system

Abstract

The system according to the embodiment aims to propose appropriate agricultural crop selection, cultivation methods, sales route decisions, and disaster response measures. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a selection unit, and a disaster prevention unit. The collection unit collects past success stories, soil conditions, and surrounding environmental data. The analysis unit analyzes the data collected by the collection unit and identifies crops to be grown and the precautions to take. The proposal unit proposes cultivation methods and sales routes based on the crops to be grown and the precautions to take identified by the analysis unit. The selection unit narrows down the options proposed by the proposal unit to the next option to be taken and presents it. The disaster prevention unit proposes countermeasures to be taken in the event of a disaster based on the options presented by the selection 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] With conventional technology, it was difficult to select appropriate crops, cultivation methods, and sales routes in agriculture, and there were issues such as a lack of adequate disaster response measures.

[0005] The system according to the embodiment aims to propose appropriate agricultural crop selection, cultivation methods, sales route decisions, and disaster response measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, a selection unit, and a disaster prevention unit. The collection unit collects past success stories, soil conditions, and surrounding environmental data. The analysis unit analyzes the data collected by the collection unit and identifies crops to be grown and points to be careful about. The proposal unit proposes cultivation methods and sales routes based on the crops to be grown and points to be careful about identified by the analysis unit. The selection unit narrows down the options proposed by the proposal unit to the next option to be taken and presents it. The disaster prevention unit proposes countermeasures to be taken in the event of a disaster based on the options presented by the selection unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose appropriate agricultural crop selection, cultivation methods, sales route decisions, and disaster response measures. [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 support system according to an embodiment of the present invention uses AI to provide self-sustaining support for agricultural practices from the very first step. The system collects data on past success stories, soil conditions, and the surrounding environment, and then analyzes this data using AI. Based on the analysis results, the AI ​​then proposes crops to cultivate, important considerations, optimal cultivation methods, and sales routes. Furthermore, the system narrows down and presents options for next steps, allowing farmers to repeat the hit-and-error process among options with a high probability of success. For example, the agricultural support system collects detailed data such as the location of farmland, climate data, and soil composition analysis results. For example, it collects data on cultivation methods, yields, and sales routes for crops that have been successful in the same region in the past. Next, the agricultural support system uses AI to analyze the collected data. The AI ​​analyzes past success stories, soil composition, and climate data to identify crops to cultivate and important considerations. For example, if the soil composition is suitable for a particular crop, it suggests growing that crop. It also suggests cultivation methods and important considerations depending on the climatic conditions. The agricultural support system then proposes optimal sales routes. For example, it suggests specific markets and distribution channels as being effective based on past success stories. Next, the agricultural support system narrows down and presents the next options to be taken. The AI ​​presents several options with a high probability of success, allowing farmers to choose from them. For example, it shows that choosing a specific cultivation method or sales route increases the probability of success. This enables the agricultural support system to provide self-sufficient support from the very first step of farming, allowing farmers to grow and sell crops efficiently. As a disaster prevention measure, the agricultural support system can also use AI to analyze weather data and propose countermeasures in the event of a disaster. For example, it can propose crop protection methods and evacuation routes based on typhoon and heavy rain forecasts. This allows the agricultural support system to help farmers grow and sell crops efficiently. For example, farmers can select the optimal crops and cultivation methods, allowing them to sell their harvested crops efficiently. It can also take appropriate measures in the event of a disaster.

[0029] An agricultural support system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, a selection unit, and a disaster prevention unit. The collection unit collects data on past success stories, soil conditions, and surrounding environmental data. The collection unit collects detailed data, such as farmland location information, climate data, and soil component analysis results. For example, the collection unit can collect data on cultivation methods, yields, and sales routes of crops that have been successful in the past in the same region. The analysis unit analyzes the data collected by the collection unit to identify crops to be grown and important points to note. The analysis unit analyzes, for example, past success stories, soil components, and climate data to identify crops to be grown and important points to note. For example, the analysis unit suggests growing a specific crop if the soil components are suitable for that crop. The analysis unit can also suggest cultivation methods and important points depending on the climate conditions. The proposal unit proposes cultivation methods and sales routes based on the crops to be grown and important points identified by the analysis unit. For example, the proposal unit proposes cultivation methods and important points suitable for a specific crop. The suggestion unit can also suggest specific markets and distribution channels based on past success stories. The selection unit narrows down and presents the next option to be taken from the options proposed by the suggestion unit. The selection unit, for example, presents several options with a high probability of success, allowing the farmer to select from among them. For example, the selection unit indicates that selecting a specific cultivation method or sales route increases the probability of success. The disaster prevention unit proposes countermeasures in the event of a disaster based on the options presented by the selection unit. The disaster prevention unit, for example, analyzes weather data and proposes countermeasures in the event of a disaster. For example, the disaster prevention unit proposes crop protection methods and evacuation routes based on typhoon and heavy rain predictions. This allows the agricultural support system according to the embodiment to efficiently grow and sell crops. For example, farmers can select optimal crops and cultivation methods, and efficiently sell harvested crops. Appropriate measures can also be taken in the event of a disaster.

[0030] The collection unit can collect detailed data such as farmland location information, climate data, and soil component analysis results. The collection unit, for example, acquires farmland location information using GPS data and map information. For example, the collection unit acquires climate data using data such as temperature, precipitation, and wind speed. The collection unit can also acquire soil component analysis results using data such as soil pH value and nutrient content. By collecting detailed data, more accurate analysis and proposals become possible. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input GPS data and map information into the generation AI and cause the generation AI to acquire farmland location information.

[0031] The analysis unit can analyze past success stories, soil composition, and climate data to identify crops to grow and the precautions to take. For example, the analysis unit can analyze past success stories to identify successful cases of cultivating a specific crop or successful cases in a specific region. For example, the analysis unit can analyze soil composition to identify the soil's pH value and nutrient content. The analysis unit can also analyze climate data to identify data such as temperature, precipitation, and wind speed. By analyzing past success stories, soil composition, and climate data, it is possible to identify optimal crops and the precautions to take. Some or all of the above-described processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit can input past success stories, soil composition, and climate data into the generation AI and cause the generation AI to identify crops to grow and the precautions to take.

[0032] The suggestion unit can suggest cultivation methods and precautions suitable for specific crops. The suggestion unit can, for example, suggest cultivation methods suitable for specific crops. For example, the suggestion unit can suggest cultivation methods such as organic cultivation, greenhouse cultivation, and hydroponic cultivation. The suggestion unit can also suggest precautions for specific crops. For example, the suggestion unit can suggest risks of pests and diseases and appropriate watering methods. This allows farmers to select the optimal cultivation method by suggesting cultivation methods and precautions suitable for specific crops. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input cultivation methods and precautions suitable for specific crops into the generation AI and have the generation AI execute the suggestions.

