System

Abstract

An object of a system according to an embodiment is to efficiently evaluate and feed back a research idea and propose a new approach.SOLUTION: A system includes a research idea input part, an evaluation part, a feedback part, a data mining part, and a proposal part. The research idea input unit inputs a research idea of a researcher. The evaluation unit evaluates the research idea input by the research idea input unit. The feedback unit feeds back the result evaluated by the evaluation unit to the researcher. The data mining unit collects related data. The suggester suggests a new approach based on the data collected by the data miner.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques do not efficiently evaluate research ideas, provide feedback, or propose new approaches, leaving room for improvement.

[0005] The system according to the embodiment aims to efficiently evaluate and provide feedback on research ideas and propose new approaches. [Means for solving the problem]

[0006] The system according to the embodiment comprises a research idea input unit, an evaluation unit, a feedback unit, a data mining unit, and a proposal unit. The research idea input unit inputs research ideas from researchers. The evaluation unit evaluates the research ideas input by the research idea input unit. The feedback unit feeds back the results of the evaluation by the evaluation unit to the researchers. The data mining unit collects related data. The proposal unit proposes new approaches based on the data collected by the data mining unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently evaluate and provide feedback on research ideas and propose new approaches. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple 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), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A research and development support system according to an embodiment of the present invention is a system that improves the efficiency of researchers' research activities and supports hypothesis formulation, experimental planning, paper writing, etc. As a result, the research and development support system can improve the efficiency of researchers' research activities and support hypothesis formulation, experimental planning, paper writing, etc.

[0029] The research and development support system according to an embodiment includes a research idea input unit, an evaluation unit, a feedback unit, a data mining unit, and a proposal unit. The research idea input unit inputs a researcher's research idea. For example, a researcher can input an idea such as "developing a new energy source." The research idea input unit can also input the research idea in text format. The evaluation unit evaluates the research idea input by the research idea input unit. For example, the generation AI evaluates the novelty and feasibility of the research idea based on related previous research. The evaluation unit can also perform risk assessment by referring to past successes and failures. The feedback unit feeds back the results of the evaluation by the evaluation unit to the researcher. For example, the generation AI may provide feedback such as "This idea may overlap with existing research." The feedback unit can also suggest improvements to the researcher. The data mining unit collects related data. For example, the generation AI collects papers, patents, technical reports, experimental data, etc. from various fields and performs cross-sectional mining. The data mining unit can also identify and associate terms and concepts from different fields to link knowledge. The proposal unit proposes new approaches based on the data collected by the data mining unit. For example, the generative AI proposes new experimental methods based on knowledge gained from research in different fields. The proposal unit can also suggest new research themes to researchers. As a result, the research and development support system according to the embodiment can streamline researchers' research activities and support hypothesis formulation, experimental planning, paper writing, and so on.

[0030] The evaluation unit can perform risk assessment by referring to past successes and failures. For example, when the generative AI evaluates a research idea, the evaluation unit extracts past successes and failures from a database and performs risk assessment. For example, it analyzes what results similar ideas have produced in the past and evaluates the level of risk. In addition, when the generative AI evaluates a research idea, the evaluation unit performs risk assessment by referring to past successes and failures of researchers. For example, it analyzes what results specific technologies and methods have produced in the past and evaluates the level of risk. In addition, when the generative AI evaluates a research idea, the evaluation unit performs risk assessment by referring to past successes and failures of researchers. For example, it analyzes what results similar ideas have produced in the past and evaluates the level of risk. This makes it possible to evaluate the risk of research ideas.

[0031] The evaluation unit takes into account the life cycle information of related patents and can make evaluations based on patent expiration dates and market trends. For example, when the generative AI evaluates a research idea, the evaluation unit takes into account the life cycle information of related patents and makes evaluations based on patent expiration dates and market trends. For example, if a patent is about to expire, the evaluation unit will evaluate the impact that the technology will have on the market. In addition, when the generative AI evaluates a research idea, the evaluation unit takes into account the life cycle information of related patents and makes evaluations based on patent expiration dates and market trends. For example, if a patent is about to expire, the evaluation unit will evaluate the impact that the technology will have on the market. In addition, when the generative AI evaluates a research idea, the evaluation unit takes into account the life cycle information of related patents and makes evaluations based on patent expiration dates and market trends. For example, if a patent is about to expire, the evaluation unit will evaluate the impact that the technology will have on the market. This makes it possible to make evaluations that take patent expiration dates and market trends into account.

[0032] The evaluation unit is able to conduct evaluations from a global perspective, taking into account research trends in different cultural spheres and regions. For example, when the generative AI evaluates a research idea, the evaluation unit conducts the evaluation from a global perspective, taking into account research trends in different cultural spheres and regions. For example, the evaluation is based on research trends and market needs in a specific region. In addition, when the generative AI evaluates a research idea, the evaluation unit conducts the evaluation from a global perspective, taking into account research trends in different cultural spheres and regions. For example, the evaluation is based on research trends and market needs in a specific region. In addition, when the generative AI evaluates a research idea, the evaluation unit conducts the evaluation from a global perspective, taking into account research trends in different cultural spheres and regions. For example, the evaluation is based on research trends and market needs in a specific region. This makes evaluations possible from a global perspective.

