system

Abstract

The system according to this embodiment aims to efficiently match research and development partners and propose the most suitable partnership. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, and a suggestion unit. The reception unit registers the user's profile. The analysis unit analyzes the information registered by the reception unit. The proposal unit proposes the most suitable partner based on the analysis results obtained by the analysis unit. The suggestion unit suggests new research themes and directions for innovation based on the partnership proposed by the proposal unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to efficiently match research and development partners, and it takes time and effort to find an optimal partnership.

[0005] The system according to the embodiment aims to efficiently match research and development partners and propose an optimal partnership.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a suggestion unit. The reception unit registers the user's profile. The analysis unit analyzes the information registered by the reception unit. The proposal unit proposes the most suitable partner based on the analysis results obtained by the analysis unit. The suggestion unit suggests new research themes and directions for innovation based on the partnership proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently match research and development partners and propose the optimal partnership. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The R&D partner matching system according to an embodiment of the present invention is a system that efficiently matches R&D partners such as companies, research institutions, and universities. In this system, users register their organization's profile and input the desired partner criteria. Next, the system uses AI to deeply understand the registered information and evaluate similarities and complementarity. The system not only proposes the optimal match but also suggests new research themes and innovation directions that may arise from collaboration. For example, the system allows users to register their organization's profile and input the desired partner criteria. The system uses natural language processing and machine learning to deeply understand the registered information and evaluate similarities and complementarity. For example, the system analyzes the user's research themes, technical capabilities, resources, and goals to propose the optimal partnership. The system goes beyond mere matching, playing a role in revitalizing the R&D ecosystem and promoting open innovation. Through continuous learning and improvement by AI, the accuracy of matching is constantly improved, providing more valuable suggestions to users. For example, the system targets R&D departments of large corporations, small and medium-sized enterprises and venture companies, universities and research institutions, public institutions (such as industrial promotion departments), and individual researchers and inventors. Challenges faced by these targets include difficulty in finding suitable R&D partners, lack of opportunities for collaboration with other fields, difficulty in mutual complementarity of resources and technologies, slow progress in promoting open innovation, and declining efficiency and productivity of R&D. To address these challenges, the system provides highly accurate matching using AI, cross-disciplinary partner suggestions, analysis of mutual complementarity of resources and technologies, suggestions for new research themes and innovations, a secure information sharing platform, and online and offline networking support.Applications of generative AI include understanding research themes and technologies through natural language processing, optimizing matching algorithms using machine learning, analyzing interdisciplinary relationships using knowledge graphs, predicting future research trends with predictive models, and providing user support through conversational AI. This allows the R&D partner matching system to efficiently match users with the most suitable R&D partners by registering, analyzing, suggesting, and providing insights based on their profiles.

[0029] The R&D partner matching system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a suggestion unit. The reception unit registers the user's profile. The user's profile includes, but is not limited to, name, affiliation, field of expertise, and research theme. For example, the reception unit allows the user to register their organization's profile and input the desired partner criteria. The analysis unit analyzes the information registered by the reception unit using natural language processing and machine learning. For example, the analysis unit uses natural language processing technology to analyze the user's research theme, technical capabilities, resources, and goals. The analysis unit also uses machine learning algorithms to evaluate the similarity and complementarity of the registered information. For example, the analysis unit uses machine learning algorithms such as neural networks and support vector machines to analyze the registered information. The proposal unit proposes the most suitable partner based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes the most suitable partner based on factors such as the degree of match in field of expertise, the relevance of research themes, and past achievements. For example, the proposal unit proposes the most suitable partner using evaluation criteria such as cosine similarity and correlation coefficient. The suggestion section suggests new research themes and innovation directions based on the partnerships proposed by the proposal section. For example, the suggestion section suggests new research themes and innovation directions that may arise from collaboration. For example, the suggestion section suggests new research themes and innovation directions based on technological breakthroughs or social impact. As a result, the R&D partner matching system according to this embodiment can efficiently match the optimal R&D partner by registering, analyzing, proposing, and suggesting user profiles.

[0030] The reception desk registers user profiles. User profiles include, but are not limited to, name, affiliation, specialty, and research topic. Specifically, users can input detailed information about themselves through a web interface. In addition to name and affiliation, users can select specific keywords and research field categories for their specialty. Furthermore, research topics can be described in detail using a free-text format, accurately reflecting the user's research content and areas of interest. The reception desk also includes a function for users to input the criteria for the partner they are seeking. For example, if a user is looking for a partner with specific technical skills or expertise in a particular research field, they can set detailed criteria. This makes it easier for users to find a partner that meets their needs. The reception desk also includes security features to safely manage the information registered by users. For example, registered information is encrypted, and measures are in place to prevent unauthorized external access. This allows users to register their information with peace of mind. Furthermore, the reception desk provides a function to periodically update the information registered by users. For example, if a user starts working on a new research topic or changes their affiliation, the latest information can always be reflected. This allows the system to always perform matching based on the latest information.

[0031] The analysis unit uses natural language processing and machine learning to analyze information registered by the reception unit. Specifically, it uses natural language processing technology to analyze users' research themes, technical skills, resources, and goals. For example, it analyzes text data of research themes entered by users in a free-text format to extract keywords and understand the context. This allows for an accurate understanding of the user's research content and areas of interest. The analysis unit also uses machine learning algorithms to evaluate the similarity and complementarity of registered information. For example, it uses machine learning algorithms such as neural networks and support vector machines to analyze user profile information and evaluate similarity and complementarity with other users. This provides foundational data for finding the optimal partner. Furthermore, the analysis unit builds a feedback loop to improve matching accuracy by learning from past matching data and success stories. For example, it analyzes data from past successful partnerships to determine what factors contributed to their success. This improves the accuracy of future matching. The analysis unit can also collect user feedback to improve its analysis algorithms. For example, it collects how users evaluated proposed partners and adjusts the analysis algorithms based on that data. This allows the analysis unit to perform analyses based on the latest information and feedback at all times, in order to propose the most suitable partner.

