system

Abstract

The system according to the embodiment aims to propose an optimal lifestyle in agriculture. [Solution] A system according to an embodiment includes a collection unit, a reception unit, an analysis unit, and a proposal unit. The collection unit collects data. The reception unit accepts user input. The analysis unit analyzes the data obtained from the collection unit and the reception unit. The proposal unit proposes an optimal farming lifestyle based on the analysis results obtained by the analysis unit.

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 analyze data to propose optimal lifestyles in agriculture, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal lifestyle in agriculture. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a reception unit, an analysis unit, and a proposal unit. The collection unit collects data. The reception unit accepts user input. The analysis unit analyzes the data obtained from the collection unit and the reception unit. The proposal unit proposes an optimal farming lifestyle based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal lifestyle in agriculture. [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) The agricultural proposal system according to an embodiment of the present invention uses a generative AI to propose optimal cultivation methods. This system collects weather data, fixed-point data, geological data, and agricultural product data from across Japan over the past 10 years. Users input the desired agricultural products, region, and field data, and the generative AI then proposes optimal agricultural practices. For example, weather data includes temperature, precipitation, and sunshine hours, while geological data includes soil type and nutrient content. Agricultural product data includes the types of vegetables and fruit trees grown in each region and their yields. Users input the desired agricultural products, region (address), and field data (size, age of abandoned farmland, etc.), and the generative AI makes specific proposals. Furthermore, the generative AI obtains current status data from e-scarecrows and analyzes this data. Based on the analysis results, the generative AI proposes optimal agricultural practices. For example, the generative AI proposes optimal tomato cultivation methods based on past weather data, geological data, and agricultural product data. It also considers the current status data obtained from the e-scarecrows to propose appropriate irrigation methods and fertilizer usage. This allows the user to grow agricultural products efficiently and effectively, and the farming suggestion system can suggest an optimal farming lifestyle based on the user's input.

[0029] An agricultural proposal system according to an embodiment includes a collection unit, a reception unit, an analysis unit, and a proposal unit. The collection unit collects meteorological data, fixed-point data, geological data, and agricultural product data from across Japan for the past 10 years. For example, the collection unit collects meteorological data such as temperature, precipitation, and sunshine hours. The collection unit can also collect geological data such as soil type and nutrient content. The collection unit can also collect agricultural product data such as the types of vegetables and fruit trees grown in each region and their yields. For example, the collection unit can acquire data from the Japan Meteorological Agency or agricultural research institutions. The reception unit receives the agricultural product and region the user wants to grow, as well as the field data status. For example, if the user wants to grow tomatoes, the reception unit receives input such as the type of tomato, the address of the growing region, the size of the field, and the number of years the land has been abandoned. The analysis unit analyzes the data obtained from the collection unit and the reception unit. For example, the analysis unit analyzes the optimal cultivation method based on the meteorological data, geological data, and agricultural product data obtained from the collection unit. The analysis unit can also perform analysis to make specific proposals based on the user's input data obtained from the reception unit. The proposal unit proposes an optimal farming lifestyle based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes an optimal method for cultivating tomatoes based on the analysis results obtained from the analysis unit. The proposal unit can also propose irrigation methods, fertilizer usage amounts, etc. This allows the farming proposal system according to the embodiment to propose an optimal farming lifestyle based on the user's input.

[0030] The agricultural proposal system includes an acquisition unit that acquires current situation data. The acquisition unit acquires the current situation data from, for example, an e-scarecrow. The e-scarecrow is a sensor system that monitors the current situation of a field in real time and provides data such as soil humidity, temperature, and nutrient content. For example, the acquisition unit acquires soil humidity data and provides it to the generation AI. The acquisition unit can also acquire soil temperature data and provide it to the generation AI. Furthermore, the acquisition unit can acquire soil nutrient content data and provide it to the generation AI. In this way, by acquiring the current situation data, the acquisition unit can enable the generation AI to grasp the latest field situation. As a result, the agricultural proposal system can make more accurate proposals by acquiring the current situation data.

[0031] The suggestion unit can suggest irrigation methods and amounts of fertilizer to be used. For example, the generation AI suggests the optimal irrigation method based on past weather data, geological data, and agricultural product data. For example, the suggestion unit can suggest drip irrigation. The suggestion unit can also suggest sprinkler irrigation. Furthermore, the suggestion unit can also suggest surface irrigation. For example, the generation AI suggests the optimal amount of fertilizer to be used based on past weather data, geological data, and agricultural product data. For example, the suggestion unit can suggest the amount of fertilizer to be used based on the nutritional state of the soil. The suggestion unit can also suggest the amount of fertilizer to be used based on the type of crop. Furthermore, the suggestion unit can suggest the amount of fertilizer to be used based on the growth stage of the crop. In this way, the suggestion unit can suggest irrigation methods and amounts of fertilizer to be used, enabling efficient agriculture.

[0032] The collection unit can collect weather data, fixed-point data, geological data, and agricultural product data for the past 10 years. For example, the collection unit collects weather data for the past 10 years. The weather data includes temperature, precipitation, wind speed, etc. For example, the collection unit can acquire weather data from the Japan Meteorological Agency. The collection unit also collects fixed-point data for the past 10 years. The fixed-point data includes weather data and soil data at specific locations. For example, the collection unit can collect weather data at specific locations. The collection unit also collects geological data for the past 10 years. The geological data includes soil type, nutrient components, pH value, etc. For example, the collection unit can acquire geological data from a geological survey station. The collection unit also collects agricultural product data for the past 10 years. The agricultural product data includes crop yield, quality, disease information, etc. For example, the collection unit can acquire agricultural product data from an agricultural research institute. By collecting data for the past 10 years, the collection unit can perform more accurate analysis.

[0033] The reception unit can receive the agricultural products and region that the user wants to grow, and the field data status. The reception unit, for example, receives the agricultural products that the user wants to grow. For example, if the user wants to grow tomatoes, the reception unit receives input of the type of tomato. The reception unit also receives the region where the user will grow the products. For example, the reception unit receives input of the address of the region where the user will grow the products. The reception unit also receives the field data status. The field data status includes the size of the field and the number of years the land has been abandoned. For example, the reception unit receives input of the size of the field by the user. The reception unit can also receive input of the number of years the land has been abandoned by the user. This enables the reception unit to make suggestions based on the user's specific requests.

[0034] The analysis unit can analyze the data obtained from the collection unit and the reception unit. The analysis unit analyzes the optimal cultivation method based on, for example, the weather data, geological data, and agricultural product data obtained from the collection unit. For example, the analysis unit can analyze weather data to identify the optimal cultivation time. The analysis unit can also analyze geological data to identify the optimal soil improvement method. Furthermore, the analysis unit can analyze agricultural product data to select the optimal crop. The analysis unit performs analysis to make specific suggestions based on, for example, user input data obtained from the reception unit. For example, the analysis unit can analyze the type and region of agricultural products the user wants to grow, and the field data status, and generate data to suggest the optimal cultivation method. This allows the analysis unit to make optimal suggestions by analyzing the collected data and the user input data.

[0035] The collection unit can evaluate the reliability of data at the time of collection and prioritize collection of highly reliable data. The collection unit can, for example, check the source of the data to evaluate the reliability of the data. For example, the collection unit can evaluate meteorological data provided by the Japan Meteorological Agency as highly reliable data. The collection unit can also evaluate geological data provided by the Geological Survey as highly reliable data. The collection unit can also evaluate agricultural product data provided by agricultural research institutes as highly reliable data. The collection unit can also check the data acquisition method and past performance to evaluate the reliability of the data. For example, the collection unit checks whether the data acquisition method is based on standard methods. The collection unit can also evaluate the reliability of the data based on past performance. As a result, the collection unit can prioritize collection of highly reliable data, thereby improving the accuracy of the analysis.

[0036] The collection unit can prioritize collecting the latest data during collection, taking into account the frequency of data updates. For example, the collection unit can check the update schedule of the data provider to evaluate the frequency of data updates. For example, the collection unit can check the update frequency of weather data provided by the Japan Meteorological Agency and prioritize collecting the latest data. The collection unit can also check the update frequency of geological data provided by the Geological Survey and prioritize collecting the latest data. Furthermore, the collection unit can check the update frequency of agricultural product data provided by agricultural research institutions and prioritize collecting the latest data. The collection unit can also check the update schedules of real-time data, daily data, monthly data, etc. to evaluate the frequency of data updates. For example, the collection unit prioritizes collecting real-time data to obtain data that reflects the latest situation. The collection unit can also grasp long-term trends by appropriately collecting daily data or monthly data. This allows the collection unit to prioritize collecting the latest data, enabling more accurate analysis.