[0033] The evaluation unit can reflect relevant industrial trends and market needs in real time. For example, when the generation AI evaluates a research idea, the evaluation unit reflects relevant industrial trends and market needs in real time. For example, the evaluation is made based on the latest market data. In addition, the evaluation unit reflects relevant industrial trends and market needs in real time when the generation AI evaluates a research idea. For example, the evaluation is made based on the latest market data. In addition, the evaluation unit reflects relevant industrial trends and market needs in real time when the generation AI evaluates a research idea. For example, the evaluation is made based on the latest market data. This makes it possible to make an evaluation that reflects industrial trends and market needs in real time.

[0034] The feedback unit can provide more accurate advice by referring to past feedback history. For example, when the generation AI provides feedback, the feedback unit can provide more accurate advice by referring to past feedback history. For example, specific improvements can be suggested based on the content of past feedback. Also, when the generation AI provides feedback, the feedback unit can provide more accurate advice by referring to past feedback history. For example, specific improvements can be suggested based on the content of past feedback. Also, when the generation AI provides feedback, the feedback unit can provide more accurate advice by referring to past feedback history. For example, specific improvements can be suggested based on the content of past feedback. In this way, by referring to past feedback history, more accurate advice can be provided.

[0035] The feedback unit is able to provide individually customized feedback by taking into account the researcher's expertise and skill level. For example, when the generation AI provides feedback, the feedback unit provides individually customized feedback by taking into account the researcher's expertise and skill level. For example, it separates advice for beginners from advice for experts. Also, when the generation AI provides feedback, the feedback unit provides individually customized feedback by taking into account the researcher's expertise and skill level. For example, it separates advice for beginners from advice for experts. Also, when the generation AI provides feedback, the feedback unit provides individually customized feedback by taking into account the researcher's expertise and skill level. For example, it separates advice for beginners from advice for experts. This makes it possible to provide feedback according to the researcher's expertise and skill level.

[0036] The feedback unit incorporates opinions from experts in different fields and can provide feedback from a more multifaceted perspective. For example, when the generation AI provides feedback, the feedback unit incorporates opinions from experts in different fields and can provide feedback from a more multifaceted perspective. For example, the opinions of physicists and chemists are integrated. Also, when the generation AI provides feedback, the feedback unit incorporates opinions from experts in different fields and can provide feedback from a more multifaceted perspective. For example, the opinions of physicists and chemists are integrated. Also, when the generation AI provides feedback, the feedback unit incorporates opinions from experts in different fields and can provide feedback from a more multifaceted perspective. For example, the opinions of physicists and chemists are integrated. This makes it possible to provide multifaceted feedback that incorporates opinions from experts in different fields.

[0037] The feedback unit can promote collaboration with other researchers in real time to jointly refine the theme. For example, when the generation AI provides feedback, the feedback unit can promote collaboration with other researchers in real time to jointly refine the theme. For example, by exchanging opinions through an online platform. Also, when the generation AI provides feedback, the feedback unit can promote collaboration with other researchers in real time to jointly refine the theme. For example, by exchanging opinions through an online platform. Also, when the generation AI provides feedback, the feedback unit can promote collaboration with other researchers in real time to jointly refine the theme. For example, by exchanging opinions through an online platform. This can promote collaboration with other researchers in real time to jointly refine the theme.

[0038] The data mining department evaluates the reliability and source of the data and is able to use only highly reliable data. For example, when the generation AI performs data mining, the data mining department evaluates the reliability and source of the data and uses only highly reliable data. For example, it gives priority to using data from peer-reviewed papers and public institutions. In addition, when the generation AI performs data mining, the data mining department evaluates the reliability and source of the data and uses only highly reliable data. For example, it gives priority to using data from peer-reviewed papers and public institutions. In addition, when the generation AI performs data mining, the data mining department evaluates the reliability and source of the data and uses only highly reliable data. For example, it gives priority to using data from peer-reviewed papers and public institutions. In this way, by using only highly reliable data, data quality can be ensured.

[0039] The data mining unit can take into account changes in the data over time and reflect the latest research trends. For example, when the generation AI performs data mining, the data mining unit takes into account changes in the data over time and reflects the latest research trends. For example, it analyzes data from the past few years to identify the latest trends. Also, when the generation AI performs data mining, the data mining unit takes into account changes in the data over time and reflects the latest research trends. For example, it analyzes data from the past few years to identify the latest trends. Also, when the generation AI performs data mining, the data mining unit takes into account changes in the data over time and reflects the latest research trends. For example, it analyzes data from the past few years to identify the latest trends. In this way, by taking into account changes in the data over time, the latest research trends can be reflected.

[0040] The data mining unit can automatically translate data in different languages ​​and collect data from an international perspective. For example, when the generation AI performs data mining, the data mining unit automatically translates data in different languages ​​and collects data from an international perspective. For example, it translates and analyzes data in English, French, Chinese, etc. Also, when the generation AI performs data mining, the data mining unit automatically translates data in different languages ​​and collects data from an international perspective. For example, it translates and analyzes data in English, French, Chinese, etc. Also, when the generation AI performs data mining, the data mining unit automatically translates data in different languages ​​and collects data from an international perspective. For example, it translates and analyzes data in English, French, Chinese, etc. This allows data in different languages ​​to be automatically translated and collected from an international perspective.

[0041] The data mining unit can integrate different data formats (text, images, audio, etc.) and perform multimodal data analysis. For example, when the generation AI performs data mining, the data mining unit integrates different data formats (text, images, audio, etc.) and performs multimodal data analysis. For example, it integrates and analyzes text data and image data. Also, when the generation AI performs data mining, the data mining unit integrates different data formats (text, images, audio, etc.) and performs multimodal data analysis. For example, it integrates and analyzes text data and image data. Also, when the generation AI performs data mining, the data mining unit integrates different data formats (text, images, audio, etc.) and performs multimodal data analysis. For example, it integrates and analyzes text data and image data. In this way, different data formats can be integrated and multimodal data analysis can be performed.

