M Coding, decoding, or code conversion, in general H04B Transmission H04L Transmission of digital information, e.g., telegraphic communication H04N Pictorial communication, e.g., television H04W Wireless communication networks G06N Computer systems based on specific computational models Data processing systems or methods, specially adapted for administrative, commercial, financial, managerial, supervisory, or forecasting purposes; G06Q systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory. or forecasting purposes, not otherwise provided for G06T Image data processing or generation, in general Healthcare informatics, i.e., information and communication technology G16H (ICT) specially adapted for the handling or processing of medical or healthcare data References 1. 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Patent Portfolio Analysis of the Synergy between Machine Learning and Photonics

Photonics , Volume 9 (1) – Jan 7, 2022

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hv photonics Communication Patent Portfolio Analysis of the Synergy between Machine Learning and Photonics Shu-Hao Chang Science and Technology Policy Research and Information Center National Applied Research Laboratories, Taipei 10663, Taiwan; shchang@narlabs.org.tw; Tel.: +866-2-27377779 Abstract: Machine learning in photonics has potential in many industries. However, research on patent portfolios is still lacking. The purpose of this study was to assess the status of machine learning in photonics technology and patent portfolios and investigate major assignees to generate a better understanding of the developmental trends of machine learning in photonics. This can provide governments and industry with a resource for planning strategic development. I used data-mining methods (correspondence analysis and K-means clustering) to explore competing technological and strategic-group relationships within the field of machine learning in photonics. The data were granted patents in the USPTO database from 2019 to 2020. The results reveal that patents were primarily in image data processing, electronic digital data processing, wireless communication networks, and healthcare informatics and diagnosis. I assessed the relative technological advantages of various assignees and propose policy recommendations for technology development. Keywords: machine learning; photonics; patent portfolio; correspondence analysis 1. Introduction In recent years, scholars have focused on combining machine learning and photonics [1–3]. Machine learning is used in the field of optics for identifying abstract features and ex- traction characteristics, such as the generation and imaging of holograms, nonparametric Citation: Chang, S.-H. Patent reconstruction of digital holography, and prediction of the resonance curves of spectra. In Portfolio Analysis of the Synergy recent years, machine learning has proven to have excellent performance in decoding com- between Machine Learning and plicated data; it can rapidly and accurately analyze spectra and images [2] and has many Photonics. Photonics 2022, 9, 33. https://doi.org/10.3390/ applications in different fields. In addition, scholars have proposed replacing conventional photonics9010033 electronic technology with photonic technology to develop faster and more energy-efficient computing systems. These systems can be used in the processing and storing of data, Received: 28 November 2021 artificial intelligence (AI), and machine learning. Photonic neuromorphic computers use Accepted: 5 January 2022 neuromorphic photonics and can transmit and process signals within subnanoseconds, Published: 7 January 2022 thereby increasing the speed of the processor and reducing energy loss [4]. Publisher’s Note: MDPI stays neutral In conclusion, combining machine learning and photonics has become a new field with regard to jurisdictional claims in of research [5]. Scholars have focused on the applications of combining machine learning published maps and institutional affil- and photonics, such as optical communication, semiconductors, and image processing. In iations. addition, they have started to develop faster and more effective neural networks using photonic technology and experimentally verified the results; in one study, computing speed and efficiency increased greatly by using light instead of electricity [6]. The application of machine learning in the field of photonics has improved the performance of machine Copyright: © 2022 by the author. learning and AI. Licensee MDPI, Basel, Switzerland. More studies are starting to focus on the development of machine learning in photonics [7,8]. This article is an open access article However, these studies have focused on algorithm technologies [2,9–11] or specific distributed under the terms and applications [1,3,6,11–13], such as machine learning-based optical data decoding [2], ma- conditions of the Creative Commons chine learning techniques for computing various optical properties [1], and a fully optical Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ neural network that increases the computational speed and power efficiency of state-of- 4.0/). the-art electronics for conventional