[0032] The proposal unit proposes the most suitable partner based on the analysis results obtained by the analysis unit. Specifically, it proposes the most suitable partner based on factors such as the degree of match in areas of expertise, the relevance of research themes, and past achievements. For example, it lists other users whose areas of expertise match the user's and prioritizes proposing users with a high degree of relevance to their research themes. It also considers past achievements and proposes partners who are predicted to have a high success rate in collaborative research. The proposal unit proposes the most suitable partner using evaluation criteria such as cosine similarity and correlation coefficients. This allows users to find the partner that best suits their needs. Furthermore, the proposal unit presents the user with multiple partner candidates and assists in selecting the most suitable partner. For example, it displays a list of each partner candidate's profile information, past achievements, and detailed research themes to make it easier for users to compare and consider them. The proposal unit also provides a function for users to provide feedback on the proposed partners. For example, users can provide feedback on whether the proposed partner was appropriate, what was good about it, and what could be improved. This allows the proposal unit to improve its proposal algorithm based on user feedback and improve the accuracy of future proposals. Furthermore, the proposal team also provides support for users to contact proposed partners. For example, it offers a function to automatically send emails to proposed partners and share contact information. This allows users to smoothly initiate partnerships.

[0033] The Suggestion Section suggests new research themes and innovation directions based on the partnership proposed by the Proposal Section. Specifically, it suggests new research themes and innovation directions that could arise from collaboration. For example, it suggests what new research themes could be considered by utilizing the technological strengths and resources of the proposed partnership. It also suggests what kind of innovations can be expected, taking into account social impact and market needs. The Suggestion Section suggests new research themes and innovation directions based on, for example, technological breakthroughs and social impact. This allows users to find new research themes and innovation directions based on the proposed partnership. Furthermore, the Suggestion Section can also propose concrete action plans to users. For example, it specifically suggests what steps should be taken to proceed with research and what resources should be utilized based on the proposed partnership. This makes it easier for users to create concrete action plans. The Suggestion Section also supports users in making the most of the proposed partnership. For example, it advises on how to proceed with joint research and how to announce results based on the proposed partnership. This allows users to effectively utilize the proposed partnership and achieve maximum results. Furthermore, the insights unit can receive feedback from users regarding the results obtained through proposed partnerships, and use this feedback to improve future insights. For example, it can collect data on the results of joint research and the progress of innovations, and use this data to improve the insights algorithm. This allows the insights unit to always provide optimal insights based on the latest information and feedback.

[0034] The analysis unit can deeply understand registered information and evaluate similarity and complementarity using natural language processing and machine learning. For example, the analysis unit uses natural language processing techniques to analyze the user's research themes, technical skills, resources, and goals. For example, the analysis unit uses natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis to analyze registered information. The analysis unit can also use machine learning algorithms to evaluate the similarity and complementarity of registered information. For example, the analysis unit uses machine learning algorithms such as neural networks and support vector machines to analyze registered information. This improves the accuracy of understanding and evaluating registered information by using natural language processing and machine learning. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input information such as the user's research themes, technical skills, resources, and goals into a generative AI, which can then analyze this information and evaluate similarity and complementarity.

[0035] The proposal unit can propose the optimal match. For example, the proposal unit proposes the best partner based on factors such as the degree of agreement in areas of expertise, the relevance of research themes, and past achievements. For example, the proposal unit proposes the best partner using evaluation criteria such as cosine similarity and correlation coefficients. The proposal unit can also propose the best partner based on the user's research themes, technical capabilities, resources, and goals. For example, the proposal unit can input information such as the user's research themes, technical capabilities, resources, and goals into a generative AI, which will analyze this information and propose the best partner. This enables efficient partnerships by proposing the optimal match. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without using a generative AI.

[0036] The suggestion section can suggest new research themes and directions for innovation that may arise from collaboration. For example, the suggestion section can suggest new research themes and directions for innovation based on technological breakthroughs or social impact. The suggestion section can also suggest new research themes and directions for innovation based on the user's research themes, technical capabilities, resources, and goals. For example, the suggestion section can input information such as the user's research themes, technical capabilities, resources, and goals into a generative AI, which can then analyze this information and suggest new research themes and directions for innovation. This stimulates the research and development ecosystem by suggesting new research themes and directions for innovation. Some or all of the processing described above in the suggestion section may be performed using a generative AI, or it may be performed without using a generative AI.

[0037] The reception desk allows users to register their organization's profile and enter the criteria for the partner they are looking for. For example, the reception desk provides an interface for users to register their organization's profile and enter the criteria for the partner they are looking for. The reception desk can also provide user support using conversational AI when users register their organization's profile and enter the criteria for the partner they are looking for. For example, the reception desk uses conversational AI to support users when they register their organization's profile and enter the criteria for the partner they are looking for. This makes it easier for users to find a suitable partner by registering their organization's profile and entering the criteria for the partner they are looking for. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI.

[0038] The analysis unit can perform interdisciplinary relationship analysis using a knowledge graph. For example, the analysis unit analyzes interdisciplinary relationships using a knowledge graph. For example, the analysis unit analyzes interdisciplinary relationships using definitions of nodes and edges and relationship evaluation methods. The analysis unit can also analyze information such as the user's research themes, technical skills, resources, and goals using a knowledge graph. For example, the analysis unit analyzes information such as the user's research themes, technical skills, resources, and goals using a knowledge graph and evaluates interdisciplinary relationships. This improves the accuracy of interdisciplinary relationship analysis by using a knowledge graph. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input a knowledge graph into a generative AI, and the generative AI can analyze interdisciplinary relationships.

[0039] The suggestion unit can predict future research trends using predictive models. For example, the suggestion unit can predict future research trends using predictive models. For example, the suggestion unit can predict future research trends using predictive models such as regression models and time series analysis models. The suggestion unit can also predict future research trends based on the user's research theme, technical capabilities, resources, and goals. For example, the suggestion unit can input information such as the user's research theme, technical capabilities, resources, and goals into a generating AI, which can analyze this information and predict future research trends. This makes it possible to predict future research trends by using predictive models. Some or all of the above processing in the suggestion unit may be performed using a generating AI, for example, or without using a generating AI.

