System

Abstract

An object of a system according to an embodiment is to analyze cultivation data and propose an optimal cultivation growing method.SOLUTION: A system includes a collection unit, an analysis unit, and a proposal unit. The collection part collects cultivation data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a cultivation and growing method based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been able to effectively utilize crop cultivation data to propose optimal cultivation methods, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze cultivation data and propose optimal cultivation methods. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects cultivation data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a cultivation method based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze cultivation data and propose optimal cultivation methods. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A crop cultivation support system according to an embodiment of the present invention is a system that supports crop cultivation using a generative AI. The crop cultivation support system analyzes cultivation data, such as soil condition, temperature, humidity, and sunshine hours, input by farmers and proposes optimal cultivation methods. For example, the crop cultivation support system proposes the optimal watering timing and fertilizer amount for a specific crop. Farmers can expect to increase their yield and improve their quality by cultivating crops based on the cultivation methods proposed by the generative AI. For example, watering crops according to the methods proposed by the generative AI promotes crop growth and increases yield. This service is highly beneficial to farmers, and by following the suggestions provided by the generative AI, they can grow crops efficiently. Furthermore, because the generative AI always makes suggestions based on the latest data, farmers can always know the optimal cultivation methods. As a result, the crop cultivation support system supports farmers in efficiently cultivating crops, thereby increasing yield and improving quality. For example, by following the optimal cultivation methods proposed by the generative AI, farmers can promote crop growth and increase their yield. Furthermore, because the generative AI always makes suggestions based on the latest data, farmers can always know the optimal cultivation methods.

[0029] A crop cultivation support system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects cultivation data. The cultivation data includes, for example, soil condition, temperature, humidity, and sunshine duration, but is not limited to these examples. For example, the collection unit measures the soil condition using a sensor and collects the data. The collection unit can also measure temperature and humidity using a sensor and collect the data. The collection unit can also measure sunshine duration and collect the data. For example, the collection unit measures the soil pH value and nutrient content and collects the data. Temperature and humidity are measured using a temperature sensor and a humidity sensor and collected the data. Sunshine duration is measured using a light sensor and collected the data. The analysis unit analyzes the data collected by the collection unit. For example, the analysis is performed by comparing the collected data with past data and data from other farmers, but is not limited to these examples. For example, the analysis unit compares the collected data with past harvest yield data and performs the analysis. The analysis unit can also compare the collected data with data from other farmers and perform the analysis. The analysis unit can also analyze factors affecting crop growth based on the collected data. For example, the analysis unit analyzes optimal conditions for crop growth based on the collected data. The suggestion unit proposes a cultivation method based on the analysis results obtained by the analysis unit. The suggestions may relate to, for example, the timing of watering and the amount of fertilizer, but are not limited to such examples. For example, the suggestion unit proposes the optimal timing of watering based on the analysis results. The suggestion unit can also propose the optimal amount of fertilizer based on the analysis results. The suggestion unit can also propose a method for promoting crop growth based on the analysis results. For example, the suggestion unit proposes a method for adjusting the timing of watering based on the analysis results. The amount of fertilizer is adjusted according to the growth stage of the crop. This allows the crop cultivation support system according to the embodiment to support farmers in efficiently cultivating crops and achieve increased yields and improved quality. Some or all of the above-described processing by the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit may input the analysis results obtained by the analysis unit into the generation AI and cause the generation AI to propose an optimal cultivation method.

[0030] The collection unit can collect data on soil condition, temperature, humidity, and sunshine hours. For example, the collection unit measures the soil condition using a sensor and collects the data. For example, the collection unit measures the soil pH value and nutrient content and collects the data. The collection unit can also measure temperature using a sensor and collect the data. For example, the collection unit measures temperature using a temperature sensor and collects the data. The collection unit can also measure humidity using a sensor and collect the data. For example, the collection unit measures humidity using a humidity sensor and collects the data. The collection unit can also measure sunshine hours and collect the data. For example, the collection unit measures sunshine hours using a light sensor and collects the data. This allows basic data necessary for cultivation to be collected, thereby improving the accuracy of the analysis. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can input data acquired by a sensor into a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze the collected data and compare it with past data or data from other farmers. For example, the analysis unit compares the collected data with past yield data and performs an analysis. For example, the analysis unit compares the collected data with past weather data and performs an analysis. The analysis unit can also compare the collected data with data from other farmers and perform an analysis. For example, the analysis unit compares the collected data with yield data from other farmers and performs an analysis. The analysis unit can also compare the collected data with cultivation method data from other farmers and perform an analysis. The analysis unit can also analyze factors that affect crop growth based on the collected data. For example, the analysis unit analyzes the optimal conditions for crop growth based on the collected data. This enables more accurate analysis by comparing the collected data with past data and data from other farmers. 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 collected data into a generation AI and have the generation AI analyze the data.

[0032] The suggestion unit can suggest the timing of watering or the amount of fertilizer based on the analysis results. For example, the suggestion unit can suggest the optimal timing of watering based on the analysis results. For example, the suggestion unit can suggest the timing of watering based on the soil humidity level and the temperature. The suggestion unit can also suggest the optimal amount of fertilizer based on the analysis results. For example, the suggestion unit can suggest the amount of fertilizer based on the type of crop and the growth stage. The suggestion unit can also suggest a method for promoting crop growth based on the analysis results. For example, the suggestion unit can suggest a method for adjusting the timing of watering based on the analysis results. By suggesting the optimal timing of watering and the amount of fertilizer, crop growth can be promoted, and an increase in yield and improvement in quality can be expected. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input the analysis results into the generation AI and cause the generation AI to suggest the optimal cultivation method.

[0033] The suggestion unit can increase the yield and improve the quality by following the suggestions provided by the generation AI. The suggestion unit can increase the yield and improve the quality by following the suggestions provided by the generation AI, for example. For example, the suggestion unit can promote crop growth and increase the yield by following the optimal cultivation method proposed by the generation AI. The suggestion unit can also improve the quality of the crop by following the method proposed by the generation AI. For example, the suggestion unit can optimize crop growth by following the watering timing and fertilizer amount proposed by the generation AI. In this way, by following the suggestions of the generation AI, an increase in the yield and improvement in the quality are achieved. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI, for example. For example, the suggestion unit can provide specific instructions to the farmer based on the cultivation method proposed by the generation AI.

