System

Abstract

An object of the system according to the embodiment is to promote communication and information sharing between users by efficiently matching works and projects related to forest protection.SOLUTION: A system includes a reception part, an information analysis part, a matching part, a communication promotion part, and an information provision part. The reception unit inputs information of a user. The information analysis unit analyzes the information input by the reception unit. The matching unit matches the project and the work based on the information analyzed by the information analysis unit. The communication promotion unit promotes communication between users. The information providing unit provides information on improvement of energy efficiency and countermeasures against global warming.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 have not been able to efficiently match jobs and projects related to forest protection, and have not sufficiently promoted communication and information sharing between users.

[0005] The system according to the embodiment aims to efficiently match jobs and projects related to forest protection and promote communication and information sharing between users. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an information analysis unit, a matching unit, a communication promotion unit, and an information provision unit. The reception unit inputs user information. The information analysis unit analyzes the information input by the reception unit. The matching unit matches projects and jobs based on the information analyzed by the information analysis unit. The communication promotion unit promotes communication between users. The information provision unit provides information on improving energy efficiency and combating global warming. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently match jobs and projects related to forest protection and promote communication and information sharing between users. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A matching system according to an embodiment of the present invention efficiently analyzes user information, matches optimal projects and jobs, promotes communication, and provides information. This matching system allows users to input information such as their region, skills, and interests, and a generation AI analyzes the input information to match participants with various jobs and projects related to forest conservation. For example, the system connects people interested in jobs such as forest monitoring and surveying, reforestation activities, forestry product processing, and ecotourism management to projects in their respective regions. It also provides idea contests and information exchange forums to promote communication between users. Users can interact with others interested in the same topic and share their knowledge and experiences. Furthermore, the system provides information on improving energy efficiency and combating global warming, helping users to take sustainable actions. By expanding jobs related to forest conservation, the matching system increases demand for jobs directly related to environmental management and energy and global warming countermeasures, promoting sustainable forest conservation in society. This allows the matching system to efficiently analyze user information, match optimal projects and jobs, promote communication, and provide information. For example, users can input information such as their region, skills, and interests, and the AI ​​will analyze the information and match participants with various jobs and projects related to forest conservation. This will expand the scope of jobs related to forest conservation, increasing demand for jobs directly related to environmental management and energy and global warming countermeasures, and promoting sustainable forest conservation in society.

[0029] A matching system according to an embodiment includes a reception unit, an information analysis unit, a matching unit, a communication promotion unit, and an information provision unit. The reception unit inputs user information. The user information includes, but is not limited to, for example, name, address, skills, and areas of interest. The reception unit allows the user to input information such as region, skills, and interests. The information analysis unit analyzes the information input by the reception unit using a generation AI. The generation AI analyzes the information using, for example, a natural language processing model or an image generation model. For example, the generation AI searches for projects and jobs in each region based on the user's profile information and performs optimal matching. The matching unit matches projects and jobs based on the information analyzed by the information analysis unit. Matching is performed based on criteria such as skill matching and degree of agreement with project requirements. For example, the matching unit matches a user interested in forest monitoring and research to a local monitoring project. The communication promotion unit provides a forum for promoting information exchange between users. The forum has, for example, thread-based, real-time chat, and Q&A functions. For example, the communication promotion unit enables the user to interact with other people who are interested in the same topic and share knowledge and experiences. The information provision unit provides information on improving energy efficiency and global warming countermeasures. The provided information includes, for example, energy saving methods, renewable energy utilization methods, and the latest information on global warming countermeasures. For example, the information provision unit enables the user to learn how to reduce energy consumption at home. As a result, the matching system according to the embodiment can efficiently analyze user information, match optimal projects and jobs, promote communication, and provide information.

[0030] The reception unit can input information about the user's region, skills, interests, etc. Regions include, but are not limited to, city, ward, town, village, prefecture, country, etc. Skills include, but are not limited to, programming skills, project management skills, language skills, etc. Interests include, but are not limited to, technical interests, business interests, hobbies, etc. The reception unit can, for example, allow the user to input detailed profile information about themselves. For example, if the user is interested in forest monitoring or research, the user can input this information. This allows for more accurate matching by inputting detailed user information. Some or all of the above-described processing in the reception unit can be performed, for example, using AI, or can be performed without using AI. For example, the reception unit can input the user's input information into a generation AI, which can then analyze the information.

[0031] The information analysis unit can analyze the information input by the reception unit using a generation AI. Examples of the generation AI include, but are not limited to, natural language processing models and image generation models. For example, the information analysis unit uses the generation AI to search for projects and jobs in each region based on the user's profile information and perform optimal matching. For example, the generation AI analyzes the user's input information and suggests optimal projects and jobs. The information analysis unit can also use the generation AI to match optimal projects and jobs based on the user's skills and areas of interest. For example, the generation AI analyzes the user's skills and areas of interest and suggests optimal projects and jobs based on that. This improves the accuracy of information analysis by using the generation AI. Some or all of the above-mentioned processing in the information analysis unit is performed using the generation AI. For example, the information analysis unit uses the generation AI to analyze the user's input information and suggest optimal projects and jobs.

[0032] The matching unit can match projects and jobs based on the information analyzed by the information analysis unit. Projects and jobs include, but are not limited to, short-term projects, long-term projects, and freelance work. The matching unit performs optimal matching based on criteria such as skill matching and degree of match with project requirements. For example, the matching unit matches a user interested in forest monitoring and research to a local monitoring project. The matching unit can also suggest optimal projects and jobs based on the user's skills and areas of interest. For example, the matching unit analyzes the user's skills and areas of interest and suggests optimal projects and jobs based on the analyzed information. This enables optimal matching based on the analyzed information. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without AI. For example, the matching unit inputs the information analyzed by the information analysis unit into a generation AI, which then suggests optimal projects and jobs.

[0033] The communication promotion unit can provide a forum for promoting information exchange between users. Examples of forums include, but are not limited to, thread formats, real-time chat, and Q&A formats. The communication promotion unit, for example, enables users to interact with other people interested in the same topic and share their knowledge and experiences. For example, the communication promotion unit may hold an idea contest related to forest protection and award users with outstanding ideas. The communication promotion unit can also provide a forum for promoting information exchange between users. For example, the communication promotion unit enables users to interact with other people interested in the same topic and share their knowledge and experiences. This promotes information exchange between users. Some or all of the above-described processing in the communication promotion unit may be performed using, or without, AI. For example, the communication promotion unit inputs user information into a generation AI, which then suggests an optimal forum.

[0034] The information providing unit can provide information on improving energy efficiency and global warming countermeasures. Examples of the provided information include, but are not limited to, energy conservation methods, renewable energy utilization methods, and the latest information on global warming countermeasures. For example, the information providing unit enables a user to learn how to reduce energy consumption at home. For example, the information providing unit provides information on improving energy efficiency to support the user in taking sustainable actions. The information providing unit can also provide information on global warming countermeasures to support the user in taking sustainable actions. For example, the information providing unit provides information on improving energy efficiency to support the user in learning how to reduce energy consumption at home. This provides information on improving energy efficiency and global warming countermeasures. Some or all of the above-described processing in the information providing unit may be performed using, or without, AI. For example, the information providing unit inputs information on improving energy efficiency and global warming countermeasures into a generation AI, which then provides optimal information.

[0035] The reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit automatically displays information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest information that will be used in a specific time period based on the user's past input history. For example, the reception unit analyzes the user's past input history and selects the optimal input method. In this way, the optimal input method can be provided by analyzing the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's past input history to a generation AI, which then selects the optimal input method.

[0036] The reception unit may filter information based on the user's current project or area of ​​interest when inputting the information. For example, the reception unit may preferentially display information related to projects in which the user is currently participating. The reception unit may also filter and display related information based on the user's area of ​​interest. The reception unit may also suggest highly relevant information by referring to the user's past project history. For example, the reception unit may filter information based on the user's current project or area of ​​interest when inputting the information. This makes it possible to provide highly relevant information by filtering the information based on the current project or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input information about the user's current project or area of ​​interest to a generation AI, which then performs filtering.