[0040] The reception desk can provide user support using conversational AI. For example, the reception desk can use conversational AI to assist users when registering their organization's profile and entering the criteria for the partner they are looking for. For example, the reception desk can provide user support using conversational AI such as a chatbot or a voice recognition system. The reception desk can also provide real-time support using conversational AI when users register their organization's profile and enter the criteria for the partner they are looking for. This improves the quality of user support by using conversational AI. Some or all of the above processes in the reception desk may be performed using, for example, generative AI, or not using generative AI.

[0041] The reception desk can analyze a user's past registration history and suggest the optimal registration method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information that the user will use at a specific time of day based on their past registration history. In this way, by analyzing past registration history, the reception desk can suggest the optimal registration method for the user. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI.

[0042] The reception desk can customize input fields based on the user's current projects and areas of interest during profile registration. For example, the reception desk can prioritize displaying input fields related to the user's current projects. It can also automatically suggest relevant input fields based on the user's areas of interest. Furthermore, the reception desk can dynamically adjust the necessary input fields according to the progress of the user's projects. This improves user convenience by customizing input fields based on current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI.

[0043] The reception desk can prioritize retrieving highly relevant information by considering the user's geographical location during profile registration. For example, the reception desk can prioritize displaying information on nearby research institutions and companies based on the user's current location. The reception desk can also suggest research themes and projects related to the user's location. Furthermore, the reception desk can prioritize retrieving region-specific resources and technologies based on the user's geographical location. In this way, highly relevant information can be prioritized by considering geographical location. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI.

[0044] The reception desk can analyze a user's social media activity and obtain relevant information when they register their profile. For example, the reception desk can analyze a user's social media activity and suggest relevant research themes or projects. It can also suggest suitable partner candidates based on the user's social media connections. Furthermore, the reception desk can analyze a user's social media posts and obtain information based on their areas of interest. This allows for the efficient acquisition of relevant information by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the registered information during the analysis. For example, the analysis unit can perform a detailed analysis on important information and simplify other information. The analysis unit can also perform a detailed analysis on information that the user is particularly interested in. Furthermore, the analysis unit can perform a concise analysis on less important information, thereby shortening the overall analysis time. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the registered information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI.

[0046] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a technical analysis algorithm to technical information. It can also apply a management analysis algorithm to management information. Furthermore, it can apply a market analysis algorithm to market information. By applying different analysis algorithms depending on the category of information, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI.

[0047] The analysis unit can determine the priority of analysis based on the timing of information submission during the analysis process. For example, the analysis unit may prioritize the analysis of the latest information and postpone older information. It can also prioritize the analysis of information submitted by users within a specific period. Furthermore, it can prioritize the analysis of information with high urgency and postpone other information. This allows for efficient analysis by determining the priority of analysis based on the timing of information submission. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant information and postpone the analysis of less relevant information. The analysis unit can also prioritize the analysis of information of particular interest to the user. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI.

[0049] The proposal unit can adjust the level of detail in a proposal based on the importance of the partner. For example, it can provide detailed proposals to important partners and simplified proposals to others. It can also provide detailed proposals to partners that the user is particularly interested in. Furthermore, it can provide concise proposals to less important partners, thereby reducing the overall proposal time. This allows for efficient proposals by adjusting the level of detail based on the importance of the partner. Some or all of the above processing in the proposal unit may be performed using, for example, generative AI, or without generative AI.

[0050] The proposal unit can apply different proposal algorithms depending on the partner's category when making a proposal. For example, the proposal unit can apply a corporate analysis algorithm to corporate partners. It can also apply a research analysis algorithm to research institution partners. Furthermore, it can apply an academic analysis algorithm to university partners. By applying different proposal algorithms depending on the partner's category, the accuracy of the proposal is improved. Some or all of the above processing in the proposal unit may be performed using, for example, generative AI, or without using generative AI.

[0051] The proposal unit can determine the priority of proposals based on the partner's registration date. For example, the proposal unit can prioritize the most recent partner information and postpone older information. The proposal unit can also prioritize partner information registered by the user during a specific period. Furthermore, the proposal unit can prioritize partner information with high urgency and postpone other information. This enables efficient proposals by prioritizing proposals based on the partner's registration date. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without using a generative AI.

[0052] The proposal unit can adjust the order of proposals based on the relevance of the partners during the proposal process. For example, the proposal unit can prioritize suggesting highly relevant partner information and postpone suggesting less relevant information. The proposal unit can also prioritize suggesting partner information that the user is particularly interested in. Furthermore, the proposal unit can dynamically adjust the order of proposals based on the relevance of the partner information. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the partners. Some or all of the above processing in the proposal unit may be performed using, for example, generative AI, or without generative AI.

[0053] The suggestion unit can select the optimal suggestion method by referring to past collaboration data when providing suggestions. For example, the suggestion unit can select the optimal suggestion method based on past successful collaboration data. It can also select a suggestion method to avoid risks based on past unsuccessful collaboration data. Furthermore, the suggestion unit can analyze past collaboration data and select the most effective suggestion method. In this way, the optimal suggestion method can be selected by referring to past collaboration data. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without using generative AI.

[0054] The suggestion unit can apply different suggestion algorithms depending on the category of collaboration when providing suggestions. For example, the suggestion unit can apply a technical suggestion algorithm to technical collaborations. It can also apply a management analysis algorithm to management collaborations. Furthermore, it can apply a market analysis algorithm to market collaborations. By applying different suggestion algorithms depending on the category of collaboration, the accuracy of the suggestions is improved. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without using generative AI.

[0055] The suggestion unit can provide suggestions while considering the geographical distribution of collaborations. For example, the suggestion unit can prioritize suggesting nearby collaboration partners based on the user's location. It can also suggest region-specific collaboration partners based on the user's geographical distribution. Furthermore, it can prioritize suggesting collaboration partners related to the user's location. This makes it possible to provide highly relevant suggestions by considering the geographical distribution of collaborations. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without using generative AI.