[0034] The collection unit can collect information on the growth stage of crops or the occurrence of pests and diseases in addition to soil conditions, temperature, humidity, and sunlight hours. The collection unit, for example, periodically records the growth stage of crops and stores it in a database. For example, the collection unit measures the number of leaves and stem height of crops and records the growth stage. The collection unit can also monitor the occurrence of pests and diseases in real time and issue alerts when abnormalities occur. For example, the collection unit can detect the occurrence of pests and diseases using a sensor and notify when an abnormality occurs. The collection unit can also collect information on the color and shape of crop leaves in addition to soil conditions, temperature, humidity, and sunlight hours to grasp the growth status in detail. For example, the collection unit can capture the color and shape of leaves with a camera and collect data. By collecting information on the growth stage of crops and the occurrence of pests and diseases, more detailed data can be used for analysis. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input data on the growth stage of crops and the occurrence of pests and diseases into a generation AI and have the generation AI analyze the data.

[0035] The collection unit can improve collection accuracy by adjusting the placement and type of sensors during data collection. For example, the collection unit optimizes the placement of sensors according to the type of crop and its growth stage. For example, the collection unit adjusts the placement of sensors according to the type of crop. The collection unit can also improve collection accuracy by selecting the type of sensor according to the soil condition and weather conditions. For example, the collection unit selects an appropriate sensor according to the soil condition and collects data. The collection unit can also periodically review the placement and type of sensors to maintain collection accuracy. For example, the collection unit periodically checks the placement and type of sensors and adjusts them as necessary. By optimizing the placement and type of sensors, collection accuracy is improved and more accurate data can be obtained. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data on the placement and type of sensors to the generation AI and cause the generation AI to select the optimal placement and type.

[0036] The collection unit can dynamically adjust the collection frequency during data collection depending on the type and growth stage of the crop. For example, the collection unit increases the collection frequency to collect detailed data during the early stages of crop growth. For example, the collection unit increases the data collection frequency during the early stages of crop growth. The collection unit can also reduce the collection frequency during stable growth stages to efficiently collect data. For example, the collection unit reduces the data collection frequency during stable growth stages. The collection unit can also set an optimal collection frequency depending on the type of crop and dynamically adjust it. For example, the collection unit adjusts the collection frequency depending on the type of crop. This allows for dynamic adjustment of the collection frequency to efficiently collect data and improve the accuracy of analysis. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data on the growth stage and type of crop to the generation AI and cause the generation AI to adjust the collection frequency.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the farm. For example, the collection unit prioritizes collecting data related to weather conditions based on the geographical location information of the farm. For example, the collection unit collects weather data based on the geographical location information of the farm. The collection unit can also prioritize collecting data related to soil conditions based on the geographical location information of the farm. For example, the collection unit collects soil data based on the geographical location information of the farm. The collection unit can also prioritize collecting data related to crop growth based on the geographical location information of the farm. For example, the collection unit collects crop growth data based on the geographical location information of the farm. In this way, highly relevant data can be prioritized by taking the geographical location information of the farm into consideration. Some or all of the above-described processing in the collection unit may be performed using, or without using, AI. For example, the collection unit can input the geographical location information of the farm to the generation AI and cause the generation AI to determine the priority of highly relevant data.

[0038] The collection unit can customize the collection method by reflecting the farmer's past feedback when collecting data. The collection unit, for example, adjusts the collection method based on the farmer's past feedback to efficiently collect data. For example, the collection unit adjusts the collection method based on the farmer's past feedback. The collection unit can also adjust the collection frequency based on the farmer's past feedback to collect necessary data. For example, the collection unit adjusts the collection frequency based on the farmer's past data. The collection unit can also adjust the placement and type of sensors based on the farmer's past feedback to improve collection accuracy. For example, the collection unit adjusts the placement and type of sensors based on the farmer's past feedback. In this way, the collection method can be customized and data can be collected efficiently by reflecting the farmer's past feedback. 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 farmer's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0039] The collection unit can compare data collected with other farmers' data in real time to detect abnormal values. The collection unit, for example, compares data collected with other farmers' data in real time to detect abnormal values. For example, the collection unit compares data collected with other farmers' harvest yield data to detect abnormal values. The collection unit can also compare data collected with weather data from other farmers to detect abnormal values. For example, the collection unit compares data collected with weather data from other farmers to detect abnormal values. The collection unit can also issue an alert and notify the farmer when an abnormal value is detected. For example, the collection unit issues an alert and notifies the farmer when an abnormal value is detected. The collection unit can also analyze the cause of an abnormal value when an abnormal value is detected and propose countermeasures. For example, the collection unit analyzes the cause of an abnormal value and proposes countermeasures. This allows abnormal values ​​to be detected by comparing data collected with other farmers, and countermeasures can be taken quickly. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without AI. For example, the collection unit can input data collected with other farmers into the generation AI and have the generation AI detect abnormal values.

[0040] During analysis, the analysis unit can integrate weather forecast data in addition to past data and data from other farmers to improve the accuracy of the analysis. The analysis unit, for example, integrates past data with weather forecast data to improve the accuracy of the analysis. For example, the analysis unit integrates past harvest data with weather forecast data to perform the analysis. The analysis unit can also integrate other farmers' data with weather forecast data to improve the accuracy of the analysis. For example, the analysis unit integrates other farmers' harvest data with weather forecast data to perform the analysis. The analysis unit can also integrate past data, other farmers' data, and weather forecast data to improve the accuracy of the analysis. For example, the analysis unit integrates past harvest data, other farmers' harvest data, and weather forecast data to perform the analysis. Integrating the weather forecast data improves the accuracy of the analysis and enables more accurate proposals. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input past data, other farmers' data, and weather forecast data into the generation AI and cause the generation AI to integrate and analyze the data.