[0037] The collection unit can prioritize collecting highly relevant data by taking into account the geographical distribution of the data during collection. The collection unit can, for example, check the geographical range of the data provider to evaluate the geographical distribution of the data. For example, the collection unit can check the geographical distribution of meteorological data provided by the Japan Meteorological Agency and prioritize collecting data related to the user's region. The collection unit can also check the geographical distribution of geological data provided by the Geological Survey of Japan and prioritize collecting data related to the user's region. The collection unit can also check the geographical distribution of agricultural product data provided by agricultural research institutions and prioritize collecting data related to the user's region. The collection unit can also check the importance and relevance of data for each region to evaluate the geographical distribution of the data. For example, the collection unit can evaluate the importance of meteorological data in the user's region and prioritize collecting highly relevant data. The collection unit can also evaluate the importance of geological data in the user's region and prioritize collecting highly relevant data. In this way, the collection unit can collect more relevant data by collecting data by taking into account the geographical distribution.

[0038] The collection unit may diversify data sources and collect data from different sources during collection. For example, the collection unit may collect data from multiple providers to diversify data sources. For example, the collection unit may collect weather data from the Japan Meteorological Agency, private weather companies, and university research institutes. The collection unit may also collect geological data from geological surveys, private geological survey companies, and university research institutes. The collection unit may also collect agricultural product data from agricultural research institutes, agricultural cooperatives, and university research institutes. The collection unit may also collect data from government agencies, research institutes, private companies, etc. to diversify data sources. For example, the collection unit may obtain reliable data by collecting data provided by government agencies. The collection unit may also obtain data that reflects the latest research results by collecting data provided by research institutes. The collection unit may also obtain practical data by collecting data provided by private companies. As a result, the collection unit improves data diversity by collecting data from different sources.

[0039] The reception unit can suggest the optimal input method by referring to the user's past input history when receiving the input. The reception unit can, for example, search a database to refer to the user's past input history. For example, the reception unit can automatically display agricultural products and regions that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest agricultural products that the user wants to grow in a specific season based on the user's past input history. The reception unit can also analyze historical data to refer to the past input history. For example, the reception unit can analyze the user's past input data and suggest the optimal input method. The reception unit can also customize the input interface based on the user's past input history. In this way, the reception unit can suggest the optimal input method for the user by referring to the past input history.

[0040] The reception unit can check the consistency of the data entered by the user upon reception and automatically correct any inconsistencies. The reception unit can, for example, check the format and range of the data to check the consistency of the data entered by the user. For example, if the reception unit detects an inappropriate combination of region and agricultural product entered by the user, the generation AI can suggest an appropriate combination. The reception unit can also automatically correct any inconsistencies in the field data conditions entered by the user. The reception unit can also automatically correct any errors in the data entered by the user. The reception unit can also check the interrelationships of the data to check the consistency of the input data. For example, the reception unit can analyze the interrelationships of the data entered by the user and detect inconsistencies. The reception unit can also check the consistency of the data to maintain data consistency. As a result, the reception unit can check the consistency of the input data and automatically correct any inconsistencies, thereby enabling accurate data entry.

[0041] The reception unit can provide an optimal input interface by taking into account the user's device information when receiving the input. The reception unit can collect information such as the device type, OS, and browser to obtain the user's device information. For example, if the user is using a smartphone, the reception unit can provide an input interface that matches the screen size. Furthermore, if the user is using a tablet, the reception unit can provide an input interface that is optimized for a large screen. Furthermore, if the user is using a desktop, the reception unit can provide a detailed input interface. The reception unit can also adjust the layout, font size, color, and the like of the input interface by taking into account the device information. For example, the reception unit can provide a simple layout that is suitable for a small smartphone screen. Furthermore, the reception unit can provide a detailed layout that is suitable for a large tablet screen. Furthermore, the reception unit can provide a complex layout that is suitable for a large desktop screen. In this way, the reception unit can provide an optimal input interface based on the user's device information, thereby improving usability.

[0042] The reception unit can analyze the user's input data in real time upon reception and provide immediate feedback. The reception unit can, for example, use data streaming analysis technology to analyze the user's input data in real time. For example, the reception unit can analyze in real time whether the combination of agricultural products and regions input by the user is appropriate and provide feedback. The reception unit can also analyze in real time whether the field data status input by the user is appropriate and provide feedback. Furthermore, if there is an error in the data input by the user, the reception unit can suggest a correction in real time. The reception unit can also use data streaming analysis technology to analyze the data in real time. For example, the reception unit can analyze the data input by the user in real time and provide immediate feedback. The reception unit can also use data streaming analysis technology to analyze the user's input data in real time and provide immediate feedback. As a result, the reception unit can analyze the data in real time and provide immediate feedback, thereby improving the accuracy of the user's input data.

[0043] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between data during analysis. The analysis unit can, for example, perform correlation analysis to evaluate the interrelationships between data. For example, the analysis unit can perform analysis by taking into account the interrelationships between meteorological data and geological data. The analysis unit can also perform analysis by taking into account the interrelationships between agricultural product data and meteorological data. Furthermore, the analysis unit can perform analysis by taking into account the interrelationships between geological data and agricultural product data. The analysis unit can also perform causal analysis to evaluate the interrelationships between data. For example, the analysis unit can analyze the causal relationship between meteorological data and agricultural product data to improve the accuracy of the analysis. The analysis unit can also analyze the causal relationship between geological data and agricultural product data to improve the accuracy of the analysis. In this way, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between data.

[0044] During analysis, the analysis unit can optimize the analysis algorithm by referring to past analysis results. The analysis unit can, for example, search a database to refer to past analysis results. For example, the analysis unit can optimize the analysis algorithm by referring to past weather data analysis results. The analysis unit can also optimize the analysis algorithm by referring to past geological data analysis results. Furthermore, the analysis unit can optimize the analysis algorithm by referring to past agricultural produce data analysis results. The analysis unit can also analyze historical data to refer to past analysis results. For example, the analysis unit can optimize the analysis algorithm based on past analysis results. The analysis unit can also adjust the analysis algorithm based on past analysis results. This allows the analysis unit to optimize the analysis algorithm by referring to past analysis results.

[0045] The analysis unit can perform the analysis taking into account the geographical distribution of the data. For example, the analysis unit can check the geographical range of the data provider to evaluate the geographical distribution of the data. For example, the analysis unit can check the geographical distribution of meteorological data provided by the Japan Meteorological Agency and perform the analysis based on the data related to the user's region. The analysis unit can also check the geographical distribution of geological data provided by the Geological Survey of Japan and perform the analysis based on the data related to the user's region. Furthermore, the analysis unit can check the geographical distribution of agricultural product data provided by an agricultural research institute and perform the analysis based on the data related to the user's region. The analysis unit can also check the importance and relevance of data for each region to evaluate the geographical distribution of the data. For example, the analysis unit can evaluate the importance of meteorological data in the user's region and perform the analysis based on highly relevant data. The analysis unit can also evaluate the importance of geological data in the user's region and perform the analysis based on highly relevant data. In this way, the analysis unit can perform the analysis taking into account the geographical distribution, thereby enabling more accurate analysis.

[0046] The analysis unit can improve the accuracy of the analysis by referring to related literature data during analysis. The analysis unit can, for example, search academic papers to refer to related literature data. For example, the analysis unit can perform the analysis by referring to the latest agricultural research papers. The analysis unit can also perform the analysis by referring to related academic books. Furthermore, the analysis unit can perform the analysis by referring to patent documents related to agricultural technology. The analysis unit can also search technical reports to refer to related literature data. For example, the analysis unit can perform the analysis by referring to technical reports. The analysis unit can also perform the analysis by referring to patent documents. In this way, the analysis unit can improve the accuracy of the analysis by referring to related literature data.

