system

Abstract

The system according to this embodiment aims to create new value through collaboration with other industries and expand the demand for traditional crafts. [Solution] The system according to this embodiment comprises a reception unit, an evaluation unit, and a selection unit. The reception unit receives idea proposals from different industries. The evaluation unit creates a collaboration image, evaluates feasibility, and determines costs and risks based on the idea proposals received by the reception unit. The selection unit selects and approaches companies that are suitable for collaboration based on the collaboration image evaluated by the evaluation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is no sufficient mechanism to create new value through collaboration with different industries and expand the demand for traditional crafts, and there is room for improvement.

[0005] The system according to the embodiment aims to create new value through collaboration with different industries and expand the demand for traditional crafts.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an evaluation unit, and a selection unit. The reception unit receives idea proposals from different industries. The evaluation unit creates a collaboration image, evaluates feasibility, and determines costs and risks based on the idea proposals received by the reception unit. The selection unit selects and approaches companies that are suitable for collaboration based on the collaboration image evaluated by the evaluation unit. [Effects of the Invention]

[0007] The system according to this embodiment can create new value through collaboration with other industries and expand the demand for traditional crafts. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The traditional craft revitalization system according to an embodiment of the present invention is a system that utilizes an AI agent to prevent the decline of Japanese traditional crafts and revitalize the industry. The traditional craft revitalization system creates an AI agent that has learned the techniques of traditional crafts and autonomously makes proposals to promote collaboration with other industries. For example, it accepts idea proposals from other industries, creates collaboration images, evaluates feasibility, judges costs and risks, and selects and approaches companies that are capable of collaborating. This creates new value, expands demand, and revitalizes the industry. Furthermore, it uses AI to supplement the parts of the skills that take time to acquire, realizing efficient human resource development. For example, it uses video, AR, VR, etc. to support skill acquisition and quantifies the experience and intuition of skilled craftsmen to use as learning data. This makes it easier for new talent to enter the industry and builds a foundation for the future of traditional crafts. Through this mechanism, the techniques of traditional crafts are redefined to meet modern needs and new value is created through collaboration with other industries. In addition, efficient human resource development solves the shortage of successors and revitalizes the entire industry. This allows the traditional crafts revitalization system to redefine the techniques of Japanese traditional crafts to meet modern needs and create new value through collaboration with other industries.

[0029] The traditional craft revitalization system according to this embodiment comprises a reception unit, an evaluation unit, and a selection unit. The reception unit receives idea proposals from different industries. For example, the reception unit can receive idea proposals from different industries through an online platform. The reception unit can also receive idea proposals from different industries via email or a dedicated application. Furthermore, the reception unit can also receive idea proposals from different industries in person. For example, the reception unit has people from different industries input their idea proposals into an online form and saves the proposal content in a database. The evaluation unit creates collaboration images, evaluates feasibility, and determines costs and risks based on the idea proposals received by the reception unit. For example, the evaluation unit can use AI to visualize the collaboration image and perform simulations. Furthermore, the evaluation unit can use AI to evaluate technical feasibility and economic feasibility. Furthermore, the evaluation unit can use AI to perform cost analysis and risk assessment. For example, the evaluation unit can use AI to create a 3D model of the collaboration image and perform simulations. The selection department selects and approaches companies that are suitable for collaboration based on the collaboration image evaluated by the evaluation department. The selection department can, for example, use AI to create a list of companies suitable for collaboration and select companies to approach. The selection department can also use AI to analyze the past performance and evaluation of companies suitable for collaboration and select the most suitable company. Furthermore, the selection department can use AI to assess the risks of companies suitable for collaboration and select companies with low risk. For example, the selection department can use AI to create a list of companies suitable for collaboration and select companies to approach. As a result, the traditional craft revitalization system according to this embodiment can create new value, expand demand, and revitalize the industry by accepting and evaluating idea proposals from different industries and selecting companies suitable for collaboration.

[0030] The reception department accepts idea proposals from diverse industries. For example, the reception department can accept idea proposals from diverse industries through an online platform. Specifically, it can make it easy for companies and individuals from different industries to submit ideas through a dedicated website or application. This allows proposers to describe their ideas in detail and upload relevant documents and images. The reception department can also accept idea proposals from diverse industries via email or a dedicated application. For example, it can set up a dedicated email address so that proposers can send their ideas by email. The dedicated application can provide an interface for proposers to input and submit their ideas. Furthermore, the reception department can also accept idea proposals from diverse industries in person. For example, it can provide opportunities for proposers to present their ideas directly at regularly held idea proposal meetings or workshops. This allows proposers to explain their ideas directly and provide details through question and answer sessions. By combining these methods, the reception department can efficiently receive a variety of proposals. For example, the reception department can have idea proposals from diverse industries enter them into an online form and store the proposal content in a database. The database records the content of proposals, information about the proposers, and the submission date, which are then used for evaluation and selection. This allows the reception department to efficiently collect diverse ideas from different industries, contributing to the overall revitalization of the system.

[0031] The evaluation department creates collaborative images, assesses feasibility, and determines costs and risks based on idea proposals received by the reception department. For example, the evaluation department can use AI to visualize collaborative images and conduct simulations. Specifically, the AI ​​generates 3D models and visual images based on the proposed ideas, visually representing the concrete form of the proposal. This allows proposers and evaluators to intuitively understand the feasibility and concrete form of the idea. The evaluation department can also use AI to assess technical and economic feasibility. For example, the AI ​​analyzes the characteristics of the proposed technologies and materials, and evaluates the technical challenges and costs involved in actual product development. Furthermore, the evaluation department can use AI to conduct cost analysis and risk assessment. For example, the AI ​​predicts the manufacturing costs and market selling prices of the proposed idea, and assesses its economic feasibility. In risk assessment, the AI ​​analyzes the impact of the proposed idea on the market and the actions of competitors to assess the risks. This allows the evaluation department to comprehensively evaluate the feasibility and economic benefits of the proposed idea and create the optimal collaborative image. For example, the evaluation department uses AI to create a 3D model of the collaborative idea and conducts simulations. These simulations verify how the proposed idea actually functions and identify technical challenges and areas for improvement. This allows the evaluation department to increase the feasibility of the proposed idea and maximize the overall system's effectiveness.

[0032] The selection department selects and approaches companies that are suitable for collaboration based on the collaboration image evaluated by the evaluation department. For example, the selection department can use AI to create a list of potential collaborating companies and select which companies to approach. Specifically, the AI ​​analyzes past collaboration achievements, the company's technological capabilities, and its economic strength to identify the most suitable collaboration partner. The selection department can also use AI to analyze the past achievements and evaluations of potential collaborating companies and select the most suitable companies. For example, the AI ​​analyzes a company's past projects and market evaluation to select companies with reliability and a proven track record. Furthermore, the selection department can use AI to assess the risks of potential collaborating companies and select companies with low risk. For example, the AI ​​analyzes a company's financial situation and market reputation to identify companies with low risk. This allows the selection department to select highly reliable, low-risk companies, increasing the success rate of collaborations. For example, the selection department can use AI to create a list of potential collaborating companies and select which companies to approach. The list includes the company's name, contact information, and past achievements, and the selection department uses this information to approach companies. During the initial consultation, the proposed ideas and collaboration concepts are explained in detail to pique the company's interest. This allows the selection department to choose the most suitable collaboration partner and maximize the overall effectiveness of the system.

[0033] The Support Department utilizes video, AR, and VR to support skill acquisition. For example, the Support Department can visually demonstrate the steps for skill acquisition using video. It can also overlay the skill acquisition steps onto the actual work environment using AR. Furthermore, the Support Department can simulate skill acquisition in a virtual space using VR. For example, the Support Department can demonstrate the skill acquisition steps in video for easier visual understanding. AR is a technology that uses augmented reality to overlay the skill acquisition steps onto the actual work environment. VR is a technology that uses virtual reality to simulate skill acquisition in a virtual space. By utilizing video, AR, and VR, the efficiency of skill acquisition is improved, and the entry of new personnel becomes easier. Some or all of the above-described processes in the Support Department may be performed using, for example, generative AI, or without generative AI. For example, the Support Department inputs the skill acquisition steps into a generative AI, which then demonstrates the steps using video, AR, and VR.

