system

Abstract

The system according to this embodiment aims to analyze weather data and create an optimal growing environment. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a construction unit. The collection unit collects weather data. The analysis unit analyzes the weather data collected by the collection unit. The construction unit constructs an optimal growing environment based on the data analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the utilization of weather data to construct an optimal growth environment has not been fully carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze weather data and construct an optimal growth environment.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a construction unit. The collection unit collects weather data. The analysis unit analyzes the weather data collected by the collection unit. The construction unit constructs an optimal growth environment based on the data analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can analyze weather data and create an optimal growing environment. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An agricultural support system according to an embodiment of the present invention is a system that utilizes generative AI to construct an optimal growing environment that takes weather into consideration and to optimize post-harvest sorting work. The agricultural support system uses generative AI to analyze weather data and construct an optimal growing environment. For example, it collects weather data such as temperature, humidity, and precipitation, and the generative AI analyzes this data to provide optimal growing conditions for crops. This is expected to improve crop quality and increase yields. Next, the agricultural support system optimizes post-harvest sorting work. The generative AI determines the quality of the harvested crops and performs appropriate sorting. For example, the generative AI uses image recognition technology to analyze the appearance of crops and automatically sorts good quality crops from poor quality crops. This improves the efficiency of sorting work and contributes to solving labor shortages. The agricultural support system enables farmers to solve the problem of labor shortages and operate agriculturally efficiently. By utilizing generative AI, it is possible to improve agricultural productivity by providing an optimal growing environment that takes weather into consideration and automating post-harvest sorting work. As a result, the agricultural support system realizes improved crop quality and increased yields, enabling efficient agricultural operation.

[0029] The agricultural support system according to the embodiment comprises a collection unit, an analysis unit, and a construction unit. The collection unit collects weather data. The collection unit can collect weather data such as temperature, humidity, and precipitation. The collection unit can collect temperature data using a temperature sensor, humidity data using a humidity sensor, and precipitation data using a precipitation sensor. The analysis unit analyzes the weather data collected by the collection unit. The analysis unit analyzes the weather data using a generation AI, for example. The generation AI takes temperature data, humidity data, and precipitation data as input and outputs optimal growth conditions. The analysis unit uses the generation AI to analyze the weather data and provide optimal growth conditions for crops. The construction unit constructs an optimal growth environment based on the data analyzed by the analysis unit. The construction unit uses the generation AI to construct an optimal growth environment, for example. The generation AI adjusts conditions such as temperature, humidity, light intensity, and soil pH to provide an optimal growth environment for crops. As a result, the agricultural support system according to this embodiment can collect and analyze weather data and create an optimal growing environment, which is expected to improve crop quality and increase yields.

[0030] The data collection unit collects weather data. For example, it can collect weather data such as temperature, humidity, and precipitation. Specifically, it collects temperature data using temperature sensors. Temperature sensors are installed in various locations on farmland to monitor daily temperature fluctuations in real time. This allows for the immediate detection of sudden temperature changes and abnormally high or low temperatures. Humidity data can also be collected using humidity sensors. Humidity sensors measure soil moisture and air humidity, which are important for crop growth, providing data to determine the appropriate timing for irrigation and ventilation. Precipitation data can also be collected using precipitation sensors. Precipitation sensors accurately measure the presence and amount of rainfall, providing information to prevent flood risks due to excessive rainfall and water shortages due to drought. These sensors transmit data to a central database using wireless communication technology, and the data collection unit manages this data centrally. Furthermore, the data collection unit can also incorporate data from weather satellites and ground-based weather observation stations to collect wide-area weather information. This allows the data collection unit to integrate local and regional weather data, enabling more accurate weather forecasts. By updating this data in real time and providing the latest information at all times, the data collection unit can improve the accuracy and reliability of the entire agricultural support system.

[0031] The analysis unit analyzes the weather data collected by the collection unit. The analysis unit analyzes the weather data using, for example, a generative AI. The generative AI takes, for example, temperature data, humidity data, and precipitation data as input and outputs the optimal growing conditions. Specifically, the generative AI compares past weather data with current weather data to predict the optimal conditions for crop growth. For example, it analyzes temperature data to identify the temperature range suitable for crop growth. It analyzes humidity data to calculate the appropriate timing and amount of irrigation. It analyzes precipitation data to assess the risk of flooding due to excessive rainfall and propose necessary countermeasures. The generative AI integrates this data to provide the optimal conditions for crop growth. Furthermore, the generative AI can dynamically adjust the optimal growing conditions according to the type of crop and its growth stage. For example, young seedlings require high humidity, while mature crops require moderate dryness. Taking such changes in conditions into consideration, the generative AI provides the optimal growing conditions in real time. In addition, the generative AI can respond quickly to extreme weather and unexpected weather fluctuations. For example, if a rapid rise in temperature or an increase in rainfall is predicted, the generating AI will immediately issue a warning and suggest appropriate countermeasures. This allows the analysis unit to quickly and accurately analyze the collected weather data and provide optimal conditions for crop growth.