[0047] When making a suggestion, the suggestion unit can make an optimal suggestion by referring to the user's past behavioral history. The suggestion unit can, for example, search a database to refer to the user's past behavioral history. For example, the suggestion unit can make an optimal suggestion based on data on agricultural products that the user has grown in the past. The suggestion unit can also suggest an optimal cultivation method based on the user's past cultivation history. Furthermore, the suggestion unit can analyze the user's past behavioral history to make the most efficient suggestion. The suggestion unit can also analyze history data to refer to the past behavioral history. For example, the suggestion unit makes an optimal suggestion based on the user's past behavioral history. The suggestion unit can also customize the content of the suggestion based on the user's past behavioral history. In this way, the suggestion unit can make an optimal suggestion to the user by referring to the past behavioral history.

[0048] When making a proposal, the proposal unit can monitor the user's current situation in real time and dynamically adjust the proposal content. The proposal unit can, for example, use sensor data to monitor the user's current situation in real time. For example, the proposal unit can monitor the current humidity and temperature of the user's field in real time and propose an appropriate irrigation method. The proposal unit can also monitor the nutrient content of the user's field in real time and propose an appropriate amount of fertilizer to use. The proposal unit can also monitor the occurrence of pests and diseases in the user's field in real time and propose an appropriate control method. The proposal unit can also use user input data for real-time monitoring. For example, the proposal unit can monitor the current situation in real time and dynamically adjust the proposal content based on data input by the user. The proposal unit can also use sensor data to monitor the user's current situation in real time and dynamically adjust the proposal content. As a result, the proposal unit can provide more appropriate proposals by monitoring in real time and dynamically adjusting the proposal content.

[0049] When making a proposal, the suggestion unit can make an optimal proposal taking into account the user's geographical location information. The suggestion unit can, for example, use GPS data to acquire the user's geographical location information. For example, the suggestion unit can suggest an optimal cultivation method based on meteorological data for the user's region. The suggestion unit can also suggest an optimal cultivation method based on geological data for the user's region. Furthermore, the suggestion unit can also suggest an optimal cultivation method based on agricultural product data for the user's region. The suggestion unit can also evaluate regional characteristics to take the geographical location information into account. For example, the suggestion unit can evaluate meteorological data for the user's region to suggest an optimal cultivation method. The suggestion unit can also evaluate geological data for the user's region to suggest an optimal cultivation method. This enables the suggestion unit to make more appropriate proposals by making proposals taking into account the geographical location information.

[0050] When making a suggestion, the suggestion unit can analyze the user's social media activity and make a relevant suggestion. For example, the suggestion unit can analyze the content of posts to analyze the user's social media activity. For example, the suggestion unit can analyze agriculture-related posts shared by the user on social media and make a relevant suggestion. The suggestion unit can also make a suggestion based on information about agricultural experts the user follows on social media. Furthermore, the suggestion unit can also make a suggestion based on information about agricultural communities the user participates in on social media. The suggestion unit can also evaluate the number of likes and the number of followers to analyze the social media activity. For example, the suggestion unit can make a suggestion based on posts that the user has received many likes. The suggestion unit can also make a suggestion based on information about agricultural experts to whom the user has many followers. In this way, the suggestion unit can make a suggestion relevant to the user by analyzing social media activity.

[0051] The acquisition unit can evaluate the reliability of data at the time of acquisition and prioritize acquisition of highly reliable data. The acquisition unit can, for example, check the source of the data to evaluate the reliability of the data. For example, the acquisition unit can evaluate meteorological data provided by the Japan Meteorological Agency as highly reliable data. The acquisition unit can also evaluate geological data provided by the Geological Survey as highly reliable data. The acquisition unit can also evaluate agricultural product data provided by agricultural research institutions as highly reliable data. The acquisition unit can also check the data acquisition method and past performance to evaluate the reliability of the data. For example, the acquisition unit can check whether the data acquisition method is based on a standard method. The acquisition unit can also evaluate the reliability of the data based on past performance. As a result, the acquisition unit can prioritize acquisition of highly reliable data, thereby improving the accuracy of the analysis.

[0052] The acquisition unit can prioritize acquiring the latest data, taking into account the data update frequency. For example, the acquisition unit can check the update schedule of the data provider to evaluate the data update frequency. For example, the acquisition unit can check the update frequency of weather data provided by the Japan Meteorological Agency and prioritize acquiring the latest data. The acquisition unit can also check the update frequency of geological data provided by the Geological Survey and prioritize acquiring the latest data. Furthermore, the acquisition unit can check the update frequency of agricultural product data provided by agricultural research institutions and prioritize acquiring the latest data. The acquisition unit can also check the update schedule of real-time data, daily data, monthly data, etc. to evaluate the data update frequency. For example, the acquisition unit prioritizes acquiring real-time data to acquire data that reflects the latest situation. The acquisition unit can also grasp long-term trends by appropriately acquiring daily data or monthly data. This allows the acquisition unit to prioritize acquiring the latest data, thereby enabling more accurate analysis.

[0053] The acquisition unit can prioritize acquiring highly relevant data by taking into account the geographical distribution of the data during acquisition. For example, the acquisition unit can check the geographical range of the data provider to evaluate the geographical distribution of the data. For example, the acquisition unit can check the geographical distribution of meteorological data provided by the Japan Meteorological Agency and prioritize acquiring data related to the user's region. The acquisition unit can also check the geographical distribution of geological data provided by the Geological Survey of Japan and prioritize acquiring data related to the user's region. Furthermore, the acquisition unit can check the geographical distribution of agricultural product data provided by agricultural research institutions and prioritize acquiring data related to the user's region. The acquisition unit can also check the importance and relevance of data for each region to evaluate the geographical distribution of the data. For example, the acquisition unit can evaluate the importance of meteorological data in the user's region and prioritize acquiring highly relevant data. The acquisition unit can also evaluate the importance of geological data in the user's region and prioritize acquiring highly relevant data. In this way, the acquisition unit can acquire more relevant data by taking geographical distribution into account.

[0054] The acquisition unit can diversify data sources and acquire data from different sources during acquisition. For example, the acquisition unit can acquire data from multiple providers to diversify data sources. For example, the acquisition unit can acquire weather data from the Japan Meteorological Agency, a private weather company, and a university research institute. The acquisition unit can also acquire geological data from the Geological Survey of Japan, a private geological survey company, and a university research institute. The acquisition unit can also acquire agricultural product data from agricultural research institutes, agricultural cooperatives, and university research institutes. The acquisition unit can also acquire data from government agencies, research institutes, private companies, etc. to diversify data sources. For example, the acquisition unit can acquire highly reliable data by acquiring data provided by government agencies. The acquisition unit can also acquire data provided by research institutes to acquire data that reflects the latest research results. The acquisition unit can also acquire practical data by acquiring data provided by private companies. As a result, the acquisition unit can acquire data from different sources, thereby improving the diversity of data.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The agricultural proposal system may further include an event information unit that provides information on local agricultural events. The event information unit collects information on agricultural-related events, seminars, and workshops held in the area and provides it to the user. For example, the event information unit may provide information on local agricultural fairs and exhibitions. It may also provide information on local agricultural technology seminars and training sessions. It may also provide information on local agricultural community and networking events. This allows the agricultural proposal system to provide users with opportunities to participate in local agricultural events and learn the latest information and techniques.

[0057] The farming suggestion system can further include an experience evaluation unit that evaluates the farming experience of the user. The experience evaluation unit evaluates the user's past farming experience and skills and provides the evaluation results to the analysis unit. For example, the experience evaluation unit can evaluate the types of crops the user has grown in the past and the yields. It can also evaluate the history of agricultural training and seminars the user has attended in the past. It can also evaluate the user's level of agricultural technique and knowledge. This enables the farming suggestion system to make optimal suggestions based on the user's experience and skills.

[0058] The agricultural proposal system can further include an activity recording unit that records the user's agricultural activities. The activity recording unit records the user's agricultural activity history and provides it to the analysis unit. For example, the activity recording unit can record the content and date and time of agricultural work performed by the user. It can also record information about agricultural materials and machinery used by the user. It can also record the quantity and quality of crops harvested by the user. This enables the agricultural proposal system to make more accurate proposals based on the user's agricultural activity history.