[0042] The proposal unit can evaluate feasibility by referring to past success cases in different fields. For example, when the generative AI proposes a new approach, the proposal unit evaluates feasibility by referring to past success cases in different fields. For example, the evaluation is made based on success cases that applied technology from different fields. In addition, when the generative AI proposes a new approach, the proposal unit evaluates feasibility by referring to past success cases in different fields. For example, the evaluation is made based on success cases that applied technology from different fields. In addition, when the generative AI proposes a new approach, the proposal unit evaluates feasibility by referring to past success cases in different fields. For example, the evaluation is made based on success cases that applied technology from different fields. In this way, feasibility can be evaluated by referring to past success cases in different fields.

[0043] The proposal department can promote collaboration with researchers from different fields to jointly develop new approaches. For example, when the generative AI proposes a new approach, the proposal department can promote collaboration with researchers from different fields to jointly develop new approaches. For example, physicists and chemists can jointly develop new experimental methods. Also, when the generative AI proposes a new approach, the proposal department can promote collaboration with researchers from different fields to jointly develop new approaches. For example, physicists and chemists can jointly develop new experimental methods. Also, when the generative AI proposes a new approach, the proposal department can promote collaboration with researchers from different fields to jointly develop new approaches. For example, physicists and chemists can jointly develop new experimental methods. This can promote collaboration with researchers from different fields to jointly develop new approaches.

[0044] The proposal department can incorporate technologies and knowledge from different industries to promote crossover innovation. For example, when the generative AI proposes a new approach, the proposal department can incorporate technologies and knowledge from different industries to promote crossover innovation. For example, it can propose the development of a new energy source that applies medical technology. The proposal department can also incorporate technologies and knowledge from different industries to promote crossover innovation when the generative AI proposes a new approach, to promote crossover innovation. For example, it can propose the development of a new energy source that applies medical technology. The proposal department can also incorporate technologies and knowledge from different industries to promote crossover innovation when the generative AI proposes a new approach, to promote crossover innovation. For example, it can propose the development of a new energy source that applies medical technology. This can incorporate technologies and knowledge from different industries to promote crossover innovation.

[0045] The proposal unit is able to make proposals from a global perspective, taking into account research trends in different cultural spheres and regions. For example, when the generative AI proposes a new approach, the proposal unit makes proposals from a global perspective, taking into account research trends in different cultural spheres and regions. For example, the proposal is made based on research trends and market needs in a specific region. Furthermore, when the generative AI proposes a new approach, the proposal unit makes proposals from a global perspective, taking into account research trends in different cultural spheres and regions. For example, the proposal is made based on research trends and market needs in a specific region. Furthermore, when the generative AI proposes a new approach, the proposal unit makes proposals from a global perspective, taking into account research trends in different cultural spheres and regions. For example, the proposal is made based on research trends and market needs in a specific region. This allows the proposal to be made from a global perspective, taking into account research trends in different cultural spheres and regions.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The R&D support system can further include a researcher schedule management unit. The schedule management unit manages researchers' research activity schedules and supports efficient time allocation. For example, when researchers plan experiments, the schedule management unit tracks the progress of the experiment in real time and suggests next steps. The schedule management unit can also automatically adjust researchers' meeting and presentation schedules to avoid overlaps. Furthermore, the schedule management unit can take researchers' rest times into consideration and provide alerts to prevent overwork. This improves the efficiency of researchers' time management and improves the productivity of their research activities.

[0048] The R&D support system can further include a funding support section. The funding support section provides researchers with information to help them raise research funds and supports the application process. For example, the funding support section collects and provides information on grants and research expenses suitable for researchers. The funding support section can also help researchers prepare application forms and provide templates for the necessary documents. Furthermore, the funding support section can track the progress of applications and send reminders to ensure deadlines are met. This reduces the time and effort researchers spend on raising funds and allows them to focus on their research activities.

[0049] The R&D support system can further include an ethical review support department. The ethical review support department provides support to researchers to proceed with research while taking ethical issues into consideration. For example, the ethical review support department evaluates whether a research plan is ethically appropriate and suggests necessary modifications. The ethical review support department can also assist researchers in preparing documents to be submitted to the ethical review committee and provide necessary guidelines. Furthermore, the ethical review support department can provide advice on how to deal with ethical issues that arise during the progress of research. This enables researchers to respond to ethical issues promptly and appropriately.

[0050] The R&D support system can further include a collaboration support section. The collaboration support section provides support for researchers to collaborate effectively with other researchers and experts. For example, the collaboration support section can recommend appropriate collaboration partners based on the researcher's field of expertise and research topic. The collaboration support section can also manage the progress of collaborative research and visualize task allocation and progress. Furthermore, the collaboration support section can provide a forum for online meetings and discussions and promote the exchange of opinions in real time. This allows researchers to collaborate efficiently, improving the quality and speed of research.