[0056] The suggestion unit can improve the accuracy of its suggestions by referring to relevant literature on the collaboration. For example, the suggestion unit can improve the accuracy of its suggestions by referring to the latest research literature related to the collaboration. It can also improve the accuracy of its suggestions by referring to past research literature related to the collaboration. Furthermore, the suggestion unit can improve the accuracy of its suggestions by utilizing a literature database related to the collaboration. As a result, the accuracy of the suggestions is improved by referring to relevant literature on the collaboration. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without using generative AI.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The reception desk can analyze a user's past registration history and suggest the most suitable registration method. For example, it can automatically display information that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest information that the user will use at specific times based on their past registration history. In this way, by analyzing past registration history, the system can suggest the most suitable registration method for each user.

[0059] The analysis unit can perform interdisciplinary relationship analysis using knowledge graphs. For example, it can analyze interdisciplinary relationships using knowledge graphs. It can analyze interdisciplinary relationships using definitions of nodes and edges and relationship evaluation methods. It can also analyze information such as the user's research themes, technical skills, resources, and goals based on the knowledge graph and evaluate interdisciplinary relationships. As a result, the accuracy of interdisciplinary relationship analysis is improved by using knowledge graphs.

[0060] The proposal team can adjust the level of detail in proposals based on the importance of each partner. For example, they can provide detailed proposals to important partners and simplified ones to others. They can also provide detailed proposals to partners that the user is particularly interested in. Furthermore, they can provide concise proposals to less important partners, shortening the overall proposal time. By adjusting the level of detail in proposals based on the importance of each partner, efficient proposals become possible.

[0061] The suggestion function can select the optimal suggestion method by referring to past collaboration data when providing suggestions. For example, it can select the optimal suggestion method based on past successful collaboration data. It can also select a suggestion method that avoids risks based on past unsuccessful collaboration data. Furthermore, it can analyze past collaboration data and select the most effective suggestion method. In this way, the optimal suggestion method can be selected by referring to past collaboration data.

[0062] The reception desk can prioritize retrieving highly relevant information by considering the user's geographical location during profile registration. For example, it can prioritize displaying information on nearby research institutions and companies based on the user's current location. It can also suggest research themes and projects related to the user's location. Furthermore, it can prioritize retrieving region-specific resources and technologies based on the user's geographical location. In this way, by considering geographical location, it can prioritize retrieving highly relevant information.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk registers the user's profile. The user's profile includes name, affiliation, area of ​​expertise, research topic, etc. The user registers their organization's profile and enters the criteria for the partner they are looking for. Step 2: The analysis unit analyzes the information registered by the reception unit using natural language processing and machine learning. The analysis unit analyzes the user's research themes, technical skills, resources, goals, etc., and evaluates the similarity and complementarity of the registered information. For example, it uses machine learning algorithms such as neural networks and support vector machines. Step 3: The proposal department proposes the most suitable partner based on the analysis results obtained by the analysis department. The proposal department proposes the most suitable partner based on factors such as the degree of agreement in areas of expertise, the relevance of research themes, and past achievements. For example, evaluation criteria such as cosine similarity and correlation coefficient may be used. Step 4: The Implications team suggests new research themes and innovation directions based on the partnership proposed by the Proposal team. The Implications team suggests new research themes and innovation directions that could arise from the collaboration. For example, they might suggest these based on technological breakthroughs or social impact.

[0065] (Example of form 2) The R&D partner matching system according to an embodiment of the present invention is a system that efficiently matches R&D partners such as companies, research institutions, and universities. In this system, users register their organization's profile and input the desired partner criteria. Next, the system uses AI to deeply understand the registered information and evaluate similarities and complementarity. The system not only proposes the optimal match but also suggests new research themes and innovation directions that may arise from collaboration. For example, the system allows users to register their organization's profile and input the desired partner criteria. The system uses natural language processing and machine learning to deeply understand the registered information and evaluate similarities and complementarity. For example, the system analyzes the user's research themes, technical capabilities, resources, and goals to propose the optimal partnership. The system goes beyond mere matching, playing a role in revitalizing the R&D ecosystem and promoting open innovation. Through continuous learning and improvement by AI, the accuracy of matching is constantly improved, providing more valuable suggestions to users. For example, the system targets R&D departments of large corporations, small and medium-sized enterprises and venture companies, universities and research institutions, public institutions (such as industrial promotion departments), and individual researchers and inventors. Challenges faced by these targets include difficulty in finding suitable R&D partners, lack of opportunities for collaboration with other fields, difficulty in mutual complementarity of resources and technologies, slow progress in promoting open innovation, and declining efficiency and productivity of R&D. To address these challenges, the system provides highly accurate matching using AI, cross-disciplinary partner suggestions, analysis of mutual complementarity of resources and technologies, suggestions for new research themes and innovations, a secure information sharing platform, and online and offline networking support.Applications of generative AI include understanding research themes and technologies through natural language processing, optimizing matching algorithms using machine learning, analyzing interdisciplinary relationships using knowledge graphs, predicting future research trends with predictive models, and providing user support through conversational AI. This allows the R&D partner matching system to efficiently match users with the most suitable R&D partners by registering, analyzing, suggesting, and providing insights based on their profiles.

[0066] The R&D partner matching system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a suggestion unit. The reception unit registers the user's profile. The user's profile includes, but is not limited to, name, affiliation, field of expertise, and research theme. For example, the reception unit allows the user to register their organization's profile and input the desired partner criteria. The analysis unit analyzes the information registered by the reception unit using natural language processing and machine learning. For example, the analysis unit uses natural language processing technology to analyze the user's research theme, technical capabilities, resources, and goals. The analysis unit also uses machine learning algorithms to evaluate the similarity and complementarity of the registered information. For example, the analysis unit uses machine learning algorithms such as neural networks and support vector machines to analyze the registered information. The proposal unit proposes the most suitable partner based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes the most suitable partner based on factors such as the degree of match in field of expertise, the relevance of research themes, and past achievements. For example, the proposal unit proposes the most suitable partner using evaluation criteria such as cosine similarity and correlation coefficient. The suggestion section suggests new research themes and innovation directions based on the partnerships proposed by the proposal section. For example, the suggestion section suggests new research themes and innovation directions that may arise from collaboration. For example, the suggestion section suggests new research themes and innovation directions based on technological breakthroughs or social impact. As a result, the R&D partner matching system according to this embodiment can efficiently match the optimal R&D partner by registering, analyzing, proposing, and suggesting user profiles.