[0037] The reception unit can select the optimal input means depending on the user's input method when inputting information. For example, if the user selects voice input, the reception unit inputs information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also support keyboard input. Furthermore, if the user selects image input, the reception unit can also input information using image recognition technology. For example, the reception unit selects the optimal input means depending on the user's input method when inputting information. This makes information input more efficient by providing the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's input method to a generation AI, which then selects the optimal input means.

[0038] The reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information when inputting information. For example, the reception unit prioritizes inputting information related to the area where the user is currently located. The reception unit can also suggest related projects or jobs based on the user's geographical location information. The reception unit can also update the user's location information in real time to provide optimal information. For example, the reception unit prioritizes inputting highly relevant information in consideration of the user's geographical location information when inputting information. This makes it possible to provide highly relevant information by taking the geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's geographical location information to a generation AI, which then suggests highly relevant information.

[0039] The reception unit can analyze the user's social media activity and input relevant information when inputting information. The reception unit, for example, inputs information related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and input relevant information. The reception unit can also input relevant information by referring to the activities of the user's friends on social media. For example, the reception unit analyzes the user's social media activity and inputs relevant information when inputting information. In this way, highly relevant information can be provided by analyzing social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs data on the user's social media activity to a generation AI, which then suggests relevant information.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting information. The reception unit can improve the input method based on, for example, feedback provided by the user in the past. The reception unit can also provide an easier-to-use input interface by reflecting the user's feedback. The reception unit can also optimize the input procedure by referring to the user's feedback. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting information. In this way, an easier-to-use input method can be provided by reflecting the past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's past feedback into a generation AI, and the generation AI customizes the input method.

[0041] The information analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the information analysis unit performs a detailed analysis on information with a high level of importance. The information analysis unit can also perform a concise analysis on information with a low level of importance. The information analysis unit can also determine the priority of the analysis based on the importance of the information. For example, the information analysis unit adjusts the level of detail of the analysis based on the importance of the information. In this way, by adjusting the level of detail of the analysis according to the importance of the information, it is possible to perform a detailed analysis on important information. Some or all of the above-mentioned processing in the information analysis unit is performed using a generation AI. For example, the information analysis unit evaluates the importance of the information using the generation AI and adjusts the level of detail of the analysis based on the result.

[0042] The information analysis unit can apply different analysis algorithms depending on the category of information. For example, the information analysis unit applies a dedicated analysis algorithm to information related to environmental protection. The information analysis unit can also apply a different analysis algorithm to information related to energy efficiency. The information analysis unit can also apply a dedicated analysis algorithm to information related to measures against global warming. For example, the information analysis unit applies different analysis algorithms depending on the category of information. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the information analysis unit is performed using a generation AI. For example, the information analysis unit determines the category of information using the generation AI and applies an analysis algorithm according to that category.

[0043] The information analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the information analysis unit adjusts the analysis algorithm based on the user's past analysis results. The information analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The information analysis unit can also analyze the user's past analysis results and propose an optimal analysis method. For example, the information analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the information analysis unit is performed using a generation AI. For example, the information analysis unit analyzes the user's past analysis results using the generation AI and adjusts the analysis algorithm based on the results.

[0044] The information analysis unit can determine the priority of analysis based on the time of submission of information. For example, the information analysis unit prioritizes analysis of the most recent information. The information analysis unit can also postpone analysis of information that was submitted earlier. The information analysis unit can also determine the priority of analysis based on the time of submission. For example, the information analysis unit determines the priority of analysis based on the time of submission of information. In this way, by determining the priority of analysis based on the time of submission, the most recent information can be analyzed preferentially. Some or all of the above-mentioned processing in the information analysis unit is performed using a generation AI. For example, the information analysis unit uses a generation AI to evaluate the time of submission of information and determine the priority of analysis based on the result.

[0045] The information analysis unit can adjust the order of analysis based on the relevance of the information. For example, the information analysis unit prioritizes analysis of highly relevant information. The information analysis unit can also postpone analysis of less relevant information. The information analysis unit can also adjust the order of analysis based on the relevance of the information. For example, the information analysis unit adjusts the order of analysis based on the relevance of the information. In this way, by adjusting the order of analysis based on the relevance of the information, highly relevant information can be analyzed preferentially. Some or all of the above-mentioned processing in the information analysis unit is performed using a generation AI. For example, the information analysis unit uses a generation AI to evaluate the relevance of the information and adjust the order of analysis based on the result.

[0046] The information analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the information analysis unit can provide analysis results that make heavy use of technical terms. Furthermore, if the user does not have technical expertise, the information analysis unit can also provide analysis results in easy-to-understand language. Furthermore, the information analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the information analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the information analysis unit is performed using a generation AI. For example, the information analysis unit evaluates the user's level of expertise using a generation AI and adjusts the use of technical terms in the analysis based on the result.

[0047] The matching unit can improve the accuracy of matching by taking into account the interrelationships of information. The matching unit performs optimal matching based on, for example, related information. The matching unit can also analyze the interrelationships of information and improve the accuracy of matching. The matching unit can also propose optimal matching by taking into account the interrelationships of information. For example, the matching unit improves the accuracy of matching by taking into account the interrelationships of information. In this way, the accuracy of matching is improved by taking into account the interrelationships of information. Some or all of the above-mentioned processing in the matching unit is performed using a generation AI. For example, the matching unit analyzes the interrelationships of information using the generation AI and proposes optimal matching based on the results.

[0048] The matching unit can perform matching by taking into account attribute information of the information submitter. The matching unit performs optimal matching by taking into account, for example, the skills and experience of the information submitter. The matching unit can also perform optimal matching by taking into account regional information of the information submitter. The matching unit can also perform optimal matching by taking into account the areas of interest of the information submitter. For example, the matching unit performs matching by taking into account attribute information of the information submitter. This makes it possible to perform more appropriate matching by taking into account the attribute information of the submitter. Some or all of the above-mentioned processing in the matching unit is performed using a generation AI. For example, the matching unit analyzes the attribute information of the information submitter using the generation AI and proposes optimal matching based on the results.

[0049] The matching unit can weight the matching based on the frequency of information submission. For example, the matching unit prioritizes matching of information that is submitted more frequently. The matching unit can also postpone matching of information that is submitted less frequently. The matching unit can also weight the matching based on the submission frequency. For example, the matching unit weights the matching based on the submission frequency of the information. By weighting based on the submission frequency, more appropriate matching becomes possible. Some or all of the above-mentioned processing in the matching unit is performed using a generation AI. For example, the matching unit evaluates the submission frequency of information using the generation AI and weights the matching based on the result.

[0050] The matching unit can perform matching taking into account the geographical distribution of information. For example, the matching unit prioritizes matching projects close to the user's current location. The matching unit can also perform optimal matching based on information that is highly geographically relevant. The matching unit can also propose optimal projects to the user taking into account the geographical distribution. For example, the matching unit performs matching taking into account the geographical distribution of information. This enables more appropriate matching by taking geographical distribution into account. Some or all of the above-mentioned processing in the matching unit is performed using a generation AI. For example, the matching unit analyzes the geographical distribution of information using the generation AI and proposes optimal matching based on the results.

[0051] The matching unit can improve the accuracy of matching by referring to literature related to the information. The matching unit, for example, performs optimal matching based on related literature. The matching unit can also improve the accuracy of matching by referring to literature related to the information. The matching unit can also propose optimal matching by taking related literature into consideration. For example, the matching unit improves the accuracy of matching by referring to literature related to the information. In this way, the accuracy of matching is improved by referring to related literature. Some or all of the above-mentioned processing in the matching unit is performed using a generation AI. For example, the matching unit analyzes literature related to the information using a generation AI and proposes optimal matching based on the results.

