System

Abstract

An object of a system according to an embodiment is to perform matching for effectively utilizing a vacant land or a vacant house in a local area.SOLUTION: A system according to an embodiment includes a collection unit, a matching unit, and a suggestion unit. The collection part collects information on local idle lands and vacant houses. The matching unit matches a vacant land or an empty house in a local area with a company or a person who wants to utilize the vacant land or the empty house based on the information collected by the collecting unit. The proposal unit proposes an activity place and an appropriate crop to the company or the person matched by the matching unit.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 technology does not adequately match unused land and vacant houses in rural areas to make effective use of them, and there is room for improvement.

[0005] The system according to the embodiment aims to match unused land and vacant houses in rural areas to be used effectively. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a matching unit, and a proposal unit. The collection unit collects information on unused land and vacant houses in rural areas. The matching unit matches unused land and vacant houses in rural areas with companies and people who want to utilize them based on the information collected by the collection unit. The proposal unit proposes activity locations and appropriate agricultural products to companies and people matched by the matching unit. [Effects of the Invention]

[0007] The system according to the embodiment can perform matching to effectively utilize unused land and vacant houses in rural areas. [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 system according to an embodiment of the present invention combines AI regional resource mapping with an in-house side job system to create a new business model in which companies can play a part in regional revitalization and coexist and prosper with rural areas. This system uses generative AI to match idle land and vacant houses in rural areas with companies and people who want to utilize them, and an agricultural advisor suggests locations and optimal agricultural products. For example, the system uses generative AI to collect information on idle land and vacant houses in rural areas and companies and people who want to utilize them. For example, the generative AI analyzes data on idle land and vacant houses provided by local governments and matches them to the needs of companies and people. Next, the agricultural advisor suggests locations and optimal agricultural products. For example, it suggests crops suitable for cultivation in a specific region or crops suited to the local climate and soil. This allows companies and people to carry out agricultural activities efficiently. Furthermore, this system can synergistically solve issues facing rural areas and the needs of urban areas. For example, issues such as depopulation and the increase in idle land in rural areas can be addressed by urban companies and people through agricultural activities in rural areas. For businesses and people in urban areas, working in the fields surrounded by abundant nature on the weekends can also provide a sense of refreshment and new business opportunities. In this way, by combining AI regional resource mapping with a company's in-house side job system, a new business model can be created in which rural and urban areas can coexist and prosper together. This allows the system to make effective use of idle land and vacant houses in rural areas, and makes it possible to match and propose solutions that meet the needs of businesses and people.

[0029] A local resource utilization system according to an embodiment includes a collection unit, a matching unit, and a proposal unit. The collection unit collects information on idle land and vacant houses in local areas. For example, the collection unit collects data on idle land and vacant houses provided by local governments. The collection unit can also automatically collect information on idle land and vacant houses in local areas using a generation AI. For example, the generation AI acquires and analyzes information on idle land and vacant houses from a local government database. The collection unit can also collect information from data sources other than local governments. For example, the collection unit collects information from real estate agents and local residents and adds it to the database. The matching unit matches idle land and vacant houses in local areas with companies and people who want to utilize them based on the information collected by the collection unit. For example, the matching unit appropriately matches idle land and vacant houses with companies and people based on the needs of the companies and people. The matching unit can also use the generation AI to perform optimal matching based on the needs of the companies and people. For example, the generation AI analyzes the needs of companies and people and proposes optimal idle land and vacant houses based on the analysis. Furthermore, the matching unit can also perform matching by taking into account the past activity history of companies and individuals. For example, it analyzes a company's past project history and proposes optimal idle land or vacant houses. The proposal unit proposes activity locations and optimal agricultural products to companies and individuals matched by the matching unit. The proposal unit, for example, proposes agricultural products that are suitable for cultivation in a specific region. The proposal unit can also propose agricultural products that are suitable for the climate and soil of the region. For example, the proposal unit analyzes regional climate data and soil data and proposes optimal agricultural products based on that data. Furthermore, the proposal unit can provide advice to companies and individuals to efficiently carry out agricultural activities. For example, the proposal unit provides advice on cultivation methods and harvesting times. As a result, the local resource utilization system according to the embodiment can make effective use of idle land and vacant houses in rural areas and enable matching and proposals that meet the needs of companies and individuals.

[0030] The collection unit can collect data on idle land and vacant houses provided by local governments. The collection unit, for example, collects data on idle land and vacant houses provided by local governments. For example, it acquires and analyzes information on idle land and vacant houses from the database of the local government. The collection unit can also evaluate the frequency of data provided by the local government and prioritize collecting frequently updated data. For example, the generation AI evaluates the frequency of data provided by the local government and prioritizes collecting frequently updated data. Furthermore, the collection unit can also evaluate the reliability of the data provider of the local government and prioritize collecting data from highly reliable providers. For example, the generation AI evaluates the reliability of the data provider of the local government and prioritizes collecting data from highly reliable providers. In this way, highly reliable information can be obtained by collecting data from local governments.

[0031] The matching unit can appropriately match idle land or vacant houses with companies or people based on the needs of the companies or people. For example, the matching unit appropriately matches idle land or vacant houses with companies or people based on the needs of the companies or people. For example, the generation AI analyzes the needs of companies or people and suggests the most suitable idle land or vacant house based on that. The matching unit can also perform matching taking into account the past activity history of companies or people. For example, it analyzes the past project history of a company and suggests the most suitable idle land or vacant house. Furthermore, the matching unit can perform long-term matching taking into account the future plans and goals of companies and people. For example, it analyzes a company's future plans and suggests the optimal match from a long-term perspective. This makes it possible to perform optimal matching according to the needs of companies and people.

[0032] The proposal unit can propose crops that are suitable for cultivation in a specific region. For example, the generation AI analyzes climate and soil data for the region and proposes the optimal crops based on that data. The proposal unit can also refer to past agricultural data to improve the accuracy of proposals based on success stories and failures. For example, the generation AI analyzes past agricultural data and proposes the optimal crops based on success stories. Furthermore, the proposal unit can also propose appropriate crops and activity methods based on the farming experience and skill level of companies and individuals. For example, the generation AI analyzes the farming experience of companies and individuals and proposes crops for beginners. This makes it possible to propose crops that are suitable for the region.