[0041] The analysis unit can automatically detect abnormal values ​​and outliers during analysis and exclude them from the analysis results. The analysis unit, for example, automatically detects abnormal values ​​and outliers and excludes them from the analysis results. For example, the analysis unit detects abnormal values ​​and outliers using statistical methods and excludes them from the analysis results. When an abnormal value or outlier is detected, the analysis unit can analyze the cause and propose countermeasures. For example, the analysis unit analyzes the cause of the abnormal value or outlier and proposes countermeasures. The analysis unit can also periodically review the algorithm for detecting abnormal values ​​and outliers to improve its accuracy. For example, the analysis unit periodically updates the algorithm for detecting abnormal values ​​and outliers to improve its accuracy. This eliminates abnormal values ​​and outliers, thereby improving the accuracy of the analysis results. 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 cause a generation AI to detect abnormal values ​​and outliers and cause the generation AI to exclude them from the analysis results.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the type of crop and its growth stage. The analysis unit applies the optimal analysis algorithm depending on, for example, the type of crop. For example, the analysis unit applies an analysis algorithm such as regression analysis or clustering depending on the type of crop. The analysis unit can also apply the optimal analysis algorithm depending on the growth stage of the crop. For example, the analysis unit applies a different analysis algorithm depending on the growth stage of the crop. The analysis unit can also dynamically adjust the analysis algorithm depending on the type of crop and its growth stage. For example, the analysis unit dynamically changes the analysis algorithm depending on the type of crop and its growth stage. This improves the analysis accuracy by applying an analysis algorithm depending on the type of crop and its growth stage. 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 data on the type of crop and its growth stage to the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0043] The analysis unit can customize the analysis results by taking into account the geographical conditions of the farm during the analysis. The analysis unit customizes the analysis results based on, for example, the geographical conditions of the farm. For example, the analysis unit suggests an optimal cultivation method based on the geographical conditions of the farm. The analysis unit can also visually display the analysis results based on the geographical conditions of the farm. For example, the analysis unit displays the analysis results as a graph or chart based on the geographical conditions of the farm. This makes it possible to provide more appropriate analysis results by taking into account the geographical conditions of the farm. 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 data on the geographical conditions of the farm into the generation AI and cause the generation AI to customize the analysis results.

[0044] During analysis, the analysis unit can complement the analysis results by referring to success stories of other farmers. The analysis unit, for example, complements the analysis results by referring to success stories of other farmers. For example, the analysis unit proposes optimal cultivation methods based on the success stories of other farmers. The analysis unit can also visually display the analysis results based on the success stories of other farmers. For example, the analysis unit displays the analysis results as graphs or charts based on the success stories of other farmers. This makes it possible to complement the analysis results and make more practical proposals by referring to the success stories of other farmers. 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 data on success stories of other farmers into the generation AI and have the generation AI complement the analysis results.

[0045] When making a proposal, the proposal unit can apply different proposal algorithms depending on the type and growth stage of the crop. The proposal unit, for example, applies an optimal proposal algorithm depending on the type of crop. For example, the proposal unit applies a proposal algorithm such as a rule-based algorithm or a machine learning algorithm depending on the type of crop. The proposal unit can also apply an optimal proposal algorithm depending on the growth stage of the crop. For example, the proposal unit applies different proposal algorithms depending on the growth stage of the crop. The proposal unit can also dynamically adjust the proposal algorithm depending on the type and growth stage of the crop. For example, the proposal unit dynamically changes the proposal algorithm depending on the type and growth stage of the crop. This enables more appropriate proposals by applying a proposal algorithm depending on the type and growth stage of the crop. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input data on the type and growth stage of the crop to the generation AI and cause the generation AI to apply the optimal proposal algorithm.

[0046] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to past proposal results. The proposal unit, for example, improves the accuracy of the proposal based on past proposal results. For example, the proposal unit proposes an optimal cultivation method based on past proposal results. The proposal unit can also customize the proposal content based on past proposal results. For example, the proposal unit adjusts the proposal content based on past proposal results. By referring to past proposal results, the accuracy of the proposal can be improved, enabling more practical proposals. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data of past proposal results into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0047] When making a proposal, the proposal unit can take into account the farmer's resources. For example, the proposal unit can take into account the farmer's labor and propose an efficient work method. For example, the proposal unit can propose an optimal work schedule based on the farmer's labor. The proposal unit can also take into account the farmer's funds and propose a cost-effective cultivation method. For example, the proposal unit can propose the optimal amount of fertilizer and pesticide to be used based on the farmer's funds. The proposal unit can also take into account the farm's equipment and propose the optimal method of using equipment and tools. For example, the proposal unit can propose the optimal method of using equipment based on the farm's equipment. This makes it possible to make a feasible proposal by taking into account the farmer's resources. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal unit can input the farmer's resource data into the generation AI and have the generation AI execute the optimal proposal.

[0048] When making a proposal, the proposal unit can customize the proposal content taking into account the geographical conditions of the farm. The proposal unit, for example, proposes an optimal cultivation method based on the geographical conditions of the farm. For example, the proposal unit customizes the proposal content based on the geographical conditions of the farm. The proposal unit can also visually display the proposal content based on the geographical conditions of the farm. For example, the proposal unit displays the proposal content as a graph or chart based on the geographical conditions of the farm. This enables more appropriate proposals to be made by taking the geographical conditions of the farm into consideration. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the geographical conditions of the farm into the generation AI and cause the generation AI to customize the proposal content.

[0049] When making a proposal, the proposal unit can adjust the proposal method by reflecting the farmer's past feedback. The proposal unit, for example, adjusts the proposal method based on the farmer's past feedback. For example, the proposal unit adjusts the proposal method based on the farmer's survey results. The proposal unit can also customize the proposal content based on the farmer's past feedback. For example, the proposal unit adjusts the proposal content based on the farmer's past data. The proposal unit can also improve the proposal accuracy based on the farmer's past feedback. For example, the proposal unit improves the proposal accuracy based on the farmer's past feedback. In this way, the proposal method is adjusted by reflecting the farmer's past feedback, and the proposal accuracy is improved. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the farmer's past feedback data into the generation AI and cause the generation AI to adjust the proposal method.

[0050] When making a proposal, the proposal unit can complement the proposal content by referring to success stories of other farmers. The proposal unit, for example, complements the proposal content by referring to success stories of other farmers. For example, the proposal unit proposes an optimal cultivation method based on the success stories of other farmers. The proposal unit can also visually display the proposal content based on the success stories of other farmers. For example, the proposal unit displays the proposal content as a graph or chart based on the success stories of other farmers. This makes it possible to complement the proposal content by referring to the success stories of other farmers and make more practical proposals. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on success stories of other farmers into the generation AI and have the generation AI complement the proposal content.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The collection unit can adjust the timing of data collection taking into account the farmer's work schedule. For example, the collection unit collects data while avoiding times when the farmer is busy. The collection unit can also adjust the frequency of data collection based on the farmer's work schedule. For example, the collection unit collects data during times when the farmer is not working. The collection unit can also dynamically change the timing of data collection according to the farmer's work schedule. For example, the collection unit automatically adjusts the timing of data collection when the farmer's work schedule changes. This makes it possible to collect data while taking into account the farmer's work schedule, thereby reducing the burden on the farmer.

[0053] The analysis unit can predict crop diseases based on the collected data. For example, the analysis unit predicts the risk of disease occurrence based on the collected data. The analysis unit can also analyze the risk of disease occurrence by comparing with past disease occurrence data. For example, the analysis unit predicts the current risk of disease occurrence based on past disease occurrence data. The analysis unit can also analyze the risk of disease occurrence by comparing with disease occurrence data of other farmers. For example, the analysis unit predicts the risk of disease occurrence based on disease occurrence data of other farmers. This allows farmers to take appropriate measures by predicting the risk of disease occurrence in advance.