[0052] The matching unit can perform matching taking into account the market value of the information. For example, the matching unit prioritizes matching of information with high market value. The matching unit can also perform optimal matching based on market value. The matching unit can also propose optimal projects to users taking market value into account. For example, the matching unit performs matching taking into account the market value of the information. This makes it possible to perform more appropriate matching by taking market value into account. Some or all of the above-mentioned processing in the matching unit is performed using a generation AI. For example, the matching unit evaluates the market value of the information using the generation AI and proposes optimal matching based on the results.

[0053] The communication promotion unit can predict current communication by referring to past communication data. For example, the communication promotion unit suggests an optimal communication method based on the past communication data. The communication promotion unit can also predict current communication by referring to past data. The communication promotion unit can also analyze past communication patterns and provide an optimal communication method. For example, the communication promotion unit predicts current communication by referring to past communication data. In this way, by referring to the past data, it is possible to predict current communication and provide an optimal method. Some or all of the above-mentioned processing in the communication promotion unit is performed using a generation AI. For example, the communication promotion unit analyzes past communication data using a generation AI and predicts current communication based on the results.

[0054] The communication promotion unit can apply different communication methods to different information categories. For example, the communication promotion unit applies a dedicated communication method to information related to environmental protection. The communication promotion unit can also apply a different communication method to information related to energy efficiency. The communication promotion unit can also apply a dedicated communication method to information related to global warming countermeasures. For example, the communication promotion unit applies different communication methods to different information categories. This improves the accuracy of communication by applying the optimal communication method according to the information category. Some or all of the above-mentioned processing in the communication promotion unit is performed using a generation AI. For example, the communication promotion unit determines the category of information using the generation AI and applies a communication method according to that category.

[0055] The communication promotion unit can analyze communication taking into account attribute information of the information submitter. The communication promotion unit can propose the optimal communication method taking into account, for example, the skills and experience of the information submitter. The communication promotion unit can also propose the optimal communication method taking into account regional information of the information submitter. The communication promotion unit can also propose the optimal communication method taking into account the information submitter's areas of interest. For example, the communication promotion unit analyzes communication taking into account attribute information of the information submitter. This enables more appropriate communication by taking into account the submitter's attribute information. Some or all of the above-mentioned processing in the communication promotion unit is performed using a generation AI. For example, the communication promotion unit analyzes attribute information of the information submitter using a generation AI and proposes the optimal communication method based on the results.

[0056] The communication promotion unit can analyze changes in communication based on the time of information submission. For example, the communication promotion unit prioritizes reflecting information that was submitted recently in the communication. The communication promotion unit can also reflect information that was submitted older in the communication later. The communication promotion unit can also analyze changes in communication based on the time of submission. For example, the communication promotion unit analyzes changes in communication based on the time of information submission. In this way, by analyzing changes in communication based on the time of submission, more appropriate information can be provided. Some or all of the above-mentioned processing in the communication promotion unit is performed using a generation AI. For example, the communication promotion unit evaluates the time of information submission using a generation AI, and analyzes changes in communication based on the results.

[0057] The communication promotion unit can analyze communication by referring to market data related to the information. The communication promotion unit, for example, proposes an optimal communication method based on the related market data. The communication promotion unit can also improve the accuracy of communication by referring to market data. The communication promotion unit can also provide an optimal communication method by taking the related market data into consideration. For example, the communication promotion unit analyzes communication by referring to market data related to the information. As a result, the accuracy of communication is improved by referring to the related market data. Some or all of the above-mentioned processing in the communication promotion unit is performed using a generation AI. For example, the communication promotion unit analyzes market data related to the information using a generation AI, and proposes an optimal communication method based on the results.

[0058] The communication promotion unit can analyze communication taking into account the technical maturity of the information. For example, the communication promotion unit prioritizes reflecting technically mature information in communication. The communication promotion unit can also reflect technically immature information in communication later. The communication promotion unit can also provide an optimal communication method taking into account the technical maturity. For example, the communication promotion unit analyzes communication taking into account the technical maturity of the information. This enables more appropriate communication by taking into account the technical maturity. Some or all of the above-mentioned processing in the communication promotion unit is performed using a generative AI. For example, the communication promotion unit uses a generative AI to evaluate the technical maturity of the information and propose an optimal communication method based on the results.

[0059] The information providing unit can improve the accuracy of the information provided by taking into account the interrelationships between information. The information providing unit provides optimal information based on, for example, related information. The information providing unit can also analyze the interrelationships between information and improve the accuracy of the information provided. The information providing unit can also provide optimal information by taking into account the interrelationships between information. For example, the information providing unit improves the accuracy of the information provided by taking into account the interrelationships between information. As a result, the accuracy of the information provided is improved by taking into account the interrelationships between information. Some or all of the above-mentioned processing in the information providing unit is performed using a generation AI. For example, the information providing unit analyzes the interrelationships between information using the generation AI and provides optimal information based on the results.

[0060] The information providing unit can provide information taking into consideration the attribute information of the information submitter. The information providing unit can provide the most appropriate information taking into consideration, for example, the skills and experience of the information submitter. The information providing unit can also provide the most appropriate information taking into consideration the regional information of the information submitter. The information providing unit can also provide the most appropriate information taking into consideration the areas of interest of the information submitter. For example, the information providing unit provides information taking into consideration the attribute information of the information submitter. In this way, more appropriate information can be provided by taking into consideration the attribute information of the submitter. Some or all of the above-mentioned processing in the information providing unit is performed using a generation AI. For example, the information providing unit analyzes the attribute information of the information submitter using the generation AI and provides the most appropriate information based on the results.

[0061] The information providing unit can weight the provision of information based on the frequency of information submission. For example, the information providing unit provides information with a high submission frequency preferentially. The information providing unit can also provide information with a low submission frequency later. The information providing unit can also weight the provision of information based on the submission frequency. For example, the information providing unit weights the provision of information based on the submission frequency. In this way, by weighting based on the submission frequency, more appropriate information can be provided. Some or all of the above-mentioned processing in the information providing unit is performed using a generation AI. For example, the information providing unit evaluates the submission frequency of information using the generation AI, and weights the provision of information based on the result.

[0062] The information providing unit can provide information taking into account the geographical distribution of the information. For example, the information providing unit can prioritize providing information related to the user's current location. The information providing unit can also provide optimal information based on information that is highly geographically relevant. The information providing unit can also provide optimal information to the user taking into account the geographical distribution. For example, the information providing unit provides information taking into account the geographical distribution. This makes it possible to provide more appropriate information by taking the geographical distribution into account. Some or all of the above-mentioned processing in the information providing unit is performed using a generation AI. For example, the information providing unit analyzes the geographical distribution of the information using the generation AI and provides optimal information based on the results.

[0063] The information providing unit can improve the accuracy of the information provided by referring to literature related to the information. The information providing unit, for example, provides optimal information based on related literature. The information providing unit can also improve the accuracy of the information provided by referring to literature related to the information. The information providing unit can also provide optimal information by taking related literature into consideration. For example, the information providing unit improves the accuracy of the information provided by referring to literature related to the information. As a result, the accuracy of the information provided is improved by referring to the related literature. Some or all of the above-mentioned processing in the information providing unit is performed using a generation AI. For example, the information providing unit analyzes literature related to the information using the generation AI, and provides optimal information based on the results.