[0033] The suggestion unit can suggest specific markets and distribution channels based on past success stories. The suggestion unit can, for example, suggest specific markets based on past success stories. For example, the suggestion unit can suggest markets such as local markets and online markets. The suggestion unit can also suggest specific distribution channels based on past success stories. For example, the suggestion unit can suggest distribution channels such as direct sales and sales through intermediaries. This allows farmers to sell their crops efficiently by suggesting markets and distribution channels based on past success stories. Some or all of the above-described processing in the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input markets and distribution channels based on past success stories into the generation AI and have the generation AI execute the suggestions.

[0034] The selection unit can present multiple options with a high probability of success, allowing the farmer to select from among them. The selection unit, for example, presents several options with a high probability of success. For example, the selection unit indicates that the probability of success increases by selecting a specific cultivation method or sales route. The selection unit can also present options with a high probability of success, allowing the farmer to select from among them. In this way, by presenting options with a high probability of success, the farmer can select the optimal option. Some or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can input options with a high probability of success into a generation AI and cause the generation AI to present the options.

[0035] The disaster countermeasures department can analyze weather data and propose countermeasures in the event of a disaster. The disaster countermeasures department, for example, analyzes weather data and proposes countermeasures in the event of a disaster. For example, the disaster countermeasures department proposes crop protection methods and evacuation routes based on typhoon and heavy rain predictions. The disaster countermeasures department can also analyze weather data and propose countermeasures in the event of a disaster. In this way, by analyzing weather data, countermeasures in the event of a disaster can be proposed. Some or all of the above-mentioned processing in the disaster countermeasures department may be performed, for example, using AI, or may be performed without using AI. For example, the disaster countermeasures department can input weather data into a generation AI and have the generation AI execute a proposal for countermeasures in the event of a disaster.

[0036] The collection unit can analyze the accuracy of past collected data and select the optimal data collection method. The collection unit, for example, analyzes past data collection results and prioritizes the selection of a highly accurate collection method. For example, the collection unit selects a collection method with minimal error from past data collection results. The collection unit can also select the optimal collection timing based on past data collection results. This allows a highly accurate collection method to be selected by analyzing past data collection results. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past data collection results into a generation AI and have the generation AI select the optimal data collection method.

[0037] When collecting data, the collection unit can take into account historical data on the farmland and traditional farming methods in the region. The collection unit, for example, refers to past harvest data on the farmland to improve the accuracy of the collected data. For example, the collection unit selects the type of data to collect by taking into account traditional farming methods in the region. The collection unit can also refer to historical climate data on the farmland to adjust the timing of data collection. This improves the accuracy of the collected data by taking into account the historical data on the farmland and traditional farming methods in the region. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input historical data on the farmland and traditional farming methods in the region into the generation AI and have the generation AI adjust the data collection.

[0038] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects data using voice recognition technology. For example, if the user uses text input, the collection unit collects data using text analysis technology. Furthermore, if the user uses image input, the collection unit can collect data using image analysis technology. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI and have the generation AI select the optimal collection means.

[0039] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting highly relevant climate data based on the user's current location. For example, the collection unit prioritizes collecting soil component data based on the location information of the user's farmland. The collection unit can also refer to historical data for the user's area and prioritize collecting highly relevant data. This allows highly relevant data to be collected preferentially by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0040] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can analyze the user's social media posts and collect related climate data. For example, the collection unit can refer to the user's social media check-in information and collect related soil data. The collection unit can also refer to the activities of the user's friends on social media to collect related agricultural data. This allows for efficient collection of related data by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activities into the generation AI and cause the generation AI to collect related data.

[0041] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit customizes the type of data to be collected, for example, based on the user's past feedback. For example, the collection unit customizes the frequency of data to be collected, based on the user's past feedback. The collection unit can also customize the timing of data collection, based on the user's past feedback. This allows the collection method to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a soil analysis algorithm to soil component data. For example, the analysis unit applies a climate analysis algorithm to climate data. Furthermore, the analysis unit can apply a success story analysis algorithm to past success story data. This enables highly accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply different analysis algorithms.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, refers to the user's past analysis results and adjusts the analysis algorithm. For example, the analysis unit selects an analysis method with less error from the user's past analysis results. The analysis unit can also determine the priority of the analysis based on the user's past analysis results. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0045] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the most recent data. For example, the analysis unit references past data and prioritizes analyzing data of high importance. The analysis unit can also adjust the analysis priority according to the time when the data was collected. This allows the most recent data to be analyzed with priority. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit analyzes less relevant data later. The analysis unit can also adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0047] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terminology. For example, if the user does not have technical expertise, the analysis unit provides analysis results that are explained in simple terms. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI use technical terminology in the analysis.

[0048] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the crop when making a suggestion. For example, the suggestion unit makes a detailed suggestion for a crop with high importance. For example, the suggestion unit makes a simplified suggestion for a crop with low importance. The suggestion unit can also determine the priority of the suggestion according to the importance of the crop. This allows for efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the crop. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the importance of the crop to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0049] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the crop. For example, the proposal unit can apply a vegetable cultivation algorithm to vegetables. For example, the proposal unit can apply a fruit cultivation algorithm to fruits. Furthermore, the proposal unit can apply a grain cultivation algorithm to grains. This enables highly accurate proposals by applying different proposal algorithms depending on the category of the crop. Some or all of the above-mentioned processing in the proposal unit can be performed using AI, for example, or can be performed without using AI. For example, the proposal unit can input the category of the crop to the generation AI and cause the generation AI to apply different proposal algorithms.

[0050] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, refers to the user's past proposal results and adjusts the proposal algorithm. For example, the suggestion unit selects a proposal method with less error from the user's past proposal results. The suggestion unit can also determine the priority of proposals based on the user's past proposal results. This improves the accuracy of the proposal by referring to the user's past proposal results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0051] When making a proposal, the proposal unit can determine the priority of the proposal based on the harvest time of the crop. For example, the proposal unit prioritizes proposals for crops whose harvest time is approaching. For example, the proposal unit postpones proposals for crops whose harvest time is far away. The proposal unit can also adjust the priority of the proposal according to the harvest time of the crop. This enables efficient proposals by determining the priority of the proposal based on the harvest time of the crop. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI, for example. For example, the proposal unit can input the harvest time of the crop to the generation AI and cause the generation AI to determine the priority of the proposals.

[0052] The suggestion unit can adjust the order of suggestions based on the relevance of the crops when making suggestions. For example, the suggestion unit prioritizes suggestions for highly relevant crops. For example, the suggestion unit puts suggestions later for less relevant crops. The suggestion unit can also adjust the order of suggestions according to the relevance of the crops. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of the crops. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the relevance of the crops to the generation AI and cause the generation AI to adjust the order of suggestions.

