System

Abstract

An object of a system according to an embodiment is to efficiently perform matching for participating in works and activities related to forest protection.SOLUTION: A system includes a registration interface generation unit, a different-industry matching unit, a communication promotion unit, and an information provision unit. The enrollment interface generator may allow a user to easily create an account and enroll their skills and experiences using the generated AI. The different-industry matching unit analyzes the skill and experience of the user and performs matching between different industries. The communication promotion unit promotes communication and information sharing between users. The information providing unit provides information for forest protection.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not efficiently match people to participate in jobs and activities related to forest conservation, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently perform matching for participation in jobs and activities related to forest conservation. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration interface generation unit, a cross-industry matching unit, a communication promotion unit, and an information provision unit. The registration interface generation unit uses a generation AI to allow users to easily create an account and register their own skills and experience. The cross-industry matching unit analyzes the user's skills and experience and performs matching between different industries. The communication promotion unit promotes communication and information sharing between users. The information provision unit provides information for forest conservation. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently perform matching for participation in jobs and activities related to forest protection. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A matching service according to an embodiment of the present invention is a system that allows users to easily create accounts, promotes matching and communication between different industries, and realizes sustainable forest conservation. As a result, the matching service allows users to easily create accounts, promotes matching and communication between different industries, and realizes sustainable forest conservation.

[0029] A matching service according to an embodiment includes a registration interface generation unit, a cross-industry matching unit, a communication promotion unit, and an information provision unit. The registration interface generation unit uses a generation AI to allow users to easily create an account and register their skills and experience. For example, the generation AI analyzes information input by the user and automatically generates an appropriate registration form. Furthermore, when a user inputs "I want to participate in forest conservation activities," the generation AI generates a form for entering related skills and experience based on that information. The cross-industry matching unit analyzes the user's skills and experience and performs cross-industry matching. For example, the generation AI can match IT companies interested in forest conservation with NPOs engaged in forest conservation activities. The generation AI also proposes optimal matches based on the user's registration information. The communication promotion unit promotes communication and information sharing between users. For example, the generation AI suggests related forums and groups based on the user's interests. The generation AI also analyzes information posted by users and automatically shares related information. The information provision unit provides information for sustainable forest conservation. For example, the generation AI collects the latest information and research results on forest conservation and provides them to users. The generation AI also analyzes users' activities and makes suggestions for sustainable forest conservation. As a result, the matching service according to the embodiment allows users to easily create accounts, promotes matching and communication between different industries, and realizes sustainable forest conservation.

[0030] The registration interface generation unit can suggest the optimal registration form based on the user's input, referring to past success stories and failure stories. For example, the generation AI in the registration interface generation unit analyzes the user's input and automatically generates the optimal registration form by referring to past success stories and failure stories. For example, if a user inputs "I want to participate in forest conservation activities," the generation AI uses that information to generate a form for entering related skills and experience. The generation AI also refers to a past database based on the information entered by the user to suggest the most appropriate registration form. For example, it automatically complements the required skills and experience based on data from users who have participated in similar activities in the past. The generation AI also analyzes the user's input in real time, referring to past success stories and failure stories, and dynamically generates the optimal registration form. For example, if a user inputs "I want to launch a forest conservation project," the generation AI uses that information to generate a form for entering the skills and experience required for project management. This improves registration efficiency by suggesting the optimal registration form based on the user's input.

[0031] The registration interface generation unit can analyze the user's input content in real time and provide advice and complementary information as the user enters the information. For example, in the registration interface generation unit, the generation AI analyzes the user's input content in real time and provides advice and complementary information as the user enters the information. For example, if a user enters "I want to participate in forest conservation activities," the generation AI provides advice on entering related skills and experience based on that information. Furthermore, as the user continues to enter information, the generation AI performs analysis in real time and complements the necessary information. For example, if a user enters "I want to start a forest conservation project," the generation AI provides complementary information on entering skills and experience necessary for project management based on that information. Furthermore, the generation AI analyzes the user's input content in real time and dynamically provides advice and complementary information as the user enters the information. For example, if a user enters "I want to participate in forest conservation activities," the generation AI provides advice on entering related skills and experience based on that information. This improves the accuracy and efficiency of registration by analyzing the user's input content in real time and providing advice and complementary information.

[0032] The registration interface generation unit can add a voice input function and use voice recognition technology to enable smooth registration. The registration interface generation unit, for example, adds a voice input function to the registration interface and uses voice recognition technology to enable smooth registration. For example, when a user verbally inputs, "I would like to participate in forest conservation activities," the generation AI generates a form for inputting related skills and experience based on that information. The voice recognition technology is also used to analyze the information input by the user verbally, enabling smooth registration. For example, when a user verbally inputs, "I would like to start a forest conservation project," the generation AI generates a form for inputting skills and experience necessary for project management based on that information. The voice input function is also added to the registration interface and voice recognition technology is used to enable smooth user input. For example, when a user verbally inputs, "I would like to participate in forest conservation activities," the generation AI generates a form for inputting related skills and experience based on that information. In this way, by adding a voice input function and using voice recognition technology, users can smoothly register.

[0033] The registration interface generation unit can automatically complete information at the next registration based on the user's past registration history. The registration interface generation unit adds a function to automatically complete information at the next registration based on the user's past registration history, for example. For example, the skills and experience previously registered by the user are automatically reflected in the input form. In addition, a function is provided to analyze the past registration history and automatically complete information required at the next registration. For example, related skills and experience are automatically entered based on data on forest conservation activities that the user previously participated in. In addition, a system is constructed to automatically complete information at the next registration based on the user's past registration history. For example, information required at the next registration is automatically entered based on information previously registered by the user. This improves the efficiency of registration by automatically completing information at the next registration based on the user's past registration history.