[0064] The information providing unit can provide information taking into consideration the market value of the information. For example, the information providing unit provides information with a high market value preferentially. The information providing unit can also provide optimal information based on market value. The information providing unit can also provide optimal information to a user taking market value into consideration. For example, the information providing unit provides information taking into consideration the market value of the information. In this way, more appropriate information can be provided by taking market value into consideration. Some or all of the above-mentioned processing in the information providing unit is performed using a generation AI. For example, the information providing unit evaluates the market value of the information using the generation AI and provides optimal information based on the result.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The reception unit can analyze the user's past behavioral history and suggest the optimal timing for inputting information. For example, if the user has tended to input information during a specific time period in the past, a notification can be sent during that time period to prompt the user to input information. Also, if the user has been active on a specific day of the week in the past, the reception unit can suggest information input that is tailored to that day. Furthermore, the reception unit can suggest the optimal input method (voice, text, etc.) based on the user's past behavioral patterns. This allows for efficient information input by optimizing the timing for inputting information based on the user's behavioral patterns.

[0067] The matching unit can analyze the user's past matching history and propose optimal matching criteria. For example, it can apply similar criteria based on the user's past successful matching patterns. It can also propose different criteria to avoid matching patterns that the user was dissatisfied with in the past. Furthermore, it can also customize the matching criteria by referring to the user's past feedback. In this way, it is possible to improve user satisfaction by proposing optimal matching criteria based on the user's past matching history.

[0068] The information provision unit can analyze the user's past information browsing history and suggest the optimal timing for providing information. For example, if the user has tended to browse information during a specific time period in the past, the information provision unit can provide information tailored to that time period. Also, if the user has often browsed information on a specific day of the week in the past, the information provision unit can provide information tailored to that day of the week. Furthermore, based on the user's past browsing history, the information provision unit can prioritize the provision of highly relevant information. This can improve user convenience by suggesting the optimal timing for providing information based on the user's past browsing history.

[0069] The information analysis unit can optimize the analysis algorithm based on the user's past analysis results. For example, it can prioritize the use of algorithms for analysis results that the user has previously given a high rating. It can also improve algorithms for analysis results that the user has previously given a low rating. Furthermore, it can also customize the analysis algorithm by referring to the user's past feedback. In this way, by optimizing the analysis algorithm based on past analysis results, it is possible to improve the accuracy of the analysis and user satisfaction.

[0070] The communication promotion unit can analyze the user's past communication history and suggest the optimal communication method. For example, it can prioritize suggestions of communication methods (chat, email, etc.) that the user has used favorably in the past. It can also suggest similar methods based on the user's successful communication patterns in the past. It can also customize communication methods by referring to the user's past feedback. In this way, it is possible to improve user satisfaction by suggesting the optimal communication method based on the user's past communication history.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception unit inputs user information. The user information includes name, address, skills, areas of interest, etc. For example, the user can input information such as region, skills, and interests. Step 2: The information analysis unit uses the generation AI to analyze the information entered by the reception unit. The generation AI analyzes the information using natural language processing models, image generation models, etc. For example, it searches for projects and jobs in each region based on the user's profile information and performs optimal matching. Step 3: The matching department matches projects and jobs based on the information analyzed by the information analysis department. Matching is based on criteria such as skill matching and degree of match with project requirements. For example, a user interested in forest monitoring and research can be matched with a local monitoring project. Step 4: The communication promotion unit provides a forum to promote information exchange between users. The forum has functions such as thread format, real-time chat, and Q&A format. For example, it allows users to interact with other people who are interested in the same topic and share their knowledge and experiences. Step 5: The information provider provides information on improving energy efficiency and combating global warming. The information provided includes methods for saving energy, using renewable energy, and the latest information on combating global warming. For example, the user can learn how to reduce energy consumption at home.

[0073] (Example 2) A matching system according to an embodiment of the present invention efficiently analyzes user information, matches optimal projects and jobs, promotes communication, and provides information. This matching system allows users to input information such as their region, skills, and interests, and a generation AI analyzes the input information to match participants with various jobs and projects related to forest conservation. For example, the system connects people interested in jobs such as forest monitoring and surveying, reforestation activities, forestry product processing, and ecotourism management to projects in their respective regions. It also provides idea contests and information exchange forums to promote communication between users. Users can interact with others interested in the same topic and share their knowledge and experiences. Furthermore, the system provides information on improving energy efficiency and combating global warming, helping users to take sustainable actions. By expanding jobs related to forest conservation, the matching system increases demand for jobs directly related to environmental management and energy and global warming countermeasures, promoting sustainable forest conservation in society. This allows the matching system to efficiently analyze user information, match optimal projects and jobs, promote communication, and provide information. For example, users can input information such as their region, skills, and interests, and the AI ​​will analyze the information and match participants with various jobs and projects related to forest conservation. This will expand the scope of jobs related to forest conservation, increasing demand for jobs directly related to environmental management and energy and global warming countermeasures, and promoting sustainable forest conservation in society.

[0074] A matching system according to an embodiment includes a reception unit, an information analysis unit, a matching unit, a communication promotion unit, and an information provision unit. The reception unit inputs user information. The user information includes, but is not limited to, for example, name, address, skills, and areas of interest. The reception unit allows the user to input information such as region, skills, and interests. The information analysis unit analyzes the information input by the reception unit using a generation AI. The generation AI analyzes the information using, for example, a natural language processing model or an image generation model. For example, the generation AI searches for projects and jobs in each region based on the user's profile information and performs optimal matching. The matching unit matches projects and jobs based on the information analyzed by the information analysis unit. Matching is performed based on criteria such as skill matching and degree of agreement with project requirements. For example, the matching unit matches a user interested in forest monitoring and research to a local monitoring project. The communication promotion unit provides a forum for promoting information exchange between users. The forum has, for example, thread-based, real-time chat, and Q&A functions. For example, the communication promotion unit enables the user to interact with other people who are interested in the same topic and share knowledge and experiences. The information provision unit provides information on improving energy efficiency and global warming countermeasures. The provided information includes, for example, energy saving methods, renewable energy utilization methods, and the latest information on global warming countermeasures. For example, the information provision unit enables the user to learn how to reduce energy consumption at home. As a result, the matching system according to the embodiment can efficiently analyze user information, match optimal projects and jobs, promote communication, and provide information.

[0075] The reception unit can input information about the user's region, skills, interests, etc. Regions include, but are not limited to, city, ward, town, village, prefecture, country, etc. Skills include, but are not limited to, programming skills, project management skills, language skills, etc. Interests include, but are not limited to, technical interests, business interests, hobbies, etc. The reception unit can, for example, allow the user to input detailed profile information about themselves. For example, if the user is interested in forest monitoring or research, the user can input this information. This allows for more accurate matching by inputting detailed user information. Some or all of the above-described processing in the reception unit can be performed, for example, using AI, or can be performed without using AI. For example, the reception unit can input the user's input information into a generation AI, which can then analyze the information.

[0076] The information analysis unit can analyze the information input by the reception unit using a generation AI. Examples of the generation AI include, but are not limited to, natural language processing models and image generation models. For example, the information analysis unit uses the generation AI to search for projects and jobs in each region based on the user's profile information and perform optimal matching. For example, the generation AI analyzes the user's input information and suggests optimal projects and jobs. The information analysis unit can also use the generation AI to match optimal projects and jobs based on the user's skills and areas of interest. For example, the generation AI analyzes the user's skills and areas of interest and suggests optimal projects and jobs based on that. This improves the accuracy of information analysis by using the generation AI. Some or all of the above-mentioned processing in the information analysis unit is performed using the generation AI. For example, the information analysis unit uses the generation AI to analyze the user's input information and suggest optimal projects and jobs.

[0077] The matching unit can match projects and jobs based on the information analyzed by the information analysis unit. Projects and jobs include, but are not limited to, short-term projects, long-term projects, and freelance work. The matching unit performs optimal matching based on criteria such as skill matching and degree of match with project requirements. For example, the matching unit matches a user interested in forest monitoring and research to a local monitoring project. The matching unit can also suggest optimal projects and jobs based on the user's skills and areas of interest. For example, the matching unit analyzes the user's skills and areas of interest and suggests optimal projects and jobs based on the analyzed information. This enables optimal matching based on the analyzed information. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without AI. For example, the matching unit inputs the information analyzed by the information analysis unit into a generation AI, which then suggests optimal projects and jobs.