[0034] The learning unit quantifies the experience and intuition of skilled craftsmen and utilizes it as learning data. For example, the learning unit can measure the work of skilled craftsmen with sensors and collect the data. The learning unit can also record the work of skilled craftsmen on video and analyze the data. Furthermore, the learning unit can record the work of skilled craftsmen on audio and analyze the data. For example, the learning unit measures the work of skilled craftsmen with sensors and collects data on the speed and force of the work. The learning unit records the work of skilled craftsmen on video and analyzes the work procedures and movements as data. The learning unit records the work of skilled craftsmen on audio and analyzes the instructions and explanations given during the work as data. By quantifying the experience and intuition of skilled craftsmen, the acquisition of skills becomes more efficient and the training of new personnel becomes easier. Some or all of the above processing in the learning unit may be performed using, for example, generative AI, or without using generative AI. For example, the learning unit inputs work data from skilled craftsmen into a generating AI, which then analyzes the work data to generate training data.

[0035] The Feedback Department provides real-time feedback. For example, the Feedback Department can provide real-time advice to users who are learning a new skill. The Feedback Department can also monitor the work of users learning a new skill in real time and provide feedback at the appropriate time. Furthermore, the Feedback Department can evaluate the work of users learning a new skill in real time and point out areas for improvement. For example, the Feedback Department provides real-time advice to users learning a new skill and supports their progress. The Feedback Department monitors the work of users learning a new skill in real time and provides feedback at the appropriate time. The Feedback Department evaluates the work of users learning a new skill in real time and points out areas for improvement. As a result, providing real-time feedback improves the efficiency of skill acquisition and makes it easier to train new personnel. Some or all of the above processes in the Feedback Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Feedback Department inputs the work data of users learning a new skill into a generative AI, and the generative AI provides real-time feedback.

[0036] The reception department can analyze the proposer's past proposal history when receiving a proposal and select the most suitable reception method. For example, the reception department can apply a similar reception method based on the proposer's past successful proposals. It can also select a reception method that reflects improvements based on the proposer's past unsuccessful proposals. Furthermore, the reception department can suggest the most effective reception method based on the proposer's past proposal history. For example, the reception department can retrieve the proposer's past proposal history from a database and analyze it using AI. This allows for the selection of the most suitable reception method and streamlines the evaluation of proposals. Some or all of the above processes in the reception department may be performed using AI, or not. For example, the reception department can input the proposer's past proposal history data into the AI, which then selects the most suitable reception method.

[0037] The reception department can filter proposals based on the proposer's industry and area of ​​expertise when receiving them. For example, if a proposer belongs to a specific industry, the reception department will prioritize proposals related to that industry. The reception department can also evaluate the appropriateness of proposals based on the proposer's area of ​​expertise. Furthermore, the reception department can filter proposals according to the proposer's industry and area of ​​expertise. For example, the reception department can obtain information on the proposer's industry and area of ​​expertise from a database and analyze it using AI. This improves the appropriateness of proposals by filtering them based on the proposer's industry and area of ​​expertise. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department can input information on the proposer's industry and area of ​​expertise into AI, and the AI ​​will filter the proposals.

[0038] The reception department can prioritize receiving proposals that are highly relevant, taking into account the proposer's geographical location. For example, if the proposer is in a nearby area, the reception department will prioritize proposals related to that area. The reception department can also accept proposals that meet region-specific needs based on the proposer's geographical location. Furthermore, the reception department can determine the priority of proposals by considering the proposer's geographical location. For example, the reception department can obtain the proposer's geographical location from a database and analyze it using AI. This makes it possible to evaluate proposals that meet region-specific needs by considering the proposer's geographical location. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department can input the proposer's geographical location into the AI, and the AI ​​will determine the priority of proposals.

[0039] The reception department can analyze the proposer's social media activity upon receiving a proposal and accept relevant proposals. For example, the reception department can evaluate the reliability of a proposal based on the proposer's social media activity. The reception department can also prioritize accepting relevant proposals based on the proposer's social media activity. Furthermore, the reception department can analyze the proposer's social media activity to determine the appropriateness of the proposal. For example, the reception department can retrieve the proposer's social media activity from a database and analyze it using AI. This improves the reliability and appropriateness of the proposal by analyzing the proposer's social media activity. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department can input the proposer's social media activity data into AI, and the AI ​​can evaluate the reliability of the proposal.

[0040] The evaluation unit can optimize its evaluation algorithm by referring to past evaluation data when evaluating proposals. For example, the evaluation unit optimizes its evaluation algorithm based on past successful proposals. It can also improve its evaluation algorithm based on past unsuccessful proposals. Furthermore, the evaluation unit can improve the accuracy of its evaluation algorithm by referring to past evaluation data. For example, the evaluation unit retrieves past evaluation data from a database and analyzes it using AI. By referring to past evaluation data, the accuracy of the evaluation algorithm is improved, and the evaluation of proposals becomes more efficient. Some or all of the above processes in the evaluation unit may be performed using AI, or not. For example, the evaluation unit inputs past evaluation data into AI, and the AI ​​optimizes the evaluation algorithm.

[0041] The evaluation unit can apply different evaluation methods depending on the category of the proposal when evaluating it. For example, if the proposal is technical, the evaluation unit will apply a technical evaluation method. If the proposal is business-related, the evaluation unit can also apply a business-related evaluation method. Furthermore, if the proposal is creative, the evaluation unit can also apply a creative evaluation method. For example, the evaluation unit can obtain the category of the proposal from a database and analyze it using AI. By applying different evaluation methods depending on the category of the proposal, the appropriateness of the proposal can be improved. Some or all of the above processing in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit can input the category data of the proposal into the AI, and the AI ​​can apply an evaluation method.

[0042] The evaluation department can determine the priority of evaluations based on the submission timing of the proposals. For example, the evaluation department may prioritize evaluations of proposals submitted early. It can also quickly evaluate proposals submitted shortly before the deadline. Furthermore, the evaluation department can determine the priority of evaluations based on the submission timing of the proposals. For example, the evaluation department may obtain the submission timing of proposals from a database and analyze it using AI. This streamlines the evaluation of proposals by determining the priority of evaluations based on the submission timing. Some or all of the above processes in the evaluation department may be performed using AI, or not. For example, the evaluation department may input proposal submission timing data into AI, and the AI ​​may determine the priority of evaluations.

[0043] The evaluation unit can adjust the order of evaluation based on the relevance of the proposals when evaluating them. For example, the evaluation unit will prioritize evaluation of proposals that are related to the current project. It can also adjust the order of evaluation if the proposals are related to future projects. Furthermore, the evaluation unit can determine the order of evaluation based on the relevance of the proposals. For example, the evaluation unit can obtain the relevance of the proposals from a database and analyze it using AI. This makes the evaluation of proposals more efficient by adjusting the order of evaluation based on the relevance of the proposals. Some or all of the above processes in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input the relevance data of the proposals into the AI, and the AI ​​can determine the order of evaluation.

[0044] The selection unit can optimize its selection algorithm by referring to past selection data when selecting companies for collaboration. For example, the selection unit optimizes the selection algorithm based on past successful collaborations. It can also improve the selection algorithm based on past unsuccessful collaborations. Furthermore, the selection unit can improve the accuracy of the selection algorithm by referring to past selection data. For example, the selection unit retrieves past selection data from a database and analyzes it using AI. This improves the accuracy of the selection algorithm by referring to past selection data, making company selection more efficient. Some or all of the above processes in the selection unit may be performed using AI, or not. For example, the selection unit inputs past selection data into AI, and the AI ​​optimizes the selection algorithm.

[0045] The selection department can select potential collaborating companies based on their industry and area of ​​expertise. For example, if a company belongs to a specific industry, the selection department will prioritize collaborations related to that industry. The selection department can also evaluate the appropriateness of collaborations based on the company's area of ​​expertise. Furthermore, the selection department can select collaborations according to the company's industry and area of ​​expertise. For example, the selection department can obtain information on companies' industries and areas of expertise from a database and analyze it using AI. This improves the appropriateness of collaborations by selecting based on the company's industry and area of ​​expertise. Some or all of the above processes in the selection department may be performed using AI, or not. For example, the selection department can input information on companies' industries and areas of expertise into AI, and the AI ​​will select collaborations.