[0059] The agricultural proposal system can further include a goal setting unit that sets agricultural goals for the user. The goal setting unit sets agricultural goals that the user wants to achieve and provides them to the analysis unit. For example, if the user wants to increase yield, the goal setting unit can set that goal. Also, if the user wants to improve the quality of a particular crop, the goal setting unit can set that goal. Furthermore, if the user wants to improve agricultural efficiency, the goal can be set. This enables the agricultural proposal system to make optimal proposals according to the user's goals.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects meteorological data, fixed-point data, geological data, and agricultural product data from across Japan for the past 10 years. For example, meteorological data includes temperature, precipitation, and sunshine hours, while geological data includes soil type and nutrient content. Agricultural product data includes the types of vegetables and fruit trees grown in each region and their yields. This data can be obtained from the Japan Meteorological Agency and agricultural research institutions. Step 2: The reception unit accepts the agricultural products and region the user wants to grow, as well as the field data status. For example, if the user wants to grow tomatoes, the unit accepts input of the type of tomato, the address of the growing region, the size of the field, the number of years the field has been abandoned, etc. Step 3: The analysis unit analyzes the data obtained from the collection unit and the reception unit. For example, it analyzes the optimal cultivation method based on the meteorological data, geological data, and agricultural product data obtained from the collection unit, and performs analysis to make specific proposals based on the user's input data obtained from the reception unit. Step 4: The proposal unit proposes optimal farming practices based on the analysis results obtained by the analysis unit. For example, it proposes optimal methods for growing tomatoes, irrigation methods, and amounts of fertilizer to use.

[0062] (Example 2) The agricultural proposal system according to an embodiment of the present invention uses a generative AI to propose optimal cultivation methods. This system collects weather data, fixed-point data, geological data, and agricultural product data from across Japan over the past 10 years. Users input the desired agricultural products, region, and field data, and the generative AI then proposes optimal agricultural practices. For example, weather data includes temperature, precipitation, and sunshine hours, while geological data includes soil type and nutrient content. Agricultural product data includes the types of vegetables and fruit trees grown in each region and their yields. Users input the desired agricultural products, region (address), and field data (size, age of abandoned farmland, etc.), and the generative AI makes specific proposals. Furthermore, the generative AI obtains current status data from e-scarecrows and analyzes this data. Based on the analysis results, the generative AI proposes optimal agricultural practices. For example, the generative AI proposes optimal tomato cultivation methods based on past weather data, geological data, and agricultural product data. It also considers the current status data obtained from the e-scarecrows to propose appropriate irrigation methods and fertilizer usage. This allows the user to grow agricultural products efficiently and effectively, and the farming suggestion system can suggest an optimal farming lifestyle based on the user's input.

[0063] An agricultural proposal system according to an embodiment includes a collection unit, a reception unit, an analysis unit, and a proposal unit. The collection unit collects meteorological data, fixed-point data, geological data, and agricultural product data from across Japan for the past 10 years. For example, the collection unit collects meteorological data such as temperature, precipitation, and sunshine hours. The collection unit can also collect geological data such as soil type and nutrient content. The collection unit can also collect agricultural product data such as the types of vegetables and fruit trees grown in each region and their yields. For example, the collection unit can acquire data from the Japan Meteorological Agency or agricultural research institutions. The reception unit receives the agricultural product and region the user wants to grow, as well as the field data status. For example, if the user wants to grow tomatoes, the reception unit receives input such as the type of tomato, the address of the growing region, the size of the field, and the number of years the land has been abandoned. The analysis unit analyzes the data obtained from the collection unit and the reception unit. For example, the analysis unit analyzes the optimal cultivation method based on the meteorological data, geological data, and agricultural product data obtained from the collection unit. The analysis unit can also perform analysis to make specific proposals based on the user's input data obtained from the reception unit. The proposal unit proposes an optimal farming lifestyle based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes an optimal method for cultivating tomatoes based on the analysis results obtained from the analysis unit. The proposal unit can also propose irrigation methods, fertilizer usage amounts, etc. This allows the farming proposal system according to the embodiment to propose an optimal farming lifestyle based on the user's input.

[0064] The agricultural proposal system includes an acquisition unit that acquires current situation data. The acquisition unit acquires the current situation data from, for example, an e-scarecrow. The e-scarecrow is a sensor system that monitors the current situation of a field in real time and provides data such as soil humidity, temperature, and nutrient content. For example, the acquisition unit acquires soil humidity data and provides it to the generation AI. The acquisition unit can also acquire soil temperature data and provide it to the generation AI. Furthermore, the acquisition unit can acquire soil nutrient content data and provide it to the generation AI. In this way, by acquiring the current situation data, the acquisition unit can enable the generation AI to grasp the latest field situation. As a result, the agricultural proposal system can make more accurate proposals by acquiring the current situation data.

[0065] The suggestion unit can suggest irrigation methods and amounts of fertilizer to be used. For example, the generation AI suggests the optimal irrigation method based on past weather data, geological data, and agricultural product data. For example, the suggestion unit can suggest drip irrigation. The suggestion unit can also suggest sprinkler irrigation. Furthermore, the suggestion unit can also suggest surface irrigation. For example, the generation AI suggests the optimal amount of fertilizer to be used based on past weather data, geological data, and agricultural product data. For example, the suggestion unit can suggest the amount of fertilizer to be used based on the nutritional state of the soil. The suggestion unit can also suggest the amount of fertilizer to be used based on the type of crop. Furthermore, the suggestion unit can suggest the amount of fertilizer to be used based on the growth stage of the crop. In this way, the suggestion unit can suggest irrigation methods and amounts of fertilizer to be used, enabling efficient agriculture.

[0066] The collection unit can collect weather data, fixed-point data, geological data, and agricultural product data for the past 10 years. For example, the collection unit collects weather data for the past 10 years. The weather data includes temperature, precipitation, wind speed, etc. For example, the collection unit can acquire weather data from the Japan Meteorological Agency. The collection unit also collects fixed-point data for the past 10 years. The fixed-point data includes weather data and soil data at specific locations. For example, the collection unit can collect weather data at specific locations. The collection unit also collects geological data for the past 10 years. The geological data includes soil type, nutrient components, pH value, etc. For example, the collection unit can acquire geological data from a geological survey station. The collection unit also collects agricultural product data for the past 10 years. The agricultural product data includes crop yield, quality, disease information, etc. For example, the collection unit can acquire agricultural product data from an agricultural research institute. By collecting data for the past 10 years, the collection unit can perform more accurate analysis.

[0067] The reception unit can receive the agricultural products and region that the user wants to grow, and the field data status. The reception unit, for example, receives the agricultural products that the user wants to grow. For example, if the user wants to grow tomatoes, the reception unit receives input of the type of tomato. The reception unit also receives the region where the user will grow the products. For example, the reception unit receives input of the address of the region where the user will grow the products. The reception unit also receives the field data status. The field data status includes the size of the field and the number of years the land has been abandoned. For example, the reception unit receives input of the size of the field by the user. The reception unit can also receive input of the number of years the land has been abandoned by the user. This enables the reception unit to make suggestions based on the user's specific requests.

[0068] The analysis unit can analyze the data obtained from the collection unit and the reception unit. The analysis unit analyzes the optimal cultivation method based on, for example, the weather data, geological data, and agricultural product data obtained from the collection unit. For example, the analysis unit can analyze weather data to identify the optimal cultivation time. The analysis unit can also analyze geological data to identify the optimal soil improvement method. Furthermore, the analysis unit can analyze agricultural product data to select the optimal crop. The analysis unit performs analysis to make specific suggestions based on, for example, user input data obtained from the reception unit. For example, the analysis unit can analyze the type and region of agricultural products the user wants to grow, and the field data status, and generate data to suggest the optimal cultivation method. This allows the analysis unit to make optimal suggestions by analyzing the collected data and the user input data.

[0069] The agricultural suggestion system includes a collection unit that estimates a user's emotions and adjusts the type of data to be collected based on the estimated user emotions. The collection unit can, for example, conduct a survey to estimate the user's emotions. For example, the collection unit can conduct a survey asking the user about their stress level. The collection unit can also use a camera to recognize the user's facial expressions. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotions using facial recognition technology. The collection unit can also use a microphone to analyze the user's voice. For example, the collection unit can record the user's voice with a microphone and estimate the emotions using voice analysis technology. The collection unit adjusts the type of data to be collected based on the estimated user emotions. For example, if the user is stressed, the collection unit can cause the generation AI to prioritize collecting data on simple cultivation methods. If the user is relaxed, the collection unit can cause the generation AI to collect data on detailed cultivation methods. If the user is excited, the collection unit can cause the generation AI to collect data on new technologies and methods. This allows the collection unit to adjust the type of data to be collected depending on the user's emotions, thereby enabling more appropriate data collection.