[0067] The reception desk registers user profiles. User profiles include, but are not limited to, name, affiliation, specialty, and research topic. Specifically, users can input detailed information about themselves through a web interface. In addition to name and affiliation, users can select specific keywords and research field categories for their specialty. Furthermore, research topics can be described in detail using a free-text format, accurately reflecting the user's research content and areas of interest. The reception desk also includes a function for users to input the criteria for the partner they are seeking. For example, if a user is looking for a partner with specific technical skills or expertise in a particular research field, they can set detailed criteria. This makes it easier for users to find a partner that meets their needs. The reception desk also includes security features to safely manage the information registered by users. For example, registered information is encrypted, and measures are in place to prevent unauthorized external access. This allows users to register their information with peace of mind. Furthermore, the reception desk provides a function to periodically update the information registered by users. For example, if a user starts working on a new research topic or changes their affiliation, the latest information can always be reflected. This allows the system to always perform matching based on the latest information.

[0068] The analysis unit uses natural language processing and machine learning to analyze information registered by the reception unit. Specifically, it uses natural language processing technology to analyze users' research themes, technical skills, resources, and goals. For example, it analyzes text data of research themes entered by users in a free-text format to extract keywords and understand the context. This allows for an accurate understanding of the user's research content and areas of interest. The analysis unit also uses machine learning algorithms to evaluate the similarity and complementarity of registered information. For example, it uses machine learning algorithms such as neural networks and support vector machines to analyze user profile information and evaluate similarity and complementarity with other users. This provides foundational data for finding the optimal partner. Furthermore, the analysis unit builds a feedback loop to improve matching accuracy by learning from past matching data and success stories. For example, it analyzes data from past successful partnerships to determine what factors contributed to their success. This improves the accuracy of future matching. The analysis unit can also collect user feedback to improve its analysis algorithms. For example, it collects how users evaluated proposed partners and adjusts the analysis algorithms based on that data. This allows the analysis unit to perform analyses based on the latest information and feedback at all times, in order to propose the most suitable partner.

[0069] The proposal unit proposes the most suitable partner based on the analysis results obtained by the analysis unit. Specifically, it proposes the most suitable partner based on factors such as the degree of match in areas of expertise, the relevance of research themes, and past achievements. For example, it lists other users whose areas of expertise match the user's and prioritizes proposing users with a high degree of relevance to their research themes. It also considers past achievements and proposes partners who are predicted to have a high success rate in collaborative research. The proposal unit proposes the most suitable partner using evaluation criteria such as cosine similarity and correlation coefficients. This allows users to find the partner that best suits their needs. Furthermore, the proposal unit presents the user with multiple partner candidates and assists in selecting the most suitable partner. For example, it displays a list of each partner candidate's profile information, past achievements, and detailed research themes to make it easier for users to compare and consider them. The proposal unit also provides a function for users to provide feedback on the proposed partners. For example, users can provide feedback on whether the proposed partner was appropriate, what was good about it, and what could be improved. This allows the proposal unit to improve its proposal algorithm based on user feedback and improve the accuracy of future proposals. Furthermore, the proposal team also provides support for users to contact proposed partners. For example, it offers a function to automatically send emails to proposed partners and share contact information. This allows users to smoothly initiate partnerships.

[0070] The Suggestion Section suggests new research themes and innovation directions based on the partnership proposed by the Proposal Section. Specifically, it suggests new research themes and innovation directions that could arise from collaboration. For example, it suggests what new research themes could be considered by utilizing the technological strengths and resources of the proposed partnership. It also suggests what kind of innovations can be expected, taking into account social impact and market needs. The Suggestion Section suggests new research themes and innovation directions based on, for example, technological breakthroughs and social impact. This allows users to find new research themes and innovation directions based on the proposed partnership. Furthermore, the Suggestion Section can also propose concrete action plans to users. For example, it specifically suggests what steps should be taken to proceed with research and what resources should be utilized based on the proposed partnership. This makes it easier for users to create concrete action plans. The Suggestion Section also supports users in making the most of the proposed partnership. For example, it advises on how to proceed with joint research and how to announce results based on the proposed partnership. This allows users to effectively utilize the proposed partnership and achieve maximum results. Furthermore, the insights unit can receive feedback from users regarding the results obtained through proposed partnerships, and use this feedback to improve future insights. For example, it can collect data on the results of joint research and the progress of innovations, and use this data to improve the insights algorithm. This allows the insights unit to always provide optimal insights based on the latest information and feedback.

[0071] The analysis unit can deeply understand registered information and evaluate similarity and complementarity using natural language processing and machine learning. For example, the analysis unit uses natural language processing techniques to analyze the user's research themes, technical skills, resources, and goals. For example, the analysis unit uses natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis to analyze registered information. The analysis unit can also use machine learning algorithms to evaluate the similarity and complementarity of registered information. For example, the analysis unit uses machine learning algorithms such as neural networks and support vector machines to analyze registered information. This improves the accuracy of understanding and evaluating registered information by using natural language processing and machine learning. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input information such as the user's research themes, technical skills, resources, and goals into a generative AI, which can then analyze this information and evaluate similarity and complementarity.

[0072] The proposal unit can propose the optimal match. For example, the proposal unit proposes the best partner based on factors such as the degree of agreement in areas of expertise, the relevance of research themes, and past achievements. For example, the proposal unit proposes the best partner using evaluation criteria such as cosine similarity and correlation coefficients. The proposal unit can also propose the best partner based on the user's research themes, technical capabilities, resources, and goals. For example, the proposal unit can input information such as the user's research themes, technical capabilities, resources, and goals into a generative AI, which will analyze this information and propose the best partner. This enables efficient partnerships by proposing the optimal match. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without using a generative AI.

[0073] The suggestion section can suggest new research themes and directions for innovation that may arise from collaboration. For example, the suggestion section can suggest new research themes and directions for innovation based on technological breakthroughs or social impact. The suggestion section can also suggest new research themes and directions for innovation based on the user's research themes, technical capabilities, resources, and goals. For example, the suggestion section can input information such as the user's research themes, technical capabilities, resources, and goals into a generative AI, which can then analyze this information and suggest new research themes and directions for innovation. This stimulates the research and development ecosystem by suggesting new research themes and directions for innovation. Some or all of the processing described above in the suggestion section may be performed using a generative AI, or it may be performed without using a generative AI.