[0032] The construction unit constructs the optimal growing environment based on data analyzed by the analysis unit. For example, the construction unit uses generative AI to construct the optimal growing environment. The generative AI adjusts conditions such as temperature, humidity, light intensity, and soil pH to provide the optimal growing environment for crops. Specifically, it uses a temperature control system to maintain a temperature suitable for crop growth. For example, it controls heaters and cooling devices in the greenhouse to maintain an appropriate temperature according to temperature fluctuations. It uses a humidity control system to maintain appropriate humidity. For example, it controls humidifiers and dehumidifiers to maintain the humidity necessary for crop growth. It uses a light intensity adjustment system to provide the appropriate light intensity. For example, it controls LED lighting to provide the light intensity necessary for crop growth. Appropriate fertilizers and soil conditioners are used to adjust the soil pH. In this way, the construction unit provides the optimal environment for crop growth, resulting in improved quality and increased yields. Furthermore, the construction unit can automate these adjustments, monitor environmental conditions in real time, and make adjustments as needed. For example, it can automatically adjust temperature, humidity, and light intensity based on data from sensors to maintain the optimal growing environment. Furthermore, the system can immediately issue a warning and propose appropriate countermeasures if abnormal environmental conditions are detected. This allows the system to consistently provide the optimal growing environment, supporting improved crop quality and increased yields.

[0033] The judgment unit determines the quality of the harvested crops. The judgment unit determines the quality of the crops using, for example, a generating AI. The generating AI analyzes the appearance of the crops using, for example, image recognition technology and determines the quality. The judgment unit, for example, uses the generating AI to analyze the appearance of the crops and determines which crops are of good quality and which are not. This makes it possible to sort the harvested crops appropriately by determining their quality. Some or all of the above processing in the judgment unit may be performed using, for example, AI, or without AI. For example, the judgment unit can input image data of the harvested crops into the generating AI and have the generating AI perform the quality determination.

[0034] The judgment unit can analyze the appearance of crops using image recognition technology and determine their quality. The judgment unit can, for example, analyze the appearance of crops using a generative AI and determine their quality. The generative AI can, for example, analyze the appearance of crops using image recognition technology and determine which crops are of good quality and which are not. In this way, the quality of crops can be accurately determined by using image recognition technology. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input image data of crops into a generative AI and have the generative AI perform the quality determination.

[0035] The judgment unit includes a sorting unit that automatically sorts crops into good quality and poor quality crops. The sorting unit, for example, uses a generation AI to determine the quality of the crops and sorts them automatically. The generation AI, for example, uses image recognition technology to analyze the appearance of the crops and automatically sorts them into good quality and poor quality crops. This improves the efficiency of the sorting process by automatically sorting good quality and poor quality crops. Some or all of the above-described processes in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input image data of the crops into the generation AI and have the generation AI perform quality determination and sorting.

[0036] The data collection unit can collect weather data such as temperature, humidity, and precipitation. For example, the data collection unit can collect temperature data using a temperature sensor. It can also collect humidity data using a humidity sensor. It can also collect precipitation data using a precipitation sensor. By collecting weather data such as temperature, humidity, and precipitation, detailed weather information can be obtained. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired by the temperature sensor into a generating AI and have the generating AI perform analysis of the temperature data.

[0037] The construction unit can provide optimal growing conditions for crops based on analyzed weather data. For example, the construction unit provides optimal growing conditions using a generation AI. The generation AI adjusts conditions such as temperature, humidity, light intensity, and soil pH to provide the optimal growing environment for crops. By providing optimal growing conditions for crops based on analyzed weather data, it is expected that crop quality will improve and yields will increase. Some or all of the above processing in the construction unit may be performed using AI, for example, or without AI. For example, the construction unit can input analyzed weather data into the generation AI and have the generation AI perform the task of providing optimal growing conditions.