inference tasks [6]. Some studies have examined the Photonics 2022, 9, 33. https://doi.org/10.3390/photonics9010033 https://www.mdpi.com/journal/photonics Photonics 2022, 9, 33 2 of 11 developmental potential of machine learning in photonics [14–17]. They did not study the topic from a macro perspective or indicate the individual positioning of technology distribution and technology developers of machine learning in photonics. Specifically, no study has examined technological fields that employ the synergy between machine learning and photonics and the technological advantages of various assignees. Accordingly, this study investigated these topics through patent portfolio analysis. This study explored the status of technology distribution and technology deve- lopers—namely, the current development focus of this technology. This study employed patent analysis to analyze and compare the focus of each technology developer, along with correspondence analysis and K-means clustering. The patent analysis focused on the United States Patent and Trademark Office (USPTO) database from 2019 to 2020. First, this study analyzed the mainstream technology of machine learning in photonics to understand current developments of the technology. Next, this study analyzed the main assignees and patent portfolios and identified different strategic groups to clarify the trends and positioning of technology developers. This study used the patent portfolio figure to under- stand the relationship between assignees and technology and used the intuitive effect of the geometric figure to effectively present and reveal the technology portfolio of technology developers, to provide information to serve as a reference for government policies. 2. Literature Review 2.1. Current Developments in Machine Learning in Photonics The field of photonics has developed rapidly in recent years. Photonics has been combined with machine learning algorithms and optical systems to add new functions to optical systems and improve the performance of optical systems. This development has generated new research foci [1,4,18], such as the design and operation of pulsed lasers and the characterization and control of ultrafast propagation dynamics [18]. Computer calculation abilities improved dramatically after graphical processing units began to be used in nongraphic calculations, and this trend accelerated after the 2010s. This trend has resulted in engineers focusing on the development of electronic hardware accelerators of machine learning, such as Google’s Tensor Processing Unit (TPU). Current machine learning processors are limited by the electricity required to process data when executing complicated operations. In general, more complicated data require more electricity and result in slower electronic data transmission. Therefore, past studies have used light to replace electricity for calculations. The neural network TPU uses photons instead of elec- trons to overcome these limitations and create a stronger and more energy-efficient AI [19]. In addition, AI plays a crucial role in the optical communication industry, such as optical network planning and operation in both transport and access networks [20]. Therefore, combining machine learning and photonics and improving hardware performance is a future research direction. Past AI deployment in photonics has spawned much research activity. AI has a certain synergy with photonics, especially in terms of power efficiency and parallelism. Two major directions exist in applying machine learning to photonics. One of the two main directions is using AI algorithms, implemented on conventional computers, to design optical structures and devices with improved, task-specific performance. The other perhaps more ambitious direction is the attempt to implement AI computation using optical systems rather than electronic ones [21]. Now that semiconductor features have shrunk to nanometer scale and room is running out for Moore’s law to continue to hold, a new generation of integrated photonics could boost the speed and processing power of AI beyond what electronics can provide [15]. The combination of machine learning and photonics has considerable potential in technological development and market applications. This study used patent analysis to identify the technological positioning of technology developers. The following section explains the patent analysis method. Photonics 2022, 9, 33 3 of 11 2.2. Patent Analysis Patent analysis analyzes and compares different types of patent data to clarify the innovative activities in the industry [22]. Patent analysis can reveal current technological developments, stimulate new technological solutions, demonstrate the relationship be- tween technologies, and motivate investment policies [23]. Patent analysis can also help researchers understand the current research foci of different inventive organizations and provide a basis for the analysis of technological trends [24]. Patent analysis can involve statistical calculations of the external characteristic indices of patent documents, such as analyzing the number of patents or using the contents of patent documents to perform analysis by