[0051] The R&D support system can further include an intellectual property management department, which provides support to researchers in protecting their research results as intellectual property. For example, the intellectual property management department can assist with the patent application process and assist with preparing and submitting the necessary documents. The intellectual property management department can also provide researchers with the latest intellectual property laws, regulations, and guidelines, encouraging them to take appropriate action. Furthermore, the intellectual property management department can propose strategies for commercializing research results and support license agreements and technology transfer. This allows researchers to effectively protect their research results and maximize commercialization opportunities.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The research idea input unit inputs the researcher's research idea. For example, a researcher can input the idea of ​​"developing a new energy source." The research idea input unit can also input the research idea in text format. Step 2: The evaluation unit evaluates the research ideas input by the research idea input unit. For example, the generative AI evaluates the novelty and feasibility of the research idea based on related previous research. The evaluation unit can also perform risk assessment by referring to past successes and failures. Step 3: The feedback department provides the results of the evaluation by the evaluation department to the researcher. For example, the generative AI may provide feedback such as, "This idea may overlap with existing research." The feedback department can also suggest areas for improvement to the researcher. Step 4: The data mining department collects relevant data. For example, the generative AI collects papers, patents, technical reports, experimental data, etc. from all fields and performs cross-sectional mining. The data mining department can also identify and associate terms and concepts from different fields, linking knowledge. Step 5: The proposal department proposes new approaches based on the data collected by the data mining department. For example, the generative AI can propose new experimental methods based on findings from research in different fields. The proposal department can also suggest new research themes to researchers.

[0054] (Example 2) A research and development support system according to an embodiment of the present invention is a system that improves the efficiency of researchers' research activities and supports hypothesis formulation, experimental planning, paper writing, etc. As a result, the research and development support system can improve the efficiency of researchers' research activities and support hypothesis formulation, experimental planning, paper writing, etc.

[0055] The research and development support system according to an embodiment includes a research idea input unit, an evaluation unit, a feedback unit, a data mining unit, and a proposal unit. The research idea input unit inputs a researcher's research idea. For example, a researcher can input an idea such as "developing a new energy source." The research idea input unit can also input the research idea in text format. The evaluation unit evaluates the research idea input by the research idea input unit. For example, the generation AI evaluates the novelty and feasibility of the research idea based on related previous research. The evaluation unit can also perform risk assessment by referring to past successes and failures. The feedback unit feeds back the results of the evaluation by the evaluation unit to the researcher. For example, the generation AI may provide feedback such as "This idea may overlap with existing research." The feedback unit can also suggest improvements to the researcher. The data mining unit collects related data. For example, the generation AI collects papers, patents, technical reports, experimental data, etc. from various fields and performs cross-sectional mining. The data mining unit can also identify and associate terms and concepts from different fields to link knowledge. The proposal unit proposes new approaches based on the data collected by the data mining unit. For example, the generative AI proposes new experimental methods based on knowledge gained from research in different fields. The proposal unit can also suggest new research themes to researchers. As a result, the research and development support system according to the embodiment can streamline researchers' research activities and support hypothesis formulation, experimental planning, paper writing, and so on.

[0056] The evaluation unit can perform risk assessment by referring to past successes and failures. For example, when the generative AI evaluates a research idea, the evaluation unit extracts past successes and failures from a database and performs risk assessment. For example, it analyzes what results similar ideas have produced in the past and evaluates the level of risk. In addition, when the generative AI evaluates a research idea, the evaluation unit performs risk assessment by referring to past successes and failures of researchers. For example, it analyzes what results specific technologies and methods have produced in the past and evaluates the level of risk. In addition, when the generative AI evaluates a research idea, the evaluation unit performs risk assessment by referring to past successes and failures of researchers. For example, it analyzes what results similar ideas have produced in the past and evaluates the level of risk. This makes it possible to evaluate the risk of research ideas.

[0057] The evaluation unit takes into account the life cycle information of related patents and can make evaluations based on patent expiration dates and market trends. For example, when the generative AI evaluates a research idea, the evaluation unit takes into account the life cycle information of related patents and makes evaluations based on patent expiration dates and market trends. For example, if a patent is about to expire, the evaluation unit will evaluate the impact that the technology will have on the market. In addition, when the generative AI evaluates a research idea, the evaluation unit takes into account the life cycle information of related patents and makes evaluations based on patent expiration dates and market trends. For example, if a patent is about to expire, the evaluation unit will evaluate the impact that the technology will have on the market. In addition, when the generative AI evaluates a research idea, the evaluation unit takes into account the life cycle information of related patents and makes evaluations based on patent expiration dates and market trends. For example, if a patent is about to expire, the evaluation unit will evaluate the impact that the technology will have on the market. This makes it possible to make evaluations that take patent expiration dates and market trends into account.

[0058] The evaluation unit can use the emotion estimation function to analyze the motivation and interests of researchers and evaluate research ideas based on the results. The evaluation unit, for example, uses the emotion estimation function to analyze the motivation and interests of researchers and evaluate research ideas based on the results. For example, it prioritizes evaluation of ideas with a high researcher emotion score. The evaluation unit also uses the emotion estimation function to analyze the motivation and interests of researchers and evaluate research ideas based on the results. For example, it prioritizes evaluation of ideas with a high researcher emotion score. The evaluation unit also uses the emotion estimation function to analyze the motivation and interests of researchers and evaluate research ideas based on the results. For example, it prioritizes evaluation of ideas with a high researcher emotion score. This makes it possible to perform evaluation based on the motivation and interests of researchers.

[0059] The evaluation unit is able to conduct evaluations from a global perspective, taking into account research trends in different cultural spheres and regions. For example, when the generative AI evaluates a research idea, the evaluation unit conducts the evaluation from a global perspective, taking into account research trends in different cultural spheres and regions. For example, the evaluation is based on research trends and market needs in a specific region. In addition, when the generative AI evaluates a research idea, the evaluation unit conducts the evaluation from a global perspective, taking into account research trends in different cultural spheres and regions. For example, the evaluation is based on research trends and market needs in a specific region. In addition, when the generative AI evaluates a research idea, the evaluation unit conducts the evaluation from a global perspective, taking into account research trends in different cultural spheres and regions. For example, the evaluation is based on research trends and market needs in a specific region. This makes evaluations possible from a global perspective.