[0033] The proposal unit can propose crops that are suitable for the climate and soil of the region. For example, the generation AI analyzes regional climate and soil data and proposes the optimal crops based on that data. The proposal unit can also refer to past agricultural data to improve the accuracy of proposals based on success stories and failures. For example, the generation AI analyzes past agricultural data and proposes the optimal crops based on success stories. Furthermore, the proposal unit can also propose appropriate crops and activity methods based on the farming experience and skill level of companies and individuals. For example, the generation AI analyzes the farming experience of companies and individuals and proposes crops for beginners. This makes it possible to propose crops that are suitable for the climate and soil of the region.

[0034] The suggestion unit can provide advice to help companies and individuals carry out agricultural activities effectively. The suggestion unit provides advice to help companies and individuals carry out agricultural activities effectively. For example, the generation AI provides advice on cultivation methods, harvest times, etc. The suggestion unit can also suggest appropriate crops and activity methods based on the company's or individual's farming experience and skill level. For example, the generation AI can analyze the farming experience of the company or individual and suggest crops suitable for beginners. Furthermore, the suggestion unit can also suggest optimal crops by conducting a detailed analysis of regional climate and soil data. For example, the generation AI can analyze regional climate and soil data and suggest optimal crops based on that data. This provides advice for efficient agricultural activities.

[0035] The collection unit can evaluate the reliability of data provided by local governments and prioritize the collection of highly reliable data. For example, the collection unit evaluates the reliability of data provided by local governments and prioritizes the collection of highly reliable data. For example, the generation AI evaluates the frequency of data provided by local governments and prioritizes the collection of frequently updated data. The collection unit can also have the generation AI evaluate the reliability of data providers to local governments and prioritize the collection of data from highly reliable providers. Furthermore, the collection unit can have the generation AI evaluate the past accuracy of data from local governments and prioritize the collection of highly accurate data. This allows for the prioritized collection of highly reliable data, thereby improving accuracy. High-quality information can be obtained.

[0036] The collection unit can prioritize collecting the latest information, taking into account the frequency of data updates. The collection unit, for example, prioritizes collecting the latest information, taking into account the frequency of data updates. For example, the generation AI checks the last update date of the data and prioritizes collecting the latest data. The collection unit can also have the generation AI analyze the frequency of data updates and prioritize collecting data that is updated frequently. Furthermore, the collection unit can have the generation AI refer to the data update history and prioritize collecting the latest information. In this way, by prioritizing the collection of the latest information, the latest data can always be obtained.

[0037] The collection unit can apply different collection algorithms depending on the type of data. For example, the collection unit applies different collection algorithms depending on the type of data. For example, when the generation AI collects data on unused land, it applies an algorithm that emphasizes the area of ​​the land and location information. In addition, when the generation AI collects data on vacant houses, the collection unit can also apply an algorithm that emphasizes the condition and age of the building. Furthermore, when the generation AI collects data on agricultural land, the collection unit can also apply an algorithm that emphasizes the quality of the soil and climatic conditions. This enables optimal collection according to the type of data.

[0038] The collection unit can also collect information from data sources other than local governments. For example, the generation AI collects data on idle land and vacant houses provided by real estate agents. The collection unit can also have the generation AI collect word-of-mouth information from local residents and add it to the database. Furthermore, the collection unit can have the generation AI collect data provided by online platforms and integrate it with local government data. In this way, comprehensive data can be obtained by collecting information from a variety of data sources.

[0039] The collection unit can collect data over a wide area, taking into account the geographical spread. For example, the collection unit collects data over a wide area, taking into account the geographical spread. For example, the generation AI collects data from multiple local governments to obtain information on idle land and vacant houses over a wide area. The collection unit can also have the generation AI collect data across regional boundaries to provide information from a wide-area perspective. Furthermore, the collection unit can have the generation AI integrate data from different regions to build a comprehensive database. In this way, comprehensive information can be obtained by collecting data over a wide area.

[0040] The collection unit can integrate and collect multiple data sources. For example, the collection unit integrates and collects multiple data sources. For example, the generation AI integrates data from local governments, real estate agents, and local residents to provide highly accurate information. The collection unit can also cross-check information from different data sources to collect highly reliable data. Furthermore, the collection unit can also integrate information from multiple data sources and build a comprehensive database. This allows highly accurate information to be obtained by integrating multiple data sources.

[0041] The matching unit can make appropriate matches by taking into account the past activity history of companies and people. The matching unit makes appropriate matches by taking into account the past activity history of companies and people. For example, the generation AI analyzes a company's past project history and suggests the most suitable idle land or vacant house. The matching unit can also make appropriate matches by taking into account the past activity history of individuals. Furthermore, the matching unit can also suggest optimal matches based on the past success stories of companies and people. This makes it possible to make optimal matches by taking into account past activity history.

[0042] The matching unit can improve the accuracy of matching by analyzing the characteristics of idle land and vacant houses in detail. The matching unit can improve the accuracy of matching by analyzing the characteristics of idle land and vacant houses in detail, for example. For example, the generation AI can analyze the area and location of the idle land in detail to match the most suitable company or person. The matching unit can also make appropriate matches by having the generation AI take into account the condition and age of the vacant house. Furthermore, the matching unit can also propose optimal matches by having the generation AI analyze the characteristics of idle land and vacant houses in detail. In this way, the accuracy of matching is improved by analyzing the characteristics of idle land and vacant houses in detail.

[0043] The matching unit can perform matching customized according to the specific needs of companies and individuals. The matching unit can perform matching customized according to the specific needs of companies and individuals. For example, the generation AI analyzes the specific needs of a company and proposes the most suitable unused land or vacant house. The matching unit can also perform appropriate matching by having the generation AI take into account the specific needs of individuals. Furthermore, the matching unit can also propose customized matching according to the specific needs of companies and individuals. This makes it possible to perform customized matching according to specific needs.