[0034] The cross-industry matching unit can perform matching that takes into account not only the user's skills and experience, but also their values ​​and goals. For example, in the cross-industry matching unit, the generation AI performs matching by taking into account not only the user's skills and experience, but also their values ​​and goals. For example, if a user has the goal of "contributing to sustainable forest conservation," the generation AI proposes a match that matches that goal. The generation AI also analyzes the user's values ​​and goals and performs optimal matching based on them. For example, if a user has the value of "having a strong interest in environmental protection," the generation AI proposes a match that matches those values. We also build a system in which the generation AI performs matching that takes into account not only the user's skills and experience, but also their values ​​and goals. For example, if a user has the goal of "contributing to the local community," the generation AI proposes a match that matches that goal. This allows for more appropriate matching by performing matching that takes into account the user's values ​​and goals.

[0035] The cross-industry matching unit can refer to past successes and failures when matching and propose optimal combinations. For example, when the generation AI is matching, the cross-industry matching unit refers to past successes and failures to propose optimal combinations. For example, it matches users with similar conditions based on data from past successful matches. It also analyzes the user's skills and experience and proposes optimal matches by referring to past successes and failures. For example, it makes suggestions to avoid similar failures based on data from past unsuccessful matches. It also builds a system where the generation AI analyzes past successes and failures and proposes optimal matches based on that. For example, it proposes matches with a high probability of success based on past data. In this way, it proposes optimal matches by referring to past successes and failures.

[0036] The cross-industry matching unit can expand the scope of cross-industry matching and enable international matching. The cross-industry matching unit, for example, uses generation AI to build a system that expands the scope of cross-industry matching and enables international matching. For example, it matches companies and individuals from different countries. In addition, to enable international matching, the generation AI provides a matching function that supports multiple languages. For example, it analyzes information entered by users in different languages ​​and proposes optimal matches. In addition, to expand the scope of cross-industry matching and enable international matching, the generation AI references an international database. For example, it proposes optimal matches based on data on companies and individuals from different countries. This expands the scope of cross-industry matching and enables international matching, thereby realizing more diverse matching.

[0037] The cross-industry matching unit can provide support for proposing joint projects and fundraising based on the matching results. The cross-industry matching unit adds a function for the generation AI to propose joint projects and support fundraising based on the matching results. For example, it proposes joint projects to matched companies and individuals. It also analyzes the matching results and builds a system that supports joint project proposals and fundraising. For example, it proposes fundraising methods to matched companies and individuals. It also provides a function for the generation AI to propose joint projects and support fundraising based on the matching results. For example, it supports matched companies and individuals in planning joint projects. This supports user activities by proposing joint projects and supporting fundraising based on the matching results.

[0038] The communication promotion unit can analyze the content of users' posts and automatically suggest related information and past posts. For example, in the communication promotion unit, the generation AI analyzes the content of users' posts and automatically suggests related information and past posts. For example, when a user posts "new ideas for forest protection," the generation AI suggests past related posts and reference information. In addition, a system is built that analyzes the content of users' posts in real time and automatically suggests related information and past posts. For example, when a user posts "progress of forest protection activities," the generation AI suggests related past posts and reference materials. In addition, the generation AI analyzes the content of users' posts and dynamically suggests related information and past posts. For example, when a user posts "new forest protection project," the generation AI suggests past success stories and related information. In this way, the quality of communication is improved by analyzing the content of users' posts and automatically suggesting related information and past posts.

[0039] The communication promotion unit can suggest optimal communication partners based on the user's communication history. The communication promotion unit adds a function in which the generation AI analyzes the user's communication history and suggests optimal communication partners. For example, it can suggest users with similar conditions based on past successful communication partnerships. It also builds a system that analyzes the user's communication history in real time and suggests optimal communication partners. For example, if a user requests "advice on forest conservation activities," the generation AI can suggest users who have provided similar advice in the past. The generation AI can also analyze the user's communication history and dynamically suggest optimal communication partners. For example, if a user inputs "I would like to participate in a new forest conservation project," the generation AI can suggest users who have participated in similar projects in the past. This improves the quality of communication by suggesting optimal communication partners based on the user's communication history.

[0040] The communication promotion unit can add video conferencing and webinar functions to the communication platform to promote real-time information sharing. The communication promotion unit, for example, adds a video conferencing function to the communication platform to promote real-time information sharing. For example, a user can use video conferencing when holding a "meeting on forest protection activities." A webinar function can also be added to the communication platform to promote real-time information sharing. For example, a user can hold a webinar to share the "latest research results on forest protection." A video conferencing and webinar function can also be added to the communication platform to provide an environment where users can share information in real time. For example, a user can use video conferencing to share the "progress of a new forest protection project." In this way, adding the video conferencing and webinar functions promotes real-time information sharing.

[0041] The communication promotion unit can automatically suggest related news and articles based on the user's interests. For example, the communication promotion unit adds a function in which the generation AI analyzes the user's interests and automatically suggests related news and articles. For example, if a user is looking for the latest news on forest protection, the generation AI will suggest related news articles. We also build a system that analyzes users' interests in real time and automatically suggests related news and articles. For example, if a user is looking for "success stories of forest protection activities," the generation AI will suggest related articles. We also build a system in which the generation AI analyzes the user's interests and automatically dynamically suggests related news and articles. For example, if a user is interested in "new forest protection technologies," the generation AI will suggest related news and articles. This improves the quality of information sharing by suggesting related news and articles based on the user's interests and interests.

[0042] The information provision unit can analyze the user's activity history and suggest optimal forest protection activities. In the information provision unit, for example, the generation AI analyzes the user's activity history and suggests optimal forest protection activities. For example, it suggests the next activity the user should participate in based on data on activities the user has participated in in the past. In addition, a system is constructed that analyzes the user's activity history in real time and suggests optimal forest protection activities. For example, it suggests the next activity the user should participate in based on data on activities the user has successfully participated in in the past. In addition, the generation AI analyzes the user's activity history and dynamically suggests optimal forest protection activities. For example, it suggests the next activity the user should participate in based on data on activities the user has participated in in the past. In this way, the effectiveness of the activities is improved by analyzing the user's activity history and suggesting optimal forest protection activities.