[0053] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit provides a proposal that uses a lot of technical terminology. For example, if the user does not have technical expertise, the suggestion unit provides a proposal that explains the proposal in simple terms. The suggestion unit can also adjust the way the proposal is expressed according to the user's level of expertise. This allows the suggestion to be provided in a way that is easy for the user to understand by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise to a generation AI and cause the generation AI to use technical terminology in the proposal.

[0054] When presenting options, the selection unit can refer to past selection results to present the optimal option. The selection unit, for example, refers to the user's past selection results to present the optimal option. For example, the selection unit presents options with minimal error based on the user's past selection results. The selection unit can also determine the priority of options based on the user's past selection results. This makes it possible to present the optimal option by referring to the past selection results. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input past selection results into a generation AI and cause the generation AI to present the optimal option.

[0055] When presenting options, the selection unit can customize the options based on the user's current situation. The selection unit presents optimal options based on, for example, the user's current weather conditions. For example, the selection unit presents optimal options based on the user's current soil conditions. The selection unit can also present optimal options based on the user's current market conditions. This allows optimal options to be provided by customizing the options based on the user's current situation. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's current situation into the generation AI and cause the generation AI to customize the options.

[0056] The selection unit can improve the method of presenting options by reflecting user feedback when presenting options. The selection unit improves the method of presenting options based on, for example, the user's past feedback. For example, the selection unit adjusts the priority of options based on the user's past feedback. The selection unit can also improve the way options are expressed based on the user's past feedback. This allows the method of presenting options to be improved by reflecting user feedback. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input user feedback into the generation AI and cause the generation AI to improve the method of presenting options.

[0057] When presenting options, the selection unit can present the optimal option taking into consideration the user's geographical location information. The selection unit, for example, presents the optimal option based on the user's current location. For example, the selection unit presents the optimal option based on location information of the user's farmland. The selection unit can also present the optimal option based on the climatic conditions of the user's region. This makes it possible to present the optimal option by taking the user's geographical location information into consideration. Some or all of the above-described processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the user's geographical location information to the generation AI and cause the generation AI to present the optimal option.

[0058] When presenting options, the selection unit can analyze the user's social media activity and present the options. The selection unit, for example, analyzes the content of the user's social media posts and presents related options. For example, the selection unit can refer to the user's social media check-in information and present related options. The selection unit can also refer to the activities of the user's friends on social media and present related options. In this way, by analyzing the user's social media activity, related options can be presented efficiently. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's social media activity into a generation AI and cause the generation AI to present related options.

[0059] When presenting options, the selection unit can customize the way options are presented by reflecting the user's past feedback. The selection unit customizes the way options are presented based on, for example, the user's past feedback. For example, the selection unit adjusts the priority of options based on the user's past feedback. The selection unit can also customize the way options are presented based on the user's past feedback. This allows the way options are presented to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the selection unit may be performed using, or without, AI, for example. For example, the selection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the way options are presented.

[0060] When taking disaster countermeasures, the disaster countermeasures department can refer to past disaster data and propose optimal countermeasures. For example, the disaster countermeasures department refers to past disaster data and proposes optimal evacuation routes. For example, the disaster countermeasures department proposes crop protection methods based on past disaster data. The disaster countermeasures department can also analyze past disaster data and propose optimal disaster countermeasures. In this way, optimal disaster countermeasures can be proposed by referring to past disaster data. Some or all of the above-mentioned processing in the disaster countermeasures department may be performed using, for example, AI, or may be performed without using AI. For example, the disaster countermeasures department can input past disaster data into a generation AI and have the generation AI execute a proposal for optimal countermeasures.

[0061] The disaster countermeasure unit can customize disaster countermeasures based on the user's current situation when taking disaster countermeasures. The disaster countermeasure unit, for example, proposes optimal disaster countermeasures based on the user's current weather conditions. For example, the disaster countermeasure unit proposes optimal disaster countermeasures based on the user's current soil conditions. The disaster countermeasure unit can also propose optimal disaster countermeasures based on the user's current market conditions. This allows optimal disaster countermeasures to be provided by customizing countermeasures based on the user's current situation. Some or all of the above-described processing in the disaster countermeasure unit may be performed using AI, for example, or may be performed without using AI. For example, the disaster countermeasure unit can input the user's current situation into the generation AI and have the generation AI customize the countermeasures.

[0062] The disaster countermeasure unit can improve the countermeasure method by reflecting user feedback during disaster countermeasures. The disaster countermeasure unit improves the disaster countermeasure method, for example, based on the user's past feedback. For example, the disaster countermeasure unit adjusts the priority of disaster countermeasures based on the user's past feedback. The disaster countermeasure unit can also improve the way disaster countermeasures are expressed based on the user's past feedback. This allows the disaster countermeasure method to be improved by reflecting user feedback. Some or all of the above-mentioned processing in the disaster countermeasure unit may be performed using AI, for example, or may be performed without using AI. For example, the disaster countermeasure unit can input user feedback into a generation AI and have the generation AI execute improvements to the countermeasure method.

[0063] The disaster countermeasure unit can propose optimal countermeasures by taking into account the user's geographical location information when taking disaster countermeasures. The disaster countermeasure unit, for example, proposes optimal evacuation routes based on the user's current location. For example, the disaster countermeasure unit proposes crop protection methods based on location information of the user's farmland. The disaster countermeasure unit can also propose optimal disaster countermeasures based on the climatic conditions of the user's region. This allows optimal disaster countermeasures to be proposed by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the disaster countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the disaster countermeasure unit can input the user's geographical location information into the generation AI and have the generation AI execute the proposal of optimal countermeasures.

[0064] The disaster countermeasure unit can analyze the user's social media activity and propose countermeasures during disaster countermeasures. The disaster countermeasure unit, for example, analyzes the user's social media posts and proposes relevant disaster countermeasures. For example, the disaster countermeasure unit can refer to the user's social media check-in information and propose relevant disaster countermeasures. The disaster countermeasure unit can also refer to the activities of the user's friends on social media and propose relevant disaster countermeasures. In this way, by analyzing the user's social media activity, relevant disaster countermeasures can be efficiently proposed. Some or all of the above-mentioned processing in the disaster countermeasure unit may be performed, for example, using AI or may be performed without using AI. For example, the disaster countermeasure unit can input the user's social media activity into a generation AI and have the generation AI execute a proposal for relevant disaster countermeasures.