[0054] The suggestion unit can suggest measures to combat crop diseases based on the analysis results. For example, the suggestion unit can suggest appropriate pesticide application methods when the risk of disease occurrence is high. The suggestion unit can also suggest preventive measures when the risk of disease occurrence is low. For example, the suggestion unit can suggest appropriate cultivation methods when the risk of disease occurrence is low. The suggestion unit can also provide specific instructions to farmers depending on the risk of disease occurrence. For example, the suggestion unit can provide specific instructions to farmers on how to use pesticides when the risk of disease occurrence is high. This makes it possible to maintain the health of crops by suggesting appropriate measures depending on the risk of disease occurrence.

[0055] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the farm. For example, the collection unit prioritizes collecting data related to weather conditions based on the geographical location information of the farm. For example, the collection unit collects weather data based on the geographical location information of the farm. The collection unit can also prioritize collecting data related to soil conditions based on the geographical location information of the farm. For example, the collection unit collects soil data based on the geographical location information of the farm. The collection unit can also prioritize collecting data related to crop growth based on the geographical location information of the farm. For example, the collection unit collects crop growth data based on the geographical location information of the farm. In this way, highly relevant data can be prioritized by taking into account the geographical location information of the farm.

[0056] During analysis, the analysis unit can improve the accuracy of the analysis by integrating weather forecast data in addition to past data and data from other farmers. For example, the analysis unit improves the accuracy of the analysis by integrating past data with weather forecast data. For example, the analysis unit integrates past harvest data with weather forecast data to perform analysis. The analysis unit can also improve the accuracy of the analysis by integrating other farmers' data with weather forecast data. For example, the analysis unit integrates other farmers' harvest data with weather forecast data to perform analysis. The analysis unit can also improve the accuracy of the analysis by integrating past data, other farmers' data, and weather forecast data. For example, the analysis unit integrates past harvest data, other farmers' harvest data, and weather forecast data to perform analysis. Integrating weather forecast data improves the accuracy of the analysis, enabling more accurate proposals.

[0057] When making a proposal, the proposal unit can take into account the farmer's resources. For example, the proposal unit takes into account the farmer's labor force and proposes an efficient work method. For example, the proposal unit proposes an optimal work schedule based on the farmer's labor force. The proposal unit can also take into account the farmer's funds and propose a cost-effective cultivation method. For example, the proposal unit proposes the optimal amount of fertilizer and pesticide to use based on the farmer's funds. The proposal unit can also take into account the farm's equipment and propose the optimal way to use equipment and tools. For example, the proposal unit proposes the optimal way to use equipment based on the farm's equipment. This makes it possible to make feasible proposals by taking into account the farmer's resources.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit collects cultivation data. The cultivation data includes soil conditions, temperature, humidity, and sunlight hours. For example, the collection unit measures the soil's pH value and nutrient content using sensors and collects the data. Temperature and humidity are measured using temperature sensors and humidity sensors and collected. Sunshine hours are measured using a light sensor and collected. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed by comparing it with past data and data from other farmers. For example, the analysis unit compares the collected data with past harvest yield data and performs an analysis. It can also compare the collected data with data from other farmers. Furthermore, based on the collected data, it analyzes factors that affect crop growth and derives optimal conditions. Step 3: The suggestion unit proposes a cultivation method based on the analysis results obtained by the analysis unit. The proposal relates to the timing of watering and the amount of fertilizer. For example, the suggestion unit proposes the optimal timing of watering and the amount of fertilizer based on the analysis results. It can also propose methods to promote crop growth. The processing in the suggestion unit may be performed using a generation AI, and the analysis results may be input to the generation AI to have it execute a proposal for the optimal cultivation method.

[0060] (Example 2) A crop cultivation support system according to an embodiment of the present invention is a system that supports crop cultivation using a generative AI. The crop cultivation support system analyzes cultivation data, such as soil condition, temperature, humidity, and sunshine hours, input by farmers and proposes optimal cultivation methods. For example, the crop cultivation support system proposes the optimal watering timing and fertilizer amount for a specific crop. Farmers can expect to increase their yield and improve their quality by cultivating crops based on the cultivation methods proposed by the generative AI. For example, watering crops according to the methods proposed by the generative AI promotes crop growth and increases yield. This service is highly beneficial to farmers, and by following the suggestions provided by the generative AI, they can grow crops efficiently. Furthermore, because the generative AI always makes suggestions based on the latest data, farmers can always know the optimal cultivation methods. As a result, the crop cultivation support system supports farmers in efficiently cultivating crops, thereby increasing yield and improving quality. For example, by following the optimal cultivation methods proposed by the generative AI, farmers can promote crop growth and increase their yield. Furthermore, because the generative AI always makes suggestions based on the latest data, farmers can always know the optimal cultivation methods.

[0061] A crop cultivation support system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects cultivation data. The cultivation data includes, for example, soil condition, temperature, humidity, and sunshine duration, but is not limited to these examples. For example, the collection unit measures the soil condition using a sensor and collects the data. The collection unit can also measure temperature and humidity using a sensor and collect the data. The collection unit can also measure sunshine duration and collect the data. For example, the collection unit measures the soil pH value and nutrient content and collects the data. Temperature and humidity are measured using a temperature sensor and a humidity sensor and collected the data. Sunshine duration is measured using a light sensor and collected the data. The analysis unit analyzes the data collected by the collection unit. For example, the analysis is performed by comparing the collected data with past data and data from other farmers, but is not limited to these examples. For example, the analysis unit compares the collected data with past harvest yield data and performs the analysis. The analysis unit can also compare the collected data with data from other farmers and perform the analysis. The analysis unit can also analyze factors affecting crop growth based on the collected data. For example, the analysis unit analyzes optimal conditions for crop growth based on the collected data. The suggestion unit proposes a cultivation method based on the analysis results obtained by the analysis unit. The suggestions may relate to, for example, the timing of watering and the amount of fertilizer, but are not limited to such examples. For example, the suggestion unit proposes the optimal timing of watering based on the analysis results. The suggestion unit can also propose the optimal amount of fertilizer based on the analysis results. The suggestion unit can also propose a method for promoting crop growth based on the analysis results. For example, the suggestion unit proposes a method for adjusting the timing of watering based on the analysis results. The amount of fertilizer is adjusted according to the growth stage of the crop. This allows the crop cultivation support system according to the embodiment to support farmers in efficiently cultivating crops and achieve increased yields and improved quality. Some or all of the above-described processing by the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit may input the analysis results obtained by the analysis unit into the generation AI and cause the generation AI to propose an optimal cultivation method.