[0074] The reception desk allows users to register their organization's profile and enter the criteria for the partner they are looking for. For example, the reception desk provides an interface for users to register their organization's profile and enter the criteria for the partner they are looking for. The reception desk can also provide user support using conversational AI when users register their organization's profile and enter the criteria for the partner they are looking for. For example, the reception desk uses conversational AI to support users when they register their organization's profile and enter the criteria for the partner they are looking for. This makes it easier for users to find a suitable partner by registering their organization's profile and entering the criteria for the partner they are looking for. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI.

[0075] The analysis unit can perform interdisciplinary relationship analysis using a knowledge graph. For example, the analysis unit analyzes interdisciplinary relationships using a knowledge graph. For example, the analysis unit analyzes interdisciplinary relationships using definitions of nodes and edges and relationship evaluation methods. The analysis unit can also analyze information such as the user's research themes, technical skills, resources, and goals using a knowledge graph. For example, the analysis unit analyzes information such as the user's research themes, technical skills, resources, and goals using a knowledge graph and evaluates interdisciplinary relationships. This improves the accuracy of interdisciplinary relationship analysis by using a knowledge graph. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input a knowledge graph into a generative AI, and the generative AI can analyze interdisciplinary relationships.

[0076] The suggestion unit can predict future research trends using predictive models. For example, the suggestion unit can predict future research trends using predictive models. For example, the suggestion unit can predict future research trends using predictive models such as regression models and time series analysis models. The suggestion unit can also predict future research trends based on the user's research theme, technical capabilities, resources, and goals. For example, the suggestion unit can input information such as the user's research theme, technical capabilities, resources, and goals into a generating AI, which can analyze this information and predict future research trends. This makes it possible to predict future research trends by using predictive models. Some or all of the above processing in the suggestion unit may be performed using a generating AI, for example, or without using a generating AI.

[0077] The reception desk can provide user support using conversational AI. For example, the reception desk can use conversational AI to assist users when registering their organization's profile and entering the criteria for the partner they are looking for. For example, the reception desk can provide user support using conversational AI such as a chatbot or a voice recognition system. The reception desk can also provide real-time support using conversational AI when users register their organization's profile and enter the criteria for the partner they are looking for. This improves the quality of user support by using conversational AI. Some or all of the above processes in the reception desk may be performed using, for example, generative AI, or not using generative AI.

[0078] The reception desk can estimate the user's emotions and adjust the profile registration interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick profile registration. This improves user convenience by adjusting the interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or not using generative AI.

[0079] The reception desk can analyze a user's past registration history and suggest the optimal registration method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information that the user will use at a specific time of day based on their past registration history. In this way, by analyzing past registration history, the reception desk can suggest the optimal registration method for the user. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI.

[0080] The reception desk can customize input fields based on the user's current projects and areas of interest during profile registration. For example, the reception desk can prioritize displaying input fields related to the user's current projects. It can also automatically suggest relevant input fields based on the user's areas of interest. Furthermore, the reception desk can dynamically adjust the necessary input fields according to the progress of the user's projects. This improves user convenience by customizing input fields based on current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI.

[0081] The reception desk can estimate the user's emotions and determine the priority of the information to be registered based on the estimated emotions. For example, if the user is nervous, the reception desk may prompt them to enter important information first and leave other information for later. If the user is relaxed, the reception desk may also encourage them to enter detailed information. Furthermore, if the user is in a hurry, the reception desk may prompt them to enter only the most important information and allow them to add more later. This enables efficient information registration by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or not using generative AI.

[0082] The reception desk can prioritize retrieving highly relevant information by considering the user's geographical location during profile registration. For example, the reception desk can prioritize displaying information on nearby research institutions and companies based on the user's current location. The reception desk can also suggest research themes and projects related to the user's location. Furthermore, the reception desk can prioritize retrieving region-specific resources and technologies based on the user's geographical location. In this way, highly relevant information can be prioritized by considering geographical location. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI.

[0083] The reception desk can analyze a user's social media activity and obtain relevant information when they register their profile. For example, the reception desk can analyze a user's social media activity and suggest relevant research themes or projects. It can also suggest suitable partner candidates based on the user's social media connections. Furthermore, the reception desk can analyze a user's social media posts and obtain information based on their areas of interest. This allows for the efficient acquisition of relevant information by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI.

[0084] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide comprehensive results. If the user is in a hurry, the analysis unit can perform a rapid analysis and provide concise results. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. By adjusting the analysis algorithm according to the user's emotions, the accuracy of the analysis results is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, generative AI, or without generative AI.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the registered information during the analysis. For example, the analysis unit can perform a detailed analysis on important information and simplify other information. The analysis unit can also perform a detailed analysis on information that the user is particularly interested in. Furthermore, the analysis unit can perform a concise analysis on less important information, thereby shortening the overall analysis time. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the registered information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI.

[0086] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a technical analysis algorithm to technical information. It can also apply a management analysis algorithm to management information. Furthermore, it can apply a market analysis algorithm to market information. By applying different analysis algorithms depending on the category of information, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, the user's understanding is deepened. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI.

[0088] The analysis unit can determine the priority of analysis based on the timing of information submission during the analysis process. For example, the analysis unit may prioritize the analysis of the latest information and postpone older information. It can also prioritize the analysis of information submitted by users within a specific period. Furthermore, it can prioritize the analysis of information with high urgency and postpone other information. This allows for efficient analysis by determining the priority of analysis based on the timing of information submission. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant information and postpone the analysis of less relevant information. The analysis unit can also prioritize the analysis of information of particular interest to the user. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI.

[0090] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions and comprehensive information. If the user is in a hurry, the suggestion unit can provide quick suggestions and concise information. Furthermore, if the user is excited, the suggestion unit can provide visually appealing suggestions. By adjusting the way suggestions are presented according to the user's emotions, the user's understanding is enhanced. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using, for example, generative AI, or not using generative AI.