[0038] The data collection unit can analyze past weather data and select the optimal collection method. For example, the data collection unit can adjust the collection frequency to specific seasons or time periods based on past weather data. For example, the data collection unit can identify periods when extreme weather events are likely to occur from past data and focus data collection during those periods. For example, the data collection unit can analyze past data and develop algorithms to optimize the collection method. This allows the optimal collection method to be selected by analyzing past weather data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past weather data into a generating AI and have the generating AI select the optimal collection method.

[0039] The data collection unit can filter weather data based on regional characteristics. For example, the data collection unit can select the types of data to collect by considering the climate characteristics of each region. The data collection unit can also filter necessary weather data based on the characteristics of agricultural crops in each region. The data collection unit can also improve the accuracy of the collected data by analyzing weather patterns in each region. This makes it possible to collect highly accurate data by filtering weather data based on regional characteristics. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the climate characteristics of each region into a generating AI and have the generating AI execute the filtering criteria.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information when collecting weather data. For example, the data collection unit can prioritize the collection of weather data for a specific region based on geographical location information. For example, the data collection unit can also prioritize the collection of data that is likely to affect crops by considering geographical location information. For example, the data collection unit can also prioritize the collection of data for areas prone to extreme weather events based on geographical location information. This enables efficient data collection by prioritizing the collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI select highly relevant data.

[0041] The data collection unit can analyze social media activity and collect relevant data when collecting weather data. For example, the data collection unit can analyze posts on social media and collect weather-related information. For example, the data collection unit can also collect weather data for a specific region based on hashtags on social media. For example, the data collection unit can analyze images and videos on social media and collect weather-related data. This enables efficient data collection by analyzing social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media post data into a generating AI and have the generating AI perform the collection of relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the weather data during the analysis. For example, the analysis unit can perform a detailed analysis on important weather data. For example, the analysis unit can perform a simplified analysis on less important weather data. The analysis unit can also optimally allocate analysis resources according to the importance of the weather data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the weather data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the weather data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of weather data during analysis. For example, the analysis unit applies a specific algorithm to temperature data. For example, the analysis unit can apply a different algorithm to precipitation data. For example, the analysis unit can apply yet another algorithm to humidity data. By applying different analysis algorithms depending on the category of weather data, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of weather data into a generating AI and have the generating AI select the analysis algorithm to apply.

[0044] The analysis unit can determine the priority of analysis based on the timing of weather data collection during the analysis. For example, the analysis unit may prioritize the analysis of data collected recently. The analysis unit may also lower the priority of analysis for older data collected at a later date. The analysis unit may also optimize the allocation of analysis resources based on the collection timing. This enables efficient analysis by determining the priority of analysis based on the timing of weather data collection. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of weather data collection into a generating AI and have the generating AI determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the weather data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may also optimally allocate analysis resources based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the weather data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the weather data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The construction unit can analyze past growth environment data during construction to select the optimal construction method. For example, the construction unit selects the optimal construction method based on past growth environment data. The construction unit can also identify periods when abnormal weather is likely to occur from past data and select a construction method suitable for those periods. The construction unit can also analyze past data and develop algorithms to optimize construction methods. This allows the optimal construction method to be selected by analyzing past growth environment data. Some or all of the above processes in the construction unit may be performed using AI, for example, or without AI. For example, the construction unit can input past growth environment data into a generating AI and have the generating AI select the optimal construction method.

[0047] The construction unit can customize the growing environment based on regional characteristics during construction. For example, the construction unit can customize the growing environment by considering regional climatic characteristics. The construction unit can also customize the growing environment based on regional crop characteristics. The construction unit can also optimize the growing environment by analyzing regional weather patterns. This allows for the provision of an optimal growing environment by customizing the growing environment based on regional characteristics. Some or all of the above processes in the construction unit may be performed using AI, for example, or without AI. For example, the construction unit can input regional climatic characteristics into a generating AI and have the generating AI perform the customization of the growing environment.

[0048] The construction unit can select the optimal growing environment by considering geographical location information during construction. For example, the construction unit can select a growing environment suitable for a specific region based on geographical location information. For example, the construction unit can also select the optimal growing environment for a crop by considering geographical location information. For example, the construction unit can also select a growing environment suitable for a region prone to extreme weather events based on geographical location information. By selecting the optimal growing environment by considering geographical location information, it becomes possible to construct an efficient growing environment. Some or all of the above-described processes in the construction unit may be performed using AI, for example, or without using AI. For example, the construction unit can input geographical location information into a generating AI and have the generating AI perform the selection of the optimal growing environment.