[0078] The communication promotion unit can provide a forum for promoting information exchange between users. Examples of forums include, but are not limited to, thread formats, real-time chat, and Q&A formats. The communication promotion unit, for example, enables users to interact with other people interested in the same topic and share their knowledge and experiences. For example, the communication promotion unit may hold an idea contest related to forest protection and award users with outstanding ideas. The communication promotion unit can also provide a forum for promoting information exchange between users. For example, the communication promotion unit enables users to interact with other people interested in the same topic and share their knowledge and experiences. This promotes information exchange between users. Some or all of the above-described processing in the communication promotion unit may be performed using, or without, AI. For example, the communication promotion unit inputs user information into a generation AI, which then suggests an optimal forum.

[0079] The information providing unit can provide information on improving energy efficiency and global warming countermeasures. Examples of the provided information include, but are not limited to, energy conservation methods, renewable energy utilization methods, and the latest information on global warming countermeasures. For example, the information providing unit enables a user to learn how to reduce energy consumption at home. For example, the information providing unit provides information on improving energy efficiency to support the user in taking sustainable actions. The information providing unit can also provide information on global warming countermeasures to support the user in taking sustainable actions. For example, the information providing unit provides information on improving energy efficiency to support the user in learning how to reduce energy consumption at home. This provides information on improving energy efficiency and global warming countermeasures. Some or all of the above-described processing in the information providing unit may be performed using, or without, AI. For example, the information providing unit inputs information on improving energy efficiency and global warming countermeasures into a generation AI, which then provides optimal information.

[0080] The reception unit can estimate the user's emotions and adjust the timing of information input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can simplify input and prompt the user to input minimal information. Furthermore, if the user is relaxed, the reception unit can prompt the user to input detailed information and provide customizable input options. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable the user to input information quickly. For example, the reception unit can estimate the user's emotions and adjust the timing of information input based on the estimated user emotions. This allows for more appropriate information input by adjusting the timing of information input according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit inputs the user's emotion data into a generation AI, which then estimates the user's emotion, and adjusts the timing of information input based on the result.

[0081] The reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit automatically displays information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest information that will be used in a specific time period based on the user's past input history. For example, the reception unit analyzes the user's past input history and selects the optimal input method. In this way, the optimal input method can be provided by analyzing the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's past input history to a generation AI, which then selects the optimal input method.

[0082] The reception unit may filter information based on the user's current project or area of ​​interest when inputting the information. For example, the reception unit may preferentially display information related to projects in which the user is currently participating. The reception unit may also filter and display related information based on the user's area of ​​interest. The reception unit may also suggest highly relevant information by referring to the user's past project history. For example, the reception unit may filter information based on the user's current project or area of ​​interest when inputting the information. This makes it possible to provide highly relevant information by filtering the information based on the current project or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input information about the user's current project or area of ​​interest to a generation AI, which then performs filtering.

[0083] The reception unit can select the optimal input means depending on the user's input method when inputting information. For example, if the user selects voice input, the reception unit inputs information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also support keyboard input. Furthermore, if the user selects image input, the reception unit can also input information using image recognition technology. For example, the reception unit selects the optimal input means depending on the user's input method when inputting information. This makes information input more efficient by providing the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's input method to a generation AI, which then selects the optimal input means.

[0084] The reception unit can estimate the user's emotions and determine the priority of information to be input based on the estimated user emotions. For example, when the user is stressed, the reception unit prioritizes input of important information. Furthermore, when the user is relaxed, the reception unit can prioritize input of detailed information. Furthermore, when the user is in a hurry, the reception unit can prioritize input of information that can be input quickly. For example, the reception unit estimates the user's emotions and determines the priority of information to be input based on the estimated user emotions. Thus, by determining the priority of information to be input according to the user's emotions, important information can be input preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit inputs the user's emotion data to a generation AI, which then estimates the emotion, and determines the priority of information to be input based on the result.

[0085] The reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information when inputting information. For example, the reception unit prioritizes inputting information related to the area where the user is currently located. The reception unit can also suggest related projects or jobs based on the user's geographical location information. The reception unit can also update the user's location information in real time to provide optimal information. For example, the reception unit prioritizes inputting highly relevant information in consideration of the user's geographical location information when inputting information. This makes it possible to provide highly relevant information by taking the geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's geographical location information to a generation AI, which then suggests highly relevant information.

[0086] The reception unit can analyze the user's social media activity and input relevant information when inputting information. The reception unit, for example, inputs information related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and input relevant information. The reception unit can also input relevant information by referring to the activities of the user's friends on social media. For example, the reception unit analyzes the user's social media activity and inputs relevant information when inputting information. In this way, highly relevant information can be provided by analyzing social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs data on the user's social media activity to a generation AI, which then suggests relevant information.

[0087] The reception unit can customize the input method by reflecting the user's past feedback when inputting information. The reception unit can improve the input method based on, for example, feedback provided by the user in the past. The reception unit can also provide an easier-to-use input interface by reflecting the user's feedback. The reception unit can also optimize the input procedure by referring to the user's feedback. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting information. In this way, an easier-to-use input method can be provided by reflecting the past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's past feedback into a generation AI, and the generation AI customizes the input method.

[0088] The information analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the information analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the information analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the information analysis unit can provide visually easy-to-understand analysis results when the user is stressed. For example, the information analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information analysis unit is performed using the generation AI. For example, the information analysis unit estimates the user's emotions using the generation AI and adjusts the presentation method of the analysis based on the result.

[0089] The information analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the information analysis unit performs a detailed analysis on information with a high level of importance. The information analysis unit can also perform a concise analysis on information with a low level of importance. The information analysis unit can also determine the priority of the analysis based on the importance of the information. For example, the information analysis unit adjusts the level of detail of the analysis based on the importance of the information. In this way, by adjusting the level of detail of the analysis according to the importance of the information, it is possible to perform a detailed analysis on important information. Some or all of the above-mentioned processing in the information analysis unit is performed using a generation AI. For example, the information analysis unit evaluates the importance of the information using the generation AI and adjusts the level of detail of the analysis based on the result.

[0090] The information analysis unit can apply different analysis algorithms depending on the category of information. For example, the information analysis unit applies a dedicated analysis algorithm to information related to environmental protection. The information analysis unit can also apply a different analysis algorithm to information related to energy efficiency. The information analysis unit can also apply a dedicated analysis algorithm to information related to measures against global warming. For example, the information analysis unit applies different analysis algorithms depending on the category of information. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the information analysis unit is performed using a generation AI. For example, the information analysis unit determines the category of information using the generation AI and applies an analysis algorithm according to that category.

[0091] The information analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the information analysis unit adjusts the analysis algorithm based on the user's past analysis results. The information analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The information analysis unit can also analyze the user's past analysis results and propose an optimal analysis method. For example, the information analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the information analysis unit is performed using a generation AI. For example, the information analysis unit analyzes the user's past analysis results using the generation AI and adjusts the analysis algorithm based on the results.

[0092] The information analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the information analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the information analysis unit can provide a detailed analysis result. Furthermore, if the user is stressed, the information analysis unit can provide a visually easy-to-understand analysis result. For example, the information analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. This allows for more appropriate analysis results to be provided by adjusting the length of the analysis according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information analysis unit is performed using the generation AI. For example, the information analysis unit estimates the user's emotions using the generation AI and adjusts the length of the analysis based on the result.

[0093] The information analysis unit can determine the priority of analysis based on the time of submission of information. For example, the information analysis unit prioritizes analysis of the most recent information. The information analysis unit can also postpone analysis of information that was submitted earlier. The information analysis unit can also determine the priority of analysis based on the time of submission. For example, the information analysis unit determines the priority of analysis based on the time of submission of information. In this way, by determining the priority of analysis based on the time of submission, the most recent information can be analyzed preferentially. Some or all of the above-mentioned processing in the information analysis unit is performed using a generation AI. For example, the information analysis unit uses a generation AI to evaluate the time of submission of information and determine the priority of analysis based on the result.