grouping technological characteristics [25]. Past studies related to machine learning in photonics have focused on algorithm technologies [2,10] or current developments and applications [1,3,6] and rarely explored and classified the knowledge or patents of machine learning in photonics. Machine learning in photonics is an interdisciplinary field that combines computer science, communication, and optics, and technological exploration of the different aspects of the industry is essential for its development. Past studies have used patent analysis to perform technological grouping and determine the technological development trend and model of industries involving many fields [26–28]. Patent analysis was also used to create a knowledge framework for biomedical three-dimensional printing (3D printing) [29]. This study used patent analysis to explore the technology distribution of machine learning in photonics and create a patent map. Few patent maps of machine learning in photonics currently exist, particularly for the technological positioning of technology developers; therefore, this study created a patent map to serve as a reference for government and industry. 3. Research Design 3.1. Search Strategy and Data Source The United States is the largest business transaction market in the world and has a long history; its system development and data can be traced back to 1975 [30]. Therefore, this study selected data from the USPTO database to perform data analysis. The data were granted patents in the USPTO database from 2019 to 2020. This study used Derwent smart search to perform a patent search. Derwent smart search is a keyword searching method. Hundreds of experts read the official public patent data in the patent database, translated the data, rewrote the abstracts, corrected the contents, and normalized the assignees, after which they recorded the rewritten and normalized data back into the database and created the Derwent smart search. In order to find the intersection of machine learning and photonics, the search criteria were that the patent contained two topics, including machine learning and photonics. The search criteria of this study were as follows: (SSTO/Machine Learning) and (SSTO/photonics); smart search-topic (SSTO) refers to Derwent smart search. Data related to machine learning and photonics were extracted from patent documents; the technological data for both topics were explored through SSTO analysis. The search results obtained 821 patents. This framework is presented in Figure 1, and it describes the process of the study in detail. After the topics and patent search direction of this study were confirmed, the patent search began. The data acquired from the patent search were analyzed to identify main- Photonics 2022, 9, x FOR PEER REVIEW 4 of 12 stream technological trends. Subsequently, correspondence and clustering analyses were performed on these findings for patent portfolio and strategic group analyses (Figure 1). Figure 1. Figure 1. Anal Analysis ysis of of patent p patent portfolio ortfolio of m of machine achine learning learning iin n photonics. photonics. 3.2. Correspondence Analysis Correspondence analysis uses a low-dimensional perceptual map to process cate- gorical variables, analyze the relative position of studied targets, and present the rela- tionship between related attributes [31]. Correspondence analysis uses figures to present the data in a cross tabulation. It considers multiple categorical variables simultaneously and presents the relationship between the variables. In addition, it uses points to present the ratio of the elements in the rows and columns of the cross tabulation in a lower di- mensionality. In other words, correspondence analysis can change the frequency of the cross tabulation into a ratio and calculate the corresponding relationship. This study used two-stage cluster analysis to classify the assignees and the international patent classification (IPC) numbers and used the coordinates obtained from the correspondence analysis in the cluster analysis to perform grouping. The spatial positions were used to determine the patent portfolio and clustering of the assignees. This study used correspondence analysis to analyze machine learning in photonics; the categorical variables were assignees and IPC numbers. Next, the perceptual map of the correspondence map was used to demonstrate the relationships among the assignees and the relationships between the assignees and IPC numbers. Assignees with a closer distribution signify that they have more patents on similar technologies and can be clas- sified into the same group. An assignee with a closer distance to an IPC number signifies that the assignee has a superior number of patents in that IPC category. 