[0046] The selection unit can select the most suitable companies for collaboration by considering their geographical location. For example, if a company is located in a nearby area, the selection unit will prioritize collaborations related to that area. The selection unit can also select collaborations that meet the specific needs of a region based on the geographical location of the companies. Furthermore, the selection unit can select collaborations while considering the geographical location of the companies. For example, the selection unit can obtain the geographical location of companies from a database and analyze it using AI. This makes it possible to select companies that meet the specific needs of a region by considering the geographical location of the companies. Some or all of the above processes in the selection unit may be performed using AI, or not. For example, the selection unit can input the geographical location of companies into AI, and the AI ​​will select the most suitable company.

[0047] The selection unit can analyze a company's social media activity when selecting potential collaborating partners. For example, the selection unit can evaluate the reliability of a collaboration based on the company's social media activity. The selection unit can also prioritize relevant collaborations based on the company's social media activity. Furthermore, the selection unit can analyze a company's social media activity to determine the appropriateness of a collaboration. For example, the selection unit can retrieve a company's social media activity from a database and analyze it using AI. This improves the reliability and appropriateness of collaborations by analyzing the company's social media activity. Some or all of the above processes in the selection unit may be performed using AI, or not. For example, the selection unit can input company social media activity data into AI, which then evaluates the reliability of the collaboration.

[0048] The support department can optimize support methods by referring to past support data when providing support for skill acquisition. For example, the support department can optimize support content based on past successful support methods. It can also improve support content based on past unsuccessful support methods. Furthermore, the support department can improve the accuracy of support methods by referring to past support data. For example, the support department can retrieve past support data from a database and analyze it using AI. This improves the accuracy of support methods and increases the efficiency of skill acquisition by referring to past support data. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input past support data into AI, which then optimizes the support methods.

[0049] The support unit can provide the optimal support method when assisting with technical skill acquisition, taking into account the user's device information. For example, if the user is using a smartphone, the support unit can provide a support method adapted to the screen size. Furthermore, if the user is using a tablet, the support unit can provide a support method optimized for the larger screen. Additionally, if the user is using a smartwatch, the support unit can provide a concise and highly visible support method. For example, the support unit can retrieve the user's device information from a database and analyze it using AI. This allows for the provision of the optimal support method by considering the user's device information, thereby improving the efficiency of technical skill acquisition. Some or all of the above-described processes in the support unit may be performed using AI, or not. For example, the support unit can input the user's device information into the AI, which then provides the optimal support method.

[0050] The support department can analyze a user's learning history and customize the support provided during technical skill acquisition. For example, the support department can provide optimal support based on the user's past learning history. Furthermore, the support department can identify areas of difficulty from the user's learning history and provide focused support. In addition, the support department can analyze the user's learning history and provide individually customized support. For example, the support department can retrieve the user's learning history from a database and analyze it using AI. This allows for the provision of individually customized support by analyzing the user's learning history, thereby improving the efficiency of technical skill acquisition. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input the user's learning history data into AI, which then customizes the support.

[0051] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit optimizes the learning algorithm based on past successful learning data. It can also improve the learning algorithm based on past unsuccessful learning data. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. For example, the learning unit retrieves past learning data from a database and analyzes it using AI. This improves the accuracy of the learning algorithm and increases learning efficiency by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, or not. For example, the learning unit inputs past learning data into the AI, and the AI ​​optimizes the learning algorithm.

[0052] The learning unit can weight the learning data based on the user's learning history during the learning process. For example, the learning unit can weight important learning data based on the user's past learning history. The learning unit can also focus on weighting areas where the user struggles, based on the user's learning history. Furthermore, the learning unit can analyze the user's learning history and perform individually customized weighting. For example, the learning unit can retrieve the user's learning history from a database and analyze it using AI. This improves the efficiency of learning by weighting the learning data based on the user's learning history. Some or all of the above processes in the learning unit may be performed using AI, or not. For example, the learning unit can input the user's learning history data into the AI, and the AI ​​will weight the learning data.

[0053] The learning unit can customize learning content based on the user's industry and area of ​​expertise during the learning process. For example, if the user belongs to a specific industry, the learning unit will provide learning content relevant to that industry. The learning unit can also customize learning content based on the user's area of ​​expertise. Furthermore, the learning unit can individually adjust the learning content according to the user's industry and area of ​​expertise. For example, the learning unit can retrieve information about the user's industry and area of ​​expertise from a database and analyze it using AI. This improves learning efficiency by customizing learning content based on the user's industry and area of ​​expertise. Some or all of the above processes in the learning unit may be performed using AI, or not. For example, the learning unit can input information about the user's industry and area of ​​expertise into the AI, and the AI ​​will customize the learning content.

[0054] The feedback unit can optimize its feedback method by referring to past feedback data during real-time feedback. For example, the feedback unit optimizes the feedback content based on past successful feedback methods. It can also improve the feedback content based on past unsuccessful feedback methods. Furthermore, the feedback unit can improve the accuracy of its feedback method by referring to past feedback data. For example, the feedback unit retrieves past feedback data from a database and analyzes it using AI. This improves the accuracy of the feedback method and increases the efficiency of feedback by referring to past feedback data. Some or all of the above processes in the feedback unit may be performed using AI, or not. For example, the feedback unit inputs past feedback data into the AI, and the AI ​​optimizes the feedback method.

[0055] The feedback unit can provide the optimal feedback method in real time, taking into account the user's device information. For example, if the user is using a smartphone, the feedback unit can provide a feedback method that matches the screen size. Furthermore, if the user is using a tablet, the feedback unit can provide a feedback method optimized for a larger screen. Additionally, if the user is using a smartwatch, the feedback unit can provide a concise and highly visible feedback method. For example, the feedback unit can retrieve the user's device information from a database and analyze it using AI. This allows the feedback unit to provide the optimal feedback method by considering the user's device information, thereby improving the efficiency of the feedback. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input the user's device information into the AI, which then provides the optimal feedback method.

[0056] The feedback unit can analyze the user's learning history and customize the feedback content during real-time feedback. For example, the feedback unit can provide optimal feedback based on the user's past learning history. It can also identify areas of weakness from the user's learning history and provide focused feedback. Furthermore, the feedback unit can analyze the user's learning history and provide individually customized feedback. For example, the feedback unit can retrieve the user's learning history from a database and analyze it using AI. This allows for the provision of individually customized feedback by analyzing the user's learning history, improving the efficiency of feedback. Some or all of the above-described processes in the feedback unit may be performed using AI, or not. For example, the feedback unit can input the user's learning history data into AI, which then customizes the feedback content.

[0057] The feedback unit can analyze the user's learning history and customize the feedback content during real-time feedback. For example, the feedback unit can provide optimal feedback based on the user's past learning history. It can also identify areas of weakness from the user's learning history and provide focused feedback. Furthermore, the feedback unit can analyze the user's learning history and provide individually customized feedback. For example, the feedback unit can retrieve the user's learning history from a database and analyze it using AI. This allows for the provision of individually customized feedback by analyzing the user's learning history, improving the efficiency of feedback. Some or all of the above-described processes in the feedback unit may be performed using AI, or not. For example, the feedback unit can input the user's learning history data into AI, which then customizes the feedback content.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The reception department can analyze the proposer's past proposal history when receiving a proposal and select the most suitable reception method. For example, it can apply a similar reception method based on the proposer's past successful proposals. It can also select a reception method that reflects improvements based on the proposer's past unsuccessful proposals. Furthermore, it can suggest the most effective reception method based on the proposer's past proposal history. In this way, by analyzing the proposer's past proposal history, the optimal reception method can be selected, and the evaluation of proposal content becomes more efficient.

[0060] The reception department can filter proposals based on the proposer's industry and area of ​​expertise. For example, if a proposer belongs to a specific industry, proposals related to that industry will be given priority. The department can also evaluate the appropriateness of proposals based on the proposer's area of ​​expertise. Furthermore, it can filter proposals according to the proposer's industry and area of ​​expertise. This filtering based on the proposer's industry and area of ​​expertise improves the appropriateness of the proposals.