[0094] The information analysis unit can adjust the order of analysis based on the relevance of the information. For example, the information analysis unit prioritizes analysis of highly relevant information. The information analysis unit can also postpone analysis of less relevant information. The information analysis unit can also adjust the order of analysis based on the relevance of the information. For example, the information analysis unit adjusts the order of analysis based on the relevance of the information. In this way, by adjusting the order of analysis based on the relevance of the information, highly relevant information can be analyzed preferentially. Some or all of the above-mentioned processing in the information analysis unit is performed using a generation AI. For example, the information analysis unit uses a generation AI to evaluate the relevance of the information and adjust the order of analysis based on the result.

[0095] The information analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the information analysis unit can provide analysis results that make heavy use of technical terms. Furthermore, if the user does not have technical expertise, the information analysis unit can also provide analysis results in easy-to-understand language. Furthermore, the information analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the information analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the information analysis unit is performed using a generation AI. For example, the information analysis unit evaluates the user's level of expertise using a generation AI and adjusts the use of technical terms in the analysis based on the result.

[0096] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated user emotions. For example, the matching unit can apply detailed matching criteria when the user is relaxed. The matching unit can also apply simple matching criteria when the user is in a hurry. The matching unit can also apply visually easy-to-understand matching criteria when the user is stressed. For example, the matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated user emotions. This enables more appropriate matching by adjusting the matching criteria according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the matching unit is performed using the generation AI. For example, the matching unit estimates the user's emotions using the generation AI and adjusts the matching criteria based on the results.

[0097] The matching unit can improve the accuracy of matching by taking into account the interrelationships of information. The matching unit performs optimal matching based on, for example, related information. The matching unit can also analyze the interrelationships of information and improve the accuracy of matching. The matching unit can also propose optimal matching by taking into account the interrelationships of information. For example, the matching unit improves the accuracy of matching by taking into account the interrelationships of information. In this way, the accuracy of matching is improved by taking into account the interrelationships of information. Some or all of the above-mentioned processing in the matching unit is performed using a generation AI. For example, the matching unit analyzes the interrelationships of information using the generation AI and proposes optimal matching based on the results.

[0098] The matching unit can perform matching by taking into account attribute information of the information submitter. The matching unit performs optimal matching by taking into account, for example, the skills and experience of the information submitter. The matching unit can also perform optimal matching by taking into account regional information of the information submitter. The matching unit can also perform optimal matching by taking into account the areas of interest of the information submitter. For example, the matching unit performs matching by taking into account attribute information of the information submitter. This makes it possible to perform more appropriate matching by taking into account the attribute information of the submitter. Some or all of the above-mentioned processing in the matching unit is performed using a generation AI. For example, the matching unit analyzes the attribute information of the information submitter using the generation AI and proposes optimal matching based on the results.

[0099] The matching unit can weight the matching based on the frequency of information submission. For example, the matching unit prioritizes matching of information that is submitted more frequently. The matching unit can also postpone matching of information that is submitted less frequently. The matching unit can also weight the matching based on the submission frequency. For example, the matching unit weights the matching based on the submission frequency of the information. By weighting based on the submission frequency, more appropriate matching becomes possible. Some or all of the above-mentioned processing in the matching unit is performed using a generation AI. For example, the matching unit evaluates the submission frequency of information using the generation AI and weights the matching based on the result.

[0100] The matching unit can estimate the user's emotions and adjust the order in which matching results are displayed based on the estimated user emotions. For example, when the user is relaxed, the matching unit displays detailed matching results. Furthermore, when the user is in a hurry, the matching unit can display matching results that are more concise. Furthermore, when the user is stressed, the matching unit can display matching results that are easier to understand visually. For example, the matching unit estimates the user's emotions and adjusts the order in which matching results are displayed based on the estimated user emotions. This allows for more appropriate information to be provided by adjusting the display order of matching results according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the matching unit is performed using the generation AI. For example, the matching unit estimates the user's emotions using the generation AI and adjusts the order in which matching results are displayed based on the result.

[0101] The matching unit can perform matching taking into account the geographical distribution of information. For example, the matching unit prioritizes matching projects close to the user's current location. The matching unit can also perform optimal matching based on information that is highly geographically relevant. The matching unit can also propose optimal projects to the user taking into account the geographical distribution. For example, the matching unit performs matching taking into account the geographical distribution of information. This enables more appropriate matching by taking geographical distribution into account. Some or all of the above-mentioned processing in the matching unit is performed using a generation AI. For example, the matching unit analyzes the geographical distribution of information using the generation AI and proposes optimal matching based on the results.

[0102] The matching unit can improve the accuracy of matching by referring to literature related to the information. The matching unit, for example, performs optimal matching based on related literature. The matching unit can also improve the accuracy of matching by referring to literature related to the information. The matching unit can also propose optimal matching by taking related literature into consideration. For example, the matching unit improves the accuracy of matching by referring to literature related to the information. In this way, the accuracy of matching is improved by referring to related literature. Some or all of the above-mentioned processing in the matching unit is performed using a generation AI. For example, the matching unit analyzes literature related to the information using a generation AI and proposes optimal matching based on the results.

[0103] The matching unit can perform matching taking into account the market value of the information. For example, the matching unit prioritizes matching of information with high market value. The matching unit can also perform optimal matching based on market value. The matching unit can also propose optimal projects to users taking market value into account. For example, the matching unit performs matching taking into account the market value of the information. This makes it possible to perform more appropriate matching by taking market value into account. Some or all of the above-mentioned processing in the matching unit is performed using a generation AI. For example, the matching unit evaluates the market value of the information using the generation AI and proposes optimal matching based on the results.

[0104] The communication promotion unit can estimate the user's emotions and adjust the display method of the communication based on the estimated user's emotions. For example, when the user is relaxed, the communication promotion unit displays detailed communication content. Furthermore, when the user is in a hurry, the communication promotion unit can also display communication content that focuses on the main points. Furthermore, when the user is stressed, the communication promotion unit can display communication content that is visually easy to understand. For example, the communication promotion unit estimates the user's emotions and adjusts the display method of the communication based on the estimated user's emotions. This allows for more appropriate information to be provided by adjusting the display method of the communication according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the communication promotion unit is performed using the generation AI. For example, the communication promotion unit estimates the user's emotions using the generation AI and adjusts the display method of the communication based on the result.

[0105] The communication promotion unit can predict current communication by referring to past communication data. For example, the communication promotion unit suggests an optimal communication method based on the past communication data. The communication promotion unit can also predict current communication by referring to past data. The communication promotion unit can also analyze past communication patterns and provide an optimal communication method. For example, the communication promotion unit predicts current communication by referring to past communication data. In this way, by referring to the past data, it is possible to predict current communication and provide an optimal method. Some or all of the above-mentioned processing in the communication promotion unit is performed using a generation AI. For example, the communication promotion unit analyzes past communication data using a generation AI and predicts current communication based on the results.

[0106] The communication promotion unit can apply different communication methods to different information categories. For example, the communication promotion unit applies a dedicated communication method to information related to environmental protection. The communication promotion unit can also apply a different communication method to information related to energy efficiency. The communication promotion unit can also apply a dedicated communication method to information related to global warming countermeasures. For example, the communication promotion unit applies different communication methods to different information categories. This improves the accuracy of communication by applying the optimal communication method according to the information category. Some or all of the above-mentioned processing in the communication promotion unit is performed using a generation AI. For example, the communication promotion unit determines the category of information using the generation AI and applies a communication method according to that category.