[0060] The evaluation unit can reflect relevant industrial trends and market needs in real time. For example, when the generation AI evaluates a research idea, the evaluation unit reflects relevant industrial trends and market needs in real time. For example, the evaluation is made based on the latest market data. In addition, the evaluation unit reflects relevant industrial trends and market needs in real time when the generation AI evaluates a research idea. For example, the evaluation is made based on the latest market data. In addition, the evaluation unit reflects relevant industrial trends and market needs in real time when the generation AI evaluates a research idea. For example, the evaluation is made based on the latest market data. This makes it possible to make an evaluation that reflects industrial trends and market needs in real time.

[0061] The evaluation unit can use the emotion estimation function to monitor the researcher's emotions in real time and provide positive feedback. The evaluation unit, for example, uses the emotion estimation function to monitor the researcher's emotions in real time when evaluating a research idea and provide positive feedback. For example, an encouraging message is displayed if the researcher's emotion score is low. The evaluation unit can also use the emotion estimation function to monitor the researcher's emotions in real time when evaluating a research idea and provide positive feedback. For example, an encouraging message is displayed if the researcher's emotion score is low. The evaluation unit can also use the emotion estimation function to monitor the researcher's emotions in real time when evaluating a research idea and provide positive feedback. For example, an encouraging message is displayed if the researcher's emotion score is low. This makes it possible to monitor the researcher's emotions in real time and provide positive feedback.

[0062] The feedback unit can provide more accurate advice by referring to past feedback history. For example, when the generation AI provides feedback, the feedback unit can provide more accurate advice by referring to past feedback history. For example, specific improvements can be suggested based on the content of past feedback. Also, when the generation AI provides feedback, the feedback unit can provide more accurate advice by referring to past feedback history. For example, specific improvements can be suggested based on the content of past feedback. Also, when the generation AI provides feedback, the feedback unit can provide more accurate advice by referring to past feedback history. For example, specific improvements can be suggested based on the content of past feedback. In this way, by referring to past feedback history, more accurate advice can be provided.

[0063] The feedback unit is able to provide individually customized feedback by taking into account the researcher's expertise and skill level. For example, when the generation AI provides feedback, the feedback unit provides individually customized feedback by taking into account the researcher's expertise and skill level. For example, it separates advice for beginners from advice for experts. Also, when the generation AI provides feedback, the feedback unit provides individually customized feedback by taking into account the researcher's expertise and skill level. For example, it separates advice for beginners from advice for experts. Also, when the generation AI provides feedback, the feedback unit provides individually customized feedback by taking into account the researcher's expertise and skill level. For example, it separates advice for beginners from advice for experts. This makes it possible to provide feedback according to the researcher's expertise and skill level.

[0064] The feedback unit can use the emotion estimation function to analyze the emotion of the researcher and provide positive feedback to maintain motivation. The feedback unit, for example, uses the emotion estimation function to analyze the emotion of the researcher when providing feedback and provide positive feedback to maintain motivation. For example, an encouraging message is displayed if the emotion score of the researcher is low. The feedback unit can also use the emotion estimation function to analyze the emotion of the researcher when providing feedback and provide positive feedback to maintain motivation. For example, an encouraging message is displayed if the emotion score of the researcher is low. The feedback unit can also use the emotion estimation function to analyze the emotion of the researcher when providing feedback and provide positive feedback to maintain motivation. For example, an encouraging message is displayed if the emotion score of the researcher is low. This makes it possible to provide positive feedback to maintain motivation of the researcher.

[0065] The feedback unit incorporates opinions from experts in different fields and can provide feedback from a more multifaceted perspective. For example, when the generation AI provides feedback, the feedback unit incorporates opinions from experts in different fields and can provide feedback from a more multifaceted perspective. For example, the opinions of physicists and chemists are integrated. Also, when the generation AI provides feedback, the feedback unit incorporates opinions from experts in different fields and can provide feedback from a more multifaceted perspective. For example, the opinions of physicists and chemists are integrated. Also, when the generation AI provides feedback, the feedback unit incorporates opinions from experts in different fields and can provide feedback from a more multifaceted perspective. For example, the opinions of physicists and chemists are integrated. This makes it possible to provide multifaceted feedback that incorporates opinions from experts in different fields.

[0066] The feedback unit can promote collaboration with other researchers in real time to jointly refine the theme. For example, when the generation AI provides feedback, the feedback unit can promote collaboration with other researchers in real time to jointly refine the theme. For example, by exchanging opinions through an online platform. Also, when the generation AI provides feedback, the feedback unit can promote collaboration with other researchers in real time to jointly refine the theme. For example, by exchanging opinions through an online platform. Also, when the generation AI provides feedback, the feedback unit can promote collaboration with other researchers in real time to jointly refine the theme. For example, by exchanging opinions through an online platform. This can promote collaboration with other researchers in real time to jointly refine the theme.