[0044] The matching unit can perform long-term matching by taking into account the future plans and goals of companies and individuals. The matching unit can perform long-term matching by taking into account the future plans and goals of companies and individuals. For example, the generation AI analyzes a company's future plans and proposes optimal matching from a long-term perspective. The matching unit can also perform appropriate matching by taking into account the future goals of individuals. Furthermore, the matching unit can also propose long-term matching based on the future plans and goals of companies and individuals. This makes it possible to perform long-term matching by taking into account future plans and goals.

[0045] The matching unit can perform matching to strengthen collaboration with the local community, taking into account local community activities and event information. For example, the generation AI analyzes local community activities and matches the most suitable companies and people. The matching unit can also perform appropriate matching by having the generation AI consider local event information. Furthermore, the matching unit can also perform appropriate matching by having the generation AI consider local event information. It can also propose matches based on information about community activities and events, making it possible to make matches that strengthen ties with the local community by taking into account local community activities and event information.

[0046] The matching unit can perform matching with a high degree of community contribution by taking into account the social responsibility activities of companies and individuals. The matching unit can perform matching with a high degree of community contribution by taking into account the social responsibility activities of companies and individuals. For example, the generation AI analyzes a company's CSR activities and proposes matching with a high degree of community contribution. The matching unit can also perform appropriate matching by taking into account the social responsibility activities of individuals. Furthermore, the matching unit can also propose matching with a high degree of community contribution based on the CSR activities of companies and individuals. This makes it possible to perform matching with a high degree of community contribution by taking into account social responsibility activities.

[0047] The proposal unit can propose optimal crops by analyzing regional climate and soil data in detail. The proposal unit, for example, proposes optimal crops by analyzing regional climate and soil data in detail. For example, the generation AI analyzes regional climate data and proposes optimal crops. The proposal unit can also propose appropriate crops by analyzing regional soil data in detail. The proposal unit can also propose optimal crops by integrating regional climate and soil data. This makes it possible to propose optimal crops by analyzing regional climate and soil data in detail.

[0048] The proposal unit can refer to past agricultural data and improve the accuracy of proposals based on success and failure cases. The proposal unit, for example, refers to past agricultural data and improves the accuracy of proposals based on success and failure cases. For example, the generation AI analyzes past agricultural data and proposes optimal crops based on success cases. The proposal unit can also refer to past failure cases and make proposals to avoid risks. Furthermore, the proposal unit can also integrate past agricultural data and improve the accuracy of proposals based on success and failure cases. In this way, the accuracy of proposals is improved by referring to past agricultural data.

[0049] The suggestion unit can suggest appropriate crops and activity methods according to the farming experience and skill level of the company or person. The suggestion unit can suggest appropriate crops and activity methods according to, for example, the farming experience and skill level of the company or person. For example, the generation AI can analyze the farming experience of the company or person and suggest crops for beginners. The suggestion unit can also suggest appropriate activity methods by having the generation AI take the skill level into consideration. Furthermore, the suggestion unit can also suggest optimal crops and activity methods based on the farming experience and skill level of the company or person. This makes it possible to make appropriate suggestions according to farming experience and skill level.

[0050] The proposal unit can propose agricultural experience programs that utilize local tourism resources and cultural resources. For example, the proposal unit proposes agricultural experience programs that utilize local tourism resources and cultural resources. For example, the generation AI analyzes local tourism resources and proposes agricultural experience programs. The proposal unit can also propose unique agricultural experience programs by using local cultural resources. Furthermore, the proposal unit can propose attractive agricultural experience programs by using the generation AI to integrate tourism resources and cultural resources. This makes it possible to propose agricultural experience programs that utilize local tourism resources and cultural resources.

[0051] The proposal department can propose business models that utilize local specialty products and branded agricultural products. For example, the proposal department proposes business models that utilize local specialty products and branded agricultural products. For example, the generation AI analyzes local specialty products and proposes a business model. The proposal department can also propose a highly profitable business model in which the generation AI utilizes branded agricultural products. Furthermore, the proposal department can propose an optimal business model in which the generation AI integrates specialty products and branded agricultural products. This makes it possible to propose business models that utilize local specialty products and branded agricultural products.

[0052] The suggestion unit can suggest health-conscious agricultural products and activity methods based on the health condition and lifestyle of the company or person. The suggestion unit can suggest health-conscious agricultural products and activity methods based on the health condition and lifestyle of the company or person. For example, the generation AI analyzes the health condition of the company or person and suggests health-conscious agricultural products. The suggestion unit can also suggest appropriate activity methods by having the generation AI take lifestyle into consideration. Furthermore, the suggestion unit can suggest optimal agricultural products and activity methods by having the generation AI take into consideration the health condition and lifestyle of the company or person. This makes it possible to make health-conscious suggestions based on health condition and lifestyle.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The local resource utilization system may further include a proposal unit that proposes agricultural experience programs that utilize local tourism resources. The proposal unit may, for example, analyze local tourism resources and propose agricultural experience programs. The proposal unit may also propose unique agricultural experience programs that utilize local cultural resources. Furthermore, the proposal unit may integrate tourism resources and cultural resources to propose attractive agricultural experience programs. This makes it possible to propose agricultural experience programs that utilize local tourism resources and cultural resources.

[0055] The local resource utilization system may further include a proposal unit that proposes health-conscious agricultural products and activity methods based on the health status and lifestyle of the company or individual. The proposal unit, for example, analyzes the health status of the company or individual and proposes health-conscious agricultural products. The proposal unit may also consider the lifestyle and propose appropriate activity methods. Furthermore, the proposal unit may propose optimal agricultural products and activity methods based on the health status and lifestyle of the company or individual. This makes it possible to make health-conscious proposals based on the health status and lifestyle.