[0043] The information provision unit can automatically collect the latest research results and technical information related to forest protection and provide it to the user. The information provision unit adds a function, for example, that the generation AI automatically collects the latest research results and technical information related to forest protection and provides it to the user. For example, the latest research papers and technical reports are automatically collected and provided to the user. Also, a system is constructed that collects the latest research results and technical information related to forest protection in real time and provides it to the user. For example, the latest research results and technical information is automatically collected and provided to the user. Also, a function is dynamically provided that the generation AI automatically collects the latest research results and technical information related to forest protection and provides it to the user. For example, the latest research results and technical information is automatically collected and provided to the user. In this way, the effectiveness of forest protection activities is improved by automatically collecting the latest research results and technical information and providing it to the user.

[0044] The information provision unit can visualize the results of forest protection activities and provide feedback to the user. The information provision unit adds, for example, a function whereby the generation AI visualizes the results of forest protection activities and provides feedback to the user. For example, the information provision unit displays the results of the activities in graphs and charts and provides feedback to the user. In addition, a system is built that visualizes the results of forest protection activities in real time and provides feedback to the user. For example, the information provision unit visualizes the results of the activities and provides feedback to the user on progress and achievement. In addition, the information provision unit dynamically provides a function whereby the generation AI visualizes the results of forest protection activities and provides feedback to the user. For example, the information provision unit visualizes the results of the activities and suggests specific improvements and next steps to the user. In this way, the information provision unit visualizes the results of forest protection activities and provides feedback to the user, thereby improving the effectiveness of the activities.

[0045] The information provision unit can link forest protection activities in different regions and countries and promote activities from a global perspective. The information provision unit, for example, uses the generation AI to build a system that links forest protection activities in different regions and countries and promotes activities from a global perspective. For example, it integrates activity data from different regions and proposes an activity plan from a global perspective. In addition, in order to link forest protection activities in different regions and countries, the generation AI provides a multilingual linking function. For example, it analyzes information entered in different languages ​​and proposes activities from a global perspective. In addition, the generation AI builds a database to link forest protection activities in different regions and countries and promote activities from a global perspective. For example, it proposes an activity plan from a global perspective based on activity data from different regions. This links forest protection activities in different regions and promotes activities from a global perspective, thereby improving the effectiveness of the activities.

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

[0047] The registration interface generation unit can suggest the optimal registration form based on the user's input, referring to past successes and failures. For example, if a user inputs "I want to participate in forest conservation activities," the generation AI uses that information to generate a form for entering related skills and experience. The generation AI also refers to a past database based on the information entered by the user and suggests the most appropriate registration form. For example, it automatically completes the required skills and experience based on data from users who have participated in similar activities in the past. The generation AI also analyzes the user's input in real time and dynamically generates the optimal registration form by referring to past successes and failures. For example, if a user inputs "I want to launch a forest conservation project," the generation AI uses that information to generate a form for entering the skills and experience required for project management. This improves registration efficiency by suggesting the optimal registration form based on the user's input.

[0048] The registration interface generation unit can analyze the user's input in real time and provide advice and complementary information as the user enters the information. For example, if a user enters "I want to participate in forest conservation activities," the generation AI uses that information to provide advice on entering related skills and experience. Furthermore, as the user continues to enter information, the generation AI performs analysis in real time and complements the necessary information. For example, if a user enters "I want to start a forest conservation project," the generation AI uses that information to provide complementary information on entering the skills and experience necessary for project management. Furthermore, the generation AI analyzes the user's input in real time and dynamically provides advice and complementary information as the user enters the information. For example, if a user enters "I want to participate in forest conservation activities," the generation AI uses that information to provide advice on entering related skills and experience in real time. This improves the accuracy and efficiency of registration by analyzing the user's input in real time and providing advice and complementary information.

[0049] The registration interface generation unit can add a voice input function and use voice recognition technology to enable smooth registration. For example, if a user verbally inputs, "I would like to participate in forest conservation activities," the generation AI uses that information to generate a form for entering related skills and experience. Furthermore, voice recognition technology is used to analyze the information entered by the user's voice, enabling smooth registration. For example, if a user verbally inputs, "I would like to start a forest conservation project," the generation AI uses that information to generate a form for entering skills and experience necessary for project management. Furthermore, a voice input function can be added to the registration interface and voice recognition technology can be used to enable smooth user input. For example, if a user verbally inputs, "I would like to participate in forest conservation activities," the generation AI uses that information to generate a form for entering related skills and experience. Thus, by adding a voice input function and using voice recognition technology, users can smoothly register.

[0050] The registration interface generation unit can automatically complete information at the next registration based on the user's past registration history. For example, the skills and experience previously registered by the user are automatically reflected in the input form. It also provides a function to analyze the past registration history and automatically complete information required at the next registration. For example, related skills and experience are automatically entered based on data on forest conservation activities the user previously participated in. It also builds a system that automatically completes information at the next registration based on the user's past registration history. For example, information required at the next registration is automatically entered based on information previously registered by the user. This improves the efficiency of registration by automatically completing information at the next registration based on the user's past registration history.

[0051] The cross-industry matching unit can perform matching that takes into account not only the user's skills and experience, but also their values ​​and goals. For example, if a user has the goal of "contributing to sustainable forest conservation," the generation AI will propose a match that matches that goal. It also analyzes the user's values ​​and goals and performs optimal matching based on them. For example, if a user has the value of "having a strong interest in environmental protection," the generation AI will propose a match that matches those values. We will also build a system in which the generation AI performs matching that takes into account not only the user's skills and experience, but also their values ​​and goals. For example, if a user has the goal of "contributing to the local community," the generation AI will propose a match that matches that goal. This allows for more appropriate matching by performing matching that takes into account the user's values ​​and goals.