4. Results 4.1. Patent Search Results To understand current technological developments, the patent search results must be analyzed prior to the correspondence analysis and cluster analysis. Table 1 presents the top ten three-level IPC numbers related to machine learning and photonics. Table 1 shows a comprehensive overview of the IPC distribution of the patents. Table 1. Top ten three-level IPC numbers related to machine learning and photonics. Ranking IPC Number Quantity Percentage G06T 212 7.11% G06F 197 6.61% G02B 172 5.77% 4 A61B 162 5.44% G06K 137 4.60% H04L 134 4.50% H04N 110 3.69% G06N 80 2.68% 9 G16H 77 2.58% G06Q 68 2.28% Photonics 2022, 9, 33 4 of 11 3.2. Correspondence Analysis Correspondence analysis uses a low-dimensional perceptual map to process categori- cal variables, analyze the relative position of studied targets, and present the relationship between related attributes [31]. Correspondence analysis uses figures to present the data in a cross tabulation. It considers multiple categorical variables simultaneously and presents the relationship between the variables. In addition, it uses points to present the ratio of the elements in the rows and columns of the cross tabulation in a lower dimensionality. In other words, correspondence analysis can change the frequency of the cross tabulation into a ratio and calculate the corresponding relationship. This study used two-stage cluster analysis to classify the assignees and the international patent classification (IPC) numbers and used the coordinates obtained from the correspondence analysis in the cluster analysis to perform grouping. The spatial positions were used to determine the patent portfolio and clustering of the assignees. This study used correspondence analysis to analyze machine learning in photonics; the categorical variables were assignees and IPC numbers. Next, the perceptual map of the correspondence map was used to demonstrate the relationships among the assignees and the relationships between the assignees and IPC numbers. Assignees with a closer distribution signify that they have more patents on similar technologies and can be classified into the same group. An assignee with a closer distance to an IPC number signifies that the assignee has a superior number of patents in that IPC category. 4. Results 4.1. Patent Search Results To understand current technological developments, the patent search results must be analyzed prior to the correspondence analysis and cluster analysis. Table 1 presents the top ten three-level IPC numbers related to machine learning and photonics. Table 1 shows a comprehensive overview of the IPC distribution of the patents. Table 1. Top ten three-level IPC numbers related to machine learning and photonics. Ranking IPC Number Quantity Percentage 1 G06T 212 7.11% 2 G06F 197 6.61% 3 G02B 172 5.77% 4 A61B 162 5.44% 5 G06K 137 4.60% 6 H04L 134 4.50% 7 H04N 110 3.69% 8 G06N 80 2.68% 9 G16H 77 2.58% 10 G06Q 68 2.28% The data presented in Table 1 reveal that technologies are concentrated around G06T, G06F, G02B, A61B, G06K, H04L, and H04N. G06T concerns image data processing or generation; G06F concerns electric digital data processing; G02B concerns optical elements, systems, or apparatus; A61B concerns diagnosis, surgery, and identification; G06K concerns the recognition of data; H04L concerns the transmission of digital information; and H04N concerns pictorial communication. The technologies related to machine learning and photonics mainly involve image processing, computing, and medical applications; optical components (G06T, G06F, G02B, A61B, G06K, H04N); and digital information transmission and data processing methods (H04L, G06N, G16H, G06Q) (Table 1). Appendix A displays the definition of IPC categories. The results of the analysis of the top ten assignees are listed in Table 2. It shows the top ten assignees with the highest number of patents. The results indicate that AT&T Intellectual Property (San Antonio, TX, USA) has the highest number of granted patents; Photonics 2022, 9, 33 5 of 11 it innovates in the development of communications. In the era of Internet of Things, op- tical communication and machine learning have become the primary means to improve communication transmission. AT&T Intellectual Property owns a large number of patents and patent portfolios used in the field of communications. AT&T Intellectual Property is followed by Intel Corporation (Santa Clara, CA, USA), Magic Leap (Plantation, FL, USA), and International Business Machines Corporation (Armonk, NY, USA), in that order; they are three leading companies of global smart software, augmented reality devices, and related services. This study indicates that these companies prioritize not only AI develop- ment but also the synergy between machine learning and photonics. Microsoft Technology Licensing (Redmond, DC, USA) is responsible for authorizing the patents of Microsoft and those of related technologies companies. As a computer technology company, Microsoft has dedicated itself to integrating machine learning into photonics to create Computer Vision, which visualizes the world. The top ten assignees were mostly related to optical communication and equipment (such as AT&T Intellectual Property), smart computing (such as Intel, Magic Leap (Plantation, FL, USA), International Business Machines, and Microsoft Technology Licensing (Redmond, DC, USA)), medical applications involving machine learning and photonics (such as HeartFlow (Redwood City, CA, USA) and Align Technology (San Jose, CA, USA)), and academic institutions that utilize machine learning and photonics (such