[0065] The disaster countermeasure unit can customize the countermeasure method by reflecting the user's past feedback when taking disaster countermeasures. The disaster countermeasure unit customizes the disaster countermeasure method, for example, based on the user's past feedback. For example, the disaster countermeasure unit adjusts the priority of disaster countermeasures based on the user's past feedback. The disaster countermeasure unit can also customize the way disaster countermeasures are expressed based on the user's past feedback. This allows the countermeasure method to be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the disaster countermeasure unit may be performed using AI, for example, or may be performed without using AI. For example, the disaster countermeasure unit can input the user's past feedback into a generation AI and have the generation AI customize the countermeasure method.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The agricultural support system can further include a real-time monitoring unit. The real-time monitoring unit monitors environmental data of farmland in real time and can immediately notify if an abnormality is detected. For example, the real-time monitoring unit constantly monitors soil humidity, temperature, weather conditions, etc. and issues an alert if an abnormality occurs. The real-time monitoring unit can also monitor the growth status of crops using cameras and sensors and notify if an abnormality is detected. This allows farmers to respond quickly and maintain the quality of their crops.

[0068] The agricultural support system can further include a predictive analysis unit, which can predict future agricultural environments and market trends based on collected data. For example, the predictive analysis unit can predict future climate changes based on meteorological data and propose appropriate cultivation plans. The predictive analysis unit can also forecast crop demand based on market data and propose optimal sales strategies. This allows farmers to reduce future risks and manage their farms efficiently.

[0069] The agricultural support system can further include a communications section, which can support information sharing between farmers and agricultural experts. For example, the communications section can provide a platform for farmers to share their experiences and knowledge. The communications section can also enable online consultations with agricultural experts, allowing farmers to receive professional advice. This allows farmers to utilize the latest agricultural technology and knowledge and manage their farms efficiently.

[0070] The agricultural support system can further include an automation section. The automation section can reduce the labor required by farmers by automating some agricultural tasks. For example, the automation section can automatically control irrigation systems to supply water at the appropriate time. The automation section can also use drones to monitor crops and spray pesticides. This allows farmers to work more efficiently and improve the quality of their crops.

[0071] The agricultural support system can also have an education department. The education department can provide farmers with the latest agricultural techniques and knowledge. For example, the education department can provide training in agricultural techniques through online courses and webinars. The education department can also share the latest agricultural research results and case studies so that farmers can use them in their own agricultural management. This allows farmers to always have the latest information and manage their farms efficiently.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The collection department collects data on past success stories, soil conditions, and the surrounding environment. For example, detailed data such as the location of the farmland, climate data, and soil composition analysis results are collected. In addition, data on the cultivation methods, yields, and sales routes of crops that have been successful in the same area in the past is also collected. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies the crops to be grown and the precautions to be taken. For example, it analyzes past success stories, soil composition, and climate data to propose cultivation methods and precautions suitable for specific crops. Step 3: The proposal department proposes cultivation methods and sales routes based on the crops to be grown and the points to be considered identified by the analysis department. For example, it proposes cultivation methods and points to be considered suitable for specific crops, as well as markets and distribution routes based on past success stories. Step 4: The selection unit narrows down the options proposed by the proposal unit and presents them to farmers. For example, it presents several options with a high probability of success, allowing farmers to choose from them. Step 5: The Disaster Management Department proposes measures to be taken in the event of a disaster based on the options presented by the Selection Department. For example, it analyzes meteorological data and proposes methods for protecting crops and evacuation routes based on typhoon and heavy rain forecasts.

[0074] (Example 2) The agricultural support system according to an embodiment of the present invention uses AI to provide self-sustaining support for agricultural practices from the very first step. The system collects data on past success stories, soil conditions, and the surrounding environment, and then analyzes this data using AI. Based on the analysis results, the AI ​​then proposes crops to cultivate, important considerations, optimal cultivation methods, and sales routes. Furthermore, the system narrows down and presents options for next steps, allowing farmers to repeat the hit-and-error process among options with a high probability of success. For example, the agricultural support system collects detailed data such as the location of farmland, climate data, and soil composition analysis results. For example, it collects data on cultivation methods, yields, and sales routes for crops that have been successful in the same region in the past. Next, the agricultural support system uses AI to analyze the collected data. The AI ​​analyzes past success stories, soil composition, and climate data to identify crops to cultivate and important considerations. For example, if the soil composition is suitable for a particular crop, it suggests growing that crop. It also suggests cultivation methods and important considerations depending on the climatic conditions. The agricultural support system then proposes optimal sales routes. For example, it suggests specific markets and distribution channels as being effective based on past success stories. Next, the agricultural support system narrows down and presents the next options to be taken. The AI ​​presents several options with a high probability of success, allowing farmers to choose from them. For example, it shows that choosing a specific cultivation method or sales route increases the probability of success. This enables the agricultural support system to provide self-sufficient support from the very first step of farming, allowing farmers to grow and sell crops efficiently. As a disaster prevention measure, the agricultural support system can also use AI to analyze weather data and propose countermeasures in the event of a disaster. For example, it can propose crop protection methods and evacuation routes based on typhoon and heavy rain forecasts. This allows the agricultural support system to help farmers grow and sell crops efficiently. For example, farmers can select the optimal crops and cultivation methods, allowing them to sell their harvested crops efficiently. It can also take appropriate measures in the event of a disaster.

[0075] An agricultural support system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, a selection unit, and a disaster prevention unit. The collection unit collects data on past success stories, soil conditions, and surrounding environmental data. The collection unit collects detailed data, such as farmland location information, climate data, and soil component analysis results. For example, the collection unit can collect data on cultivation methods, yields, and sales routes of crops that have been successful in the past in the same region. The analysis unit analyzes the data collected by the collection unit to identify crops to be grown and important points to note. The analysis unit analyzes, for example, past success stories, soil components, and climate data to identify crops to be grown and important points to note. For example, the analysis unit suggests growing a specific crop if the soil components are suitable for that crop. The analysis unit can also suggest cultivation methods and important points depending on the climate conditions. The proposal unit proposes cultivation methods and sales routes based on the crops to be grown and important points identified by the analysis unit. For example, the proposal unit proposes cultivation methods and important points suitable for a specific crop. The suggestion unit can also suggest specific markets and distribution channels based on past success stories. The selection unit narrows down and presents the next option to be taken from the options proposed by the suggestion unit. The selection unit, for example, presents several options with a high probability of success, allowing the farmer to select from among them. For example, the selection unit indicates that selecting a specific cultivation method or sales route increases the probability of success. The disaster prevention unit proposes countermeasures in the event of a disaster based on the options presented by the selection unit. The disaster prevention unit, for example, analyzes weather data and proposes countermeasures in the event of a disaster. For example, the disaster prevention unit proposes crop protection methods and evacuation routes based on typhoon and heavy rain predictions. This allows the agricultural support system according to the embodiment to efficiently grow and sell crops. For example, farmers can select optimal crops and cultivation methods, and efficiently sell harvested crops. Appropriate measures can also be taken in the event of a disaster.