[0052] When matching, the cross-industry matching unit can refer to past successes and failures to propose optimal combinations. For example, it matches users with similar conditions based on data from past successful matches. It also analyzes the user's skills and experience and proposes optimal matches by referring to past successes and failures. For example, it makes suggestions to avoid similar failures based on data from past unsuccessful matches. It also builds a system where the generation AI analyzes past successes and failures and proposes optimal matches based on that. For example, it proposes matches with a high probability of success based on past data. This allows it to propose optimal matches by referring to past successes and failures.

[0053] The cross-industry matching unit can expand the scope of cross-industry matching and enable international matching. For example, a system can be built using generation AI to expand the scope of cross-industry matching and enable international matching. For example, it can match companies and individuals from different countries. To achieve international matching, the generation AI also provides a multilingual matching function. For example, it can analyze information entered by users in different languages ​​and propose optimal matches. To expand the scope of cross-industry matching and enable international matching, the generation AI also references an international database. For example, it can propose optimal matches based on data from companies and individuals from different countries. This expands the scope of cross-industry matching and enables international matching, thereby realizing more diverse matching.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The registration interface generation unit uses the generation AI to allow users to easily create an account and register their skills and experience. For example, the generation AI analyzes the information entered by the user and automatically generates an appropriate registration form. For example, if a user enters "I want to participate in forest conservation activities," the generation AI generates a form for entering related skills and experience based on that information. Step 2: The cross-industry matching unit analyzes the user's skills and experience and performs cross-industry matching. For example, the generation AI can match an IT company interested in forest conservation with an NPO engaged in forest conservation activities. The generation AI also suggests optimal matches based on the user's registered information. Step 3: The communication promotion unit promotes communication and information sharing between users. For example, the generation AI suggests relevant forums and groups based on the user's interests. The generation AI also analyzes information posted by users and automatically shares related information. Step 4: The information provider provides information for sustainable forest conservation. For example, the generator collects the latest information and research results on forest conservation and provides them to users. The generator also analyzes users' activities and makes suggestions for sustainable forest conservation.

[0056] (Example 2) A matching service according to an embodiment of the present invention is a system that allows users to easily create accounts, promotes matching and communication between different industries, and realizes sustainable forest conservation. As a result, the matching service allows users to easily create accounts, promotes matching and communication between different industries, and realizes sustainable forest conservation.

[0057] A matching service according to an embodiment includes a registration interface generation unit, a cross-industry matching unit, a communication promotion unit, and an information provision unit. The registration interface generation unit uses a generation AI to allow users to easily create an account and register their skills and experience. For example, the generation AI analyzes information input by the user and automatically generates an appropriate registration form. Furthermore, when a user inputs "I want to participate in forest conservation activities," the generation AI generates a form for entering related skills and experience based on that information. The cross-industry matching unit analyzes the user's skills and experience and performs cross-industry matching. For example, the generation AI can match IT companies interested in forest conservation with NPOs engaged in forest conservation activities. The generation AI also proposes optimal matches based on the user's registration information. The communication promotion unit promotes communication and information sharing between users. For example, the generation AI suggests related forums and groups based on the user's interests. The generation AI also analyzes information posted by users and automatically shares related information. The information provision unit provides information for sustainable forest conservation. For example, the generation AI collects the latest information and research results on forest conservation and provides them to users. The generation AI also analyzes users' activities and makes suggestions for sustainable forest conservation. As a result, the matching service according to the embodiment allows users to easily create accounts, promotes matching and communication between different industries, and realizes sustainable forest conservation.

[0058] The registration interface generation unit can suggest the optimal registration form based on the user's input, referring to past success stories and failure stories. For example, the generation AI in the registration interface generation unit analyzes the user's input and automatically generates the optimal registration form by referring to past success stories and failure stories. For example, if a user inputs "I want to participate in forest conservation activities," the generation AI uses that information to generate a form for entering related skills and experience. The generation AI also refers to a past database based on the information entered by the user to suggest the most appropriate registration form. For example, it automatically complements the required skills and experience based on data from users who have participated in similar activities in the past. The generation AI also analyzes the user's input in real time, referring to past success stories and failure stories, and dynamically generates the optimal registration form. For example, if a user inputs "I want to launch a forest conservation project," the generation AI uses that information to generate a form for entering the skills and experience required for project management. This improves registration efficiency by suggesting the optimal registration form based on the user's input.

[0059] The registration interface generation unit can analyze the user's input content in real time and provide advice and complementary information as the user enters the information. For example, in the registration interface generation unit, the generation AI analyzes the user's input content in real time and provides advice and complementary information as the user enters the information. For example, if a user enters "I want to participate in forest conservation activities," the generation AI provides advice on entering related skills and experience based on that information. Furthermore, as the user continues to enter information, the generation AI performs analysis in real time and complements the necessary information. For example, if a user enters "I want to start a forest conservation project," the generation AI provides complementary information on entering skills and experience necessary for project management based on that information. Furthermore, the generation AI analyzes the user's input content in real time and dynamically provides advice and complementary information as the user enters the information. For example, if a user enters "I want to participate in forest conservation activities," the generation AI provides advice on entering related skills and experience based on that information. This improves the accuracy and efficiency of registration by analyzing the user's input content in real time and providing advice and complementary information.

[0060] The registered interface generation unit can use the emotion estimation function to analyze the user's emotions when typing and automatically adjust the interface design to reduce stress. The registered interface generation unit, for example, uses the emotion estimation function to analyze the user's emotions when typing and automatically adjust the interface design to reduce stress. For example, if the user is nervous, the generation AI suggests a design and color scheme that will help them relax. The unit also analyzes the user's emotions when typing in real time and dynamically adjusts the interface design to reduce stress. For example, if the user is feeling anxious, the generation AI displays a design or message that gives a sense of security. The unit also uses the emotion estimation function to analyze the user's emotions when typing and automatically changes the interface design to reduce stress. For example, if the user is tired, the generation AI suggests a simple and intuitive design. This improves user satisfaction by analyzing the user's emotions and automatically adjusting the interface design to reduce stress.