[0062] The collection unit can collect data on soil condition, temperature, humidity, and sunshine hours. For example, the collection unit measures the soil condition using a sensor and collects the data. For example, the collection unit measures the soil pH value and nutrient content and collects the data. The collection unit can also measure temperature using a sensor and collect the data. For example, the collection unit measures temperature using a temperature sensor and collects the data. The collection unit can also measure humidity using a sensor and collect the data. For example, the collection unit measures humidity using a humidity sensor and collects the data. The collection unit can also measure sunshine hours and collect the data. For example, the collection unit measures sunshine hours using a light sensor and collects the data. This allows basic data necessary for cultivation to be collected, thereby improving the accuracy of the analysis. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can input data acquired by a sensor into a generation AI and have the generation AI analyze the data.

[0063] The analysis unit can analyze the collected data and compare it with past data or data from other farmers. For example, the analysis unit compares the collected data with past yield data and performs an analysis. For example, the analysis unit compares the collected data with past weather data and performs an analysis. The analysis unit can also compare the collected data with data from other farmers and perform an analysis. For example, the analysis unit compares the collected data with yield data from other farmers and performs an analysis. The analysis unit can also compare the collected data with cultivation method data from other farmers and perform an analysis. The analysis unit can also analyze factors that affect crop growth based on the collected data. For example, the analysis unit analyzes the optimal conditions for crop growth based on the collected data. This enables more accurate analysis by comparing the collected data with past data and data from other farmers. 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 collected data into a generation AI and have the generation AI analyze the data.

[0064] The suggestion unit can suggest the timing of watering or the amount of fertilizer based on the analysis results. For example, the suggestion unit can suggest the optimal timing of watering based on the analysis results. For example, the suggestion unit can suggest the timing of watering based on the soil humidity level and the temperature. The suggestion unit can also suggest the optimal amount of fertilizer based on the analysis results. For example, the suggestion unit can suggest the amount of fertilizer based on the type of crop and the growth stage. The suggestion unit can also suggest a method for promoting crop growth based on the analysis results. For example, the suggestion unit can suggest a method for adjusting the timing of watering based on the analysis results. By suggesting the optimal timing of watering and the amount of fertilizer, crop growth can be promoted, and an increase in yield and improvement in quality can be expected. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input the analysis results into the generation AI and cause the generation AI to suggest the optimal cultivation method.

[0065] The suggestion unit can increase the yield and improve the quality by following the suggestions provided by the generation AI. The suggestion unit can increase the yield and improve the quality by following the suggestions provided by the generation AI, for example. For example, the suggestion unit can promote crop growth and increase the yield by following the optimal cultivation method proposed by the generation AI. The suggestion unit can also improve the quality of the crop by following the method proposed by the generation AI. For example, the suggestion unit can optimize crop growth by following the watering timing and fertilizer amount proposed by the generation AI. In this way, by following the suggestions of the generation AI, an increase in the yield and improvement in the quality are achieved. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI, for example. For example, the suggestion unit can provide specific instructions to the farmer based on the cultivation method proposed by the generation AI.

[0066] 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, the collection unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. For example, the collection unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the collection unit can automatically adjust the timing of data collection to collect data quickly. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. This adjusts the timing of data collection according to the user's emotions, reducing the user's burden and enabling efficient data collection. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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 AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the generation AI and cause the generation AI to adjust the timing of data collection.

[0067] The collection unit can collect information on the growth stage of crops or the occurrence of pests and diseases in addition to soil conditions, temperature, humidity, and sunlight hours. The collection unit, for example, periodically records the growth stage of crops and stores it in a database. For example, the collection unit measures the number of leaves and stem height of crops and records the growth stage. The collection unit can also monitor the occurrence of pests and diseases in real time and issue alerts when abnormalities occur. For example, the collection unit can detect the occurrence of pests and diseases using a sensor and notify when an abnormality occurs. The collection unit can also collect information on the color and shape of crop leaves in addition to soil conditions, temperature, humidity, and sunlight hours to grasp the growth status in detail. For example, the collection unit can capture the color and shape of leaves with a camera and collect data. By collecting information on the growth stage of crops and the occurrence of pests and diseases, more detailed data can be used for analysis. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input data on the growth stage of crops and the occurrence of pests and diseases into a generation AI and have the generation AI analyze the data.

[0068] The collection unit can improve collection accuracy by adjusting the placement and type of sensors during data collection. For example, the collection unit optimizes the placement of sensors according to the type of crop and its growth stage. For example, the collection unit adjusts the placement of sensors according to the type of crop. The collection unit can also improve collection accuracy by selecting the type of sensor according to the soil condition and weather conditions. For example, the collection unit selects an appropriate sensor according to the soil condition and collects data. The collection unit can also periodically review the placement and type of sensors to maintain collection accuracy. For example, the collection unit periodically checks the placement and type of sensors and adjusts them as necessary. By optimizing the placement and type of sensors, collection accuracy is improved and more accurate data can be obtained. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data on the placement and type of sensors to the generation AI and cause the generation AI to select the optimal placement and type.

[0069] The collection unit can dynamically adjust the collection frequency during data collection depending on the type and growth stage of the crop. For example, the collection unit increases the collection frequency to collect detailed data during the early stages of crop growth. For example, the collection unit increases the data collection frequency during the early stages of crop growth. The collection unit can also reduce the collection frequency during stable growth stages to efficiently collect data. For example, the collection unit reduces the data collection frequency during stable growth stages. The collection unit can also set an optimal collection frequency depending on the type of crop and dynamically adjust it. For example, the collection unit adjusts the collection frequency depending on the type of crop. This allows for dynamic adjustment of the collection frequency to efficiently collect data and improve the accuracy of analysis. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data on the growth stage and type of crop to the generation AI and cause the generation AI to adjust the collection frequency.

[0070] 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 only important data. For example, the collection unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed data. For example, the collection unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. Thus, by prioritizing data according to the user's emotions, important data can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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 collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the generation AI and have the generation AI determine the priority of the data.