[0067] The feedback unit can use the emotion estimation function to monitor the emotions of the researcher in real time and provide advice to reduce negative emotions. The feedback unit, for example, uses the emotion estimation function to monitor the emotions of the researcher in real time when providing feedback and provide advice to reduce negative emotions. For example, if the researcher is feeling stressed, the feedback unit can suggest ways to relax. The feedback unit can also use the emotion estimation function to monitor the emotions of the researcher in real time when providing feedback and provide advice to reduce negative emotions. For example, if the researcher is feeling stressed, the feedback unit can suggest ways to relax. The feedback unit can also use the emotion estimation function to monitor the emotions of the researcher in real time when providing feedback and provide advice to reduce negative emotions. For example, if the researcher is feeling stressed, the feedback unit can suggest ways to relax. In this way, advice to reduce negative emotions can be provided.

[0068] The data mining department evaluates the reliability and source of the data and is able to use only highly reliable data. For example, when the generation AI performs data mining, the data mining department evaluates the reliability and source of the data and uses only highly reliable data. For example, it gives priority to using data from peer-reviewed papers and public institutions. In addition, when the generation AI performs data mining, the data mining department evaluates the reliability and source of the data and uses only highly reliable data. For example, it gives priority to using data from peer-reviewed papers and public institutions. In addition, when the generation AI performs data mining, the data mining department evaluates the reliability and source of the data and uses only highly reliable data. For example, it gives priority to using data from peer-reviewed papers and public institutions. In this way, by using only highly reliable data, data quality can be ensured.

[0069] The data mining unit can take into account changes in the data over time and reflect the latest research trends. For example, when the generation AI performs data mining, the data mining unit takes into account changes in the data over time and reflects the latest research trends. For example, it analyzes data from the past few years to identify the latest trends. Also, when the generation AI performs data mining, the data mining unit takes into account changes in the data over time and reflects the latest research trends. For example, it analyzes data from the past few years to identify the latest trends. Also, when the generation AI performs data mining, the data mining unit takes into account changes in the data over time and reflects the latest research trends. For example, it analyzes data from the past few years to identify the latest trends. In this way, by taking into account changes in the data over time, the latest research trends can be reflected.

[0070] The data mining unit can use the emotion estimation function to analyze the emotions of researchers and prioritize presenting data that attracts their interest. The data mining unit, for example, uses the emotion estimation function to analyze the emotions of researchers during data mining and prioritize presenting data that attracts their interest. For example, data with a high emotion score of the researcher is displayed preferentially. The data mining unit also uses the emotion estimation function to analyze the emotions of researchers during data mining and prioritize presenting data that attracts their interest. For example, data with a high emotion score of the researcher is displayed preferentially. The data mining unit also uses the emotion estimation function to analyze the emotions of researchers during data mining and prioritize presenting data that attracts their interest. For example, data with a high emotion score of the researcher is displayed preferentially. This makes it possible to analyze the emotions of researchers and prioritize presenting data that attracts their interest.

[0071] The data mining unit can automatically translate data in different languages ​​and collect data from an international perspective. For example, when the generation AI performs data mining, the data mining unit automatically translates data in different languages ​​and collects data from an international perspective. For example, it translates and analyzes data in English, French, Chinese, etc. Also, when the generation AI performs data mining, the data mining unit automatically translates data in different languages ​​and collects data from an international perspective. For example, it translates and analyzes data in English, French, Chinese, etc. Also, when the generation AI performs data mining, the data mining unit automatically translates data in different languages ​​and collects data from an international perspective. For example, it translates and analyzes data in English, French, Chinese, etc. This allows data in different languages ​​to be automatically translated and collected from an international perspective.

[0072] The data mining unit can integrate different data formats (text, images, audio, etc.) and perform multimodal data analysis. For example, when the generation AI performs data mining, the data mining unit integrates different data formats (text, images, audio, etc.) and performs multimodal data analysis. For example, it integrates and analyzes text data and image data. Also, when the generation AI performs data mining, the data mining unit integrates different data formats (text, images, audio, etc.) and performs multimodal data analysis. For example, it integrates and analyzes text data and image data. Also, when the generation AI performs data mining, the data mining unit integrates different data formats (text, images, audio, etc.) and performs multimodal data analysis. For example, it integrates and analyzes text data and image data. In this way, different data formats can be integrated and multimodal data analysis can be performed.

[0073] The data mining unit can use the emotion estimation function to monitor the emotions of researchers in real time and preferentially present data that elicits positive emotions. The data mining unit, for example, uses the emotion estimation function to monitor the emotions of researchers in real time during data mining and preferentially present data that elicits positive emotions. For example, data with a high researcher emotion score is preferentially displayed. The data mining unit also uses the emotion estimation function to monitor the emotions of researchers in real time during data mining and preferentially present data that elicits positive emotions. For example, data with a high researcher emotion score is preferentially displayed. The data mining unit also uses the emotion estimation function to monitor the emotions of researchers in real time during data mining and preferentially present data that elicits positive emotions. For example, data with a high researcher emotion score is preferentially displayed. This makes it possible to monitor the emotions of researchers in real time and preferentially present data that elicits positive emotions.

[0074] The proposal unit can evaluate feasibility by referring to past success cases in different fields. For example, when the generative AI proposes a new approach, the proposal unit evaluates feasibility by referring to past success cases in different fields. For example, the evaluation is made based on success cases that applied technology from different fields. In addition, when the generative AI proposes a new approach, the proposal unit evaluates feasibility by referring to past success cases in different fields. For example, the evaluation is made based on success cases that applied technology from different fields. In addition, when the generative AI proposes a new approach, the proposal unit evaluates feasibility by referring to past success cases in different fields. For example, the evaluation is made based on success cases that applied technology from different fields. In this way, feasibility can be evaluated by referring to past success cases in different fields.