[0061] The reception department can prioritize proposals based on their relevance, taking into account the proposer's geographical location. For example, if a proposer is in a nearby area, proposals related to that area will be prioritized. Furthermore, proposals tailored to specific regional needs can also be prioritized based on the proposer's geographical location. The priority of proposals can also be determined by considering the proposer's geographical location. This allows for the evaluation of proposals that align with regional needs by taking the proposer's geographical location into account.

[0062] The reception department can analyze the proposer's social media activity when receiving proposals and accept relevant proposals. For example, it can evaluate the reliability of a proposal based on the proposer's social media activity. It can also prioritize accepting relevant proposals based on the proposer's social media activity. Furthermore, it can determine the appropriateness of a proposal by analyzing the proposer's social media activity. In this way, analyzing the proposer's social media activity improves the reliability and appropriateness of the proposal.

[0063] The evaluation unit can optimize its evaluation algorithm by referring to past evaluation data when evaluating proposals. For example, it can optimize the evaluation algorithm based on successful proposals in the past. It can also improve the evaluation algorithm based on unsuccessful proposals in the past. Furthermore, it can improve the accuracy of the evaluation algorithm by referring to past evaluation data. As a result, by referring to past evaluation data, the accuracy of the evaluation algorithm is improved and the evaluation of proposals becomes more efficient.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The reception department accepts idea proposals from different industries. The reception department can accept idea proposals via, for example, an online platform, email, a dedicated application, or in person. Furthermore, the reception department has idea proposals from different industries enter them into an online form and saves the proposal content in a database. Step 2: The evaluation department creates a collaborative image, assesses feasibility, and determines costs and risks based on the idea proposals received by the reception department. The evaluation department can, for example, use AI to visualize the collaborative image and conduct simulations. It can also evaluate technical and economic feasibility and perform cost analysis and risk assessment. Step 3: The selection department selects and approaches potential collaborating companies based on the collaboration image evaluated by the evaluation department. The selection department can, for example, use AI to create a list of potential collaborating companies and select which companies to approach. It can also analyze past performance and evaluations to select the most suitable companies. Furthermore, it can conduct risk assessments and select companies with low risk.

[0066] (Example of form 2) The traditional craft revitalization system according to an embodiment of the present invention is a system that utilizes an AI agent to prevent the decline of Japanese traditional crafts and revitalize the industry. The traditional craft revitalization system creates an AI agent that has learned the techniques of traditional crafts and autonomously makes proposals to promote collaboration with other industries. For example, it accepts idea proposals from other industries, creates collaboration images, evaluates feasibility, judges costs and risks, and selects and approaches companies that are capable of collaborating. This creates new value, expands demand, and revitalizes the industry. Furthermore, it uses AI to supplement the parts of the skills that take time to acquire, realizing efficient human resource development. For example, it uses video, AR, VR, etc. to support skill acquisition and quantifies the experience and intuition of skilled craftsmen to use as learning data. This makes it easier for new talent to enter the industry and builds a foundation for the future of traditional crafts. Through this mechanism, the techniques of traditional crafts are redefined to meet modern needs and new value is created through collaboration with other industries. In addition, efficient human resource development solves the shortage of successors and revitalizes the entire industry. This allows the traditional crafts revitalization system to redefine the techniques of Japanese traditional crafts to meet modern needs and create new value through collaboration with other industries.

[0067] The traditional craft revitalization system according to this embodiment comprises a reception unit, an evaluation unit, and a selection unit. The reception unit receives idea proposals from different industries. For example, the reception unit can receive idea proposals from different industries through an online platform. The reception unit can also receive idea proposals from different industries via email or a dedicated application. Furthermore, the reception unit can also receive idea proposals from different industries in person. For example, the reception unit has people from different industries input their idea proposals into an online form and saves the proposal content in a database. The evaluation unit creates collaboration images, evaluates feasibility, and determines costs and risks based on the idea proposals received by the reception unit. For example, the evaluation unit can use AI to visualize the collaboration image and perform simulations. Furthermore, the evaluation unit can use AI to evaluate technical feasibility and economic feasibility. Furthermore, the evaluation unit can use AI to perform cost analysis and risk assessment. For example, the evaluation unit can use AI to create a 3D model of the collaboration image and perform simulations. The selection department selects and approaches companies that are suitable for collaboration based on the collaboration image evaluated by the evaluation department. The selection department can, for example, use AI to create a list of companies suitable for collaboration and select companies to approach. The selection department can also use AI to analyze the past performance and evaluation of companies suitable for collaboration and select the most suitable company. Furthermore, the selection department can use AI to assess the risks of companies suitable for collaboration and select companies with low risk. For example, the selection department can use AI to create a list of companies suitable for collaboration and select companies to approach. As a result, the traditional craft revitalization system according to this embodiment can create new value, expand demand, and revitalize the industry by accepting and evaluating idea proposals from different industries and selecting companies suitable for collaboration.

[0068] The reception department accepts idea proposals from diverse industries. For example, the reception department can accept idea proposals from diverse industries through an online platform. Specifically, it can make it easy for companies and individuals from different industries to submit ideas through a dedicated website or application. This allows proposers to describe their ideas in detail and upload relevant documents and images. The reception department can also accept idea proposals from diverse industries via email or a dedicated application. For example, it can set up a dedicated email address so that proposers can send their ideas by email. The dedicated application can provide an interface for proposers to input and submit their ideas. Furthermore, the reception department can also accept idea proposals from diverse industries in person. For example, it can provide opportunities for proposers to present their ideas directly at regularly held idea proposal meetings or workshops. This allows proposers to explain their ideas directly and provide details through question and answer sessions. By combining these methods, the reception department can efficiently receive a variety of proposals. For example, the reception department can have idea proposals from diverse industries enter them into an online form and store the proposal content in a database. The database records the content of proposals, information about the proposers, and the submission date, which are then used for evaluation and selection. This allows the reception department to efficiently collect diverse ideas from different industries, contributing to the overall revitalization of the system.

[0069] The evaluation department creates collaborative images, assesses feasibility, and determines costs and risks based on idea proposals received by the reception department. For example, the evaluation department can use AI to visualize collaborative images and conduct simulations. Specifically, the AI ​​generates 3D models and visual images based on the proposed ideas, visually representing the concrete form of the proposal. This allows proposers and evaluators to intuitively understand the feasibility and concrete form of the idea. The evaluation department can also use AI to assess technical and economic feasibility. For example, the AI ​​analyzes the characteristics of the proposed technologies and materials, and evaluates the technical challenges and costs involved in actual product development. Furthermore, the evaluation department can use AI to conduct cost analysis and risk assessment. For example, the AI ​​predicts the manufacturing costs and market selling prices of the proposed idea, and assesses its economic feasibility. In risk assessment, the AI ​​analyzes the impact of the proposed idea on the market and the actions of competitors to assess the risks. This allows the evaluation department to comprehensively evaluate the feasibility and economic benefits of the proposed idea and create the optimal collaborative image. For example, the evaluation department uses AI to create a 3D model of the collaborative idea and conducts simulations. These simulations verify how the proposed idea actually functions and identify technical challenges and areas for improvement. This allows the evaluation department to increase the feasibility of the proposed idea and maximize the overall system's effectiveness.

[0070] The selection department selects and approaches companies that are suitable for collaboration based on the collaboration image evaluated by the evaluation department. For example, the selection department can use AI to create a list of potential collaborating companies and select which companies to approach. Specifically, the AI ​​analyzes past collaboration achievements, the company's technological capabilities, and its economic strength to identify the most suitable collaboration partner. The selection department can also use AI to analyze the past achievements and evaluations of potential collaborating companies and select the most suitable companies. For example, the AI ​​analyzes a company's past projects and market evaluation to select companies with reliability and a proven track record. Furthermore, the selection department can use AI to assess the risks of potential collaborating companies and select companies with low risk. For example, the AI ​​analyzes a company's financial situation and market reputation to identify companies with low risk. This allows the selection department to select highly reliable, low-risk companies, increasing the success rate of collaborations. For example, the selection department can use AI to create a list of potential collaborating companies and select which companies to approach. The list includes the company's name, contact information, and past achievements, and the selection department uses this information to approach companies. During the initial consultation, the proposed ideas and collaboration concepts are explained in detail to pique the company's interest. This allows the selection department to choose the most suitable collaboration partner and maximize the overall effectiveness of the system.