as California Institute of Technology (Pasadena, CA, USA)) (Table 2). Table 2. Number of granted patents of the top ten assignees. Relevance to Machine Learning and Photonics Assignee AT&T Intellectual Property (81, 9.87%), Samsung Electronics Co., Optical communication equipment Ltd. (Suwon, Korea) (13, 1.58%) Intel Corporation (56, 6.82%), Magic Leap (40, 4.87%), International Smart computing Business Machines Corporation (35, 4.26%), Microsoft Technology Licensing (20, 2.44%) Medical applications HeartFlow, Inc. (15, 1.83%), Align Technology Inc. (13, 1.58%) California Institute of Technology (15, 1.83%), Leland Stanford Academic institutions using machine learning and photonics Junior University (13, 1.58%) Note: The first number in the bracket represents the number of patents. The second number in the bracket represents the patent share. 4.2. Patent Portfolio Positioning Analysis This study used correspondence analysis to perform patent portfolio positioning analysis. Past scholars have used correspondence analysis to study the distribution of ecosystems [32], brand positioning [33], applications of medical management [34], and innovation management strategies [35]. This study used assignees and IPC numbers as categorical variables and selected data of the top ten assignees in machine learning in photonics patents; these patents were distributed over 77 classes of the three-level IPC patents and are related to diverse technological fields. Sometimes the positioning figure of the correspondence analysis cannot completely explain the relationship between the variables, particularly when too many variables exist and overlap or blur the figure, resulting in difficulty in understanding the figure. Therefore, this study used correspondence–cluster analysis to clarify the relationship and connection between multiple variables and classes; the original data were quantified with correspondence analysis, and cluster analysis was used to conduct research [36]. This study used a two-stage clustering method by splitting the clustering calculations into two stages. The first stage involved using the hierarchical clustering method to collect the agglomeration schedule of the samples during merging to observe the clustering process. Next, the cluster distance coefficient was used as the standard to determine the number of clusters. After the optimal number of clusters was decided, the second stage used the nonhierarchical clustering method to perform clustering. The results are displayed in Figure 2. Figure 2 reveals the cluster distance coefficients for different numbers of clusters. Photonics 2022, 9, x FOR PEER REVIEW 6 of 12 innovation management strategies [35]. This study used assignees and IPC numbers as categorical variables and selected data of the top ten assignees in machine learning in photonics patents; these patents were distributed over 77 classes of the three-level IPC patents and are related to diverse technological fields. Photonics 2022, 9, 33 6 of 11 Sometimes the positioning figure of the correspondence analysis cannot completely explain the relationship between the variables, particularly when too many variables ex- ist and overlap or blur the figure, resulting in difficulty in understanding the figure. The cluster distance coefficients were used to determine the number of clusters. The Therefore, this study used correspondence–cluster analysis to clarify the relationship coefficients underwent the most considerable change when the number of clusters changed and connection between multiple variables and classes; the original data were quantified from three groups to two groups, which signifies that more effort is required to merge three wit gr houps corresp into otwo ndence groups. analTher ysis, efor and c e, the lust optimal er analnumber ysis was ofuse groups d to co was nduct determined researcto h [ be 36]. three groups based on the coefficient changes. Next, the K-means cluster method divided This study used a two-stage clustering method by splitting the clustering calcula- the top ten assignees and 77 IPC patent classes into three groups. The grouped patent tions into two stages. The first stage involved using the hierarchical clustering method to portfolios of assignees and IPC numbers of machine learning in photonics are illustrated collect the agglomeration schedule of the samples during merging to observe the clus- in Figure 3. It reveals the visual results of correspondence analysis and cluster analysis. tering process. Next, the cluster distance coefficient was used as the standard to deter- The axes represent the two main factors determined through principal component analysis, mine the number of clusters. After the optimal number of clusters was decided, the sec- and the axis scores are presented as x and y coordinates on a 2-dimensional plane. Table 3 ond stage used the nonhierarchical clustering method to perform clustering. The results shows the coordinates of each assignee in Figure 3. Table 4 demonstrates the main members are displayed in Figure 2. Figure 2 reveals the cluster distance coefficients for different of each group. numbers of clusters. The cluster distance coefficients were used