[0061] The registration interface generation unit can add a voice input function and use voice recognition technology to enable smooth registration. The registration interface generation unit, for example, adds a voice input function to the registration interface and uses voice recognition technology to enable smooth registration. For example, when a user verbally inputs, "I would like to participate in forest conservation activities," the generation AI generates a form for inputting related skills and experience based on that information. The voice recognition technology is also used to analyze the information input by the user verbally, enabling smooth registration. For example, when a user verbally inputs, "I would like to start a forest conservation project," the generation AI generates a form for inputting skills and experience necessary for project management based on that information. The voice input function is also added to the registration interface and voice recognition technology is used to enable smooth user input. For example, when a user verbally inputs, "I would like to participate in forest conservation activities," the generation AI generates a form for inputting related skills and experience based on that information. In this way, by adding a voice input function and using voice recognition technology, users can smoothly register.

[0062] The registration interface generation unit can automatically complete information at the next registration based on the user's past registration history. The registration interface generation unit adds a function to automatically complete information at the next registration based on the user's past registration history, for example. For example, the skills and experience previously registered by the user are automatically reflected in the input form. In addition, a function is provided to analyze the past registration history and automatically complete information required at the next registration. For example, related skills and experience are automatically entered based on data on forest conservation activities that the user previously participated in. In addition, a system is constructed to automatically complete information at the next registration based on the user's past registration history. For example, information required at the next registration is automatically entered based on information previously registered by the user. This improves the efficiency of registration by automatically completing information at the next registration based on the user's past registration history.

[0063] The registration interface generation unit can use the emotion estimation function to detect in real time any anxiety or questions the user may have during registration and provide appropriate support. The registration interface generation unit, for example, uses the emotion estimation function to detect in real time any anxiety or questions the user may have during registration and provide appropriate support. For example, if the user is feeling anxious, the generation AI provides a message or support that gives a sense of security. The unit also analyzes the user's emotions in real time, detects any anxiety or questions the user may have during registration, and provides support. For example, if the user has a question, the generation AI provides an answer to that question or supplementary information. The unit also uses the emotion estimation function to detect in real time any anxiety or questions the user may have during registration and dynamically provide appropriate support. For example, if the user is feeling stressed, the generation AI provides advice or support that will help them relax. In this way, the unit can detect in real time any anxiety or questions the user may have during registration and provide appropriate support, thereby improving user satisfaction.

[0064] The cross-industry matching unit can perform matching that takes into account not only the user's skills and experience, but also their values ​​and goals. For example, in the cross-industry matching unit, the generation AI performs matching by taking into account not only the user's skills and experience, but also their values ​​and goals. For example, if a user has the goal of "contributing to sustainable forest conservation," the generation AI proposes a match that matches that goal. The generation AI also analyzes the user's values ​​and goals and performs optimal matching based on them. For example, if a user has the value of "having a strong interest in environmental protection," the generation AI proposes a match that matches those values. We also build a system in which the generation AI performs matching that takes into account not only the user's skills and experience, but also their values ​​and goals. For example, if a user has the goal of "contributing to the local community," the generation AI proposes a match that matches that goal. This allows for more appropriate matching by performing matching that takes into account the user's values ​​and goals.

[0065] The cross-industry matching unit can refer to past successes and failures when matching and propose optimal combinations. For example, when the generation AI is matching, the cross-industry matching unit refers to past successes and failures to propose optimal combinations. For example, it matches users with similar conditions based on data from past successful matches. It also analyzes the user's skills and experience and proposes optimal matches by referring to past successes and failures. For example, it makes suggestions to avoid similar failures based on data from past unsuccessful matches. It also builds a system where the generation AI analyzes past successes and failures and proposes optimal matches based on that. For example, it proposes matches with a high probability of success based on past data. In this way, it proposes optimal matches by referring to past successes and failures.

[0066] The cross-industry matching unit can use the emotion estimation function to analyze the emotional compatibility of matching candidates and achieve better matching. The cross-industry matching unit, for example, uses the emotion estimation function to analyze the emotional compatibility of matching candidates and achieve better matching. For example, it proposes candidates with good compatibility based on the user's emotion score. It also analyzes the user's emotions in real time and performs matching taking emotional compatibility into consideration. For example, if the user has positive emotions, it proposes candidates with similarly positive emotions. It also uses the emotion estimation function to analyze the emotional compatibility of matching candidates and builds a system that achieves better matching. For example, it dynamically proposes candidates with good compatibility based on the user's emotion data. In this way, better matching is achieved by analyzing emotional compatibility.

[0067] The cross-industry matching unit can expand the scope of cross-industry matching and enable international matching. The cross-industry matching unit, for example, uses generation AI to build a system that expands the scope of cross-industry matching and enables international matching. For example, it matches companies and individuals from different countries. In addition, to enable international matching, the generation AI provides a matching function that supports multiple languages. For example, it analyzes information entered by users in different languages ​​and proposes optimal matches. In addition, to expand the scope of cross-industry matching and enable international matching, the generation AI references an international database. For example, it proposes optimal matches based on data on companies and individuals from different countries. This expands the scope of cross-industry matching and enables international matching, thereby realizing more diverse matching.

[0068] The cross-industry matching unit can provide support for proposing joint projects and fundraising based on the matching results. The cross-industry matching unit adds a function for the generation AI to propose joint projects and support fundraising based on the matching results. For example, it proposes joint projects to matched companies and individuals. It also analyzes the matching results and builds a system that supports joint project proposals and fundraising. For example, it proposes fundraising methods to matched companies and individuals. It also provides a function for the generation AI to propose joint projects and support fundraising based on the matching results. For example, it supports matched companies and individuals in planning joint projects. This supports user activities by proposing joint projects and supporting fundraising based on the matching results.