[0071] The Support Department utilizes video, AR, and VR to support skill acquisition. For example, the Support Department can visually demonstrate the steps for skill acquisition using video. It can also overlay the skill acquisition steps onto the actual work environment using AR. Furthermore, the Support Department can simulate skill acquisition in a virtual space using VR. For example, the Support Department can demonstrate the skill acquisition steps in video for easier visual understanding. AR is a technology that uses augmented reality to overlay the skill acquisition steps onto the actual work environment. VR is a technology that uses virtual reality to simulate skill acquisition in a virtual space. By utilizing video, AR, and VR, the efficiency of skill acquisition is improved, and the entry of new personnel becomes easier. Some or all of the above-described processes in the Support Department may be performed using, for example, generative AI, or without generative AI. For example, the Support Department inputs the skill acquisition steps into a generative AI, which then demonstrates the steps using video, AR, and VR.

[0072] The learning unit quantifies the experience and intuition of skilled craftsmen and utilizes it as learning data. For example, the learning unit can measure the work of skilled craftsmen with sensors and collect the data. The learning unit can also record the work of skilled craftsmen on video and analyze the data. Furthermore, the learning unit can record the work of skilled craftsmen on audio and analyze the data. For example, the learning unit measures the work of skilled craftsmen with sensors and collects data on the speed and force of the work. The learning unit records the work of skilled craftsmen on video and analyzes the work procedures and movements as data. The learning unit records the work of skilled craftsmen on audio and analyzes the instructions and explanations given during the work as data. By quantifying the experience and intuition of skilled craftsmen, the acquisition of skills becomes more efficient and the training of new personnel becomes easier. Some or all of the above processing in the learning unit may be performed using, for example, generative AI, or without using generative AI. For example, the learning unit inputs work data from skilled craftsmen into a generating AI, which then analyzes the work data to generate training data.

[0073] The Feedback Department provides real-time feedback. For example, the Feedback Department can provide real-time advice to users who are learning a new skill. The Feedback Department can also monitor the work of users learning a new skill in real time and provide feedback at the appropriate time. Furthermore, the Feedback Department can evaluate the work of users learning a new skill in real time and point out areas for improvement. For example, the Feedback Department provides real-time advice to users learning a new skill and supports their progress. The Feedback Department monitors the work of users learning a new skill in real time and provides feedback at the appropriate time. The Feedback Department evaluates the work of users learning a new skill in real time and points out areas for improvement. As a result, providing real-time feedback improves the efficiency of skill acquisition and makes it easier to train new personnel. Some or all of the above processes in the Feedback Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Feedback Department inputs the work data of users learning a new skill into a generative AI, and the generative AI provides real-time feedback.

[0074] The reception department, when receiving idea proposals from different industries, can estimate the proposer's emotions and prioritize the proposals based on those emotions. For example, if the proposer is excited, the reception department will highly value the urgency of the proposal and process it with priority. If the proposer is feeling anxious, the reception department can also carefully review the details of the proposal and evaluate it thoroughly. Furthermore, if the proposer is relaxed, the reception department can prioritize the creativity of the proposal and respond flexibly. For example, the reception department can capture the proposer's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. This allows for a more appropriate evaluation of proposals by prioritizing them based on the proposer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception desk inputs the proposer's facial expression data into a generating AI, which then estimates the proposer's emotions.

[0075] The reception department can analyze the proposer's past proposal history when receiving a proposal and select the most suitable reception method. For example, the reception department can apply a similar reception method based on the proposer's past successful proposals. It can also select a reception method that reflects improvements based on the proposer's past unsuccessful proposals. Furthermore, the reception department can suggest the most effective reception method based on the proposer's past proposal history. For example, the reception department can retrieve the proposer's past proposal history from a database and analyze it using AI. This allows for the selection of the most suitable reception method and streamlines the evaluation of proposals. Some or all of the above processes in the reception department may be performed using AI, or not. For example, the reception department can input the proposer's past proposal history data into the AI, which then selects the most suitable reception method.

[0076] The reception department can filter proposals based on the proposer's industry and area of ​​expertise when receiving them. For example, if a proposer belongs to a specific industry, the reception department will prioritize proposals related to that industry. The reception department can also evaluate the appropriateness of proposals based on the proposer's area of ​​expertise. Furthermore, the reception department can filter proposals according to the proposer's industry and area of ​​expertise. For example, the reception department can obtain information on the proposer's industry and area of ​​expertise from a database and analyze it using AI. This improves the appropriateness of proposals by filtering them based on the proposer's industry and area of ​​expertise. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department can input information on the proposer's industry and area of ​​expertise into AI, and the AI ​​will filter the proposals.

[0077] The reception department can prioritize receiving proposals that are highly relevant, taking into account the proposer's geographical location. For example, if the proposer is in a nearby area, the reception department will prioritize proposals related to that area. The reception department can also accept proposals that meet region-specific needs based on the proposer's geographical location. Furthermore, the reception department can determine the priority of proposals by considering the proposer's geographical location. For example, the reception department can obtain the proposer's geographical location from a database and analyze it using AI. This makes it possible to evaluate proposals that meet region-specific needs by considering the proposer's geographical location. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department can input the proposer's geographical location into the AI, and the AI ​​will determine the priority of proposals.

[0078] The reception department can analyze the proposer's social media activity upon receiving a proposal and accept relevant proposals. For example, the reception department can evaluate the reliability of a proposal based on the proposer's social media activity. The reception department can also prioritize accepting relevant proposals based on the proposer's social media activity. Furthermore, the reception department can analyze the proposer's social media activity to determine the appropriateness of the proposal. For example, the reception department can retrieve the proposer's social media activity from a database and analyze it using AI. This improves the reliability and appropriateness of the proposal by analyzing the proposer's social media activity. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department can input the proposer's social media activity data into AI, and the AI ​​can evaluate the reliability of the proposal.

[0079] The evaluation unit can estimate the proposer's emotions when evaluating the proposal and adjust the evaluation criteria based on the estimated emotions. For example, if the proposer is excited, the evaluation unit will prioritize the innovativeness of the proposal. If the proposer is feeling anxious, the evaluation unit can also carefully evaluate the feasibility of the proposal. Furthermore, if the proposer is relaxed, the evaluation unit can also prioritize the creativity of the proposal. For example, the evaluation unit can capture the proposer's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows for a more appropriate evaluation by adjusting the evaluation criteria based on the proposer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit inputs the proposer's facial expression data into the generative AI, and the generative AI estimates the proposer's emotions.

[0080] The evaluation unit can optimize its evaluation algorithm by referring to past evaluation data when evaluating proposals. For example, the evaluation unit optimizes its evaluation algorithm based on past successful proposals. It can also improve its evaluation algorithm based on past unsuccessful proposals. Furthermore, the evaluation unit can improve the accuracy of its evaluation algorithm by referring to past evaluation data. For example, the evaluation unit retrieves past evaluation data from a database and analyzes it using AI. By referring to past evaluation data, the accuracy of the evaluation algorithm is improved, and the evaluation of proposals becomes more efficient. Some or all of the above processes in the evaluation unit may be performed using AI, or not. For example, the evaluation unit inputs past evaluation data into AI, and the AI ​​optimizes the evaluation algorithm.

[0081] The evaluation unit can apply different evaluation methods depending on the category of the proposal when evaluating it. For example, if the proposal is technical, the evaluation unit will apply a technical evaluation method. If the proposal is business-related, the evaluation unit can also apply a business-related evaluation method. Furthermore, if the proposal is creative, the evaluation unit can also apply a creative evaluation method. For example, the evaluation unit can obtain the category of the proposal from a database and analyze it using AI. By applying different evaluation methods depending on the category of the proposal, the appropriateness of the proposal can be improved. Some or all of the above processing in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit can input the category data of the proposal into the AI, and the AI ​​can apply an evaluation method.