[0076] The collection unit can collect detailed data such as farmland location information, climate data, and soil component analysis results. The collection unit, for example, acquires farmland location information using GPS data and map information. For example, the collection unit acquires climate data using data such as temperature, precipitation, and wind speed. The collection unit can also acquire soil component analysis results using data such as soil pH value and nutrient content. By collecting detailed data, more accurate analysis and proposals become possible. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input GPS data and map information into the generation AI and cause the generation AI to acquire farmland location information.

[0077] The analysis unit can analyze past success stories, soil composition, and climate data to identify crops to grow and the precautions to take. For example, the analysis unit can analyze past success stories to identify successful cases of cultivating a specific crop or successful cases in a specific region. For example, the analysis unit can analyze soil composition to identify the soil's pH value and nutrient content. The analysis unit can also analyze climate data to identify data such as temperature, precipitation, and wind speed. By analyzing past success stories, soil composition, and climate data, it is possible to identify optimal crops and the precautions to take. Some or all of the above-described processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit can input past success stories, soil composition, and climate data into the generation AI and cause the generation AI to identify crops to grow and the precautions to take.

[0078] The suggestion unit can suggest cultivation methods and precautions suitable for specific crops. The suggestion unit can, for example, suggest cultivation methods suitable for specific crops. For example, the suggestion unit can suggest cultivation methods such as organic cultivation, greenhouse cultivation, and hydroponic cultivation. The suggestion unit can also suggest precautions for specific crops. For example, the suggestion unit can suggest risks of pests and diseases and appropriate watering methods. This allows farmers to select the optimal cultivation method by suggesting cultivation methods and precautions suitable for specific crops. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input cultivation methods and precautions suitable for specific crops into the generation AI and have the generation AI execute the suggestions.

[0079] The suggestion unit can suggest specific markets and distribution channels based on past success stories. The suggestion unit can, for example, suggest specific markets based on past success stories. For example, the suggestion unit can suggest markets such as local markets and online markets. The suggestion unit can also suggest specific distribution channels based on past success stories. For example, the suggestion unit can suggest distribution channels such as direct sales and sales through intermediaries. This allows farmers to sell their crops efficiently by suggesting markets and distribution channels based on past success stories. Some or all of the above-described processing in the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input markets and distribution channels based on past success stories into the generation AI and have the generation AI execute the suggestions.

[0080] The selection unit can present multiple options with a high probability of success, allowing the farmer to select from among them. The selection unit, for example, presents several options with a high probability of success. For example, the selection unit indicates that the probability of success increases by selecting a specific cultivation method or sales route. The selection unit can also present options with a high probability of success, allowing the farmer to select from among them. In this way, by presenting options with a high probability of success, the farmer can select the optimal option. Some or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can input options with a high probability of success into a generation AI and cause the generation AI to present the options.

[0081] The disaster countermeasures department can analyze weather data and propose countermeasures in the event of a disaster. The disaster countermeasures department, for example, analyzes weather data and proposes countermeasures in the event of a disaster. For example, the disaster countermeasures department proposes crop protection methods and evacuation routes based on typhoon and heavy rain predictions. The disaster countermeasures department can also analyze weather data and propose countermeasures in the event of a disaster. In this way, by analyzing weather data, countermeasures in the event of a disaster can be proposed. Some or all of the above-mentioned processing in the disaster countermeasures department may be performed, for example, using AI, or may be performed without using AI. For example, the disaster countermeasures department can input weather data into a generation AI and have the generation AI execute a proposal for countermeasures in the event of a disaster.

[0082] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the user's burden. For example, if the user is relaxed, the collection unit increases the frequency of data collection and collects detailed data. Furthermore, if the user is in a hurry, the collection unit can quickly collect data and collect the minimum amount of data necessary. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0083] The collection unit can analyze the accuracy of past collected data and select the optimal data collection method. The collection unit, for example, analyzes past data collection results and prioritizes the selection of a highly accurate collection method. For example, the collection unit selects a collection method with minimal error from past data collection results. The collection unit can also select the optimal collection timing based on past data collection results. This allows a highly accurate collection method to be selected by analyzing past data collection results. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past data collection results into a generation AI and have the generation AI select the optimal data collection method.

[0084] When collecting data, the collection unit can take into account historical data on the farmland and traditional farming methods in the region. The collection unit, for example, refers to past harvest data on the farmland to improve the accuracy of the collected data. For example, the collection unit selects the type of data to collect by taking into account traditional farming methods in the region. The collection unit can also refer to historical climate data on the farmland to adjust the timing of data collection. This improves the accuracy of the collected data by taking into account the historical data on the farmland and traditional farming methods in the region. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input historical data on the farmland and traditional farming methods in the region into the generation AI and have the generation AI adjust the data collection.

[0085] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects data using voice recognition technology. For example, if the user uses text input, the collection unit collects data using text analysis technology. Furthermore, if the user uses image input, the collection unit can collect data using image analysis technology. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI and have the generation AI select the optimal collection means.

[0086] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting data of high importance. For example, when the user is relaxed, the collection unit prioritizes collecting detailed data. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly. This allows important data to be collected preferentially by determining the priority of data to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0087] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting highly relevant climate data based on the user's current location. For example, the collection unit prioritizes collecting soil component data based on the location information of the user's farmland. The collection unit can also refer to historical data for the user's area and prioritize collecting highly relevant data. This allows highly relevant data to be collected preferentially by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0088] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can analyze the user's social media posts and collect related climate data. For example, the collection unit can refer to the user's social media check-in information and collect related soil data. The collection unit can also refer to the activities of the user's friends on social media to collect related agricultural data. This allows for efficient collection of related data by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activities into the generation AI and cause the generation AI to collect related data.

[0089] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit customizes the type of data to be collected, for example, based on the user's past feedback. For example, the collection unit customizes the frequency of data to be collected, based on the user's past feedback. The collection unit can also customize the timing of data collection, based on the user's past feedback. This allows the collection method to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0090] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that are concise. By adjusting the way the analysis is presented according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0091] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0092] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a soil analysis algorithm to soil component data. For example, the analysis unit applies a climate analysis algorithm to climate data. Furthermore, the analysis unit can apply a success story analysis algorithm to past success story data. This enables highly accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply different analysis algorithms.

[0093] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, refers to the user's past analysis results and adjusts the analysis algorithm. For example, the analysis unit selects an analysis method with less error from the user's past analysis results. The analysis unit can also determine the priority of the analysis based on the user's past analysis results. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0094] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short, to-the-point analysis result. For example, if the user is relaxed, the analysis unit can provide a longer analysis result with detailed explanations. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. This allows the analysis length to be adjusted according to the user's emotions, thereby providing an analysis result of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0095] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the most recent data. For example, the analysis unit references past data and prioritizes analyzing data of high importance. The analysis unit can also adjust the analysis priority according to the time when the data was collected. This allows the most recent data to be analyzed with priority. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0096] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit analyzes less relevant data later. The analysis unit can also adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0097] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terminology. For example, if the user does not have technical expertise, the analysis unit provides analysis results that are explained in simple terms. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI use technical terminology in the analysis.