[0056] The local resource utilization system can also be equipped with a matching unit that takes into account the social responsibility activities of companies and individuals to perform matching with a high degree of local contribution. The matching unit, for example, analyzes the CSR activities of companies and individuals to propose matching with a high degree of local contribution. The matching unit can also consider the social responsibility activities of individuals to perform appropriate matching. Furthermore, the matching unit can propose matching with a high degree of local contribution based on the CSR activities of companies and individuals. This makes it possible to perform matching with a high degree of local contribution by taking social responsibility activities into account.

[0057] The local resource utilization system can also include a proposal unit that proposes business models that utilize local specialty products and branded agricultural products. The proposal unit, for example, analyzes local specialty products and proposes business models. The proposal unit can also propose highly profitable business models that utilize branded agricultural products. Furthermore, the proposal unit can integrate local specialty products and branded agricultural products and propose optimal business models. This makes it possible to propose business models that utilize local specialty products and branded agricultural products.

[0058] The local resource utilization system can also be equipped with a matching unit that performs matching to strengthen ties with the local community, taking into account local community activities and event information. The matching unit, for example, analyzes local community activities and matches the most suitable companies and people. The matching unit can also perform appropriate matching by taking into account local event information. Furthermore, the matching unit can also propose matching based on community activities and event information in order to strengthen ties with the local community. This makes it possible to perform matching that strengthens ties with the local community by taking into account local community activities and event information.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects information on idle land and vacant houses in rural areas. For example, the collection unit collects data on idle land and vacant houses provided by local governments. The collection unit can also use the generation AI to automatically collect information on idle land and vacant houses in rural areas. For example, the generation AI obtains and analyzes information on idle land and vacant houses from the local government's database. Furthermore, the collection unit can collect information from data sources other than local governments. For example, it collects information from real estate agents and local residents and adds it to the database. Step 2: The matching unit matches idle land and vacant houses in rural areas with companies and people who want to utilize them based on the information collected by the collection unit. For example, the matching unit appropriately matches idle land and vacant houses with companies and people based on the needs of the companies and people. The matching unit can also use generation AI to perform optimal matching based on the needs of companies and people. For example, the generation AI can analyze the needs of companies and people and propose the most suitable idle land and vacant houses based on that. Furthermore, the matching unit can also perform matching taking into account the past activity history of companies and people. For example, it can analyze the past project history of a company and propose the most suitable idle land and vacant house. Step 3: The proposal unit proposes activity locations and optimal agricultural products to companies and individuals matched by the matching unit. For example, the proposal unit proposes agricultural products that are suitable for cultivation in a specific region. The proposal unit can also propose agricultural products that are suited to the climate and soil of the region. For example, the proposal unit analyzes regional climate and soil data and proposes optimal agricultural products based on that data. Furthermore, the proposal unit can provide advice to help companies and individuals carry out agricultural activities efficiently. For example, the proposal unit provides advice on cultivation methods, harvest times, etc.

[0061] (Example 2) A system according to an embodiment of the present invention combines AI regional resource mapping with an in-house side job system to create a new business model in which companies can play a part in regional revitalization and coexist and prosper with rural areas. This system uses generative AI to match idle land and vacant houses in rural areas with companies and people who want to utilize them, and an agricultural advisor suggests locations and optimal agricultural products. For example, the system uses generative AI to collect information on idle land and vacant houses in rural areas and companies and people who want to utilize them. For example, the generative AI analyzes data on idle land and vacant houses provided by local governments and matches them to the needs of companies and people. Next, the agricultural advisor suggests locations and optimal agricultural products. For example, it suggests crops suitable for cultivation in a specific region or crops suited to the local climate and soil. This allows companies and people to carry out agricultural activities efficiently. Furthermore, this system can synergistically solve issues facing rural areas and the needs of urban areas. For example, issues such as depopulation and the increase in idle land in rural areas can be addressed by urban companies and people through agricultural activities in rural areas. For businesses and people in urban areas, working in the fields surrounded by abundant nature on the weekends can also provide a sense of refreshment and new business opportunities. In this way, by combining AI regional resource mapping with a company's in-house side job system, a new business model can be created in which rural and urban areas can coexist and prosper together. This allows the system to make effective use of idle land and vacant houses in rural areas, and makes it possible to match and propose solutions that meet the needs of businesses and people.

[0062] A local resource utilization system according to an embodiment includes a collection unit, a matching unit, and a proposal unit. The collection unit collects information on idle land and vacant houses in local areas. For example, the collection unit collects data on idle land and vacant houses provided by local governments. The collection unit can also automatically collect information on idle land and vacant houses in local areas using a generation AI. For example, the generation AI acquires and analyzes information on idle land and vacant houses from a local government database. The collection unit can also collect information from data sources other than local governments. For example, the collection unit collects information from real estate agents and local residents and adds it to the database. The matching unit matches idle land and vacant houses in local areas with companies and people who want to utilize them based on the information collected by the collection unit. For example, the matching unit appropriately matches idle land and vacant houses with companies and people based on the needs of the companies and people. The matching unit can also use the generation AI to perform optimal matching based on the needs of the companies and people. For example, the generation AI analyzes the needs of companies and people and proposes optimal idle land and vacant houses based on the analysis. Furthermore, the matching unit can also perform matching by taking into account the past activity history of companies and individuals. For example, it analyzes a company's past project history and proposes optimal idle land or vacant houses. The proposal unit proposes activity locations and optimal agricultural products to companies and individuals matched by the matching unit. The proposal unit, for example, proposes agricultural products that are suitable for cultivation in a specific region. The proposal unit can also propose agricultural products that are suitable for the climate and soil of the region. For example, the proposal unit analyzes regional climate data and soil data and proposes optimal agricultural products based on that data. Furthermore, the proposal unit can provide advice to companies and individuals to efficiently carry out agricultural activities. For example, the proposal unit provides advice on cultivation methods and harvesting times. As a result, the local resource utilization system according to the embodiment can make effective use of idle land and vacant houses in rural areas and enable matching and proposals that meet the needs of companies and individuals.