[0082] The evaluation department can determine the priority of evaluations based on the submission timing of the proposals. For example, the evaluation department may prioritize evaluations of proposals submitted early. It can also quickly evaluate proposals submitted shortly before the deadline. Furthermore, the evaluation department can determine the priority of evaluations based on the submission timing of the proposals. For example, the evaluation department may obtain the submission timing of proposals from a database and analyze it using AI. This streamlines the evaluation of proposals by determining the priority of evaluations based on the submission timing. Some or all of the above processes in the evaluation department may be performed using AI, or not. For example, the evaluation department may input proposal submission timing data into AI, and the AI ​​may determine the priority of evaluations.

[0083] The evaluation unit can adjust the order of evaluation based on the relevance of the proposals when evaluating them. For example, the evaluation unit will prioritize evaluation of proposals that are related to the current project. It can also adjust the order of evaluation if the proposals are related to future projects. Furthermore, the evaluation unit can determine the order of evaluation based on the relevance of the proposals. For example, the evaluation unit can obtain the relevance of the proposals from a database and analyze it using AI. This makes the evaluation of proposals more efficient by adjusting the order of evaluation based on the relevance of the proposals. Some or all of the above processes in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input the relevance data of the proposals into the AI, and the AI ​​can determine the order of evaluation.

[0084] The selection unit can estimate a company's emotions when selecting potential collaborating partners and adjust the selection criteria based on the estimated emotions. For example, if a company is proactive, the selection unit will rate the possibility of collaboration higher. If a company is cautious, the selection unit can also adjust the selection criteria to account for risks. Furthermore, if a company is excited, the selection unit can quickly approach them about collaboration. For example, the selection unit can capture a company's facial expression with a camera and estimate its emotions using an emotion estimation algorithm. This allows for the selection of more appropriate companies by adjusting the selection criteria based on the company's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit inputs the company's facial expression data into a generative AI, which then estimates the company's emotions.

[0085] The selection unit can optimize its selection algorithm by referring to past selection data when selecting companies for collaboration. For example, the selection unit optimizes the selection algorithm based on past successful collaborations. It can also improve the selection algorithm based on past unsuccessful collaborations. Furthermore, the selection unit can improve the accuracy of the selection algorithm by referring to past selection data. For example, the selection unit retrieves past selection data from a database and analyzes it using AI. This improves the accuracy of the selection algorithm by referring to past selection data, making company selection more efficient. Some or all of the above processes in the selection unit may be performed using AI, or not. For example, the selection unit inputs past selection data into AI, and the AI ​​optimizes the selection algorithm.

[0086] The selection department can select potential collaborating companies based on their industry and area of ​​expertise. For example, if a company belongs to a specific industry, the selection department will prioritize collaborations related to that industry. The selection department can also evaluate the appropriateness of collaborations based on the company's area of ​​expertise. Furthermore, the selection department can select collaborations according to the company's industry and area of ​​expertise. For example, the selection department can obtain information on companies' industries and areas of expertise from a database and analyze it using AI. This improves the appropriateness of collaborations by selecting based on the company's industry and area of ​​expertise. Some or all of the above processes in the selection department may be performed using AI, or not. For example, the selection department can input information on companies' industries and areas of expertise into AI, and the AI ​​will select collaborations.

[0087] The selection unit can select the most suitable companies for collaboration by considering their geographical location. For example, if a company is located in a nearby area, the selection unit will prioritize collaborations related to that area. The selection unit can also select collaborations that meet the specific needs of a region based on the geographical location of the companies. Furthermore, the selection unit can select collaborations while considering the geographical location of the companies. For example, the selection unit can obtain the geographical location of companies from a database and analyze it using AI. This makes it possible to select companies that meet the specific needs of a region by considering the geographical location of the companies. Some or all of the above processes in the selection unit may be performed using AI, or not. For example, the selection unit can input the geographical location of companies into AI, and the AI ​​will select the most suitable company.

[0088] The selection unit can analyze a company's social media activity when selecting potential collaborating partners. For example, the selection unit can evaluate the reliability of a collaboration based on the company's social media activity. The selection unit can also prioritize relevant collaborations based on the company's social media activity. Furthermore, the selection unit can analyze a company's social media activity to determine the appropriateness of a collaboration. For example, the selection unit can retrieve a company's social media activity from a database and analyze it using AI. This improves the reliability and appropriateness of collaborations by analyzing the company's social media activity. Some or all of the above processes in the selection unit may be performed using AI, or not. For example, the selection unit can input company social media activity data into AI, which then evaluates the reliability of the collaboration.

[0089] The support unit can estimate the user's emotions when providing technical training support and adjust the support content based on the estimated emotions. For example, if the user is nervous, the support unit can provide a relaxing environment and adjust the support content. If the user is enjoying themselves, the support unit can also provide challenging tasks and adjust the support content. Furthermore, if the user is tired, the support unit can provide easy tasks and adjust the support content. For example, the support unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate technical training support by adjusting the support content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, or not. For example, the support unit inputs the user's facial expression data into the generative AI, which then estimates the user's emotions.

[0090] The support department can optimize support methods by referring to past support data when providing support for skill acquisition. For example, the support department can optimize support content based on past successful support methods. It can also improve support content based on past unsuccessful support methods. Furthermore, the support department can improve the accuracy of support methods by referring to past support data. For example, the support department can retrieve past support data from a database and analyze it using AI. This improves the accuracy of support methods and increases the efficiency of skill acquisition by referring to past support data. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input past support data into AI, which then optimizes the support methods.

[0091] The support unit can provide the optimal support method when assisting with technical skill acquisition, taking into account the user's device information. For example, if the user is using a smartphone, the support unit can provide a support method adapted to the screen size. Furthermore, if the user is using a tablet, the support unit can provide a support method optimized for the larger screen. Additionally, if the user is using a smartwatch, the support unit can provide a concise and highly visible support method. For example, the support unit can retrieve the user's device information from a database and analyze it using AI. This allows for the provision of the optimal support method by considering the user's device information, thereby improving the efficiency of technical skill acquisition. Some or all of the above-described processes in the support unit may be performed using AI, or not. For example, the support unit can input the user's device information into the AI, which then provides the optimal support method.

[0092] The support department can analyze a user's learning history and customize the support provided during technical skill acquisition. For example, the support department can provide optimal support based on the user's past learning history. Furthermore, the support department can identify areas of difficulty from the user's learning history and provide focused support. In addition, the support department can analyze the user's learning history and provide individually customized support. For example, the support department can retrieve the user's learning history from a database and analyze it using AI. This allows for the provision of individually customized support by analyzing the user's learning history, thereby improving the efficiency of technical skill acquisition. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input the user's learning history data into AI, which then customizes the support.

[0093] The learning unit can estimate the user's emotions when selecting training data and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit can provide detailed training data. If the user is in a hurry, the learning unit can also provide concise training data. Furthermore, if the user is excited, the learning unit can provide visually stimulating training data. For example, the learning unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. By selecting training data based on the user's emotions, more appropriate training data is provided, improving the efficiency of learning. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit inputs the user's facial expression data into the generative AI, and the generative AI estimates the user's emotions.

[0094] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit optimizes the learning algorithm based on past successful learning data. It can also improve the learning algorithm based on past unsuccessful learning data. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. For example, the learning unit retrieves past learning data from a database and analyzes it using AI. This improves the accuracy of the learning algorithm and increases learning efficiency by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, or not. For example, the learning unit inputs past learning data into the AI, and the AI ​​optimizes the learning algorithm.

[0095] The learning unit can weight the learning data based on the user's learning history during the learning process. For example, the learning unit can weight important learning data based on the user's past learning history. The learning unit can also focus on weighting areas where the user struggles, based on the user's learning history. Furthermore, the learning unit can analyze the user's learning history and perform individually customized weighting. For example, the learning unit can retrieve the user's learning history from a database and analyze it using AI. This improves the efficiency of learning by weighting the learning data based on the user's learning history. Some or all of the above processes in the learning unit may be performed using AI, or not. For example, the learning unit can input the user's learning history data into the AI, and the AI ​​will weight the learning data.