[0091] The proposal unit can adjust the level of detail in a proposal based on the importance of the partner. For example, it can provide detailed proposals to important partners and simplified proposals to others. It can also provide detailed proposals to partners that the user is particularly interested in. Furthermore, it can provide concise proposals to less important partners, thereby reducing the overall proposal time. This allows for efficient proposals by adjusting the level of detail based on the importance of the partner. Some or all of the above processing in the proposal unit may be performed using, for example, generative AI, or without generative AI.

[0092] The proposal unit can apply different proposal algorithms depending on the partner's category when making a proposal. For example, the proposal unit can apply a corporate analysis algorithm to corporate partners. It can also apply a research analysis algorithm to research institution partners. Furthermore, it can apply an academic analysis algorithm to university partners. By applying different proposal algorithms depending on the partner's category, the accuracy of the proposal is improved. Some or all of the above processing in the proposal unit may be performed using, for example, generative AI, or without using generative AI.

[0093] The suggestion section can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion section will provide a short, concise suggestion. If the user is relaxed, the suggestion section may provide a longer suggestion with detailed explanations. Furthermore, if the user is excited, the suggestion section may provide a suggestion with visually stimulating effects. By adjusting the length of the suggestion according to the user's emotions, the user's understanding is enhanced. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion section may be performed using, for example, generative AI, or not using generative AI.

[0094] The proposal unit can determine the priority of proposals based on the partner's registration date. For example, the proposal unit can prioritize the most recent partner information and postpone older information. The proposal unit can also prioritize partner information registered by the user during a specific period. Furthermore, the proposal unit can prioritize partner information with high urgency and postpone other information. This enables efficient proposals by prioritizing proposals based on the partner's registration date. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without using a generative AI.

[0095] The proposal unit can adjust the order of proposals based on the relevance of the partners during the proposal process. For example, the proposal unit can prioritize suggesting highly relevant partner information and postpone suggesting less relevant information. The proposal unit can also prioritize suggesting partner information that the user is particularly interested in. Furthermore, the proposal unit can dynamically adjust the order of proposals based on the relevance of the partner information. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the partners. Some or all of the above processing in the proposal unit may be performed using, for example, generative AI, or without generative AI.

[0096] The suggestion unit can estimate the user's emotions and adjust its suggestion methods based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions and comprehensive information. If the user is in a hurry, the suggestion unit can provide quick suggestions and concise information. Furthermore, if the user is excited, the suggestion unit can provide visually appealing suggestions. By adjusting the suggestion methods according to the user's emotions, the user's understanding is deepened. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using, for example, generative AI, or not using generative AI.

[0097] The suggestion unit can select the optimal suggestion method by referring to past collaboration data when providing suggestions. For example, the suggestion unit can select the optimal suggestion method based on past successful collaboration data. It can also select a suggestion method to avoid risks based on past unsuccessful collaboration data. Furthermore, the suggestion unit can analyze past collaboration data and select the most effective suggestion method. In this way, the optimal suggestion method can be selected by referring to past collaboration data. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without using generative AI.

[0098] The suggestion unit can apply different suggestion algorithms depending on the category of collaboration when providing suggestions. For example, the suggestion unit can apply a technical suggestion algorithm to technical collaborations. It can also apply a management analysis algorithm to management collaborations. Furthermore, it can apply a market analysis algorithm to market collaborations. By applying different suggestion algorithms depending on the category of collaboration, the accuracy of the suggestions is improved. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without using generative AI.

[0099] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit may prioritize important suggestions and postpone others. If the user is relaxed, the suggestion unit may provide detailed suggestions and comprehensive information. Furthermore, if the user is in a hurry, the suggestion unit may provide only the most important suggestions, allowing for additional suggestions to be added later. This enables efficient suggestions by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using, for example, generative AI, or not using generative AI.

[0100] The suggestion unit can provide suggestions while considering the geographical distribution of collaborations. For example, the suggestion unit can prioritize suggesting nearby collaboration partners based on the user's location. It can also suggest region-specific collaboration partners based on the user's geographical distribution. Furthermore, it can prioritize suggesting collaboration partners related to the user's location. This makes it possible to provide highly relevant suggestions by considering the geographical distribution of collaborations. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without using generative AI.

[0101] The suggestion unit can improve the accuracy of its suggestions by referring to relevant literature on the collaboration. For example, the suggestion unit can improve the accuracy of its suggestions by referring to the latest research literature related to the collaboration. It can also improve the accuracy of its suggestions by referring to past research literature related to the collaboration. Furthermore, the suggestion unit can improve the accuracy of its suggestions by utilizing a literature database related to the collaboration. As a result, the accuracy of the suggestions is improved by referring to relevant literature on the collaboration. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without using generative AI.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on those emotions. For example, if the user is relaxed, it can perform a detailed analysis and provide comprehensive results. If the user is in a hurry, it can perform a rapid analysis and provide concise results. Furthermore, if the user is excited, it can provide visually appealing analysis results. By adjusting the depth of the analysis according to the user's emotions, the accuracy of the analysis results is improved.

[0104] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions and comprehensive information. If the user is in a hurry, it can provide quick suggestions and concise information. Furthermore, if the user is excited, it can provide visually appealing suggestions. By adjusting the way suggestions are presented according to the user's emotions, the user's understanding is deepened.

[0105] The suggestion function can estimate the user's emotions and adjust the suggestion method based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions and comprehensive information. If the user is in a hurry, it can provide quick suggestions and concise information. Furthermore, if the user is excited, it can provide visually appealing suggestions. By adjusting the suggestion method according to the user's emotions, a deeper understanding of the user is achieved.

[0106] The reception desk can estimate the user's emotions and adjust the profile registration interface based on those emotions. For example, if the user is stressed, it can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick profile registration. This improves user convenience by adjusting the interface according to the user's emotions.

[0107] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, the user's understanding is deepened.

[0108] The reception desk can analyze a user's past registration history and suggest the most suitable registration method. For example, it can automatically display information that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest information that the user will use at specific times based on their past registration history. In this way, by analyzing past registration history, the system can suggest the most suitable registration method for each user.