[0075] The proposal department can promote collaboration with researchers from different fields to jointly develop new approaches. For example, when the generative AI proposes a new approach, the proposal department can promote collaboration with researchers from different fields to jointly develop new approaches. For example, physicists and chemists can jointly develop new experimental methods. Also, when the generative AI proposes a new approach, the proposal department can promote collaboration with researchers from different fields to jointly develop new approaches. For example, physicists and chemists can jointly develop new experimental methods. Also, when the generative AI proposes a new approach, the proposal department can promote collaboration with researchers from different fields to jointly develop new approaches. For example, physicists and chemists can jointly develop new experimental methods. This can promote collaboration with researchers from different fields to jointly develop new approaches.

[0076] The suggestion unit can use the emotion estimation function to analyze the emotion of the researcher when proposing a new approach and make a proposal that elicits positive emotions. The suggestion unit, for example, uses the emotion estimation function to analyze the emotion of the researcher when proposing a new approach and make a proposal that elicits positive emotions. For example, it prioritizes proposals with a high emotion score from the researcher. Also, the suggestion unit can use the emotion estimation function to analyze the emotion of the researcher when proposing a new approach and make a proposal that elicits positive emotions. For example, it prioritizes proposals with a high emotion score from the researcher. Also, the suggestion unit can use the emotion estimation function to analyze the emotion of the researcher when proposing a new approach and make a proposal that elicits positive emotions. For example, it prioritizes proposals with a high emotion score from the researcher. In this way, it is possible to analyze the emotion of the researcher and make a proposal that elicits positive emotions.

[0077] The proposal department can incorporate technologies and knowledge from different industries to promote crossover innovation. For example, when the generative AI proposes a new approach, the proposal department can incorporate technologies and knowledge from different industries to promote crossover innovation. For example, it can propose the development of a new energy source that applies medical technology. The proposal department can also incorporate technologies and knowledge from different industries to promote crossover innovation when the generative AI proposes a new approach, to promote crossover innovation. For example, it can propose the development of a new energy source that applies medical technology. The proposal department can also incorporate technologies and knowledge from different industries to promote crossover innovation when the generative AI proposes a new approach, to promote crossover innovation. For example, it can propose the development of a new energy source that applies medical technology. This can incorporate technologies and knowledge from different industries to promote crossover innovation.

[0078] The proposal unit is able to make proposals from a global perspective, taking into account research trends in different cultural spheres and regions. For example, when the generative AI proposes a new approach, the proposal unit makes proposals from a global perspective, taking into account research trends in different cultural spheres and regions. For example, the proposal is made based on research trends and market needs in a specific region. Furthermore, when the generative AI proposes a new approach, the proposal unit makes proposals from a global perspective, taking into account research trends in different cultural spheres and regions. For example, the proposal is made based on research trends and market needs in a specific region. Furthermore, when the generative AI proposes a new approach, the proposal unit makes proposals from a global perspective, taking into account research trends in different cultural spheres and regions. For example, the proposal is made based on research trends and market needs in a specific region. This allows the proposal to be made from a global perspective, taking into account research trends in different cultural spheres and regions.

[0079] The suggestion unit can use the emotion estimation function to monitor the researcher's emotions in real time when proposing a new approach and provide advice to reduce negative emotions. The suggestion unit, for example, uses the emotion estimation function to monitor the researcher's emotions in real time when proposing a new approach and provide advice to reduce negative emotions. For example, if the researcher is feeling anxious, the suggestion unit can suggest a relaxation method. The suggestion unit can also use the emotion estimation function to monitor the researcher's emotions in real time when proposing a new approach and provide advice to reduce negative emotions. For example, if the researcher is feeling anxious, the suggestion unit can suggest a relaxation method. The suggestion unit can also use the emotion estimation function to monitor the researcher's emotions in real time when proposing a new approach and provide advice to reduce negative emotions. For example, if the researcher is feeling anxious, the suggestion unit can suggest a relaxation method. In this way, the suggestion unit can monitor the researcher's emotions in real time and provide advice to reduce negative emotions.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The R&D support system can further include a researcher schedule management unit. The schedule management unit manages researchers' research activity schedules and supports efficient time allocation. For example, when researchers plan experiments, the schedule management unit tracks the progress of the experiment in real time and suggests next steps. The schedule management unit can also automatically adjust researchers' meeting and presentation schedules to avoid overlaps. Furthermore, the schedule management unit can take researchers' rest times into consideration and provide alerts to prevent overwork. This improves the efficiency of researchers' time management and improves the productivity of their research activities.

[0082] The R&D support system can further include a funding support section. The funding support section provides researchers with information to help them raise research funds and supports the application process. For example, the funding support section collects and provides information on grants and research expenses suitable for researchers. The funding support section can also help researchers prepare application forms and provide templates for the necessary documents. Furthermore, the funding support section can track the progress of applications and send reminders to ensure deadlines are met. This reduces the time and effort researchers spend on raising funds and allows them to focus on their research activities.

[0083] The R&D support system can further include an ethical review support department. The ethical review support department provides support to researchers to proceed with research while taking ethical issues into consideration. For example, the ethical review support department evaluates whether a research plan is ethically appropriate and suggests necessary modifications. The ethical review support department can also assist researchers in preparing documents to be submitted to the ethical review committee and provide necessary guidelines. Furthermore, the ethical review support department can provide advice on how to deal with ethical issues that arise during the progress of research. This enables researchers to respond to ethical issues promptly and appropriately.