[0107] The communication promotion unit can analyze communication taking into account attribute information of the information submitter. The communication promotion unit can propose the optimal communication method taking into account, for example, the skills and experience of the information submitter. The communication promotion unit can also propose the optimal communication method taking into account regional information of the information submitter. The communication promotion unit can also propose the optimal communication method taking into account the information submitter's areas of interest. For example, the communication promotion unit analyzes communication taking into account attribute information of the information submitter. This enables more appropriate communication by taking into account the submitter's attribute information. Some or all of the above-mentioned processing in the communication promotion unit is performed using a generation AI. For example, the communication promotion unit analyzes attribute information of the information submitter using a generation AI and proposes the optimal communication method based on the results.

[0108] The communication promotion unit can estimate the user's emotions and adjust the importance of the communication based on the estimated user's emotions. For example, when the user is relaxed, the communication promotion unit displays detailed communication content. Furthermore, when the user is in a hurry, the communication promotion unit can also display communication content that focuses on the main points. Furthermore, when the user is stressed, the communication promotion unit can display communication content that is visually easy to understand. For example, the communication promotion unit estimates the user's emotions and adjusts the importance of the communication based on the estimated user's emotions. This allows for more appropriate information to be provided by adjusting the importance of the communication according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the communication promotion unit is performed using the generation AI. For example, the communication promotion unit estimates the user's emotions using the generation AI and adjusts the importance of the communication based on the result.

[0109] The communication promotion unit can analyze changes in communication based on the time of information submission. For example, the communication promotion unit prioritizes reflecting information that was submitted recently in the communication. The communication promotion unit can also reflect information that was submitted older in the communication later. The communication promotion unit can also analyze changes in communication based on the time of submission. For example, the communication promotion unit analyzes changes in communication based on the time of information submission. In this way, by analyzing changes in communication based on the time of submission, more appropriate information can be provided. Some or all of the above-mentioned processing in the communication promotion unit is performed using a generation AI. For example, the communication promotion unit evaluates the time of information submission using a generation AI, and analyzes changes in communication based on the results.

[0110] The communication promotion unit can analyze communication by referring to market data related to the information. The communication promotion unit, for example, proposes an optimal communication method based on the related market data. The communication promotion unit can also improve the accuracy of communication by referring to market data. The communication promotion unit can also provide an optimal communication method by taking the related market data into consideration. For example, the communication promotion unit analyzes communication by referring to market data related to the information. As a result, the accuracy of communication is improved by referring to the related market data. Some or all of the above-mentioned processing in the communication promotion unit is performed using a generation AI. For example, the communication promotion unit analyzes market data related to the information using a generation AI, and proposes an optimal communication method based on the results.

[0111] The communication promotion unit can analyze communication taking into account the technical maturity of the information. For example, the communication promotion unit prioritizes reflecting technically mature information in communication. The communication promotion unit can also reflect technically immature information in communication later. The communication promotion unit can also provide an optimal communication method taking into account the technical maturity. For example, the communication promotion unit analyzes communication taking into account the technical maturity of the information. This enables more appropriate communication by taking into account the technical maturity. Some or all of the above-mentioned processing in the communication promotion unit is performed using a generative AI. For example, the communication promotion unit uses a generative AI to evaluate the technical maturity of the information and propose an optimal communication method based on the results.

[0112] The information providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, when the user is relaxed, the information providing unit can prioritize providing detailed information. Furthermore, when the user is in a hurry, the information providing unit can prioritize providing information that is easy to understand visually. Furthermore, when the user is stressed, the information providing unit can prioritize providing information that is easy to understand visually. For example, the information providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. This allows more appropriate information to be provided by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the information providing unit is performed using the generation AI. For example, the information providing unit estimates the user's emotions using the generation AI and determines the priority of information to be provided based on the result.

[0113] The information providing unit can improve the accuracy of the information provided by taking into account the interrelationships between information. The information providing unit provides optimal information based on, for example, related information. The information providing unit can also analyze the interrelationships between information and improve the accuracy of the information provided. The information providing unit can also provide optimal information by taking into account the interrelationships between information. For example, the information providing unit improves the accuracy of the information provided by taking into account the interrelationships between information. As a result, the accuracy of the information provided is improved by taking into account the interrelationships between information. Some or all of the above-mentioned processing in the information providing unit is performed using a generation AI. For example, the information providing unit analyzes the interrelationships between information using the generation AI and provides optimal information based on the results.

[0114] The information providing unit can provide information taking into consideration the attribute information of the information submitter. The information providing unit can provide the most appropriate information taking into consideration, for example, the skills and experience of the information submitter. The information providing unit can also provide the most appropriate information taking into consideration the regional information of the information submitter. The information providing unit can also provide the most appropriate information taking into consideration the areas of interest of the information submitter. For example, the information providing unit provides information taking into consideration the attribute information of the information submitter. In this way, more appropriate information can be provided by taking into consideration the attribute information of the submitter. Some or all of the above-mentioned processing in the information providing unit is performed using a generation AI. For example, the information providing unit analyzes the attribute information of the information submitter using the generation AI and provides the most appropriate information based on the results.

[0115] The information providing unit can weight the provision of information based on the frequency of information submission. For example, the information providing unit provides information with a high submission frequency preferentially. The information providing unit can also provide information with a low submission frequency later. The information providing unit can also weight the provision of information based on the submission frequency. For example, the information providing unit weights the provision of information based on the submission frequency. In this way, by weighting based on the submission frequency, more appropriate information can be provided. Some or all of the above-mentioned processing in the information providing unit is performed using a generation AI. For example, the information providing unit evaluates the submission frequency of information using the generation AI, and weights the provision of information based on the result.

[0116] The information providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, the information providing unit can display detailed information when the user is relaxed. Furthermore, the information providing unit can display information that focuses on the main points when the user is in a hurry. Furthermore, the information providing unit can display visually easy-to-understand information when the user is stressed. For example, the information providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. This allows more appropriate information to be provided by adjusting the display method of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the information providing unit is performed using the generation AI. For example, the information providing unit estimates the user's emotions using the generation AI and adjusts the display method of the information to be provided based on the result.

[0117] The information providing unit can provide information taking into account the geographical distribution of the information. For example, the information providing unit can prioritize providing information related to the user's current location. The information providing unit can also provide optimal information based on information that is highly geographically relevant. The information providing unit can also provide optimal information to the user taking into account the geographical distribution. For example, the information providing unit provides information taking into account the geographical distribution. This makes it possible to provide more appropriate information by taking the geographical distribution into account. Some or all of the above-mentioned processing in the information providing unit is performed using a generation AI. For example, the information providing unit analyzes the geographical distribution of the information using the generation AI and provides optimal information based on the results.

[0118] The information providing unit can improve the accuracy of the information provided by referring to literature related to the information. The information providing unit, for example, provides optimal information based on related literature. The information providing unit can also improve the accuracy of the information provided by referring to literature related to the information. The information providing unit can also provide optimal information by taking related literature into consideration. For example, the information providing unit improves the accuracy of the information provided by referring to literature related to the information. As a result, the accuracy of the information provided is improved by referring to the related literature. Some or all of the above-mentioned processing in the information providing unit is performed using a generation AI. For example, the information providing unit analyzes literature related to the information using the generation AI, and provides optimal information based on the results.