[0109] The analysis unit can perform interdisciplinary relationship analysis using knowledge graphs. For example, it can analyze interdisciplinary relationships using knowledge graphs. It can analyze interdisciplinary relationships using definitions of nodes and edges and relationship evaluation methods. It can also analyze information such as the user's research themes, technical skills, resources, and goals based on the knowledge graph and evaluate interdisciplinary relationships. As a result, the accuracy of interdisciplinary relationship analysis is improved by using knowledge graphs.

[0110] The proposal team can adjust the level of detail in proposals based on the importance of each partner. For example, they can provide detailed proposals to important partners and simplified ones to others. They can also provide detailed proposals to partners that the user is particularly interested in. Furthermore, they can provide concise proposals to less important partners, shortening the overall proposal time. By adjusting the level of detail in proposals based on the importance of each partner, efficient proposals become possible.

[0111] The suggestion function can select the optimal suggestion method by referring to past collaboration data when providing suggestions. For example, it can select the optimal suggestion method based on past successful collaboration data. It can also select a suggestion method that avoids risks based on past unsuccessful collaboration data. Furthermore, it can analyze past collaboration data and select the most effective suggestion method. In this way, the optimal suggestion method can be selected by referring to past collaboration data.

[0112] The reception desk can prioritize retrieving highly relevant information by considering the user's geographical location during profile registration. For example, it can prioritize displaying information on nearby research institutions and companies based on the user's current location. It can also suggest research themes and projects related to the user's location. Furthermore, it can prioritize retrieving region-specific resources and technologies based on the user's geographical location. In this way, by considering geographical location, it can prioritize retrieving highly relevant information.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The reception desk registers the user's profile. The user's profile includes name, affiliation, area of ​​expertise, research topic, etc. The user registers their organization's profile and enters the criteria for the partner they are looking for. Step 2: The analysis unit analyzes the information registered by the reception unit using natural language processing and machine learning. The analysis unit analyzes the user's research themes, technical skills, resources, goals, etc., and evaluates the similarity and complementarity of the registered information. For example, it uses machine learning algorithms such as neural networks and support vector machines. Step 3: The proposal department proposes the most suitable partner based on the analysis results obtained by the analysis department. The proposal department proposes the most suitable partner based on factors such as the degree of agreement in areas of expertise, the relevance of research themes, and past achievements. For example, evaluation criteria such as cosine similarity and correlation coefficient may be used. Step 4: The Implications team suggests new research themes and innovation directions based on the partnership proposed by the Proposal team. The Implications team suggests new research themes and innovation directions that could arise from the collaboration. For example, they might suggest these based on technological breakthroughs or social impact.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and suggestion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and registers the user's profile. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the registered information using natural language processing and machine learning. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable partner based on the analysis results. The suggestion unit is implemented by the control unit 46A of the smart device 14 and suggests new research themes or directions for innovation based on the proposed partnership. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and registers the user's profile. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the registered information using natural language processing and machine learning. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable partner based on the analysis results. The suggestion unit is implemented by the control unit 46A of the smart glasses 214 and suggests new research themes or directions for innovation based on the proposed partnership. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and registers the user's profile. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the registered information using natural language processing and machine learning. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable partner based on the analysis results. The suggestion unit is implemented by the control unit 46A of the headset terminal 314 and suggests new research themes or directions for innovation based on the proposed partnership. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and suggestion unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and registers the user's profile. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the registered information using natural language processing and machine learning. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable partner based on the analysis results. The suggestion unit is implemented by the control unit 46A of the robot 414 and suggests new research themes or directions for innovation based on the proposed partnership. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0168] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) The reception area where users register their profiles, An analysis unit analyzes the information registered by the reception unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the most suitable partner, The system comprises a suggestion section that indicates new research themes and directions for innovation based on the partnership proposed by the aforementioned proposal section. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Using natural language processing and machine learning, we gain a deep understanding of registered information and evaluate similarities and complementarity. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose the optimal match The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned suggestion unit is, This suggests new research themes and innovation directions that may emerge through collaboration. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Users register their organization's profile and enter the criteria for the partner they are looking for. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We will perform interdisciplinary relationship analysis using knowledge graphs. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned suggestion unit is, Predicting future research trends using predictive models. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We provide user support using conversational AI. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and adjusts the profile registration interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is We analyze the user's past registration history and suggest the optimal registration method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When registering a profile, input fields are customized based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the information to be registered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When registering a profile, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When a user registers their profile, their social media activity is analyzed and relevant information is retrieved. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the registered information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the partner. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the partner's category. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When submitting a proposal, we prioritize proposals based on when the partner registered. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the partners. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned suggestion unit is, We estimate the user's emotions and adjust the suggestion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned suggestion unit is, When providing suggestions, we will select the optimal suggestion method by referring to past collaborative data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned suggestion unit is, When providing suggestions, different suggestion algorithms are applied depending on the category of collaboration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned suggestion unit is, It estimates the user's emotions and prioritizes suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned suggestion unit is, When making suggestions, consider the geographical distribution of collaborations. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned suggestion unit is, When making suggestions, refer to relevant literature on collaboration to improve the accuracy of those suggestions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area where users register their profiles, An analysis unit analyzes the information registered by the reception unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the most suitable partner, The system comprises a suggestion section that indicates new research themes and directions for innovation based on the partnership proposed by the aforementioned proposal section. A system characterized by the following features.

2. The aforementioned analysis unit, Using natural language processing and machine learning, we gain a deep understanding of registered information and evaluate similarities and complementarity. The system according to feature 1.

3. The aforementioned proposal section is, We propose the optimal match The system according to feature 1.

4. The aforementioned suggestion unit is, This suggests new research themes and innovation directions that may emerge through collaboration. The system according to feature 1.

5. The aforementioned reception unit is Users register their organization's profile and enter the criteria for the partner they are looking for. The system according to feature 1.

6. The aforementioned analysis unit, We will perform interdisciplinary relationship analysis using knowledge graphs. The system according to feature 1.

7. The aforementioned suggestion unit is, Predicting future research trends using predictive models. The system according to feature 1.

8. The aforementioned reception unit is We provide user support using conversational AI. The system according to feature 1.

Images