[0098] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit provides simple, highly visible suggestions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that are concise. This allows the suggestion unit to adjust the way suggestions are expressed according to the user's emotions, making it possible to provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way suggestions are expressed.

[0099] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the crop when making a suggestion. For example, the suggestion unit makes a detailed suggestion for a crop with high importance. For example, the suggestion unit makes a simplified suggestion for a crop with low importance. The suggestion unit can also determine the priority of the suggestion according to the importance of the crop. This allows for efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the crop. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the importance of the crop to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0100] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the crop. For example, the proposal unit can apply a vegetable cultivation algorithm to vegetables. For example, the proposal unit can apply a fruit cultivation algorithm to fruits. Furthermore, the proposal unit can apply a grain cultivation algorithm to grains. This enables highly accurate proposals by applying different proposal algorithms depending on the category of the crop. Some or all of the above-mentioned processing in the proposal unit can be performed using AI, for example, or can be performed without using AI. For example, the proposal unit can input the category of the crop to the generation AI and cause the generation AI to apply different proposal algorithms.

[0101] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, refers to the user's past proposal results and adjusts the proposal algorithm. For example, the suggestion unit selects a proposal method with less error from the user's past proposal results. The suggestion unit can also determine the priority of proposals based on the user's past proposal results. This improves the accuracy of the proposal by referring to the user's past proposal results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0102] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, to-the-point suggestions. For example, if the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows the length of the suggestions to be adjusted according to the user's emotions, thereby providing suggestions of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0103] When making a proposal, the proposal unit can determine the priority of the proposal based on the harvest time of the crop. For example, the proposal unit prioritizes proposals for crops whose harvest time is approaching. For example, the proposal unit postpones proposals for crops whose harvest time is far away. The proposal unit can also adjust the priority of the proposal according to the harvest time of the crop. This enables efficient proposals by determining the priority of the proposal based on the harvest time of the crop. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI, for example. For example, the proposal unit can input the harvest time of the crop to the generation AI and cause the generation AI to determine the priority of the proposals.

[0104] The suggestion unit can adjust the order of suggestions based on the relevance of the crops when making suggestions. For example, the suggestion unit prioritizes suggestions for highly relevant crops. For example, the suggestion unit puts suggestions later for less relevant crops. The suggestion unit can also adjust the order of suggestions according to the relevance of the crops. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of the crops. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the relevance of the crops to the generation AI and cause the generation AI to adjust the order of suggestions.

[0105] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit provides a proposal that uses a lot of technical terminology. For example, if the user does not have technical expertise, the suggestion unit provides a proposal that explains the proposal in simple terms. The suggestion unit can also adjust the way the proposal is expressed according to the user's level of expertise. This allows the suggestion to be provided in a way that is easy for the user to understand by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise to a generation AI and cause the generation AI to use technical terminology in the proposal.

[0106] The selection unit can estimate the user's emotions and adjust the way options are presented based on the estimated user emotions. For example, if the user is nervous, the selection unit provides simple, highly visible options. For example, if the user is relaxed, the selection unit can provide detailed options. Furthermore, if the user is in a hurry, the selection unit can provide options that are concise. This allows the user to be provided with options that are easy to understand by adjusting the way options are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the selection unit can input the user's emotion data into the generation AI and have the generation AI adjust the way options are presented.

[0107] When presenting options, the selection unit can refer to past selection results to present the optimal option. The selection unit, for example, refers to the user's past selection results to present the optimal option. For example, the selection unit presents options with minimal error based on the user's past selection results. The selection unit can also determine the priority of options based on the user's past selection results. This makes it possible to present the optimal option by referring to the past selection results. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input past selection results into a generation AI and cause the generation AI to present the optimal option.

[0108] When presenting options, the selection unit can customize the options based on the user's current situation. The selection unit presents optimal options based on, for example, the user's current weather conditions. For example, the selection unit presents optimal options based on the user's current soil conditions. The selection unit can also present optimal options based on the user's current market conditions. This allows optimal options to be provided by customizing the options based on the user's current situation. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's current situation into the generation AI and cause the generation AI to customize the options.

[0109] The selection unit can improve the method of presenting options by reflecting user feedback when presenting options. The selection unit improves the method of presenting options based on, for example, the user's past feedback. For example, the selection unit adjusts the priority of options based on the user's past feedback. The selection unit can also improve the way options are expressed based on the user's past feedback. This allows the method of presenting options to be improved by reflecting user feedback. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input user feedback into the generation AI and cause the generation AI to improve the method of presenting options.

[0110] The selection unit can estimate the user's emotions and prioritize options based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit prioritizes presenting options with high importance. For example, if the user is relaxed, the selection unit prioritizes presenting detailed options. Furthermore, if the user is in a hurry, the selection unit can prioritize presenting options that can be selected quickly. This allows options to be prioritized according to the user's emotions, thereby prioritizing important options. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the options.

[0111] When presenting options, the selection unit can present the optimal option taking into consideration the user's geographical location information. The selection unit, for example, presents the optimal option based on the user's current location. For example, the selection unit presents the optimal option based on location information of the user's farmland. The selection unit can also present the optimal option based on the climatic conditions of the user's region. This makes it possible to present the optimal option by taking the user's geographical location information into consideration. Some or all of the above-described processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the user's geographical location information to the generation AI and cause the generation AI to present the optimal option.

[0112] When presenting options, the selection unit can analyze the user's social media activity and present the options. The selection unit, for example, analyzes the content of the user's social media posts and presents related options. For example, the selection unit can refer to the user's social media check-in information and present related options. The selection unit can also refer to the activities of the user's friends on social media and present related options. In this way, by analyzing the user's social media activity, related options can be presented efficiently. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's social media activity into a generation AI and cause the generation AI to present related options.

[0113] When presenting options, the selection unit can customize the way options are presented by reflecting the user's past feedback. The selection unit customizes the way options are presented based on, for example, the user's past feedback. For example, the selection unit adjusts the priority of options based on the user's past feedback. The selection unit can also customize the way options are presented based on the user's past feedback. This allows the way options are presented to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the selection unit may be performed using, or without, AI, for example. For example, the selection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the way options are presented.