[0096] The learning unit can customize learning content based on the user's industry and area of ​​expertise during the learning process. For example, if the user belongs to a specific industry, the learning unit will provide learning content relevant to that industry. The learning unit can also customize learning content based on the user's area of ​​expertise. Furthermore, the learning unit can individually adjust the learning content according to the user's industry and area of ​​expertise. For example, the learning unit can retrieve information about the user's industry and area of ​​expertise from a database and analyze it using AI. This improves learning efficiency by customizing learning content based on the user's industry and area of ​​expertise. Some or all of the above processes in the learning unit may be performed using AI, or not. For example, the learning unit can input information about the user's industry and area of ​​expertise into the AI, and the AI ​​will customize the learning content.

[0097] The feedback unit can estimate the user's emotions during real-time feedback and adjust the feedback content based on the estimated emotions. For example, if the user is tense, the feedback unit can provide relaxing feedback. It can also provide challenging feedback if the user is enjoying themselves. Furthermore, if the user is tired, the feedback unit can provide simple feedback. For example, the feedback unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate feedback to be provided by adjusting the feedback content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the feedback unit may be performed using AI, or not. For example, the feedback unit inputs the user's facial expression data into the generative AI, which then estimates the user's emotions.

[0098] The feedback unit can optimize its feedback method by referring to past feedback data during real-time feedback. For example, the feedback unit optimizes the feedback content based on past successful feedback methods. It can also improve the feedback content based on past unsuccessful feedback methods. Furthermore, the feedback unit can improve the accuracy of its feedback method by referring to past feedback data. For example, the feedback unit retrieves past feedback data from a database and analyzes it using AI. This improves the accuracy of the feedback method and increases the efficiency of feedback by referring to past feedback data. Some or all of the above processes in the feedback unit may be performed using AI, or not. For example, the feedback unit inputs past feedback data into the AI, and the AI ​​optimizes the feedback method.

[0099] The feedback unit can provide the optimal feedback method in real time, taking into account the user's device information. For example, if the user is using a smartphone, the feedback unit can provide a feedback method that matches the screen size. Furthermore, if the user is using a tablet, the feedback unit can provide a feedback method optimized for a larger screen. Additionally, if the user is using a smartwatch, the feedback unit can provide a concise and highly visible feedback method. For example, the feedback unit can retrieve the user's device information from a database and analyze it using AI. This allows the feedback unit to provide the optimal feedback method by considering the user's device information, thereby improving the efficiency of the feedback. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input the user's device information into the AI, which then provides the optimal feedback method.

[0100] The feedback unit can analyze the user's learning history and customize the feedback content during real-time feedback. For example, the feedback unit can provide optimal feedback based on the user's past learning history. It can also identify areas of weakness from the user's learning history and provide focused feedback. Furthermore, the feedback unit can analyze the user's learning history and provide individually customized feedback. For example, the feedback unit can retrieve the user's learning history from a database and analyze it using AI. This allows for the provision of individually customized feedback by analyzing the user's learning history, improving the efficiency of feedback. Some or all of the above-described processes in the feedback unit may be performed using AI, or not. For example, the feedback unit can input the user's learning history data into AI, which then customizes the feedback content.

[0101] The feedback unit can analyze the user's learning history and customize the feedback content during real-time feedback. For example, the feedback unit can provide optimal feedback based on the user's past learning history. It can also identify areas of weakness from the user's learning history and provide focused feedback. Furthermore, the feedback unit can analyze the user's learning history and provide individually customized feedback. For example, the feedback unit can retrieve the user's learning history from a database and analyze it using AI. This allows for the provision of individually customized feedback by analyzing the user's learning history, improving the efficiency of feedback. Some or all of the above-described processes in the feedback unit may be performed using AI, or not. For example, the feedback unit can input the user's learning history data into AI, which then customizes the feedback content.

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

[0103] The reception desk can estimate the proposer's emotions and prioritize the proposal based on those emotions. For example, if the proposer is excited, the urgency of the proposal will be highly valued and processed with priority. If the proposer is feeling anxious, the details of the proposal can be reviewed and evaluated carefully. Furthermore, if the proposer is relaxed, the creativity of the proposal can be emphasized and a flexible approach can be taken. In this way, prioritizing proposals based on the proposer's emotions allows for a more appropriate evaluation of the proposals.

[0104] The evaluation unit can estimate the proposer's emotions when evaluating a proposal and adjust the evaluation criteria based on those emotions. For example, if the proposer is excited, the evaluation will prioritize the innovativeness of the proposal. If the proposer is feeling anxious, the feasibility of the proposal can be carefully evaluated. Furthermore, if the proposer is relaxed, the evaluation will prioritize the creativity of the proposal. By adjusting the evaluation criteria based on the proposer's emotions, a more appropriate evaluation becomes possible.

[0105] The selection team can estimate the sentiment of potential collaborating companies and adjust the selection criteria based on that estimation. For example, if a company is enthusiastic, the potential for collaboration will be highly valued. If a company is cautious, the selection criteria can be adjusted to take risks into consideration. Furthermore, if a company is excited, a collaboration proposal can be approached quickly. By adjusting the selection criteria based on the company's sentiment, it becomes possible to select more appropriate companies.

[0106] The support department can estimate the user's emotions during technical training and adjust the support content based on those estimates. For example, if the user is feeling nervous, it can provide a relaxing environment and adjust the support content accordingly. If the user is enjoying themselves, it can offer challenging tasks and adjust the support content. Furthermore, if the user is feeling tired, it can offer easier tasks and adjust the support content accordingly. By adjusting the support content based on the user's emotions, it becomes possible to provide more appropriate technical training support.

[0107] The learning unit can estimate the user's emotions when selecting training data and select training data based on those estimated emotions. For example, if the user is relaxed, it can provide detailed training data. If the user is in a hurry, it can provide training data that gets straight to the point. Furthermore, if the user is excited, it can provide visually stimulating training data. By selecting training data based on the user's emotions, it can provide more appropriate training data and improve learning efficiency.

[0108] The reception department can analyze the proposer's past proposal history when receiving a proposal and select the most suitable reception method. For example, it can apply a similar reception method based on the proposer's past successful proposals. It can also select a reception method that reflects improvements based on the proposer's past unsuccessful proposals. Furthermore, it can suggest the most effective reception method based on the proposer's past proposal history. In this way, by analyzing the proposer's past proposal history, the optimal reception method can be selected, and the evaluation of proposal content becomes more efficient.

[0109] The reception department can filter proposals based on the proposer's industry and area of ​​expertise. For example, if a proposer belongs to a specific industry, proposals related to that industry will be given priority. The department can also evaluate the appropriateness of proposals based on the proposer's area of ​​expertise. Furthermore, it can filter proposals according to the proposer's industry and area of ​​expertise. This filtering based on the proposer's industry and area of ​​expertise improves the appropriateness of the proposals.

[0110] The reception department can prioritize proposals based on their relevance, taking into account the proposer's geographical location. For example, if a proposer is in a nearby area, proposals related to that area will be prioritized. Furthermore, proposals tailored to specific regional needs can also be prioritized based on the proposer's geographical location. The priority of proposals can also be determined by considering the proposer's geographical location. This allows for the evaluation of proposals that align with regional needs by taking the proposer's geographical location into account.

[0111] The reception department can analyze the proposer's social media activity when receiving proposals and accept relevant proposals. For example, it can evaluate the reliability of a proposal based on the proposer's social media activity. It can also prioritize accepting relevant proposals based on the proposer's social media activity. Furthermore, it can determine the appropriateness of a proposal by analyzing the proposer's social media activity. In this way, analyzing the proposer's social media activity improves the reliability and appropriateness of the proposal.

[0112] The evaluation unit can optimize its evaluation algorithm by referring to past evaluation data when evaluating proposals. For example, it can optimize the evaluation algorithm based on successful proposals in the past. It can also improve the evaluation algorithm based on unsuccessful proposals in the past. Furthermore, it can improve the accuracy of the evaluation algorithm by referring to past evaluation data. As a result, by referring to past evaluation data, the accuracy of the evaluation algorithm is improved and the evaluation of proposals becomes more efficient.