[0063] The collection unit can collect data on idle land and vacant houses provided by local governments. The collection unit, for example, collects data on idle land and vacant houses provided by local governments. For example, it acquires and analyzes information on idle land and vacant houses from the database of the local government. The collection unit can also evaluate the frequency of data provided by the local government and prioritize collecting frequently updated data. For example, the generation AI evaluates the frequency of data provided by the local government and prioritizes collecting frequently updated data. Furthermore, the collection unit can also evaluate the reliability of the data provider of the local government and prioritize collecting data from highly reliable providers. For example, the generation AI evaluates the reliability of the data provider of the local government and prioritizes collecting data from highly reliable providers. In this way, highly reliable information can be obtained by collecting data from local governments.

[0064] The matching unit can appropriately match idle land or vacant houses with companies or people based on the needs of the companies or people. For example, the matching unit appropriately matches idle land or vacant houses with companies or people based on the needs of the companies or people. For example, the generation AI analyzes the needs of companies or people and suggests the most suitable idle land or vacant house based on that. The matching unit can also perform matching taking into account the past activity history of companies or people. For example, it analyzes the past project history of a company and suggests the most suitable idle land or vacant house. Furthermore, the matching unit can perform long-term matching taking into account the future plans and goals of companies and people. For example, it analyzes a company's future plans and suggests the optimal match from a long-term perspective. This makes it possible to perform optimal matching according to the needs of companies and people.

[0065] The proposal unit can propose crops that are suitable for cultivation in a specific region. For example, the generation AI analyzes climate and soil data for the region and proposes the optimal crops based on that data. The proposal unit can also refer to past agricultural data to improve the accuracy of proposals based on success stories and failures. For example, the generation AI analyzes past agricultural data and proposes the optimal crops based on success stories. Furthermore, the proposal unit can also propose appropriate crops and activity methods based on the farming experience and skill level of companies and individuals. For example, the generation AI analyzes the farming experience of companies and individuals and proposes crops for beginners. This makes it possible to propose crops that are suitable for the region.

[0066] The proposal unit can propose crops that are suitable for the climate and soil of the region. For example, the generation AI analyzes regional climate and soil data and proposes the optimal crops based on that data. The proposal unit can also refer to past agricultural data to improve the accuracy of proposals based on success stories and failures. For example, the generation AI analyzes past agricultural data and proposes the optimal crops based on success stories. Furthermore, the proposal unit can also propose appropriate crops and activity methods based on the farming experience and skill level of companies and individuals. For example, the generation AI analyzes the farming experience of companies and individuals and proposes crops for beginners. This makes it possible to propose crops that are suitable for the climate and soil of the region.

[0067] The suggestion unit can provide advice to help companies and individuals carry out agricultural activities effectively. The suggestion unit provides advice to help companies and individuals carry out agricultural activities effectively. For example, the generation AI provides advice on cultivation methods, harvest times, etc. The suggestion unit can also suggest appropriate crops and activity methods based on the company's or individual's farming experience and skill level. For example, the generation AI can analyze the farming experience of the company or individual and suggest crops suitable for beginners. Furthermore, the suggestion unit can also suggest optimal crops by conducting a detailed analysis of regional climate and soil data. For example, the generation AI can analyze regional climate and soil data and suggest optimal crops based on that data. This provides advice for efficient agricultural activities.

[0068] The local resource utilization system further includes a collection unit that estimates the user's emotions and prioritizes the data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and prioritizes the data to be collected based on the estimated user emotions. For example, if the user is excited, the generation AI prioritizes collecting information on new vacant land or vacant houses. Furthermore, if the user is relaxed, the collection unit can also cause the generation AI to reevaluate existing data and prioritize collecting reliable information. Furthermore, if the user is stressed, the collection unit can also prioritize collecting data that the generation AI can easily access. This allows the data collection priority to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] The collection unit can evaluate the reliability of data provided by local governments and prioritize the collection of highly reliable data. For example, the collection unit evaluates the reliability of data provided by local governments and prioritizes the collection of highly reliable data. For example, the generation AI evaluates the frequency of data provided by local governments and prioritizes the collection of frequently updated data. The collection unit can also have the generation AI evaluate the reliability of data providers to local governments and prioritize the collection of data from highly reliable providers. Furthermore, the collection unit can have the generation AI evaluate the past accuracy of data from local governments and prioritize the collection of highly accurate data. This allows for the prioritized collection of highly reliable data, thereby improving accuracy. High-quality information can be obtained.

[0070] The collection unit can prioritize collecting the latest information, taking into account the frequency of data updates. The collection unit, for example, prioritizes collecting the latest information, taking into account the frequency of data updates. For example, the generation AI checks the last update date of the data and prioritizes collecting the latest data. The collection unit can also have the generation AI analyze the frequency of data updates and prioritize collecting data that is updated frequently. Furthermore, the collection unit can have the generation AI refer to the data update history and prioritize collecting the latest information. In this way, by prioritizing the collection of the latest information, the latest data can always be obtained.

[0071] The collection unit can apply different collection algorithms depending on the type of data. For example, the collection unit applies different collection algorithms depending on the type of data. For example, when the generation AI collects data on unused land, it applies an algorithm that emphasizes the area of ​​the land and location information. In addition, when the generation AI collects data on vacant houses, the collection unit can also apply an algorithm that emphasizes the condition and age of the building. Furthermore, when the generation AI collects data on agricultural land, the collection unit can also apply an algorithm that emphasizes the quality of the soil and climatic conditions. This enables optimal collection according to the type of data.

[0072] The local resource utilization system further includes a collection unit that estimates a user's emotions and adjusts the display method of the collected data based on the estimated user emotions. The collection unit, for example, estimates a user's emotions and adjusts the display method of the collected data based on the estimated user emotions. For example, if the user is excited, the generation AI displays the data in visually appealing graphics. Furthermore, if the user is relaxed, the collection unit can also display the data in a simple, easy-to-read format. Furthermore, if the user is stressed, the collection unit can also display the most important information by highlighting it. This adjusts the display method of the data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The collection unit can also collect information from data sources other than local governments. For example, the generation AI collects data on idle land and vacant houses provided by real estate agents. The collection unit can also have the generation AI collect word-of-mouth information from local residents and add it to the database. Furthermore, the collection unit can have the generation AI collect data provided by online platforms and integrate it with local government data. In this way, comprehensive data can be obtained by collecting information from a variety of data sources.