[0114] The disaster prevention unit can estimate the user's emotions and adjust disaster prevention measures based on the estimated user emotions. For example, if the user is nervous, the disaster prevention unit can provide simple, highly visible disaster prevention measures. For example, if the user is relaxed, the disaster prevention unit can provide detailed disaster prevention measures. Furthermore, if the user is in a hurry, the disaster prevention unit can provide disaster prevention measures that focus on the key points. This allows disaster prevention measures to be adjusted according to the user's emotions, making it possible to provide disaster prevention measures that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the disaster prevention unit can be performed using, for example, AI, or without AI. For example, the disaster prevention unit can input the user's emotion data into the generation AI and have the generation AI adjust the disaster prevention measures.

[0115] When taking disaster countermeasures, the disaster countermeasures department can refer to past disaster data and propose optimal countermeasures. For example, the disaster countermeasures department refers to past disaster data and proposes optimal evacuation routes. For example, the disaster countermeasures department proposes crop protection methods based on past disaster data. The disaster countermeasures department can also analyze past disaster data and propose optimal disaster countermeasures. In this way, optimal disaster countermeasures can be proposed by referring to past disaster data. Some or all of the above-mentioned processing in the disaster countermeasures department may be performed using, for example, AI, or may be performed without using AI. For example, the disaster countermeasures department can input past disaster data into a generation AI and have the generation AI execute a proposal for optimal countermeasures.

[0116] The disaster countermeasure unit can customize disaster countermeasures based on the user's current situation when taking disaster countermeasures. The disaster countermeasure unit, for example, proposes optimal disaster countermeasures based on the user's current weather conditions. For example, the disaster countermeasure unit proposes optimal disaster countermeasures based on the user's current soil conditions. The disaster countermeasure unit can also propose optimal disaster countermeasures based on the user's current market conditions. This allows optimal disaster countermeasures to be provided by customizing countermeasures based on the user's current situation. Some or all of the above-described processing in the disaster countermeasure unit may be performed using AI, for example, or may be performed without using AI. For example, the disaster countermeasure unit can input the user's current situation into the generation AI and have the generation AI customize the countermeasures.

[0117] The disaster countermeasure unit can improve the countermeasure method by reflecting user feedback during disaster countermeasures. The disaster countermeasure unit improves the disaster countermeasure method, for example, based on the user's past feedback. For example, the disaster countermeasure unit adjusts the priority of disaster countermeasures based on the user's past feedback. The disaster countermeasure unit can also improve the way disaster countermeasures are expressed based on the user's past feedback. This allows the disaster countermeasure method to be improved by reflecting user feedback. Some or all of the above-mentioned processing in the disaster countermeasure unit may be performed using AI, for example, or may be performed without using AI. For example, the disaster countermeasure unit can input user feedback into a generation AI and have the generation AI execute improvements to the countermeasure method.

[0118] The disaster prevention unit can estimate the user's emotions and prioritize disaster prevention measures based on the estimated user emotions. For example, if the user is feeling stressed, the disaster prevention unit prioritizes the presentation of disaster prevention measures with high importance. For example, if the user is relaxed, the disaster prevention unit prioritizes the presentation of detailed disaster prevention measures. Furthermore, if the user is in a hurry, the disaster prevention unit can prioritize the presentation of disaster prevention measures that can be implemented quickly. This allows the prioritization of disaster prevention measures according to the user's emotions, thereby prioritizing the presentation of important disaster prevention measures. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the disaster prevention unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the disaster prevention unit can input the user's emotion data into the generation AI and have the generation AI determine the priorities of disaster prevention measures.

[0119] The disaster countermeasure unit can propose optimal countermeasures by taking into account the user's geographical location information when taking disaster countermeasures. The disaster countermeasure unit, for example, proposes optimal evacuation routes based on the user's current location. For example, the disaster countermeasure unit proposes crop protection methods based on location information of the user's farmland. The disaster countermeasure unit can also propose optimal disaster countermeasures based on the climatic conditions of the user's region. This allows optimal disaster countermeasures to be proposed by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the disaster countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the disaster countermeasure unit can input the user's geographical location information into the generation AI and have the generation AI execute the proposal of optimal countermeasures.

[0120] The disaster countermeasure unit can analyze the user's social media activity and propose countermeasures during disaster countermeasures. The disaster countermeasure unit, for example, analyzes the user's social media posts and proposes relevant disaster countermeasures. For example, the disaster countermeasure unit can refer to the user's social media check-in information and propose relevant disaster countermeasures. The disaster countermeasure unit can also refer to the activities of the user's friends on social media and propose relevant disaster countermeasures. In this way, by analyzing the user's social media activity, relevant disaster countermeasures can be efficiently proposed. Some or all of the above-mentioned processing in the disaster countermeasure unit may be performed, for example, using AI or may be performed without using AI. For example, the disaster countermeasure unit can input the user's social media activity into a generation AI and have the generation AI execute a proposal for relevant disaster countermeasures.

[0121] The disaster countermeasure unit can customize the countermeasure method by reflecting the user's past feedback when taking disaster countermeasures. The disaster countermeasure unit customizes the disaster countermeasure method, for example, based on the user's past feedback. For example, the disaster countermeasure unit adjusts the priority of disaster countermeasures based on the user's past feedback. The disaster countermeasure unit can also customize the way disaster countermeasures are expressed based on the user's past feedback. This allows the countermeasure method to be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the disaster countermeasure unit may be performed using AI, for example, or may be performed without using AI. For example, the disaster countermeasure unit can input the user's past feedback into a generation AI and have the generation AI customize the countermeasure method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, selection unit, and disaster prevention unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects farmland location information and weather data using the camera 42 and sensors of the smart device 14, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data to identify crops to be grown and the precautions to take. The proposal unit, realized by the specific processing unit 290 of the data processing device 12, proposes optimal cultivation methods and sales routes based on the analysis results. The selection unit, realized by the control unit 46A of the smart device 14, narrows down the proposed options and presents them as the next option. The disaster prevention unit, realized by the specific processing unit 290 of the data processing device 12, analyzes weather data and proposes countermeasures in the event of a disaster. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, selection unit, and disaster prevention unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit uses the camera 42 and sensors of the smart glasses 214 to collect farmland location information and weather data, which is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data to identify crops to be grown and the precautions to take. The proposal unit, realized by the specific processing unit 290 of the data processing device 12, proposes optimal cultivation methods and sales routes based on the analysis results. The selection unit, realized by the control unit 46A of the smart glasses 214, narrows down the proposed options and presents them as the next option. The disaster prevention unit, realized by the specific processing unit 290 of the data processing device 12, analyzes weather data and proposes countermeasures in the event of a disaster. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, selection unit, and disaster prevention unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects farmland location information and weather data using the camera 42 and sensors of the headset terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data to identify crops to be grown and the necessary precautions. The proposal unit, realized by the specific processing unit 290 of the data processing device 12, proposes optimal cultivation methods and sales routes based on the analysis results. The selection unit, realized by the control unit 46A of the headset terminal 314, narrows down the proposed options and presents them as the next option. The disaster prevention unit, realized by the specific processing unit 290 of the data processing device 12, analyzes weather data and proposes countermeasures in the event of a disaster. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, selection unit, and disaster prevention unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit uses the camera 42 and sensors of the robot 414 to collect farmland location information and weather data, which is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data to identify crops to be grown and the precautions to take. The proposal unit, realized by the specific processing unit 290 of the data processing device 12, proposes optimal cultivation methods and sales routes based on the analysis results. The selection unit, realized by the control unit 46A of the robot 414, narrows down the proposed options and presents them as the next option. The disaster prevention unit, realized by the specific processing unit 290 of the data processing device 12, analyzes weather data and proposes countermeasures in the event of a disaster.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The agricultural support system can further include a real-time monitoring unit. The real-time monitoring unit monitors environmental data of farmland in real time and can immediately notify if an abnormality is detected. For example, the real-time monitoring unit constantly monitors soil humidity, temperature, weather conditions, etc. and issues an alert if an abnormality occurs. The real-time monitoring unit can also monitor the growth status of crops using cameras and sensors and notify if an abnormality is detected. This allows farmers to respond quickly and maintain the quality of their crops.