[0069] The cross-industry matching unit can use the emotion estimation function to provide advice for improving the quality of communication after matching. The cross-industry matching unit, for example, uses the emotion estimation function to provide advice for improving the quality of communication after matching. For example, the cross-industry matching unit suggests improvements to communication based on the user's emotion score. The cross-industry matching unit also analyzes the user's emotions in real time and provides advice for improving the quality of communication after matching. For example, if the user is feeling stressed, the cross-industry matching unit suggests a communication method that will help the user relax. The cross-industry matching unit also uses the emotion estimation function to dynamically provide advice for improving the quality of communication after matching. For example, the cross-industry matching unit suggests improvements to communication in real time based on the user's emotion data. In this way, the cross-industry matching unit uses the emotion estimation function to provide advice for improving the quality of communication after matching, thereby improving user satisfaction.

[0070] The communication promotion unit can analyze the content of users' posts and automatically suggest related information and past posts. For example, in the communication promotion unit, the generation AI analyzes the content of users' posts and automatically suggests related information and past posts. For example, when a user posts "new ideas for forest protection," the generation AI suggests past related posts and reference information. In addition, a system is built that analyzes the content of users' posts in real time and automatically suggests related information and past posts. For example, when a user posts "progress of forest protection activities," the generation AI suggests related past posts and reference materials. In addition, the generation AI analyzes the content of users' posts and dynamically suggests related information and past posts. For example, when a user posts "new forest protection project," the generation AI suggests past success stories and related information. In this way, the quality of communication is improved by analyzing the content of users' posts and automatically suggesting related information and past posts.

[0071] The communication promotion unit can suggest optimal communication partners based on the user's communication history. The communication promotion unit adds a function in which the generation AI analyzes the user's communication history and suggests optimal communication partners. For example, it can suggest users with similar conditions based on past successful communication partnerships. It also builds a system that analyzes the user's communication history in real time and suggests optimal communication partners. For example, if a user requests "advice on forest conservation activities," the generation AI can suggest users who have provided similar advice in the past. The generation AI can also analyze the user's communication history and dynamically suggest optimal communication partners. For example, if a user inputs "I would like to participate in a new forest conservation project," the generation AI can suggest users who have participated in similar projects in the past. This improves the quality of communication by suggesting optimal communication partners based on the user's communication history.

[0072] The communication promotion unit can use the emotion estimation function to suggest a communication method that corresponds to the user's emotional state. The communication promotion unit, for example, uses the emotion estimation function to suggest a communication method that corresponds to the user's emotional state. For example, if the user is feeling stressed, a communication method that helps the user relax is suggested. In addition, a system is constructed that analyzes the user's emotions in real time and suggests a communication method that corresponds to the emotional state. For example, if the user is feeling anxious, a communication method that gives a sense of security is suggested. In addition, the emotion estimation function is used to dynamically suggest a communication method that corresponds to the user's emotional state. For example, if the user is tired, a simple and intuitive communication method is suggested. In this way, the quality of communication is improved by suggesting a communication method that corresponds to the user's emotional state.

[0073] The communication promotion unit can add video conferencing and webinar functions to the communication platform to promote real-time information sharing. The communication promotion unit, for example, adds a video conferencing function to the communication platform to promote real-time information sharing. For example, a user can use video conferencing when holding a "meeting on forest protection activities." A webinar function can also be added to the communication platform to promote real-time information sharing. For example, a user can hold a webinar to share the "latest research results on forest protection." A video conferencing and webinar function can also be added to the communication platform to provide an environment where users can share information in real time. For example, a user can use video conferencing to share the "progress of a new forest protection project." In this way, adding the video conferencing and webinar functions promotes real-time information sharing.

[0074] The communication promotion unit can automatically suggest related news and articles based on the user's interests. For example, the communication promotion unit adds a function in which the generation AI analyzes the user's interests and automatically suggests related news and articles. For example, if a user is looking for the latest news on forest protection, the generation AI will suggest related news articles. We also build a system that analyzes users' interests in real time and automatically suggests related news and articles. For example, if a user is looking for "success stories of forest protection activities," the generation AI will suggest related articles. We also build a system in which the generation AI analyzes the user's interests and automatically dynamically suggests related news and articles. For example, if a user is interested in "new forest protection technologies," the generation AI will suggest related news and articles. This improves the quality of information sharing by suggesting related news and articles based on the user's interests and interests.

[0075] The communication promotion unit can use the emotion estimation function to provide feedback to improve the quality of communication in real time. The communication promotion unit, for example, uses the emotion estimation function to provide feedback to improve the quality of communication in real time. For example, if a user is feeling stressed, a communication method that will help the user relax is suggested. Also, a system is constructed that analyzes the user's emotions in real time and provides feedback to improve the quality of communication. For example, if a user is feeling anxious, a communication method that gives a sense of security is suggested. Also, the emotion estimation function is used to dynamically provide feedback to improve the quality of communication. For example, if the user is tired, a simple and intuitive communication method is suggested. In this way, by using the emotion estimation function to provide feedback to improve the quality of communication in real time, user satisfaction is improved.

[0076] The information provision unit can analyze the user's activity history and suggest optimal forest protection activities. In the information provision unit, for example, the generation AI analyzes the user's activity history and suggests optimal forest protection activities. For example, it suggests the next activity the user should participate in based on data on activities the user has participated in in the past. In addition, a system is constructed that analyzes the user's activity history in real time and suggests optimal forest protection activities. For example, it suggests the next activity the user should participate in based on data on activities the user has successfully participated in in the past. In addition, the generation AI analyzes the user's activity history and dynamically suggests optimal forest protection activities. For example, it suggests the next activity the user should participate in based on data on activities the user has participated in in the past. In this way, the effectiveness of the activities is improved by analyzing the user's activity history and suggesting optimal forest protection activities.

[0077] The information provision unit can automatically collect the latest research results and technical information related to forest protection and provide it to the user. The information provision unit adds a function, for example, that the generation AI automatically collects the latest research results and technical information related to forest protection and provides it to the user. For example, the latest research papers and technical reports are automatically collected and provided to the user. Also, a system is constructed that collects the latest research results and technical information related to forest protection in real time and provides it to the user. For example, the latest research results and technical information is automatically collected and provided to the user. Also, a function is dynamically provided that the generation AI automatically collects the latest research results and technical information related to forest protection and provides it to the user. For example, the latest research results and technical information is automatically collected and provided to the user. In this way, the effectiveness of forest protection activities is improved by automatically collecting the latest research results and technical information and providing it to the user.