[0074] The collection unit can collect data over a wide area, taking into account the geographical spread. For example, the collection unit collects data over a wide area, taking into account the geographical spread. For example, the generation AI collects data from multiple local governments to obtain information on idle land and vacant houses over a wide area. The collection unit can also have the generation AI collect data across regional boundaries to provide information from a wide-area perspective. Furthermore, the collection unit can have the generation AI integrate data from different regions to build a comprehensive database. In this way, comprehensive information can be obtained by collecting data over a wide area.

[0075] The collection unit can integrate and collect multiple data sources. For example, the collection unit integrates and collects multiple data sources. For example, the generation AI integrates data from local governments, real estate agents, and local residents to provide highly accurate information. The collection unit can also cross-check information from different data sources to collect highly reliable data. Furthermore, the collection unit can also integrate information from multiple data sources and build a comprehensive database. This allows highly accurate information to be obtained by integrating multiple data sources.

[0076] The local resource utilization system further includes a matching unit that estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The matching unit, for example, estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. For example, if the user is excited, the generation AI prioritizes proposing new matches. The matching unit can also cause the generation AI to reevaluate existing matches if the user is relaxed. Furthermore, if the user is stressed, the matching unit can cause the generation AI to prioritize simple and intuitive matches. This adjusts the matching criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The matching unit can make appropriate matches by taking into account the past activity history of companies and people. The matching unit makes appropriate matches by taking into account the past activity history of companies and people. For example, the generation AI analyzes a company's past project history and suggests the most suitable idle land or vacant house. The matching unit can also make appropriate matches by taking into account the past activity history of individuals. Furthermore, the matching unit can also suggest optimal matches based on the past success stories of companies and people. This makes it possible to make optimal matches by taking into account past activity history.

[0078] The matching unit can improve the accuracy of matching by analyzing the characteristics of idle land and vacant houses in detail. The matching unit can improve the accuracy of matching by analyzing the characteristics of idle land and vacant houses in detail, for example. For example, the generation AI can analyze the area and location of the idle land in detail to match the most suitable company or person. The matching unit can also make appropriate matches by having the generation AI take into account the condition and age of the vacant house. Furthermore, the matching unit can also propose optimal matches by having the generation AI analyze the characteristics of idle land and vacant houses in detail. In this way, the accuracy of matching is improved by analyzing the characteristics of idle land and vacant houses in detail.

[0079] The matching unit can perform matching customized according to the specific needs of companies and individuals. The matching unit can perform matching customized according to the specific needs of companies and individuals. For example, the generation AI analyzes the specific needs of a company and proposes the most suitable unused land or vacant house. The matching unit can also perform appropriate matching by having the generation AI take into account the specific needs of individuals. Furthermore, the matching unit can also propose customized matching according to the specific needs of companies and individuals. This makes it possible to perform customized matching according to specific needs.

[0080] The local resource utilization system further includes a matching unit that estimates the user's emotions and adjusts the display method of the matching results based on the estimated user emotions. The matching unit, for example, estimates the user's emotions and adjusts the display method of the matching results based on the estimated user emotions. For example, if the user is excited, the generation AI displays the matching results with visually appealing graphics. Furthermore, if the user is relaxed, the matching unit can display the matching results in a simple, easy-to-read format. Furthermore, if the user is stressed, the matching unit can display the most important information by highlighting it. This adjusts the display method of the matching results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The matching unit can perform long-term matching by taking into account the future plans and goals of companies and individuals. The matching unit can perform long-term matching by taking into account the future plans and goals of companies and individuals. For example, the generation AI analyzes a company's future plans and proposes optimal matching from a long-term perspective. The matching unit can also perform appropriate matching by taking into account the future goals of individuals. Furthermore, the matching unit can also propose long-term matching based on the future plans and goals of companies and individuals. This makes it possible to perform long-term matching by taking into account future plans and goals.

[0082] The matching unit can perform matching to strengthen collaboration with the local community, taking into account local community activities and event information. For example, the generation AI analyzes local community activities and matches the most suitable companies and people. The matching unit can also perform appropriate matching by having the generation AI consider local event information. Furthermore, the matching unit can also perform appropriate matching by having the generation AI consider local event information. It can also propose matches based on information about community activities and events, making it possible to make matches that strengthen ties with the local community by taking into account local community activities and event information.

[0083] The matching unit can perform matching with a high degree of community contribution by taking into account the social responsibility activities of companies and individuals. The matching unit can perform matching with a high degree of community contribution by taking into account the social responsibility activities of companies and individuals. For example, the generation AI analyzes a company's CSR activities and proposes matching with a high degree of community contribution. The matching unit can also perform appropriate matching by taking into account the social responsibility activities of individuals. Furthermore, the matching unit can also propose matching with a high degree of community contribution based on the CSR activities of companies and individuals. This makes it possible to perform matching with a high degree of community contribution by taking into account social responsibility activities.

[0084] The local resource utilization system further includes a suggestion unit that estimates the user's emotions and adjusts the way in which suggestions are presented based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions and adjusts the way in which suggestions are presented based on the estimated user emotions. For example, if the user is excited, the suggestion unit displays suggestions using visually appealing graphics. If the user is relaxed, the suggestion unit can also display suggestions in a simple, easy-to-read format. Furthermore, if the user is stressed, the suggestion unit can also display suggestions by emphasizing the most important information. This adjusts the way in which suggestions are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The proposal unit can propose optimal crops by analyzing regional climate and soil data in detail. The proposal unit, for example, proposes optimal crops by analyzing regional climate and soil data in detail. For example, the generation AI analyzes regional climate data and proposes optimal crops. The proposal unit can also propose appropriate crops by analyzing regional soil data in detail. The proposal unit can also propose optimal crops by integrating regional climate and soil data. This makes it possible to propose optimal crops by analyzing regional climate and soil data in detail.