to determine the number Table 3. Top ten assignees. of clusters. The coefficients underwent the most considerable change when the number of clusters changed from three groups to two groups, which signifies that more effort is Assignee x y required to merge three groups into two groups. Therefore, the optimal number of AT&T Intellectual Property –0.877 1.770 groups was determined to be three groups based on the coefficient changes. Next, the Intel Corporation –0.834 –0.850 K-means cluster method divided the top ten assignees and 77 IPC patent classes into Magic Leap 1.320 –0.089 International Business Machines Corporation –0.318 –0.319 three groups. The grouped patent portfolios of assignees and IPC numbers of machine Microsoft Technology Licensing –0.253 0.284 learning in photonics are illustrated in Figure 3. It reveals the visual results of corre- California Institute of Technology 0.441 0.214 spondence analysis and cluster analysis. The axes represent the two main factors deter- HeartFlow, Inc. 1.027 –0.001 mined throug Align h princip Technology al component analysis, Inc. and the 0.332 axis scores are presented 0.099 as x and Samsung Electronics Co., Ltd. 0.296 0.146 y coordinates on a 2-dimensional plane. Table 3 shows the coordinates of each assignee Leland Stanford Junior University 0.898 0.776 in Figure 3. Table 4 demonstrates the main members of each group. Figure 2. Cluster coefficient change. Figure 2. Cluster coefficient change. Photonics 2022, 9, x FOR PEER REVIEW 7 of 12 Photonics 2022, 9, 33 7 of 11 Figure 3. Figure 3.G Gr rou ouped ped patent por patent portfolios tfoliosof ofmachine machine learning learning in in photonics. photonics. Table 3. Top ten assignees. Table 4. Main members of each group. Assignee x y Group Main Group Member Main Patent Application Field AT&T Intellectual Property –0.877 1.770 International Business Machines Corporation, Intel Corporation, I G06F, H04L, G06N, G06Q, H03M Intel Corporation –0.834 –0.850 Microsoft Technology Licensing II AT&T Intellectual Property H04B, H04W, H01Q, H01P Magic Leap 1.320 –0.089 Align Technology Inc., California Institute of Technology, International Business Machines Corporation –0.318 –0.319 III HeartFlow, Inc., Magic Leap, Samsung Electronics Co., Ltd., G06T, G02B, A61B, G06K, G16H Microsoft Technology Licensing –0.253 0.284 Leland Stanford Junior University California Institute of Technology 0.441 0.214 HeartFlow, Inc. 1.027 –0.001 Figure 3, Tables 3 and 4 indicate that most assignees are in Group III, which signifies that most assignees Align T have achieved echnology Inc similar. developments in machine 0.33 learning 2 in photonics 0.099 technologies. These main developments include image data processing (G06T), optical Samsung Electronics Co., Ltd. 0.296 0.146 elements, systems, or apparatuses (G02B), healthcare informatics and diagnosis (G16H, Leland Stanford Junior University 0.898 0.776 A61B), and recognition of data (G16H). In addition, International Business Machines Cor- poration, Intel Corporation, and Microsoft Technology Licensing have similar positions in Table 4. Main members of each group. the machine learning in photonics patent portfolio, and their patents are mainly related to electric digital data processing (G06F), the transmission of digital information (H04L), com- Group Main Group Member Main Patent Application Field puter systems based on specific computational models (G06N), commercial data processing International Business Machines methods (G06Q), and coding (H03M). AT&T Intellectual Property is related to transmission I Corporation, Intel Corporation, Microsoft G06F, H04L, G06N, G06Q, H03M (H04B), wireless communication networks (H04W), antennas (H01Q), and waveguides Technology Licensing (H01P); it focuses on technologies related to wireless communication. II AT&T Intellectual Property H04B, H04W, H01Q, H01P Align Technology Inc., California Institute of Technology, HeartFlow, Inc., Magic III G06T, G02B, A61B, G06K, G16H Leap, Samsung Electronics Co., Ltd., Leland Stanford Junior University Photonics 2022, 9, x FOR PEER REVIEW 8 of 12 Figure 3, Tables 3 and 4 indicate that most assignees are in Group III, which signi- fies that most assignees have achieved similar developments in machine learning in photonics technologies. These main developments include image data processing (G06T), optical elements, systems, or apparatuses (G02B), healthcare informatics and diagnosis (G16H, A61B), and recognition of data (G16H). In addition, International Business Machines Corporation, Intel Corporation, and Microsoft Technology Licensing have similar positions in the machine learning in photonics patent portfolio, and their patents are mainly related to electric digital data processing (G06F), the transmission of digital information (H04L), computer systems based on specific computational models (G06N), commercial data processing methods (G06Q), and coding (H03M). AT&T Intel- lectual Property is related to transmission (H04B), wireless communication networks Photonics 2022, 9, 33 8 of 11 (H04W), antennas (H01Q), and waveguides (H01P); it focuses on technologies related to wireless communication. 