[0124] The agricultural support system can further include a predictive analysis unit, which can predict future agricultural environments and market trends based on collected data. For example, the predictive analysis unit can predict future climate changes based on meteorological data and propose appropriate cultivation plans. The predictive analysis unit can also forecast crop demand based on market data and propose optimal sales strategies. This allows farmers to reduce future risks and manage their farms efficiently.

[0125] The agricultural support system can further include a communications section, which can support information sharing between farmers and agricultural experts. For example, the communications section can provide a platform for farmers to share their experiences and knowledge. The communications section can also enable online consultations with agricultural experts, allowing farmers to receive professional advice. This allows farmers to utilize the latest agricultural technology and knowledge and manage their farms efficiently.

[0126] The agricultural support system can further include an automation section. The automation section can reduce the labor required by farmers by automating some agricultural tasks. For example, the automation section can automatically control irrigation systems to supply water at the appropriate time. The automation section can also use drones to monitor crops and spray pesticides. This allows farmers to work more efficiently and improve the quality of their crops.

[0127] The agricultural support system can also have an education department. The education department can provide farmers with the latest agricultural techniques and knowledge. For example, the education department can provide training in agricultural techniques through online courses and webinars. The education department can also share the latest agricultural research results and case studies so that farmers can use them in their own agricultural management. This allows farmers to always have the latest information and manage their farms efficiently.

[0128] The agricultural support system may further include an emotion estimation unit. The emotion estimation unit may estimate the user's emotion and adjust the system interface and suggestions based on the estimated emotion. For example, if the user is feeling stressed, the emotion estimation unit may provide a simple, highly visible interface. If the user is relaxed, the emotion estimation unit may provide detailed information. This makes it possible to provide optimal support according to the user's emotion.

[0129] The agricultural support system may further include an emotion feedback unit. The emotion feedback unit may improve the system's suggestions based on the user's emotions. For example, if the user is dissatisfied with a suggestion, the emotion feedback unit may collect that feedback and reflect it in the next suggestion. Also, if the user is satisfied with a suggestion, the emotion feedback unit may collect that feedback and strengthen similar suggestions. This allows the system's suggestions to be continuously improved based on the user's emotions.

[0130] The agricultural support system may further include an emotion monitoring unit. The emotion monitoring unit may monitor the user's emotions in real time and provide support at an appropriate time. For example, if the user is feeling stressed, the emotion monitoring unit may provide advice on how to relax. Also, if the user is excited, the emotion monitoring unit may provide advice on how to stay calm. This makes it possible to provide appropriate support according to the user's emotions.

[0131] The agricultural support system may further include a sentiment analysis unit. The sentiment analysis unit may analyze the user's sentiment data and grasp long-term sentiment trends. For example, the sentiment analysis unit may analyze how the user feels about a particular task and suggest ways to improve the task. The sentiment analysis unit may also provide stress management and mental health support based on the user's sentiment data. This may enable long-term support based on the user's sentiment.

[0132] The agricultural support system may further include an emotion prediction unit. The emotion prediction unit can predict future emotions based on the user's past emotion data and provide appropriate support. For example, the emotion prediction unit can predict that the user is likely to feel stressed at a certain time and provide advice on relaxing in advance. The emotion prediction unit can also predict that the user will feel positive emotions toward a specific task and recommend that task. This makes it possible to provide predictive support based on the user's emotions.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The collection department collects data on past success stories, soil conditions, and the surrounding environment. For example, detailed data such as the location of the farmland, climate data, and soil composition analysis results are collected. In addition, data on the cultivation methods, yields, and sales routes of crops that have been successful in the same area in the past is also collected. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies the crops to be grown and the precautions to be taken. For example, it analyzes past success stories, soil composition, and climate data to propose cultivation methods and precautions suitable for specific crops. Step 3: The proposal department proposes cultivation methods and sales routes based on the crops to be grown and the points to be considered identified by the analysis department. For example, it proposes cultivation methods and points to be considered suitable for specific crops, as well as markets and distribution routes based on past success stories. Step 4: The selection unit narrows down the options proposed by the proposal unit and presents them to farmers. For example, it presents several options with a high probability of success, allowing farmers to choose from them. Step 5: The Disaster Management Department proposes measures to be taken in the event of a disaster based on the options presented by the Selection Department. For example, it analyzes meteorological data and proposes methods for protecting crops and evacuation routes based on typhoon and heavy rain forecasts.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0186] 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.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Explanation of symbols]

[0207] 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. The collection department collects data on past success stories, soil conditions, and the surrounding environment. an analysis unit that analyzes the data collected by the collection unit and identifies crops to be grown and points to be careful about; a proposal unit that proposes a cultivation method and a sales route based on the crop to be grown and points to be considered, which are identified by the analysis unit; a selection unit that narrows down and presents an option to be taken next from the options proposed by the proposal unit; a disaster countermeasure unit that proposes countermeasures in the event of a disaster based on the options presented by the selection unit. A system characterized by:

2. The collecting unit Collect detailed data on farmland location, climate data, and soil composition analysis results 2. The system of claim 1.

3. The analysis unit Analyzing past success stories, soil composition, and climate data, we identify crops that should be grown and what precautions to take.

2. The system of claim 1.

4. The proposal unit Propose cultivation methods and precautions suitable for specific crops 2. The system of claim 1.

5. The proposal unit Suggest specific markets and distribution channels based on past success stories 2. The system of claim 1.

6. The selection unit Present multiple options with high probability of success and allow farmers to choose from them 2. The system of claim 1.

7. The disaster response department Analyzing meteorological data and proposing countermeasures in the event of a disaster 2. The system of claim 1.

8. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

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