[0086] The proposal unit can refer to past agricultural data and improve the accuracy of proposals based on success and failure cases. The proposal unit, for example, refers to past agricultural data and improves the accuracy of proposals based on success and failure cases. For example, the generation AI analyzes past agricultural data and proposes optimal crops based on success cases. The proposal unit can also refer to past failure cases and make proposals to avoid risks. Furthermore, the proposal unit can also integrate past agricultural data and improve the accuracy of proposals based on success and failure cases. In this way, the accuracy of proposals is improved by referring to past agricultural data.

[0087] The suggestion unit can suggest appropriate crops and activity methods according to the farming experience and skill level of the company or person. The suggestion unit can suggest appropriate crops and activity methods according to, for example, the farming experience and skill level of the company or person. For example, the generation AI can analyze the farming experience of the company or person and suggest crops for beginners. The suggestion unit can also suggest appropriate activity methods by having the generation AI take the skill level into consideration. Furthermore, the suggestion unit can also suggest optimal crops and activity methods based on the farming experience and skill level of the company or person. This makes it possible to make appropriate suggestions according to farming experience and skill level.

[0088] The local resource utilization system further includes a suggestion unit that estimates the user's emotions and prioritizes suggestions based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions and prioritizes suggestions based on the estimated user emotions. For example, if the user is excited, the generation AI prioritizes displaying new suggestions. The suggestion unit can also reevaluate existing suggestions and prioritize them if the user is relaxed. Furthermore, if the user is stressed, the suggestion unit can also prioritize displaying simple and intuitive suggestions. This allows the priority of suggestions to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The proposal unit can propose agricultural experience programs that utilize local tourism resources and cultural resources. For example, the proposal unit proposes agricultural experience programs that utilize local tourism resources and cultural resources. For example, the generation AI analyzes local tourism resources and proposes agricultural experience programs. The proposal unit can also propose unique agricultural experience programs by using local cultural resources. Furthermore, the proposal unit can propose attractive agricultural experience programs by using the generation AI to integrate tourism resources and cultural resources. This makes it possible to propose agricultural experience programs that utilize local tourism resources and cultural resources.

[0090] The proposal department can propose business models that utilize local specialty products and branded agricultural products. For example, the proposal department proposes business models that utilize local specialty products and branded agricultural products. For example, the generation AI analyzes local specialty products and proposes a business model. The proposal department can also propose a highly profitable business model in which the generation AI utilizes branded agricultural products. Furthermore, the proposal department can propose an optimal business model in which the generation AI integrates specialty products and branded agricultural products. This makes it possible to propose business models that utilize local specialty products and branded agricultural products.

[0091] The suggestion unit can suggest health-conscious agricultural products and activity methods based on the health condition and lifestyle of the company or person. The suggestion unit can suggest health-conscious agricultural products and activity methods based on the health condition and lifestyle of the company or person. For example, the generation AI analyzes the health condition of the company or person and suggests health-conscious agricultural products. The suggestion unit can also suggest appropriate activity methods by having the generation AI take lifestyle into consideration. Furthermore, the suggestion unit can suggest optimal agricultural products and activity methods by having the generation AI take into consideration the health condition and lifestyle of the company or person. This makes it possible to make health-conscious suggestions based on health condition and lifestyle. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, matching unit, and suggestion unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information on unused land and vacant houses in rural areas using the camera 42 and communication I / F 44 of the smart device 14, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs matching that meets the needs of companies and people based on the collected information. The suggestion unit is realized, for example, by the control unit 46A of the smart device 14, and suggests optimal agricultural products and activity locations. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, matching unit, and suggestion 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 collection unit collects information on unused land and vacant houses in rural areas using the camera 42 and communication I / F 44 of the smart glasses 214, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The matching unit, realized, for example, by the specific processing unit 290 of the data processing device 12, performs matching based on the collected information to meet the needs of companies and individuals. The suggestion unit, realized, for example, by the control unit 46A of the smart glasses 214, suggests optimal crops and activity locations. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, matching unit, and suggestion 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 collection unit collects information on unused land and vacant houses in rural areas using the camera 42 and communication I / F 44 of the headset terminal 314, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs matching that meets the needs of companies and people based on the collected information. The suggestion unit is realized, for example, by the control unit 46A of the headset terminal 314, and suggests optimal agricultural products and activity locations. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, matching unit, and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit uses the camera 42 and communication I / F 44 of the robot 414 to collect information on unused land and vacant houses in rural areas, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs matching that meets the needs of companies and people based on the collected information. The suggestion unit is realized, for example, by the control unit 46A of the robot 414, and suggests optimal agricultural products and activity locations.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The local resource utilization system may further include a proposal unit that proposes agricultural experience programs that utilize local tourism resources. The proposal unit may, for example, analyze local tourism resources and propose agricultural experience programs. The proposal unit may also propose unique agricultural experience programs that utilize local cultural resources. Furthermore, the proposal unit may integrate tourism resources and cultural resources to propose attractive agricultural experience programs. This makes it possible to propose agricultural experience programs that utilize local tourism resources and cultural resources.

[0094] The local resource utilization system may further include a proposal unit that proposes health-conscious agricultural products and activity methods based on the health status and lifestyle of the company or individual. The proposal unit, for example, analyzes the health status of the company or individual and proposes health-conscious agricultural products. The proposal unit may also consider the lifestyle and propose appropriate activity methods. Furthermore, the proposal unit may propose optimal agricultural products and activity methods based on the health status and lifestyle of the company or individual. This makes it possible to make health-conscious proposals based on the health status and lifestyle.

[0095] The local resource utilization system can also be equipped with a matching unit that takes into account the social responsibility activities of companies and individuals to perform matching with a high degree of local contribution. The matching unit, for example, analyzes the CSR activities of companies and individuals to propose matching with a high degree of local contribution. The matching unit can also consider the social responsibility activities of individuals to perform appropriate matching. Furthermore, the matching unit can propose matching with a high degree of local contribution based on the CSR activities of companies and individuals. This makes it possible to perform matching with a high degree of local contribution by taking social responsibility activities into account.