4.3. Post analysis: Change in Number of Patents and Papers 4.3. Post analysis: Change in Number of Patents and Papers The The re relationship lationship between between number o number of f publ published ished pa papers pers aand nd nu number mber oof f pa patents tents wa was s further further investigated investigated tto o ide identify ntify deve developmental lopmental tr tr end ends s in mach in machine ine lea learning rning in p in h photonics. otonics. Web of Science (WOS) was used to search relevant papers with the following criteria: Web of Science (WOS) was used to search relevant papers with the following criteria: ((TTL/machine ((TTL/machine le learning) arning) o or r (AB (ABST/machine ST/machine le learning) arning) or (K or (KW/machine W/machine lelearning)) arning)) and and ((TTL/photonics) or (ABST/photonics) or (KW/photonics)). TTL, ABST, and KW indicate ((TTL/photonics) or (ABST/photonics) or (KW/photonics)). TTL, ABST, and KW indicate titles, titles, abstracts, abstracts, and ke and keywor ywords, re ds, respectively spectively. . FFigur igure e4 presents th 4 presents the e change changein number of in number of patents and papers. patents and papers. Figure Figure 4. 4. Number Number of patents a of patents and nd papers in papers in 20 2019 19 and 2020 and 2020. . Patents and papers on machine learning in photonics have received increasing attention, Patents and papers on machine learning in photonics have received increasing at- and the increase in the number of patents (1.20%) is similar to that of papers (1.14%). tention, and the increase in the number of patents (1.20%) is similar to that of papers (1.14%). 5. Conclusions 5.1. Discussion and Implications 5. Conclusions This study used correspondence analysis and cluster analysis to explore the main- 5.1. Discussion and Implications stream technologies of machine learning in photonics and conduct patent portfolio analysis. This study used correspondence analysis and cluster analysis to explore the main- The empirical results indicate that the mainstream technologies include image data and stream technologies of machine learning in photonics and conduct patent portfolio electric digital data processing, optical elements, wireless communication networks, and analysis. The empirical results indicate that the mainstream technologies include image healthcare informatics and diagnosis; these technologies are not located within a specific field. In addition, the main developers of machine learning in photonics are leading com- panies of information and communication technology (ICT), wafers, and smart software and services. These companies include AT&T Intellectual Property, Intel Corporation, Inter- national Business Machines Corporation, Microsoft Technology Licensing, and Samsung Electronics Co., Ltd. Magic Leap focuses on augmented reality technologies that map the light field into the retina. California Institute of Technology and Leland Stanford Junior University (Stanford University) (Stanford, CA, USA) are known in academia for their natural sciences, engineering, and entrepreneurial atmospheres. HeartFlow, Inc. and Align Technology Inc. are devoted to using deep learning technologies to develop innovative medical materials. In conclusion, companies concerned with ICT, wafers, and medical ma- terials, along with academia, all play a crucial role in the development of new technologies of machine learning in photonics. Governments should consider allocating more research funds to academia to support technological developments in this field. Photonics 2022, 9, 33 9 of 11 A field that is being developed by more companies is more competitive. Align Tech- nology Inc., California Institute of Technology, HeartFlow, Inc., Magic Leap, Samsung Electronics Co., Ltd., and Stanford University are all investing in research in image data processing and optical elements, systems, or apparatuses. Therefore, these fields are more competitive. The main company investing in research and development in wireless commu- nication networks is AT&T Intellectual Property, which signifies that this field is relatively less competitive. Conventional information technology companies such as International Business Machines Corporation, Intel Corporation, and Microsoft Technology Licensing fo- cus on electric digital data processing and specific computational models, such as computer systems based on biological models. The analysis demonstrated that the synergy between the technological development of machine learning and photonics is centralized on optical communication equipment, smart computing, and medical applications. The use of machine learning and photonics increased communication efficiency and improved the performance of artificial intelligence and quality of medical diagnosis. In addition, the patent portfolio positioning analysis demonstrated that academic institutions among the top ten assignees, such as California Institute of Technology and Leland Stanford Junior University, were grouped in the same cluster as firms related to medical applications, such as HeartFlow and Align Technology. This suggests that academic institutions have a strong interest in the integration of machine learning and