[0071] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the farm. For example, the collection unit prioritizes collecting data related to weather conditions based on the geographical location information of the farm. For example, the collection unit collects weather data based on the geographical location information of the farm. The collection unit can also prioritize collecting data related to soil conditions based on the geographical location information of the farm. For example, the collection unit collects soil data based on the geographical location information of the farm. The collection unit can also prioritize collecting data related to crop growth based on the geographical location information of the farm. For example, the collection unit collects crop growth data based on the geographical location information of the farm. In this way, highly relevant data can be prioritized by taking the geographical location information of the farm into consideration. Some or all of the above-described processing in the collection unit may be performed using, or without using, AI. For example, the collection unit can input the geographical location information of the farm to the generation AI and cause the generation AI to determine the priority of highly relevant data.

[0072] The collection unit can customize the collection method by reflecting the farmer's past feedback when collecting data. The collection unit, for example, adjusts the collection method based on the farmer's past feedback to efficiently collect data. For example, the collection unit adjusts the collection method based on the farmer's past feedback. The collection unit can also adjust the collection frequency based on the farmer's past feedback to collect necessary data. For example, the collection unit adjusts the collection frequency based on the farmer's past data. The collection unit can also adjust the placement and type of sensors based on the farmer's past feedback to improve collection accuracy. For example, the collection unit adjusts the placement and type of sensors based on the farmer's past feedback. In this way, the collection method can be customized and data can be collected efficiently by reflecting the farmer's past feedback. 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 farmer's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0073] The collection unit can compare data collected with other farmers' data in real time to detect abnormal values. The collection unit, for example, compares data collected with other farmers' data in real time to detect abnormal values. For example, the collection unit compares data collected with other farmers' harvest yield data to detect abnormal values. The collection unit can also compare data collected with weather data from other farmers to detect abnormal values. For example, the collection unit compares data collected with weather data from other farmers to detect abnormal values. The collection unit can also issue an alert and notify the farmer when an abnormal value is detected. For example, the collection unit issues an alert and notifies the farmer when an abnormal value is detected. The collection unit can also analyze the cause of an abnormal value when an abnormal value is detected and propose countermeasures. For example, the collection unit analyzes the cause of an abnormal value and proposes countermeasures. This allows abnormal values ​​to be detected by comparing data collected with other farmers, and countermeasures can be taken quickly. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without AI. For example, the collection unit can input data collected with other farmers into the generation AI and have the generation AI detect abnormal values.

[0074] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, the analysis unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, the analysis unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the key points. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the display method of the analysis results to be adjusted according to the user's emotions, thereby enabling a display that is easy for the user to view. 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 analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and have the generation AI adjust the display method of the analysis results.

[0075] During analysis, the analysis unit can integrate weather forecast data in addition to past data and data from other farmers to improve the accuracy of the analysis. The analysis unit, for example, integrates past data with weather forecast data to improve the accuracy of the analysis. For example, the analysis unit integrates past harvest data with weather forecast data to perform the analysis. The analysis unit can also integrate other farmers' data with weather forecast data to improve the accuracy of the analysis. For example, the analysis unit integrates other farmers' harvest data with weather forecast data to perform the analysis. The analysis unit can also integrate past data, other farmers' data, and weather forecast data to improve the accuracy of the analysis. For example, the analysis unit integrates past harvest data, other farmers' harvest data, and weather forecast data to perform the analysis. Integrating the weather forecast data improves the accuracy of the analysis and enables more accurate proposals. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input past data, other farmers' data, and weather forecast data into the generation AI and cause the generation AI to integrate and analyze the data.

[0076] The analysis unit can automatically detect abnormal values ​​and outliers during analysis and exclude them from the analysis results. The analysis unit, for example, automatically detects abnormal values ​​and outliers and excludes them from the analysis results. For example, the analysis unit detects abnormal values ​​and outliers using statistical methods and excludes them from the analysis results. When an abnormal value or outlier is detected, the analysis unit can analyze the cause and propose countermeasures. For example, the analysis unit analyzes the cause of the abnormal value or outlier and proposes countermeasures. The analysis unit can also periodically review the algorithm for detecting abnormal values ​​and outliers to improve its accuracy. For example, the analysis unit periodically updates the algorithm for detecting abnormal values ​​and outliers to improve its accuracy. This eliminates abnormal values ​​and outliers, thereby improving the accuracy of the analysis results. 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 cause a generation AI to detect abnormal values ​​and outliers and cause the generation AI to exclude them from the analysis results.

[0077] During analysis, the analysis unit can apply different analysis algorithms depending on the type of crop and its growth stage. The analysis unit applies the optimal analysis algorithm depending on, for example, the type of crop. For example, the analysis unit applies an analysis algorithm such as regression analysis or clustering depending on the type of crop. The analysis unit can also apply the optimal analysis algorithm depending on the growth stage of the crop. For example, the analysis unit applies a different analysis algorithm depending on the growth stage of the crop. The analysis unit can also dynamically adjust the analysis algorithm depending on the type of crop and its growth stage. For example, the analysis unit dynamically changes the analysis algorithm depending on the type of crop and its growth stage. This improves the analysis accuracy by applying an analysis algorithm depending on the type of crop and its growth stage. 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 data on the type of crop and its growth stage to the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0078] The analysis unit can estimate the user's emotions and prioritize analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize displaying only important analysis results. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. For example, the analysis unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying analysis results that can be quickly confirmed. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the analysis results to be prioritized according to the user's emotions, thereby prioritizing the display of important analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative 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 analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and have the generation AI determine the priority of the analysis results.

[0079] The analysis unit can customize the analysis results by taking into account the geographical conditions of the farm during the analysis. The analysis unit customizes the analysis results based on, for example, the geographical conditions of the farm. For example, the analysis unit suggests an optimal cultivation method based on the geographical conditions of the farm. The analysis unit can also visually display the analysis results based on the geographical conditions of the farm. For example, the analysis unit displays the analysis results as a graph or chart based on the geographical conditions of the farm. This makes it possible to provide more appropriate analysis results by taking into account the geographical conditions of the farm. 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 data on the geographical conditions of the farm into the generation AI and cause the generation AI to customize the analysis results.

[0080] During analysis, the analysis unit can complement the analysis results by referring to success stories of other farmers. The analysis unit, for example, complements the analysis results by referring to success stories of other farmers. For example, the analysis unit proposes optimal cultivation methods based on the success stories of other farmers. The analysis unit can also visually display the analysis results based on the success stories of other farmers. For example, the analysis unit displays the analysis results as graphs or charts based on the success stories of other farmers. This makes it possible to complement the analysis results and make more practical proposals by referring to the success stories of other farmers. 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 data on success stories of other farmers into the generation AI and have the generation AI complement the analysis results.