[0096] The local resource utilization system can also include a proposal unit that proposes business models that utilize local specialty products and branded agricultural products. The proposal unit, for example, analyzes local specialty products and proposes business models. The proposal unit can also propose highly profitable business models that utilize branded agricultural products. Furthermore, the proposal unit can integrate local specialty products and branded agricultural products and propose optimal business models. This makes it possible to propose business models that utilize local specialty products and branded agricultural products.

[0097] The local resource utilization system can also be equipped with a matching unit that performs matching to strengthen ties with the local community, taking into account local community activities and event information. The matching unit, for example, analyzes local community activities and matches the most suitable companies and people. The matching unit can also perform appropriate matching by taking into account local event information. Furthermore, the matching unit can also propose matching based on community activities and event information in order to strengthen ties with the local community. This makes it possible to perform matching that strengthens ties with the local community by taking into account local community activities and event information.

[0098] The local resource utilization system may further include a collection unit that estimates the user's emotions and determines the priority of data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of data to be collected based on the estimated user emotions. For example, if the user is excited, the generation AI may prioritize collecting information on new vacant land or vacant houses. Furthermore, if the user is relaxed, the collection unit may cause the generation AI to reevaluate existing data and prioritize collecting reliable information. Furthermore, if the user is stressed, the collection unit may prioritize collecting data that the generation AI can easily access. In this way, the priority of data collection is determined according to the user's emotions.

[0099] The local resource utilization system may further include a collection unit that estimates the user's emotions and adjusts the display method of the collected data based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the display method of the collected data based on the estimated user emotions. For example, if the user is excited, the generation AI may display the data in visually appealing graphics. The collection unit may also cause the generation AI to display the data in a simple, easy-to-read format if the user is relaxed. Furthermore, the collection unit may cause the generation AI to highlight and display the most important information if the user is feeling stressed. In this way, the display method of the data is adjusted according to the user's emotions.

[0100] The local resource utilization system may further include a matching unit that estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The matching unit, for example, estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. For example, if the user is excited, the generation AI prioritizes proposing new matches. The matching unit may also cause the generation AI to reevaluate existing matches if the user is relaxed. Furthermore, the matching unit may cause the generation AI to prioritize simple and intuitive matches if the user is feeling stressed. In this way, the matching criteria are adjusted according to the user's emotions.

[0101] The local resource utilization system may further include a matching unit that estimates the user's emotions and adjusts the display method of the matching results based on the estimated user emotions. The matching unit, for example, estimates the user's emotions and adjusts the display method of the matching results based on the estimated user emotions. For example, if the user is excited, the generation AI may display the matching results with visually appealing graphics. Furthermore, if the user is relaxed, the matching unit may display the matching results in a simple, easy-to-read format. Furthermore, if the user is feeling stressed, the matching unit may display the most important information with emphasis. In this way, the display method of the matching results is adjusted according to the user's emotions.

[0102] The local resource utilization system may further include a suggestion unit that estimates the user's emotions and adjusts the way in which suggestions are expressed based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions and adjusts the way in which suggestions are expressed based on the estimated user emotions. For example, if the user is excited, the suggestion unit may display suggestions using visually appealing graphics. If the user is relaxed, the suggestion unit may also display suggestions in a simple, easy-to-read format. Furthermore, if the user is stressed, the suggestion unit may display suggestions by emphasizing the most important information. In this way, the way in which suggestions are expressed is adjusted according to the user's emotions.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection unit collects information on idle land and vacant houses in rural areas. For example, the collection unit collects data on idle land and vacant houses provided by local governments. The collection unit can also use the generation AI to automatically collect information on idle land and vacant houses in rural areas. For example, the generation AI obtains and analyzes information on idle land and vacant houses from the local government's database. Furthermore, the collection unit can collect information from data sources other than local governments. For example, it collects information from real estate agents and local residents and adds it to the database. Step 2: The matching unit matches idle land and vacant houses in rural areas with companies and people who want to utilize them based on the information collected by the collection unit. For example, the matching unit appropriately matches idle land and vacant houses with companies and people based on the needs of the companies and people. The matching unit can also use generation AI to perform optimal matching based on the needs of companies and people. For example, the generation AI can analyze the needs of companies and people and propose the most suitable idle land and vacant houses based on that. Furthermore, the matching unit can also perform matching taking into account the past activity history of companies and people. For example, it can analyze the past project history of a company and propose the most suitable idle land and vacant house. Step 3: The proposal unit proposes activity locations and optimal agricultural products to companies and individuals matched by the matching unit. For example, the proposal unit proposes agricultural products that are suitable for cultivation in a specific region. The proposal unit can also propose agricultural products that are suited to the climate and soil of the region. For example, the proposal unit analyzes regional climate and soil data and proposes optimal agricultural products based on that data. Furthermore, the proposal unit can provide advice to help companies and individuals carry out agricultural activities efficiently. For example, the proposal unit provides advice on cultivation methods, harvest times, etc.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 collection department that collects information on unused land and vacant houses in rural areas, a matching unit that matches unused land and vacant houses in rural areas with companies and people who want to utilize them based on the information collected by the collection unit; a proposal unit that proposes activity locations and suitable agricultural products to companies and people matched by the matching unit; A system characterized by:

2. The collecting unit Collect data on unused land and vacant houses provided by local governments 2. The system of claim 1.

3. The matching unit Matching idle land and vacant houses with businesses and people based on their needs 2. The system of claim 1.

4. The proposal unit Suggesting crops suitable for cultivation in specific regions 2. The system of claim 1.

5. The proposal unit Propose crops suited to the local climate and soil 2. The system of claim 1.

6. The proposal unit Providing advice to businesses and individuals on how to effectively carry out agricultural activities 2. The system of claim 1.

7. The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Evaluate the reliability of data provided by local governments and prioritize collection of reliable data 2. The system of claim 1.

Images