[0070] The collection unit can evaluate the reliability of data at the time of collection and prioritize collection of highly reliable data. The collection unit can, for example, check the source of the data to evaluate the reliability of the data. For example, the collection unit can evaluate meteorological data provided by the Japan Meteorological Agency as highly reliable data. The collection unit can also evaluate geological data provided by the Geological Survey as highly reliable data. The collection unit can also evaluate agricultural product data provided by agricultural research institutes as highly reliable data. The collection unit can also check the data acquisition method and past performance to evaluate the reliability of the data. For example, the collection unit checks whether the data acquisition method is based on standard methods. The collection unit can also evaluate the reliability of the data based on past performance. As a result, the collection unit can prioritize collection of highly reliable data, thereby improving the accuracy of the analysis.

[0071] The collection unit can prioritize collecting the latest data during collection, taking into account the frequency of data updates. For example, the collection unit can check the update schedule of the data provider to evaluate the frequency of data updates. For example, the collection unit can check the update frequency of weather data provided by the Japan Meteorological Agency and prioritize collecting the latest data. The collection unit can also check the update frequency of geological data provided by the Geological Survey and prioritize collecting the latest data. Furthermore, the collection unit can check the update frequency of agricultural product data provided by agricultural research institutions and prioritize collecting the latest data. The collection unit can also check the update schedules of real-time data, daily data, monthly data, etc. to evaluate the frequency of data updates. For example, the collection unit prioritizes collecting real-time data to obtain data that reflects the latest situation. The collection unit can also grasp long-term trends by appropriately collecting daily data or monthly data. This allows the collection unit to prioritize collecting the latest data, enabling more accurate analysis.

[0072] The agricultural suggestion system includes a collection unit that estimates a user's emotions and prioritizes data to be collected based on the estimated user emotions. The collection unit can, for example, conduct a survey to estimate the user's emotions. For example, the collection unit can conduct a survey asking the user about their stress level. The collection unit can also use a camera to recognize the user's facial expressions. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotions using facial recognition technology. The collection unit can also use a microphone to analyze the user's voice. For example, the collection unit can record the user's voice with a microphone and estimate the emotions using voice analysis technology. The collection unit prioritizes data to be collected based on the estimated user emotions. For example, if the user is stressed, the collection unit can cause the generation AI to prioritize collecting data on simple cultivation methods. If the user is relaxed, the collection unit can cause the generation AI to collect data on detailed cultivation methods. If the user is excited, the collection unit can cause the generation AI to collect data on new technologies and methods. This allows the collection unit to determine the priority of data according to the user's emotions, thereby enabling more appropriate data collection.

[0073] The collection unit can prioritize collecting highly relevant data by taking into account the geographical distribution of the data during collection. The collection unit can, for example, check the geographical range of the data provider to evaluate the geographical distribution of the data. For example, the collection unit can check the geographical distribution of meteorological data provided by the Japan Meteorological Agency and prioritize collecting data related to the user's region. The collection unit can also check the geographical distribution of geological data provided by the Geological Survey of Japan and prioritize collecting data related to the user's region. The collection unit can also check the geographical distribution of agricultural product data provided by agricultural research institutions and prioritize collecting data related to the user's region. The collection unit can also check the importance and relevance of data for each region to evaluate the geographical distribution of the data. For example, the collection unit can evaluate the importance of meteorological data in the user's region and prioritize collecting highly relevant data. The collection unit can also evaluate the importance of geological data in the user's region and prioritize collecting highly relevant data. In this way, the collection unit can collect more relevant data by collecting data by taking into account the geographical distribution.

[0074] The collection unit may diversify data sources and collect data from different sources during collection. For example, the collection unit may collect data from multiple providers to diversify data sources. For example, the collection unit may collect weather data from the Japan Meteorological Agency, private weather companies, and university research institutes. The collection unit may also collect geological data from geological surveys, private geological survey companies, and university research institutes. The collection unit may also collect agricultural product data from agricultural research institutes, agricultural cooperatives, and university research institutes. The collection unit may also collect data from government agencies, research institutes, private companies, etc. to diversify data sources. For example, the collection unit may obtain reliable data by collecting data provided by government agencies. The collection unit may also obtain data that reflects the latest research results by collecting data provided by research institutes. The collection unit may also obtain practical data by collecting data provided by private companies. As a result, the collection unit improves data diversity by collecting data from different sources.

[0075] The agricultural suggestion system includes a reception unit that estimates a user's emotions and adjusts the display method of the input interface based on the estimated user emotions. The reception unit can, for example, conduct a survey to estimate the user's emotions. For example, the reception unit can conduct a survey asking the user about their stress level. The reception unit can also use a camera to recognize the user's facial expressions. For example, the reception unit can capture the user's facial expressions with a camera and estimate the user's emotions using facial recognition technology. The reception unit can also use a microphone to analyze the user's voice. For example, the reception unit can record the user's voice with a microphone and estimate the user's emotions using voice analysis technology. The reception unit adjusts the display method of the input interface based on the estimated user emotions. For example, if the user is nervous, the reception unit can provide an interface with calm colors to reduce visual stress. If the user is having fun, the reception unit can provide an interface with bright colors to make input work more enjoyable. If the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. This allows the reception unit to provide a more user-friendly interface by adjusting the display method of the input interface in accordance with the user's emotions.

[0076] The reception unit can suggest the optimal input method by referring to the user's past input history when receiving the input. The reception unit can, for example, search a database to refer to the user's past input history. For example, the reception unit can automatically display agricultural products and regions that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest agricultural products that the user wants to grow in a specific season based on the user's past input history. The reception unit can also analyze historical data to refer to the past input history. For example, the reception unit can analyze the user's past input data and suggest the optimal input method. The reception unit can also customize the input interface based on the user's past input history. In this way, the reception unit can suggest the optimal input method for the user by referring to the past input history.

[0077] The reception unit can check the consistency of the data entered by the user upon reception and automatically correct any inconsistencies. The reception unit can, for example, check the format and range of the data to check the consistency of the data entered by the user. For example, if the reception unit detects an inappropriate combination of region and agricultural product entered by the user, the generation AI can suggest an appropriate combination. The reception unit can also automatically correct any inconsistencies in the field data conditions entered by the user. The reception unit can also automatically correct any errors in the data entered by the user. The reception unit can also check the interrelationships of the data to check the consistency of the input data. For example, the reception unit can analyze the interrelationships of the data entered by the user and detect inconsistencies. The reception unit can also check the consistency of the data to maintain data consistency. As a result, the reception unit can check the consistency of the input data and automatically correct any inconsistencies, thereby enabling accurate data entry.

[0078] The agricultural suggestion system includes a reception unit that estimates a user's emotions and prioritizes input data based on the estimated user emotions. The reception unit can, for example, conduct a survey to estimate the user's emotions. For example, the reception unit can conduct a survey asking the user about their stress level. The reception unit can also use a camera to recognize the user's facial expressions. For example, the reception unit can capture the user's facial expressions with a camera and estimate the user's emotions using facial recognition technology. The reception unit can also use a microphone to analyze the user's voice. For example, the reception unit can record the user's voice with a microphone and estimate the user's emotions using voice analysis technology. The reception unit prioritizes the input data based on the estimated user emotions. For example, if the user is stressed, the reception unit can cause the generation AI to prioritize simple input items. If the user is relaxed, the reception unit can cause the generation AI to display detailed input items. If the user is excited, the reception unit can cause the generation AI to display input items related to new technologies or methods. This allows the reception unit to prioritize the input data based on the user's emotions, enabling more appropriate data entry.

[0079] The reception unit can provide an optimal input interface by taking into account the user's device information when receiving the input. The reception unit can collect information such as the device type, OS, and browser to obtain the user's device information. For example, if the user is using a smartphone, the reception unit can provide an input interface that matches the screen size. Furthermore, if the user is using a tablet, the reception unit can provide an input interface that is optimized for a large screen. Furthermore, if the user is using a desktop, the reception unit can provide a detailed input interface. The reception unit can also adjust the layout, font size, color, and the like of the input interface by taking into account the device information. For example, the reception unit can provide a simple layout that is suitable for a small smartphone screen. Furthermore, the reception unit can provide a detailed layout that is suitable for a large tablet screen. Furthermore, the reception unit can provide a complex layout that is suitable for a large desktop screen. In this way, the reception unit can provide an optimal input interface based on the user's device information, thereby improving usability.