[0081] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, if the user is nervous, the suggestion unit provides a simple and highly visible suggestion method. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the suggestion unit can provide a suggestion method that includes detailed information. For example, the suggestion unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the suggestion unit can provide a suggestion method that focuses on the key points. For example, the suggestion unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the suggestion to be easily understood by adjusting the way suggestions are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative 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 suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input user emotion data to the generation AI and cause the generation AI to adjust the way the suggestion is expressed.

[0082] When making a proposal, the proposal unit can apply different proposal algorithms depending on the type and growth stage of the crop. The proposal unit, for example, applies an optimal proposal algorithm depending on the type of crop. For example, the proposal unit applies a proposal algorithm such as a rule-based algorithm or a machine learning algorithm depending on the type of crop. The proposal unit can also apply an optimal proposal algorithm depending on the growth stage of the crop. For example, the proposal unit applies different proposal algorithms depending on the growth stage of the crop. The proposal unit can also dynamically adjust the proposal algorithm depending on the type and growth stage of the crop. For example, the proposal unit dynamically changes the proposal algorithm depending on the type and growth stage of the crop. This enables more appropriate proposals by applying a proposal algorithm depending on the type and growth stage of the crop. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input data on the type and growth stage of the crop to the generation AI and cause the generation AI to apply the optimal proposal algorithm.

[0083] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to past proposal results. The proposal unit, for example, improves the accuracy of the proposal based on past proposal results. For example, the proposal unit proposes an optimal cultivation method based on past proposal results. The proposal unit can also customize the proposal content based on past proposal results. For example, the proposal unit adjusts the proposal content based on past proposal results. By referring to past proposal results, the accuracy of the proposal can be improved, enabling more practical proposals. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data of past proposal results into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0084] When making a proposal, the proposal unit can take into account the farmer's resources. For example, the proposal unit can take into account the farmer's labor and propose an efficient work method. For example, the proposal unit can propose an optimal work schedule based on the farmer's labor. The proposal unit can also take into account the farmer's funds and propose a cost-effective cultivation method. For example, the proposal unit can propose the optimal amount of fertilizer and pesticide to be used based on the farmer's funds. The proposal unit can also take into account the farm's equipment and propose the optimal method of using equipment and tools. For example, the proposal unit can propose the optimal method of using equipment based on the farm's equipment. This makes it possible to make a feasible proposal by taking into account the farmer's resources. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal unit can input the farmer's resource data into the generation AI and have the generation AI execute the optimal proposal.

[0085] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize displaying only important suggestions. For example, the suggestion unit can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the suggestion unit can prioritize displaying detailed suggestions. For example, the suggestion unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the suggestion unit can prioritize displaying suggestions that can be quickly confirmed. For example, the suggestion unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the priority of suggestions to be prioritized according to the user's emotions, thereby enabling important suggestions to be displayed preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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 suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input user emotion data to the generation AI and have the generation AI determine the priority of suggestions.

[0086] When making a proposal, the proposal unit can customize the proposal content taking into account the geographical conditions of the farm. The proposal unit, for example, proposes an optimal cultivation method based on the geographical conditions of the farm. For example, the proposal unit customizes the proposal content based on the geographical conditions of the farm. The proposal unit can also visually display the proposal content based on the geographical conditions of the farm. For example, the proposal unit displays the proposal content as a graph or chart based on the geographical conditions of the farm. This enables more appropriate proposals to be made by taking the geographical conditions of the farm into consideration. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the geographical conditions of the farm into the generation AI and cause the generation AI to customize the proposal content.

[0087] When making a proposal, the proposal unit can adjust the proposal method by reflecting the farmer's past feedback. The proposal unit, for example, adjusts the proposal method based on the farmer's past feedback. For example, the proposal unit adjusts the proposal method based on the farmer's survey results. The proposal unit can also customize the proposal content based on the farmer's past feedback. For example, the proposal unit adjusts the proposal content based on the farmer's past data. The proposal unit can also improve the proposal accuracy based on the farmer's past feedback. For example, the proposal unit improves the proposal accuracy based on the farmer's past feedback. In this way, the proposal method is adjusted by reflecting the farmer's past feedback, and the proposal accuracy is improved. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the farmer's past feedback data into the generation AI and cause the generation AI to adjust the proposal method.

[0088] When making a proposal, the proposal unit can complement the proposal content by referring to success stories of other farmers. The proposal unit, for example, complements the proposal content by referring to success stories of other farmers. For example, the proposal unit proposes an optimal cultivation method based on the success stories of other farmers. The proposal unit can also visually display the proposal content based on the success stories of other farmers. For example, the proposal unit displays the proposal content as a graph or chart based on the success stories of other farmers. This makes it possible to complement the proposal content by referring to the success stories of other farmers and make more practical proposals. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on success stories of other farmers into the generation AI and have the generation AI complement the proposal content. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect cultivation data such as soil condition, temperature, humidity, and sunshine hours using the sensors and camera 42 of the smart device 14. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can acquire the collected data from the smart device 14 via the network 54. The analysis unit can also be realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit can also be realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal cultivation method based on the analysis results. The suggestion unit can also be realized by the control unit 46A of the smart device 14 and can make suggestions using a generation AI. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect cultivation data such as soil condition, temperature, humidity, and sunshine hours using the sensors and camera 42 of the smart glasses 214. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can acquire the collected data from the smart glasses 214 via the network 54. The analysis unit can be realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit can be realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal cultivation method based on the analysis results. The suggestion unit can also be realized by the control unit 46A of the smart glasses 214 and can make suggestions using a generation AI. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect cultivation data such as soil condition, temperature, humidity, and sunshine hours using the sensors and camera 42 of the headset terminal 314. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can acquire the collected data from the headset terminal 314 via the network 54. The analysis unit can also be realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit can also be realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal cultivation method based on the analysis results. The suggestion unit can also be realized by the control unit 46A of the headset terminal 314 and can make suggestions using a generation AI. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect cultivation data such as soil condition, temperature, humidity, and sunshine hours using the sensors and camera 42 of the robot 414. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can acquire the collected data from the robot 414 via the network 54. The analysis unit can be realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit can be realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal cultivation method based on the analysis results. The suggestion unit can also be realized by the control unit 46A of the robot 414 and can make suggestions using a generative AI.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The collection unit can adjust the timing of data collection taking into account the farmer's work schedule. For example, the collection unit collects data while avoiding times when the farmer is busy. The collection unit can also adjust the frequency of data collection based on the farmer's work schedule. For example, the collection unit collects data during times when the farmer is not working. The collection unit can also dynamically change the timing of data collection according to the farmer's work schedule. For example, the collection unit automatically adjusts the timing of data collection when the farmer's work schedule changes. This makes it possible to collect data while taking into account the farmer's work schedule, thereby reducing the burden on the farmer.