photonics in the field of medicine. Subsequent studies can investigate patents in academia. In terms of the theoretical contributions of this study, past studies of machine learning in photonics focused on algorithm technologies [2,9–11] or specific applications [1,3,6,11–13]; however, they did not determine the technology distribution or the patent portfolio of technology developers from a macro perspective. This study revealed the distribution of primary technology and positioning of patentees in the synergy between machine learning and photonics. In addition, this study identified mainstream technological trends in photonics machine learning and strategies and positioning in the patent layout to provide reference for enterprises to devise patent layout strategies, identify investment opportunities, and develop the industry strategically. Few studies have investigated the patent layout and patentee positioning in the synergy between machine learning and photonics. Machine learning in photonics will become indispensable, and analyzing the distribution of its technology is crucial and was therefore the motivation for this study. This study bridged this research gap and used a new perspective to explore the technological foci of each technology developer. This study proposes a technology road map of machine learning in photonics. The conclusions of this study can be used by businesses to allocate their research and develop- ment resources and used by the government to promote new technologies. More attention is required on companies and academia involved in photonics, applications of machine learning, and the usage of photonics to improve the performance of AI. This study deter- mined that current research on machine learning in photonics was related to image data processing, electric digital data processing, wireless communication networks, and health- care informatics and diagnosis. Therefore, the government can provide funding to train researchers in these fields to improve the future development of the photonics industry. 5.2. Limitations and Future Research Directions First, this study generalized the developments of each assignee in the three-level IPC. Future studies can conduct further research in each essential direction. For example, future studies can perform four-level IPC analysis or use more combinations of patent variables, such as inventor and nationality, to acquire additional patent information. Second, patent portfolio analysis was performed through correspondence analysis and K-means clustering. Subsequent studies should use WOS, in which a cluster with specific densities for research items and available patents can be created to acquire detailed information. Future studies can use different patent searches or search criteria, such as including triadic patent family Photonics 2022, 9, 33 10 of 11 data, to reveal crucial patent information. Due to limitations on the human resources and funding of this study, this study only used the USPTO database as a data source. The present patent analysis focused on a limited geographical and temporal segment. Subsequent studies should include other data sources, such as the European Patent Office, the Japan Patent Office, arXiv for articles, and preprints for existing patents, to draw informative conclusions. Funding: This research was funded by the Ministry of Science and Technology of the Taiwan, grant number MOST 109-2410-H-492-001. Acknowledgments: The author would like to thank the Ministry of Science and Technology of the Taiwan for financially supporting this research under Contract No. MOST 109-2410-H-492-001. Conflicts of Interest: The author declare no conflict of interest. Appendix A Table A1. Definition of IPC categories. IPC Categories Meaning A61B Diagnosis; surgery; identification G02B Optical elements, systems, or apparatus G06F Electric digital data processing Recognition of data; presentation of data; record carriers; handling G06K record carriers G06T Image data processing or generation, in general H01P Waveguides; resonators, lines, or other devices of the waveguide type H01Q Antennas, i.e., radio aerials H03M Coding, decoding, or code conversion, in general H04B Transmission H04L Transmission of digital information, e.g., telegraphic communication H04N Pictorial communication, e.g., television H04W Wireless communication networks G06N Computer systems based on specific computational models Data processing systems or methods, specially adapted for administrative, commercial, financial, managerial, supervisory, or forecasting purposes; G06Q systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory. or forecasting purposes, not otherwise provided for G06T Image data processing or generation, in general Healthcare informatics, i.e., information and communication technology G16H (ICT) specially adapted for the handling or processing of medical or healthcare data References 1. 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Journal

PhotonicsMultidisciplinary Digital Publishing Institute

Published: Jan 7, 2022

Keywords: machine learning; photonics; patent portfolio; correspondence analysis

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