[0080] The reception unit can analyze the user's input data in real time upon reception and provide immediate feedback. The reception unit can, for example, use data streaming analysis technology to analyze the user's input data in real time. For example, the reception unit can analyze in real time whether the combination of agricultural products and regions input by the user is appropriate and provide feedback. The reception unit can also analyze in real time whether the field data status input by the user is appropriate and provide feedback. Furthermore, if there is an error in the data input by the user, the reception unit can suggest a correction in real time. The reception unit can also use data streaming analysis technology to analyze the data in real time. For example, the reception unit can analyze the data input by the user in real time and provide immediate feedback. The reception unit can also use data streaming analysis technology to analyze the user's input data in real time and provide immediate feedback. As a result, the reception unit can analyze the data in real time and provide immediate feedback, thereby improving the accuracy of the user's input data.

[0081] The agricultural suggestion system includes an analysis unit that estimates a user's emotions and adjusts an analysis algorithm based on the estimated user emotions. The analysis unit can, for example, conduct a survey to estimate the user's emotions. For example, the analysis unit can conduct a survey asking the user about their stress level. The analysis unit can also use a camera to recognize the user's facial expressions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using facial recognition technology. The analysis unit can also use a microphone to analyze the user's voice. For example, the analysis unit can record the user's voice with a microphone and estimate the emotions using voice analysis technology. The analysis unit adjusts the analysis algorithm based on the estimated user emotions. For example, if the user is stressed, the analysis unit can cause the generation AI to use a simple analysis algorithm. If the user is relaxed, the analysis unit can cause the generation AI to use a more detailed analysis algorithm. Furthermore, if the user is excited, the analysis unit can cause the generation AI to use an analysis algorithm that uses new technologies or methods. This allows the analysis unit to adjust the analysis algorithm according to the user's emotions, enabling more appropriate analysis.

[0082] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between data during analysis. The analysis unit can, for example, perform correlation analysis to evaluate the interrelationships between data. For example, the analysis unit can perform analysis by taking into account the interrelationships between meteorological data and geological data. The analysis unit can also perform analysis by taking into account the interrelationships between agricultural product data and meteorological data. Furthermore, the analysis unit can perform analysis by taking into account the interrelationships between geological data and agricultural product data. The analysis unit can also perform causal analysis to evaluate the interrelationships between data. For example, the analysis unit can analyze the causal relationship between meteorological data and agricultural product data to improve the accuracy of the analysis. The analysis unit can also analyze the causal relationship between geological data and agricultural product data to improve the accuracy of the analysis. In this way, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between data.

[0083] During analysis, the analysis unit can optimize the analysis algorithm by referring to past analysis results. The analysis unit can, for example, search a database to refer to past analysis results. For example, the analysis unit can optimize the analysis algorithm by referring to past weather data analysis results. The analysis unit can also optimize the analysis algorithm by referring to past geological data analysis results. Furthermore, the analysis unit can optimize the analysis algorithm by referring to past agricultural produce data analysis results. The analysis unit can also analyze historical data to refer to past analysis results. For example, the analysis unit can optimize the analysis algorithm based on past analysis results. The analysis unit can also adjust the analysis algorithm based on past analysis results. This allows the analysis unit to optimize the analysis algorithm by referring to past analysis results.

[0084] The agricultural suggestion system includes an analysis unit that estimates a user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. The analysis unit can, for example, conduct a survey to estimate the user's emotions. For example, the analysis unit can conduct a survey asking the user about their stress level. The analysis unit can also use a camera to recognize the user's facial expressions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the user's emotions using facial recognition technology. The analysis unit can also use a microphone to analyze the user's voice. For example, the analysis unit can record the user's voice with a microphone and estimate the user's emotions using voice analysis technology. The analysis unit adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. In this way, the analysis unit can adjust the display method of the analysis results according to the user's emotions, thereby enabling a more user-friendly display.

[0085] The analysis unit can perform the analysis taking into account the geographical distribution of the data. For example, the analysis unit can check the geographical range of the data provider to evaluate the geographical distribution of the data. For example, the analysis unit can check the geographical distribution of meteorological data provided by the Japan Meteorological Agency and perform the analysis based on the data related to the user's region. The analysis unit can also check the geographical distribution of geological data provided by the Geological Survey of Japan and perform the analysis based on the data related to the user's region. Furthermore, the analysis unit can check the geographical distribution of agricultural product data provided by an agricultural research institute and perform the analysis based on the data related to the user's region. The analysis unit can also check the importance and relevance of data for each region to evaluate the geographical distribution of the data. For example, the analysis unit can evaluate the importance of meteorological data in the user's region and perform the analysis based on highly relevant data. The analysis unit can also evaluate the importance of geological data in the user's region and perform the analysis based on highly relevant data. In this way, the analysis unit can perform the analysis taking into account the geographical distribution, thereby enabling more accurate analysis.

[0086] The analysis unit can improve the accuracy of the analysis by referring to related literature data during analysis. The analysis unit can, for example, search academic papers to refer to related literature data. For example, the analysis unit can perform the analysis by referring to the latest agricultural research papers. The analysis unit can also perform the analysis by referring to related academic books. Furthermore, the analysis unit can perform the analysis by referring to patent documents related to agricultural technology. The analysis unit can also search technical reports to refer to related literature data. For example, the analysis unit can perform the analysis by referring to technical reports. The analysis unit can also perform the analysis by referring to patent documents. In this way, the analysis unit can improve the accuracy of the analysis by referring to related literature data.

[0087] The agricultural suggestion system includes a suggestion unit that estimates a user's emotions and adjusts the way suggestions are presented based on the estimated user emotions. The suggestion unit can, for example, conduct a survey to estimate the user's emotions. For example, the suggestion unit can conduct a survey asking the user about their stress level. The suggestion unit can also use a camera to recognize the user's facial expression. For example, the suggestion unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The suggestion unit can also use a microphone to analyze the user's voice. For example, the suggestion unit can record the user's voice with a microphone and estimate the emotion using voice analysis technology. The suggestion unit adjusts the way suggestions are presented based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can make simple, highly visible suggestions. If the user is relaxed, the suggestion unit can make suggestions that include detailed information. If the user is in a hurry, the suggestion unit can make suggestions that focus on the main points. This allows the suggestion unit to adjust the way suggestions are presented based on the user's emotions, enabling more appropriate suggestions.

[0088] When making a suggestion, the suggestion unit can make an optimal suggestion by referring to the user's past behavioral history. The suggestion unit can, for example, search a database to refer to the user's past behavioral history. For example, the suggestion unit can make an optimal suggestion based on data on agricultural products that the user has grown in the past. The suggestion unit can also suggest an optimal cultivation method based on the user's past cultivation history. Furthermore, the suggestion unit can analyze the user's past behavioral history to make the most efficient suggestion. The suggestion unit can also analyze history data to refer to the past behavioral history. For example, the suggestion unit makes an optimal suggestion based on the user's past behavioral history. The suggestion unit can also customize the content of the suggestion based on the user's past behavioral history. In this way, the suggestion unit can make an optimal suggestion to the user by referring to the past behavioral history.

[0089] When making a proposal, the proposal unit can monitor the user's current situation in real time and dynamically adjust the proposal content. The proposal unit can, for example, use sensor data to monitor the user's current situation in real time. For example, the proposal unit can monitor the current humidity and temperature of the user's field in real time and propose an appropriate irrigation method. The proposal unit can also monitor the nutrient content of the user's field in real time and propose an appropriate amount of fertilizer to use. The proposal unit can also monitor the occurrence of pests and diseases in the user's field in real time and propose an appropriate control method. The proposal unit can also use user input data for real-time monitoring. For example, the proposal unit can monitor the current situation in real time and dynamically adjust the proposal content based on data input by the user. The proposal unit can also use sensor data to monitor the user's current situation in real time and dynamically adjust the proposal content. As a result, the proposal unit can provide more appropriate proposals by monitoring in real time and dynamically adjusting the proposal content.

[0090] The agricultural suggestion system includes a suggestion unit that estimates a user's emotions and prioritizes suggestions based on the estimated user emotions. The suggestion unit can, for example, conduct a survey to estimate the user's emotions. For example, the suggestion unit can conduct a survey asking the user about their stress level. The suggestion unit can also use a camera to recognize the user's facial expressions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate the emotions using facial recognition technology. The suggestion unit can also use a microphone to analyze the user's voice. For example, the suggestion unit can record the user's voice with a microphone and estimate the emotions using voice analysis technology. The suggestion unit prioritizes suggestions based on the estimated user emotions. For example, if the user is stressed, the suggestion unit can cause the generation AI to prioritize simple suggestions. If the user is relaxed, the suggestion unit can cause the generation AI to prioritize detailed suggestions. If the user is excited, the suggestion unit can cause the generation AI to prioritize suggestions related to new technologies or methods. This allows the suggestion unit to determine the priority of suggestions according to the user's feelings, thereby enabling more appropriate suggestions to be made.