[0078] The information providing unit can use the emotion estimation function to suggest activities according to the user's emotional state, thereby maintaining motivation. The information providing unit, for example, uses the emotion estimation function to suggest activities according to the user's emotional state, thereby maintaining motivation. For example, if the user is feeling stressed, the information providing unit suggests activities that will help the user relax. Furthermore, a system is constructed that analyzes the user's emotions in real time and suggests activities according to the emotional state. For example, if the user is feeling anxious, the information providing unit suggests activities that will give the user a sense of security. Furthermore, the information providing unit uses the emotion estimation function to dynamically suggest activities according to the user's emotional state, thereby maintaining motivation. For example, if the user is tired, the information providing unit suggests activities that will refresh the user. In this way, by suggesting activities according to the user's emotional state, motivation is maintained and the effectiveness of the activities is improved.

[0079] The information provision unit can visualize the results of forest protection activities and provide feedback to the user. The information provision unit adds, for example, a function whereby the generation AI visualizes the results of forest protection activities and provides feedback to the user. For example, the information provision unit displays the results of the activities in graphs and charts and provides feedback to the user. In addition, a system is built that visualizes the results of forest protection activities in real time and provides feedback to the user. For example, the information provision unit visualizes the results of the activities and provides feedback to the user on progress and achievement. In addition, the information provision unit dynamically provides a function whereby the generation AI visualizes the results of forest protection activities and provides feedback to the user. For example, the information provision unit visualizes the results of the activities and suggests specific improvements and next steps to the user. In this way, the information provision unit visualizes the results of forest protection activities and provides feedback to the user, thereby improving the effectiveness of the activities.

[0080] The information provision unit can link forest protection activities in different regions and countries and promote activities from a global perspective. The information provision unit, for example, uses the generation AI to build a system that links forest protection activities in different regions and countries and promotes activities from a global perspective. For example, it integrates activity data from different regions and proposes an activity plan from a global perspective. In addition, in order to link forest protection activities in different regions and countries, the generation AI provides a multilingual linking function. For example, it analyzes information entered in different languages ​​and proposes activities from a global perspective. In addition, the generation AI builds a database to link forest protection activities in different regions and countries and promote activities from a global perspective. For example, it proposes an activity plan from a global perspective based on activity data from different regions. This links forest protection activities in different regions and promotes activities from a global perspective, thereby improving the effectiveness of the activities.

[0081] The information providing unit can use the emotion estimation function to automatically generate and share an activity report based on the user's emotional state. The information providing unit, for example, uses the emotion estimation function to automatically generate and share an activity report based on the user's emotional state. For example, if the user has positive emotions, an activity report reflecting that emotion is generated. Furthermore, a system is constructed that analyzes the user's emotions in real time and automatically generates an activity report based on the emotional state. For example, if the user is feeling stressed, an activity report reflecting that emotion is generated. Furthermore, the emotion estimation function is used to dynamically generate and share an activity report based on the user's emotional state. For example, if the user is feeling anxious, an activity report reflecting that emotion is generated and shared. In this way, the effectiveness of activities is improved by automatically generating and sharing an activity report based on the user's emotional state.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The registration interface generation unit can suggest the optimal registration form based on the user's input, referring to past successes and failures. For example, if a user inputs "I want to participate in forest conservation activities," the generation AI uses that information to generate a form for entering related skills and experience. The generation AI also refers to a past database based on the information entered by the user and suggests the most appropriate registration form. For example, it automatically completes the required skills and experience based on data from users who have participated in similar activities in the past. The generation AI also analyzes the user's input in real time and dynamically generates the optimal registration form by referring to past successes and failures. For example, if a user inputs "I want to launch a forest conservation project," the generation AI uses that information to generate a form for entering the skills and experience required for project management. This improves registration efficiency by suggesting the optimal registration form based on the user's input.

[0084] The registration interface generation unit can analyze the user's input in real time and provide advice and complementary information as the user enters the information. For example, if a user enters "I want to participate in forest conservation activities," the generation AI uses that information to provide advice on entering related skills and experience. Furthermore, as the user continues to enter information, the generation AI performs analysis in real time and complements the necessary information. For example, if a user enters "I want to start a forest conservation project," the generation AI uses that information to provide complementary information on entering the skills and experience necessary for project management. Furthermore, the generation AI analyzes the user's input in real time and dynamically provides advice and complementary information as the user enters the information. For example, if a user enters "I want to participate in forest conservation activities," the generation AI uses that information to provide advice on entering related skills and experience in real time. This improves the accuracy and efficiency of registration by analyzing the user's input in real time and providing advice and complementary information.

[0085] The registered interface generation unit uses the emotion estimation function to analyze the user's emotions when typing and automatically adjusts the interface design to reduce stress. For example, if the user is nervous, the generation AI will suggest a relaxing design and color scheme. The unit also analyzes the user's emotions in real time when typing and dynamically adjusts the interface design to reduce stress. For example, if the user is feeling anxious, the generation AI will display a design or message that gives a sense of security. The unit also uses the emotion estimation function to analyze the user's emotions when typing and automatically changes the interface design to reduce stress. For example, if the user is tired, the generation AI will suggest a simple and intuitive design. This improves user satisfaction by analyzing the user's emotions and automatically adjusting the interface design to reduce stress.

[0086] The registration interface generation unit can add a voice input function and use voice recognition technology to enable smooth registration. For example, if a user verbally inputs, "I would like to participate in forest conservation activities," the generation AI uses that information to generate a form for entering related skills and experience. Furthermore, voice recognition technology is used to analyze the information entered by the user's voice, enabling smooth registration. For example, if a user verbally inputs, "I would like to start a forest conservation project," the generation AI uses that information to generate a form for entering skills and experience necessary for project management. Furthermore, a voice input function can be added to the registration interface and voice recognition technology can be used to enable smooth user input. For example, if a user verbally inputs, "I would like to participate in forest conservation activities," the generation AI uses that information to generate a form for entering related skills and experience. Thus, by adding a voice input function and using voice recognition technology, users can smoothly register.