[0091] The analysis unit can predict crop diseases based on the collected data. For example, the analysis unit predicts the risk of disease occurrence based on the collected data. The analysis unit can also analyze the risk of disease occurrence by comparing with past disease occurrence data. For example, the analysis unit predicts the current risk of disease occurrence based on past disease occurrence data. The analysis unit can also analyze the risk of disease occurrence by comparing with disease occurrence data of other farmers. For example, the analysis unit predicts the risk of disease occurrence based on disease occurrence data of other farmers. This allows farmers to take appropriate measures by predicting the risk of disease occurrence in advance.

[0092] The suggestion unit can suggest measures to combat crop diseases based on the analysis results. For example, the suggestion unit can suggest appropriate pesticide application methods when the risk of disease occurrence is high. The suggestion unit can also suggest preventive measures when the risk of disease occurrence is low. For example, the suggestion unit can suggest appropriate cultivation methods when the risk of disease occurrence is low. The suggestion unit can also provide specific instructions to farmers depending on the risk of disease occurrence. For example, the suggestion unit can provide specific instructions to farmers on how to use pesticides when the risk of disease occurrence is high. This makes it possible to maintain the health of crops by suggesting appropriate measures depending on the risk of disease occurrence.

[0093] The collection unit can estimate the user's emotions and adjust the frequency 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 burden on the user. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. For example, the collection unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the collection unit can automatically adjust the timing of data collection to collect data quickly. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the frequency of data collection to be adjusted according to the user's emotions, reducing the burden on the user and enabling efficient data collection.

[0094] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, the analysis unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the display method of the analysis results to be adjusted according to the user's emotions, thereby enabling a display that is easy for the user to view.

[0095] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, if the user is nervous, the suggestion unit provides a simple and highly visible suggestion method. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the suggestion unit can provide a suggestion method that includes detailed information. For example, the suggestion unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the suggestion unit can provide a suggestion method that focuses on the main points. For example, the suggestion unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the suggestion method to be adjusted according to the user's emotions, making it possible to provide suggestions that are easy for the user to understand.

[0096] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize displaying only important suggestions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The suggestion unit can also prioritize displaying detailed suggestions when the user is relaxed. For example, the suggestion unit can record the user's voice and estimate the emotion using voice analysis technology. The suggestion unit can also prioritize displaying suggestions that can be quickly confirmed when the user is in a hurry. For example, the suggestion unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. In this way, the suggestion unit can prioritize displaying important suggestions by prioritizing suggestions according to the user's emotions.

[0097] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the farm. For example, the collection unit prioritizes collecting data related to weather conditions based on the geographical location information of the farm. For example, the collection unit collects weather data based on the geographical location information of the farm. The collection unit can also prioritize collecting data related to soil conditions based on the geographical location information of the farm. For example, the collection unit collects soil data based on the geographical location information of the farm. The collection unit can also prioritize collecting data related to crop growth based on the geographical location information of the farm. For example, the collection unit collects crop growth data based on the geographical location information of the farm. In this way, highly relevant data can be prioritized by taking into account the geographical location information of the farm.

[0098] During analysis, the analysis unit can improve the accuracy of the analysis by integrating weather forecast data in addition to past data and data from other farmers. For example, the analysis unit improves the accuracy of the analysis by integrating past data with weather forecast data. For example, the analysis unit integrates past harvest data with weather forecast data to perform analysis. The analysis unit can also improve the accuracy of the analysis by integrating other farmers' data with weather forecast data. For example, the analysis unit integrates other farmers' harvest data with weather forecast data to perform analysis. The analysis unit can also improve the accuracy of the analysis by integrating past data, other farmers' data, and weather forecast data. For example, the analysis unit integrates past harvest data, other farmers' harvest data, and weather forecast data to perform analysis. Integrating weather forecast data improves the accuracy of the analysis, enabling more accurate proposals.

[0099] When making a proposal, the proposal unit can take into account the farmer's resources. For example, the proposal unit takes into account the farmer's labor force and proposes an efficient work method. For example, the proposal unit proposes an optimal work schedule based on the farmer's labor force. The proposal unit can also take into account the farmer's funds and propose a cost-effective cultivation method. For example, the proposal unit proposes the optimal amount of fertilizer and pesticide to use based on the farmer's funds. The proposal unit can also take into account the farm's equipment and propose the optimal way to use equipment and tools. For example, the proposal unit proposes the optimal way to use equipment based on the farm's equipment. This makes it possible to make feasible proposals by taking into account the farmer's resources.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The collection unit collects cultivation data. The cultivation data includes soil conditions, temperature, humidity, and sunlight hours. For example, the collection unit measures the soil's pH value and nutrient content using sensors and collects the data. Temperature and humidity are measured using temperature sensors and humidity sensors and collected. Sunshine hours are measured using a light sensor and collected. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed by comparing it with past data and data from other farmers. For example, the analysis unit compares the collected data with past harvest yield data and performs an analysis. It can also compare the collected data with data from other farmers. Furthermore, based on the collected data, it analyzes factors that affect crop growth and derives optimal conditions. Step 3: The suggestion unit proposes a cultivation method based on the analysis results obtained by the analysis unit. The proposal relates to the timing of watering and the amount of fertilizer. For example, the suggestion unit proposes the optimal timing of watering and the amount of fertilizer based on the analysis results. It can also propose methods to promote crop growth. The processing in the suggestion unit may be performed using a generation AI, and the analysis results may be input to the generation AI to have it execute a proposal for the optimal cultivation method.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[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 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.

[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 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.

[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 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.

[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] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects cultivation data; an analysis unit that analyzes the data collected by the collection unit; A proposal unit that proposes a cultivation method based on the analysis results obtained by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Collect data on soil condition, temperature, humidity, and sunshine hours 2. The system of claim 1.

3. The analysis unit Analyze the collected data and compare it with historical data or data from other farmers 2. The system of claim 1.

4. The proposal unit Suggest watering timing or fertilizer amount based on analysis results 2. The system of claim 1.

5. The proposal unit By following the suggestions provided by Generative AI, you can increase yield and improve quality.

2. The system of claim 1.

6. 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.

7. The collecting unit Collect information on soil conditions, temperature, humidity, and sunshine hours, as well as the stage of crop development or pest infestation.

2. The system of claim 1.

8. The collecting unit When collecting data, adjust the placement and type of sensors to improve collection accuracy.

2. The system of claim 1.

Images