[0119] The information providing unit can provide information taking into consideration the market value of the information. For example, the information providing unit provides information with a high market value preferentially. The information providing unit can also provide optimal information based on market value. The information providing unit can also provide optimal information to a user taking market value into consideration. For example, the information providing unit provides information taking into consideration the market value of the information. In this way, more appropriate information can be provided by taking market value into consideration. Some or all of the above-mentioned processing in the information providing unit is performed using a generation AI. For example, the information providing unit evaluates the market value of the information using the generation AI and provides optimal information based on the result. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, information analysis unit, matching unit, communication promotion unit, and information provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input user information via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. For example, the information analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. For example, the matching unit is implemented by the specific processing unit 290 of the data processing device 12 and matches projects or jobs based on the analyzed information. For example, the communication promotion unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides a forum for promoting information exchange between users. For example, the information provision unit is implemented by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides information on improving energy efficiency and measures against global warming. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, information analysis unit, matching unit, communication promotion unit, and information provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input user information via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. For example, the information analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. For example, the matching unit is realized by the specific processing unit 290 of the data processing device 12 and matches projects or jobs based on the analyzed information. For example, the communication promotion unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides a forum for promoting information exchange between users. For example, the information provision unit is realized by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides information on improving energy efficiency and measures against global warming. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, information analysis unit, matching unit, communication promotion unit, and information provision unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit can input user information via the microphone 238 of the headset terminal 314 or the communication I / F 26 of the data processing device 12. For example, the information analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. For example, the matching unit is realized by the specific processing unit 290 of the data processing device 12 and matches projects or jobs based on the analyzed information. For example, the communication promotion unit is realized by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12 and provides a forum for promoting information exchange between users. For example, the information provision unit is realized by the display 343 of the headset terminal 314 or the specific processing unit 290 of the data processing device 12 and provides information on improving energy efficiency and measures against global warming. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, information analysis unit, matching unit, communication promotion unit, and information provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input user information via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. For example, the information analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. For example, the matching unit is realized by the specific processing unit 290 of the data processing device 12 and matches projects or jobs based on the analyzed information. For example, the communication promotion unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and provides a forum for promoting information exchange between users. For example, the information provision unit is realized by the speaker 240 of the robot 414 or the specific processing unit 290 of the data processing device 12 and provides information on improving energy efficiency and measures against global warming.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The reception unit can analyze the user's past behavioral history and suggest the optimal timing for inputting information. For example, if the user has tended to input information during a specific time period in the past, a notification can be sent during that time period to prompt the user to input information. Also, if the user has been active on a specific day of the week in the past, the reception unit can suggest information input that is tailored to that day. Furthermore, the reception unit can suggest the optimal input method (voice, text, etc.) based on the user's past behavioral patterns. This allows for efficient information input by optimizing the timing for inputting information based on the user's behavioral patterns.

[0122] The information analysis unit can estimate the user's emotions and adjust the way in which the analysis results are presented based on the estimated user emotions. For example, if the user is feeling stressed, the analysis results can be presented in a concise summary. If the user is relaxed, detailed analysis results can be provided. Furthermore, if the user is in a hurry, the analysis results can be presented using visually easy-to-understand graphs and charts. This makes it possible to provide more appropriate information by adjusting the way in which the analysis results are presented according to the user's emotions.

[0123] The matching unit can analyze the user's past matching history and propose optimal matching criteria. For example, it can apply similar criteria based on the user's past successful matching patterns. It can also propose different criteria to avoid matching patterns that the user was dissatisfied with in the past. Furthermore, it can also customize the matching criteria by referring to the user's past feedback. In this way, it is possible to improve user satisfaction by proposing optimal matching criteria based on the user's past matching history.

[0124] The communication promotion unit can estimate the user's emotions and adjust the content of communication based on the estimated user emotions. For example, if the user is feeling stressed, it can send messages of encouragement and support. If the user is relaxed, it can also provide detailed information and suggestions. Furthermore, if the user is in a hurry, it can send concise messages that get to the point. This allows for more effective communication by adjusting the content of communication according to the user's emotions.

[0125] The information provision unit can analyze the user's past information browsing history and suggest the optimal timing for providing information. For example, if the user has tended to browse information during a specific time period in the past, the information provision unit can provide information tailored to that time period. Also, if the user has often browsed information on a specific day of the week in the past, the information provision unit can provide information tailored to that day of the week. Furthermore, based on the user's past browsing history, the information provision unit can prioritize the provision of highly relevant information. This can improve user convenience by suggesting the optimal timing for providing information based on the user's past browsing history.

[0126] The reception unit can estimate the user's emotions and customize the input interface based on the estimated user emotions. For example, if the user is feeling stressed, a simple and intuitive interface can be provided. Alternatively, if the user is relaxed, an interface including detailed options can be provided. Furthermore, if the user is in a hurry, voice input and auto-completion functions can be provided with priority. In this way, customizing the input interface according to the user's emotions enables more comfortable information input.

[0127] The information analysis unit can optimize the analysis algorithm based on the user's past analysis results. For example, it can prioritize the use of algorithms for analysis results that the user has previously given a high rating. It can also improve algorithms for analysis results that the user has previously given a low rating. Furthermore, it can also customize the analysis algorithm by referring to the user's past feedback. In this way, by optimizing the analysis algorithm based on past analysis results, it is possible to improve the accuracy of the analysis and user satisfaction.

[0128] The matching unit can estimate the user's emotions and adjust the display method of the matching results based on the estimated user emotions. For example, if the user is feeling stressed, a simple and visually easy-to-understand matching result can be displayed. If the user is relaxed, detailed matching results can be provided. Furthermore, if the user is in a hurry, matching results that focus on the main points can be displayed. In this way, by adjusting the display method of the matching results according to the user's emotions, more appropriate information can be provided.

[0129] The communication promotion unit can analyze the user's past communication history and suggest the optimal communication method. For example, it can prioritize suggestions of communication methods (chat, email, etc.) that the user has used favorably in the past. It can also suggest similar methods based on the user's successful communication patterns in the past. It can also customize communication methods by referring to the user's past feedback. In this way, it is possible to improve user satisfaction by suggesting the optimal communication method based on the user's past communication history.

[0130] The information providing unit can estimate the user's emotions and adjust the format of the information to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, it can provide visually easy-to-understand infographics or videos. If the user is relaxed, it can provide detailed text information. Furthermore, if the user is in a hurry, it can provide information in bullet-point format that focuses on the main points. In this way, by adjusting the format of information according to the user's emotions, it is possible to provide more effective information.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The reception unit inputs user information. The user information includes name, address, skills, areas of interest, etc. For example, the user can input information such as region, skills, and interests. Step 2: The information analysis unit uses the generation AI to analyze the information entered by the reception unit. The generation AI analyzes the information using natural language processing models, image generation models, etc. For example, it searches for projects and jobs in each region based on the user's profile information and performs optimal matching. Step 3: The matching department matches projects and jobs based on the information analyzed by the information analysis department. Matching is based on criteria such as skill matching and degree of match with project requirements. For example, a user interested in forest monitoring and research can be matched with a local monitoring project. Step 4: The communication promotion unit provides a forum to promote information exchange between users. The forum has functions such as thread format, real-time chat, and Q&A format. For example, it allows users to interact with other people who are interested in the same topic and share their knowledge and experiences. Step 5: The information provider provides information on improving energy efficiency and combating global warming. The information provided includes methods for saving energy, using renewable energy, and the latest information on combating global warming. For example, the user can learn how to reduce energy consumption at home.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] 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.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] 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.

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] 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.

[0182] 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.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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 reception unit for inputting user information; an information analysis unit that analyzes the information input by the reception unit; a matching unit that matches projects and jobs based on the information analyzed by the information analysis unit; a communication promotion unit that promotes communication between users; an information providing unit that provides information on improving energy efficiency and measures against global warming; A system characterized by:

2. The reception unit Enter information about your location, skills, interests, and The system of claim 1 .

3. The information analysis unit The generation AI analyzes the information input by the reception unit. The system of claim 1 .

4. The matching unit Matching projects and jobs based on the information analyzed by the information analysis unit The system of claim 1 .

5. The communication promotion unit Providing a forum to facilitate user-to-user information exchange The system of claim 1 .

6. The information providing unit Providing information on improving energy efficiency and combating global warming The system of claim 1 .

7. The reception unit Estimates the user's emotions and adjusts the timing of information input based on the estimated user emotions. The system of claim 1 .

8. The reception unit Analyze the user's past input history and select the optimal input method The system of claim 1 .

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