[0087] The registration interface generation unit can automatically complete information at the next registration based on the user's past registration history. For example, the skills and experience previously registered by the user are automatically reflected in the input form. It also provides a function to analyze the past registration history and automatically complete information required at the next registration. For example, related skills and experience are automatically entered based on data on forest conservation activities the user previously participated in. It also builds a system that automatically completes information at the next registration based on the user's past registration history. For example, information required at the next registration is automatically entered based on information previously registered by the user. This improves the efficiency of registration by automatically completing information at the next registration based on the user's past registration history.

[0088] The registration interface generation unit uses the emotion estimation function to detect in real time any anxieties or questions the user may have when registering and provide appropriate support. For example, if the user is feeling anxious, the generation AI will provide reassuring messages and support. The unit also analyzes the user's emotions in real time, detects any anxieties or questions the user may have when registering, and provides support. For example, if the user has a question, the generation AI will provide an answer or supplementary information to that question. The unit also uses the emotion estimation function to detect in real time any anxieties or questions the user may have when registering and dynamically provide appropriate support. For example, if the user is feeling stressed, the generation AI will provide advice and support to help them relax. This allows the unit to detect in real time any anxieties or questions the user may have when registering and provide appropriate support, thereby improving user satisfaction.

[0089] The cross-industry matching unit can perform matching that takes into account not only the user's skills and experience, but also their values ​​and goals. For example, if a user has the goal of "contributing to sustainable forest conservation," the generation AI will propose a match that matches that goal. It also analyzes the user's values ​​and goals and performs optimal matching based on them. For example, if a user has the value of "having a strong interest in environmental protection," the generation AI will propose a match that matches those values. We will also build a system in which the generation AI performs matching that takes into account not only the user's skills and experience, but also their values ​​and goals. For example, if a user has the goal of "contributing to the local community," the generation AI will propose a match that matches that goal. This allows for more appropriate matching by performing matching that takes into account the user's values ​​and goals.

[0090] When matching, the cross-industry matching unit can refer to past successes and failures to propose optimal combinations. For example, it matches users with similar conditions based on data from past successful matches. It also analyzes the user's skills and experience and proposes optimal matches by referring to past successes and failures. For example, it makes suggestions to avoid similar failures based on data from past unsuccessful matches. It also builds a system where the generation AI analyzes past successes and failures and proposes optimal matches based on that. For example, it proposes matches with a high probability of success based on past data. This allows it to propose optimal matches by referring to past successes and failures.

[0091] The cross-industry matching unit can use the emotion estimation function to analyze the emotional compatibility of matching candidates and achieve better matching. For example, it can suggest candidates with good compatibility based on the user's emotion score. It can also analyze the user's emotions in real time and perform matching taking emotional compatibility into consideration. For example, if the user has positive emotions, it can suggest candidates with similarly positive emotions. It can also use the emotion estimation function to analyze the emotional compatibility of matching candidates and build a system that achieves better matching. For example, it can dynamically suggest candidates with good compatibility based on the user's emotion data. This allows for better matching by analyzing emotional compatibility.

[0092] The cross-industry matching unit can expand the scope of cross-industry matching and enable international matching. For example, a system can be built using generation AI to expand the scope of cross-industry matching and enable international matching. For example, it can match companies and individuals from different countries. To achieve international matching, the generation AI also provides a multilingual matching function. For example, it can analyze information entered by users in different languages ​​and propose optimal matches. To expand the scope of cross-industry matching and enable international matching, the generation AI also references an international database. For example, it can propose optimal matches based on data from companies and individuals from different countries. This expands the scope of cross-industry matching and enables international matching, thereby realizing more diverse matching.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The registration interface generation unit uses the generation AI to allow users to easily create an account and register their skills and experience. For example, the generation AI analyzes the information entered by the user and automatically generates an appropriate registration form. For example, if a user enters "I want to participate in forest conservation activities," the generation AI generates a form for entering related skills and experience based on that information. Step 2: The cross-industry matching unit analyzes the user's skills and experience and performs cross-industry matching. For example, the generation AI can match an IT company interested in forest conservation with an NPO engaged in forest conservation activities. The generation AI also suggests optimal matches based on the user's registered information. Step 3: The communication promotion unit promotes communication and information sharing between users. For example, the generation AI suggests relevant forums and groups based on the user's interests. The generation AI also analyzes information posted by users and automatically shares related information. Step 4: The information provider provides information for sustainable forest conservation. For example, the generator collects the latest information and research results on forest conservation and provides them to users. The generator also analyzes users' activities and makes suggestions for sustainable forest conservation.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, a 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.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0136] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0139] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0145] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0146] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0147] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0148] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0149] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0150] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0151] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0152] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0153] 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.

[0154] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0155] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0156] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0157] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0158] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0159] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0160] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0161] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A registration interface generation unit that uses generation AI to allow users to easily create an account and register their skills and experience. a cross-industry matching unit that analyzes the skills and experience of the user and performs cross-industry matching; a communication promotion unit that promotes communication and information sharing between the users; an information providing unit that provides information for forest protection; A system characterized by:

2. The registration interface generation unit Add voice input function and use voice recognition technology to achieve smooth registration 2. The system of claim 1.

3. The cross-industry matching unit Matching takes into account not only the user's skills and experience, but also their values ​​and goals.

2. The system of claim 1.

4. The communication promotion unit Analyze the user's posts and automatically suggest related information and past posts.

2. The system of claim 1.

5. The information providing unit Analyzing the user's activity history and suggesting optimal forest conservation activities 2. The system of claim 1.

6. The registration interface generation unit Analyze the user's emotions when inputting data and automatically adjust the interface design to reduce stress.

2. The system of claim 1.

Images