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

[0114] Step 1: The reception department accepts idea proposals from different industries. The reception department can accept idea proposals via, for example, an online platform, email, a dedicated application, or in person. Furthermore, the reception department has idea proposals from different industries enter them into an online form and saves the proposal content in a database. Step 2: The evaluation department creates a collaborative image, assesses feasibility, and determines costs and risks based on the idea proposals received by the reception department. The evaluation department can, for example, use AI to visualize the collaborative image and conduct simulations. It can also evaluate technical and economic feasibility and perform cost analysis and risk assessment. Step 3: The selection department selects and approaches potential collaborating companies based on the collaboration image evaluated by the evaluation department. The selection department can, for example, use AI to create a list of potential collaborating companies and select which companies to approach. It can also analyze past performance and evaluations to select the most suitable companies. Furthermore, it can conduct risk assessments and select companies with low risk.

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

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

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

[0118] Each of the multiple elements described above, including the reception unit, evaluation unit, selection unit, support unit, learning unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives idea proposals from different industries through an online platform. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses AI to create collaborative images and evaluate their feasibility. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects companies that can collaborate. The support unit is implemented by the control unit 46A of the smart device 14 and supports skill acquisition using video, AR, and VR. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and quantifies the experience and intuition of skilled craftsmen and uses it as learning data. The feedback unit is implemented by the control unit 46A of the smart device 14 and provides real-time advice to users who are acquiring skills. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0134] Each of the multiple elements mentioned above, including the reception unit, evaluation unit, selection unit, support unit, learning unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives idea proposals from different industries through an online platform. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses AI to create collaborative images and evaluate their feasibility. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects companies that can collaborate. The support unit is implemented by the control unit 46A of the smart glasses 214 and supports skill acquisition using video, AR, and VR. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and quantifies the experience and intuition of skilled craftsmen and uses it as learning data. The feedback unit is implemented by the control unit 46A of the smart glasses 214 and provides real-time advice to users who are acquiring skills. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0150] Each of the multiple elements described above, including the reception unit, evaluation unit, selection unit, support unit, learning unit, and feedback unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives idea proposals from different industries through an online platform. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses AI to create collaborative images and evaluate their feasibility. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects companies that can collaborate. The support unit is implemented by the control unit 46A of the headset terminal 314 and supports skill acquisition using video, AR, and VR. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and quantifies the experience and intuition of skilled craftsmen and utilizes it as learning data. The feedback unit is implemented by the control unit 46A of the headset terminal 314 and provides real-time advice to users who are acquiring skills. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0167] Each of the multiple elements described above, including the reception unit, evaluation unit, selection unit, support unit, learning unit, and feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives idea proposals from different industries through an online platform. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses AI to create collaborative images and evaluate their feasibility. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects companies that can collaborate. The support unit is implemented by the control unit 46A of the robot 414 and supports skill acquisition using video, AR, and VR. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and quantifies the experience and intuition of skilled craftsmen and uses it as learning data. The feedback unit is implemented by the control unit 46A of the robot 414 and provides real-time advice to users who are acquiring skills. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0186] (Note 1) A reception department that accepts idea proposals from different industries, Based on the idea proposals received by the aforementioned reception department, the evaluation department creates a collaborative image, assesses feasibility, and determines costs and risks. The system includes a selection unit that selects and approaches companies that are capable of collaborating based on the collaboration image evaluated by the aforementioned evaluation unit. A system characterized by the following features. (Note 2) The facility includes a support department that utilizes video, AR, and VR to assist in skill acquisition. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a learning unit that quantifies the experience and intuition of skilled craftsmen and utilizes it as learning data. The system described in Appendix 1, characterized by the features described herein. (Note 4) It is equipped with a feedback unit that provides real-time feedback. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is When receiving idea proposals from different industries, we estimate the proposer's emotions and prioritize the proposal based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When receiving a proposal, the system analyzes the proposer's past proposal history and selects the most suitable submission method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving proposals, we will filter them based on the proposer's industry and area of ​​expertise. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving proposals, we will prioritize accepting proposals that are highly relevant, taking into account the proposer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving proposals, we analyze the proposer's social media activity and accept relevant proposals. The system described in Appendix 1, characterized by the features described herein. (Note 10) The evaluation unit described above, When evaluating proposals, the proposer's emotions are estimated, and the evaluation criteria are adjusted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The evaluation unit described above, When evaluating the proposed content, the evaluation algorithm is optimized by referring to past evaluation data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The evaluation unit described above, When evaluating proposals, different evaluation methods will be applied depending on the category of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 13) The evaluation unit described above, When evaluating proposals, the priority of evaluation will be determined based on when the proposals were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit described above, When evaluating proposals, the order of evaluation will be adjusted based on the relevance of the proposals. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned selection unit is When selecting potential collaborating companies, we estimate their sentiments and adjust the selection criteria based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned selection unit is When selecting potential collaborating companies, we optimize the selection algorithm by referring to past selection data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned selection unit is When selecting companies for collaboration, the selection process is based on the company's industry and area of ​​expertise. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned selection unit is When selecting potential collaborating companies, we will consider their geographical location to choose the most suitable company. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned selection unit is When selecting potential collaborating companies, we will consider their geographical location to choose the most suitable company. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned selection unit is When selecting companies for collaboration, we analyze their social media activity to make our selections. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned support unit is When providing support for acquiring technical skills, the system estimates the user's emotions and adjusts the support content based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 22) The aforementioned support unit is When providing support for skill acquisition, we optimize support methods by referring to past support data. The system described in Appendix 2, characterized by the features described herein. (Note 23) The aforementioned support unit is When providing support for acquiring technical skills, we take into account the user's device information to provide the most suitable support method. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned support unit is When providing support for acquiring technical skills, we analyze the user's learning history to customize the support content. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned learning unit, When selecting training data, the user's emotions are estimated, and the training data is selected based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 26) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 3, characterized by the features described herein. (Note 27) The aforementioned learning unit, During training, the training data is weighted based on the user's training history. The system described in Appendix 3, characterized by the features described herein. (Note 28) The aforementioned learning unit, During learning, the learning content is customized based on the user's industry and area of ​​expertise. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned feedback unit is During real-time feedback, the system estimates the user's emotions and adjusts the feedback content based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 30) The aforementioned feedback unit is During real-time feedback, the feedback method is optimized by referring to past feedback data. The system described in Appendix 4, characterized by the features described herein. (Note 31) The aforementioned feedback unit is When providing real-time feedback, the system takes into account the user's device information to provide the optimal feedback method. The system described in Appendix 4, characterized by the features described herein. (Note 32) The aforementioned feedback unit is During real-time feedback, the system analyzes the user's learning history to customize the feedback content. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. A reception department that accepts idea proposals from different industries, Based on the idea proposals received by the aforementioned reception department, the evaluation department creates a collaborative image, assesses feasibility, and determines costs and risks. The system includes a selection unit that selects and approaches companies that are capable of collaborating based on the collaboration image evaluated by the aforementioned evaluation unit. A system characterized by the following features.

2. The facility includes a support department that utilizes video, AR, and VR to assist in skill acquisition. The system according to feature 1.

3. It includes a learning unit that quantifies the experience and intuition of skilled craftsmen and utilizes it as learning data. The system according to feature 1.

4. It is equipped with a feedback unit that provides real-time feedback. The system according to feature 1.

5. The aforementioned reception unit is When receiving idea proposals from different industries, we estimate the proposer's emotions and prioritize the proposal based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When receiving a proposal, the system analyzes the proposer's past proposal history and selects the most suitable submission method. The system according to feature 1.

7. The aforementioned reception unit is When receiving proposals, we will filter them based on the proposer's industry and area of ​​expertise. The system according to feature 1.

8. The aforementioned reception unit is When receiving proposals, we will prioritize accepting proposals that are highly relevant, taking into account the proposer's geographical location. The system according to feature 1.

9. The aforementioned reception unit is When receiving proposals, we analyze the proposer's social media activity and accept relevant proposals. The system according to feature 1.

Images