[0091] When making a proposal, the suggestion unit can make an optimal proposal taking into account the user's geographical location information. The suggestion unit can, for example, use GPS data to acquire the user's geographical location information. For example, the suggestion unit can suggest an optimal cultivation method based on meteorological data for the user's region. The suggestion unit can also suggest an optimal cultivation method based on geological data for the user's region. Furthermore, the suggestion unit can also suggest an optimal cultivation method based on agricultural product data for the user's region. The suggestion unit can also evaluate regional characteristics to take the geographical location information into account. For example, the suggestion unit can evaluate meteorological data for the user's region to suggest an optimal cultivation method. The suggestion unit can also evaluate geological data for the user's region to suggest an optimal cultivation method. This enables the suggestion unit to make more appropriate proposals by making proposals taking into account the geographical location information.

[0092] When making a suggestion, the suggestion unit can analyze the user's social media activity and make a relevant suggestion. For example, the suggestion unit can analyze the content of posts to analyze the user's social media activity. For example, the suggestion unit can analyze agriculture-related posts shared by the user on social media and make a relevant suggestion. The suggestion unit can also make a suggestion based on information about agricultural experts the user follows on social media. Furthermore, the suggestion unit can also make a suggestion based on information about agricultural communities the user participates in on social media. The suggestion unit can also evaluate the number of likes and the number of followers to analyze the social media activity. For example, the suggestion unit can make a suggestion based on posts that the user has received many likes. The suggestion unit can also make a suggestion based on information about agricultural experts to whom the user has many followers. In this way, the suggestion unit can make a suggestion relevant to the user by analyzing social media activity.

[0093] The agricultural suggestion system includes an acquisition unit that estimates a user's emotions and adjusts the type of data to be acquired based on the estimated user emotions. The acquisition unit can, for example, conduct a survey to estimate the user's emotions. For example, the acquisition unit can conduct a survey asking the user about their stress level. The acquisition unit can also use a camera to recognize the user's facial expressions. For example, the acquisition unit can capture the user's facial expressions with a camera and estimate the emotions using facial recognition technology. The acquisition unit can also use a microphone to analyze the user's voice. For example, the acquisition unit can record the user's voice with a microphone and estimate the emotions using voice analysis technology. The acquisition unit adjusts the type of data to be acquired based on the estimated user emotions. For example, if the user is stressed, the acquisition unit can cause the generation AI to prioritize data on simple cultivation methods. If the user is relaxed, the acquisition unit can cause the generation AI to acquire data on detailed cultivation methods. If the user is excited, the acquisition unit can cause the generation AI to acquire data on new technologies and methods. This allows the acquisition unit to adjust the type of data to be acquired depending on the user's emotion, thereby enabling more appropriate data acquisition.

[0094] The acquisition unit can evaluate the reliability of data at the time of acquisition and prioritize acquisition of highly reliable data. The acquisition unit can, for example, check the source of the data to evaluate the reliability of the data. For example, the acquisition unit can evaluate meteorological data provided by the Japan Meteorological Agency as highly reliable data. The acquisition unit can also evaluate geological data provided by the Geological Survey as highly reliable data. The acquisition unit can also evaluate agricultural product data provided by agricultural research institutions as highly reliable data. The acquisition unit can also check the data acquisition method and past performance to evaluate the reliability of the data. For example, the acquisition unit can check whether the data acquisition method is based on a standard method. The acquisition unit can also evaluate the reliability of the data based on past performance. As a result, the acquisition unit can prioritize acquisition of highly reliable data, thereby improving the accuracy of the analysis.

[0095] The acquisition unit can prioritize acquiring the latest data, taking into account the data update frequency. For example, the acquisition unit can check the update schedule of the data provider to evaluate the data update frequency. For example, the acquisition unit can check the update frequency of weather data provided by the Japan Meteorological Agency and prioritize acquiring the latest data. The acquisition unit can also check the update frequency of geological data provided by the Geological Survey and prioritize acquiring the latest data. Furthermore, the acquisition unit can check the update frequency of agricultural product data provided by agricultural research institutions and prioritize acquiring the latest data. The acquisition unit can also check the update schedule of real-time data, daily data, monthly data, etc. to evaluate the data update frequency. For example, the acquisition unit prioritizes acquiring real-time data to acquire data that reflects the latest situation. The acquisition unit can also grasp long-term trends by appropriately acquiring daily data or monthly data. This allows the acquisition unit to prioritize acquiring the latest data, thereby enabling more accurate analysis.

[0096] The agricultural suggestion system includes an acquisition unit that estimates a user's emotions and prioritizes data to be acquired based on the estimated user emotions. The acquisition unit can, for example, conduct a survey to estimate the user's emotions. For example, the acquisition unit can conduct a survey asking the user about their stress level. The acquisition unit can also use a camera to recognize the user's facial expressions. For example, the acquisition unit can capture the user's facial expressions with a camera and estimate the emotion using facial recognition technology. The acquisition unit can also use a microphone to analyze the user's voice. For example, the acquisition unit can record the user's voice with a microphone and estimate the emotion using voice analysis technology. The acquisition unit prioritizes the data to be acquired based on the estimated user emotions. For example, if the user is stressed, the acquisition unit can cause the generation AI to prioritize data on simple cultivation methods. Also, if the user is relaxed, the acquisition unit can cause the generation AI to prioritize data on detailed cultivation methods. Furthermore, if the user is excited, the acquisition unit can cause the generation AI to prioritize data on new technologies and methods. This allows the acquisition unit to determine the priority of data according to the user's emotions, thereby enabling more appropriate data acquisition.

[0097] The acquisition unit can prioritize acquiring highly relevant data by taking into account the geographical distribution of the data during acquisition. For example, the acquisition unit can check the geographical range of the data provider to evaluate the geographical distribution of the data. For example, the acquisition unit can check the geographical distribution of meteorological data provided by the Japan Meteorological Agency and prioritize acquiring data related to the user's region. The acquisition unit can also check the geographical distribution of geological data provided by the Geological Survey of Japan and prioritize acquiring data related to the user's region. Furthermore, the acquisition unit can check the geographical distribution of agricultural product data provided by agricultural research institutions and prioritize acquiring data related to the user's region. The acquisition unit can also check the importance and relevance of data for each region to evaluate the geographical distribution of the data. For example, the acquisition unit can evaluate the importance of meteorological data in the user's region and prioritize acquiring highly relevant data. The acquisition unit can also evaluate the importance of geological data in the user's region and prioritize acquiring highly relevant data. In this way, the acquisition unit can acquire more relevant data by taking geographical distribution into account.

[0098] The acquisition unit can diversify data sources and acquire data from different sources during acquisition. For example, the acquisition unit can acquire data from multiple providers to diversify data sources. For example, the acquisition unit can acquire weather data from the Japan Meteorological Agency, a private weather company, and a university research institute. The acquisition unit can also acquire geological data from the Geological Survey of Japan, a private geological survey company, and a university research institute. The acquisition unit can also acquire agricultural product data from agricultural research institutes, agricultural cooperatives, and university research institutes. The acquisition unit can also acquire data from government agencies, research institutes, private companies, etc. to diversify data sources. For example, the acquisition unit can acquire highly reliable data by acquiring data provided by government agencies. The acquisition unit can also acquire data provided by research institutes to acquire data that reflects the latest research results. The acquisition unit can also acquire practical data by acquiring data provided by private companies. As a result, the acquisition unit can acquire data from different sources, thereby improving the diversity of data. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, reception unit, analysis unit, proposal unit, and acquisition 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 is realized by the control unit 46A of the smart device 14 and collects meteorological data and geological data. The reception unit accepts user input using the touch panel 38A or microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal farming lifestyle based on the analysis results. The acquisition unit acquires current situation data using the camera 42 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, reception unit, analysis unit, suggestion unit, and acquisition 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 is realized by the control unit 46A of the smart glasses 214 and collects meteorological data and geological data. The reception unit accepts user input using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests an optimal farming lifestyle based on the analysis results. The acquisition unit acquires current situation data using the camera 42 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, reception unit, analysis unit, proposal unit, and acquisition 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 is realized by the control unit 46A of the headset terminal 314 and collects meteorological data and geological data. The reception unit receives user input using the microphone 238 of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal farming lifestyle based on the analysis results. The acquisition unit acquires current situation data using the camera 42 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, reception unit, analysis unit, proposal unit, and acquisition 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 is realized by the control unit 46A of the robot 414 and collects meteorological data and geological data. The reception unit receives user input using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal farming lifestyle based on the analysis results. The acquisition unit acquires current situation data using the camera 42 of the robot 414.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The farming suggestion system can further include a health management unit that monitors the user's health condition. The health management unit collects vital data such as the user's heart rate, blood pressure, and body temperature, and provides it to the analysis unit. For example, if the user is feeling stressed, the health management unit can suggest farm work that will help them relax. Also, if the user is tired, the health management unit can suggest light work or rest. Furthermore, it can suggest appropriate times for nutrition and hydration depending on the user's health condition. This enables the farming suggestion system to make suggestions that take the user's health condition into consideration.