[0084] The R&D support system can further include a collaboration support section. The collaboration support section provides support for researchers to collaborate effectively with other researchers and experts. For example, the collaboration support section can recommend appropriate collaboration partners based on the researcher's field of expertise and research topic. The collaboration support section can also manage the progress of collaborative research and visualize task allocation and progress. Furthermore, the collaboration support section can provide a forum for online meetings and discussions and promote the exchange of opinions in real time. This allows researchers to collaborate efficiently, improving the quality and speed of research.

[0085] The R&D support system can further include an intellectual property management department, which provides support to researchers in protecting their research results as intellectual property. For example, the intellectual property management department can assist with the patent application process and assist with preparing and submitting the necessary documents. The intellectual property management department can also provide researchers with the latest intellectual property laws, regulations, and guidelines, encouraging them to take appropriate action. Furthermore, the intellectual property management department can propose strategies for commercializing research results and support license agreements and technology transfer. This allows researchers to effectively protect their research results and maximize commercialization opportunities.

[0086] The evaluation department can use the emotion estimation function to analyze a researcher's stress level and suggest rest at appropriate times. For example, if a researcher continues working for an extended period of time, the evaluation department can use the emotion estimation function to monitor the researcher's stress level and display an alert to encourage them to take a rest. The evaluation department can also suggest relaxation methods or activities to relieve stress if the researcher's stress level is high. Furthermore, the evaluation department can identify the cause of stress based on the researcher's emotion data and suggest improvement measures. This allows researchers to efficiently carry out their research activities while maintaining their health.

[0087] The feedback unit can use its emotion estimation function to analyze the researcher's emotions and customize the content of the feedback. For example, if the researcher is feeling positive, the feedback unit can provide encouraging messages that will further enhance those emotions. If the researcher is feeling negative, the feedback unit can provide specific advice to alleviate those emotions. Furthermore, the feedback unit can adjust the timing and frequency of feedback based on the researcher's emotion data to provide optimal support. This allows researchers to receive appropriate feedback based on their emotions, allowing them to maintain their motivation while continuing their research activities.

[0088] The Data Mining Department uses the emotion estimation function to analyze researchers' emotions and prioritize presenting data that is likely to interest them. For example, if a researcher expresses positive emotions toward a particular topic, the Data Mining Department will prioritize displaying the latest research data related to that topic. Conversely, if a researcher expresses negative emotions, the Data Mining Department can present interesting data and success stories to alleviate those emotions. Furthermore, based on the researcher's emotion data, the Data Mining Department can adjust the data presentation method and format to provide information in a form that is easier for researchers to understand. This allows researchers to receive appropriate data according to their emotions, allowing them to efficiently conduct their research activities.

[0089] The suggestion unit uses emotion estimation to analyze researchers' emotions and prioritize suggesting topics for which the researcher has positive emotions. For example, if a researcher shows a high interest in a particular field, the suggestion unit can suggest new research topics and approaches related to that field. If a researcher shows negative emotions, the suggestion unit can suggest new perspectives and approaches to alleviate those emotions. Furthermore, the suggestion unit can adjust the timing and content of suggestions based on the researcher's emotion data to provide optimal support. This allows researchers to receive appropriate suggestions based on their emotions, allowing them to maintain their motivation while continuing their research activities.

[0090] The suggestion unit can use its emotion estimation function to monitor researchers' emotions in real time and provide advice to reduce negative emotions. For example, if a researcher is feeling anxious, the suggestion unit can suggest relaxation methods or activities to relieve stress. If a researcher is feeling fatigued, the suggestion unit can display an alert to encourage them to take a rest. Furthermore, the suggestion unit can identify the cause of negative emotions based on the researcher's emotion data and suggest remedial measures. This allows researchers to receive appropriate advice based on their emotions, allowing them to efficiently conduct their research activities while maintaining their health.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The research idea input unit inputs the researcher's research idea. For example, a researcher can input the idea of ​​"developing a new energy source." The research idea input unit can also input the research idea in text format. Step 2: The evaluation unit evaluates the research ideas input by the research idea input unit. For example, the generative AI evaluates the novelty and feasibility of the research idea based on related previous research. The evaluation unit can also perform risk assessment by referring to past successes and failures. Step 3: The feedback department provides the results of the evaluation by the evaluation department to the researcher. For example, the generative AI may provide feedback such as, "This idea may overlap with existing research." The feedback department can also suggest areas for improvement to the researcher. Step 4: The data mining department collects relevant data. For example, the generative AI collects papers, patents, technical reports, experimental data, etc. from all fields and performs cross-sectional mining. The data mining department can also identify and associate terms and concepts from different fields, linking knowledge. Step 5: The proposal department proposes new approaches based on the data collected by the data mining department. For example, the generative AI can propose new experimental methods based on findings from research in different fields. The proposal department can also suggest new research themes to researchers.

[0093] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0105] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] 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.

[0152] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a research idea input section for inputting research ideas of researchers; an evaluation unit that evaluates the research idea input by the research idea input unit; a feedback unit that feeds back the results of the evaluation by the evaluation unit to a researcher; a data mining department for collecting relevant data; a suggestion unit that suggests a new approach based on the data collected by the data mining unit. A system characterized by:

2. The evaluation unit Conduct risk assessment by looking at past successes and failures 2. The system of claim 1.

3. The evaluation unit Consider the life cycle information of related patents and evaluate them based on patent expiration dates and market trends 2. The system of claim 1.

4. The evaluation unit Analyze researchers' motivations and interests and evaluate their research ideas accordingly 2. The system of claim 1.

5. The evaluation unit Evaluate from a global perspective, taking into account research trends in different cultural spheres and regions 2. The system of claim 1.

Images