[0101] The agricultural proposal system may further include an event information unit that provides information on local agricultural events. The event information unit collects information on agricultural-related events, seminars, and workshops held in the area and provides it to the user. For example, the event information unit may provide information on local agricultural fairs and exhibitions. It may also provide information on local agricultural technology seminars and training sessions. It may also provide information on local agricultural community and networking events. This allows the agricultural proposal system to provide users with opportunities to participate in local agricultural events and learn the latest information and techniques.

[0102] The farming suggestion system can further include an experience evaluation unit that evaluates the farming experience of the user. The experience evaluation unit evaluates the user's past farming experience and skills and provides the evaluation results to the analysis unit. For example, the experience evaluation unit can evaluate the types of crops the user has grown in the past and the yields. It can also evaluate the history of agricultural training and seminars the user has attended in the past. It can also evaluate the user's level of agricultural technique and knowledge. This enables the farming suggestion system to make optimal suggestions based on the user's experience and skills.

[0103] The agricultural proposal system can further include an activity recording unit that records the user's agricultural activities. The activity recording unit records the user's agricultural activity history and provides it to the analysis unit. For example, the activity recording unit can record the content and date and time of agricultural work performed by the user. It can also record information about agricultural materials and machinery used by the user. It can also record the quantity and quality of crops harvested by the user. This enables the agricultural proposal system to make more accurate proposals based on the user's agricultural activity history.

[0104] The agricultural proposal system can further include a goal setting unit that sets agricultural goals for the user. The goal setting unit sets agricultural goals that the user wants to achieve and provides them to the analysis unit. For example, if the user wants to increase yield, the goal setting unit can set that goal. Also, if the user wants to improve the quality of a particular crop, the goal setting unit can set that goal. Furthermore, if the user wants to improve agricultural efficiency, the goal can be set. This enables the agricultural proposal system to make optimal proposals according to the user's goals.

[0105] The farming suggestion system can further include a schedule adjustment unit that estimates the user's emotions and adjusts the schedule of farming activities based on the estimated user emotions. The schedule adjustment unit optimizes the schedule of farming activities by taking the user's emotions into consideration. For example, if the user is feeling stressed, the schedule adjustment unit can prioritize relaxing tasks in the schedule. Also, if the user is relaxed, the schedule adjustment unit can also incorporate efficient tasks into the schedule. Furthermore, if the user is excited, the schedule adjustment unit can also incorporate tasks to try new technologies or methods into the schedule. In this way, the farming suggestion system can provide an optimal schedule according to the user's emotions.

[0106] The agricultural suggestion system can further include a material selection unit that estimates the user's emotions and selects agricultural materials based on the estimated user's emotions. The material selection unit selects optimal agricultural materials taking the user's emotions into consideration. For example, if the user is feeling stressed, the material selection unit can select materials that are easy to use and require little effort. Furthermore, if the user is relaxed, the material selection unit can select efficient materials. Furthermore, if the user is excited, the material selection unit can select materials that incorporate new technologies or methods. This allows the agricultural suggestion system to provide optimal agricultural materials according to the user's emotions.

[0107] The farming suggestion system can further include a task adjustment unit that estimates the user's emotions and adjusts the difficulty of the farming tasks based on the estimated user emotions. The task adjustment unit optimizes the difficulty of the farming tasks by taking the user's emotions into consideration. For example, if the user is feeling stressed, the task adjustment unit can suggest easy and relaxing tasks. Also, if the user is relaxed, the task adjustment unit can suggest efficient and challenging tasks. Furthermore, if the user is excited, the task adjustment unit can suggest tasks that involve trying new techniques or methods. In this way, the farming suggestion system can provide optimal farming tasks according to the user's emotions.

[0108] The farming suggestion system may further include a feedback unit that estimates the user's emotions and provides feedback on the farming activities based on the estimated user emotions. The feedback unit optimizes the feedback on the farming activities by taking the user's emotions into consideration. For example, if the user is feeling stressed, the feedback unit may provide encouragement or advice to help the user relax. If the user is relaxed, the feedback unit may provide specific and detailed advice. Furthermore, if the user is excited, the feedback unit may provide advice on new techniques or methods. This allows the farming suggestion system to provide optimal feedback according to the user's emotions.

[0109] The farming suggestion system may further include a motivation improvement unit that estimates the user's emotions and improves the user's motivation for farming activities based on the estimated emotions. The motivation improvement unit makes suggestions to improve the user's motivation for farming activities, taking into account the user's emotions. For example, if the user is feeling stressed, the motivation improvement unit may suggest relaxing music or scenery. Furthermore, if the user is relaxed, the motivation improvement unit may suggest work that will give the user a sense of accomplishment. Furthermore, if the user is excited, the motivation improvement unit may suggest work that involves trying new techniques or methods. This allows the farming suggestion system to provide optimal motivation improvement measures according to the user's emotions.

[0110] The processing flow of the second embodiment will be briefly explained below.

[0111] Step 1: The collection unit collects meteorological data, fixed-point data, geological data, and agricultural product data from across Japan for the past 10 years. For example, meteorological data includes temperature, precipitation, and sunshine hours, while geological data includes soil type and nutrient content. Agricultural product data includes the types of vegetables and fruit trees grown in each region and their yields. This data can be obtained from the Japan Meteorological Agency and agricultural research institutions. Step 2: The reception unit accepts the agricultural products and region the user wants to grow, as well as the field data status. For example, if the user wants to grow tomatoes, the unit accepts input of the type of tomato, the address of the growing region, the size of the field, the number of years the field has been abandoned, etc. Step 3: The analysis unit analyzes the data obtained from the collection unit and the reception unit. For example, it analyzes the optimal cultivation method based on the meteorological data, geological data, and agricultural product data obtained from the collection unit, and performs analysis to make specific proposals based on the user's input data obtained from the reception unit. Step 4: The proposal unit proposes optimal farming practices based on the analysis results obtained by the analysis unit. For example, it proposes optimal methods for growing tomatoes, irrigation methods, and amounts of fertilizer to use.

[0112] 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.

[0113] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0114] 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.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0163] 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.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] [Explanation of symbols]

[0184] 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 unit that collects data; a reception unit that receives input from a user; an analysis unit that analyzes the data obtained from the collection unit and the reception unit; a proposal unit that proposes an optimal agricultural lifestyle based on the analysis results obtained by the analysis unit; Equipped with A system characterized by:

2. An acquisition unit for acquiring current situation data is provided The system of claim 1 .

3. The proposal unit Suggest irrigation methods and fertilizer usage The system of claim 1 .

4. The collecting unit Collect meteorological data, fixed point data, geological data, and agricultural product data for the past 10 years The system of claim 1 .

5. The reception unit Accepts the agricultural products that users want to grow, the region, and field data. The system of claim 1 .

6. The analysis unit Analyzing the data obtained from the collecting unit and the receiving unit The system of claim 1 .

7. The collecting unit Inferring user sentiment and adjusting the type of data collected based on the estimated user sentiment The system of claim 1 .

8. The collecting unit When collecting data, evaluate its reliability and prioritize collection of reliable data. The system of claim 1 .

9. The collecting unit When collecting data, prioritize collecting the latest data, taking into account the frequency of data updates. The system of claim 1 .

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