[0049] The construction unit can analyze social media activity during construction and propose methods for creating a suitable growing environment. For example, the construction unit can analyze posts on social media and collect information related to the growing environment. For example, the construction unit can also propose a growing environment suitable for a specific region based on hashtags on social media. For example, the construction unit can analyze images and videos on social media and propose methods related to the growing environment. This enables the efficient construction of a growing environment by analyzing social media activity and proposing methods for creating a suitable growing environment. Some or all of the above-described processes in the construction unit may be performed using AI, for example, or without AI. For example, the construction unit can input social media post data into a generating AI and have the generating AI execute proposals for growing environment methods.

[0050] The judgment unit can optimize its judgment algorithm by referring to past quality data during the judgment process. For example, the judgment unit optimizes the judgment algorithm based on past quality data. The judgment unit can also identify periods when abnormal quality is likely to occur from past data and optimize the judgment algorithm to suit those periods. For example, the judgment unit can analyze past data and develop an algorithm to optimize the judgment algorithm. This enables efficient quality judgment by optimizing the judgment algorithm by referring to past quality data. Some or all of the above processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input past quality data into a generating AI and have the generating AI perform the optimization of the judgment algorithm.

[0051] The judgment unit can weight quality data based on harvest time during the judgment process. For example, the judgment unit can prioritize weighting data with an approaching harvest time. For example, the judgment unit can also lower the weighting of data with a distant harvest time. For example, the judgment unit can also optimally adjust the weighting of quality data based on harvest time. This enables efficient quality judgment by weighting quality data based on harvest time. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input quality data based on harvest time into a generating AI and have the generating AI perform the weighting adjustment.

[0052] The sorting unit can optimize its sorting algorithm by referring to past sorting data during the sorting process. For example, the sorting unit can optimize its sorting algorithm based on past sorting data. The sorting unit can also identify periods when abnormal sorting is likely to occur from past data and optimize a sorting algorithm suitable for those periods. The sorting unit can also analyze past data and develop an algorithm to optimize the sorting algorithm. This enables efficient sorting by optimizing the sorting algorithm by referring to past sorting data. Some or all of the above processes in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input past sorting data into a generating AI and have the generating AI perform the optimization of the sorting algorithm.

[0053] The sorting unit can select the optimal sorting method while considering geographical location information. For example, the sorting unit can select a sorting method suitable for a specific region based on geographical location information. For example, the sorting unit can also select the optimal sorting method for agricultural products while considering geographical location information. For example, the sorting unit can also select a sorting method suitable for areas prone to extreme weather events based on geographical location information. By selecting the optimal sorting method while considering geographical location information, efficient sorting becomes possible. Some or all of the above-described processes in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input geographical location information into a generating AI and have the generating AI select the optimal sorting method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The agricultural support system can also be equipped with a forecasting unit. The forecasting unit predicts future weather based on weather data obtained from the data collection unit. For example, the forecasting unit can combine past and current weather data to predict the weather for the next week. This allows farmers to create appropriate farming plans based on future weather. The forecasting unit can also predict the occurrence of extreme weather events and issue warnings to farmers. For example, the forecasting unit can predict the occurrence of typhoons or heavy rains and notify farmers to take countermeasures in advance. Furthermore, the forecasting unit can also propose an optimal irrigation schedule based on weather forecasts. This allows farmers to use water resources efficiently and optimize crop growth.

[0056] The agricultural support system can also be equipped with a learning unit. This unit automatically learns agricultural knowledge based on data obtained from the data collection and analysis units. For example, the learning unit can combine historical weather data with crop growth data to learn optimal growing conditions. This allows farmers to provide more accurate growing conditions. The learning unit can also learn data related to post-harvest sorting to improve sorting accuracy. For example, it can automatically update the criteria for distinguishing good and bad quality crops based on past sorting data. Furthermore, the learning unit can continuously improve the overall system performance based on feedback from farmers. This ensures that the agricultural support system always incorporates the latest knowledge and technology, providing optimal support to farmers.

[0057] The agricultural support system can also be equipped with a notification unit. The notification unit provides appropriate notifications to farmers based on information obtained from the analysis and judgment units. For example, the notification unit can notify farmers of the appropriate timing for farm work based on the results of weather data analysis. This allows farmers to perform appropriate farm work according to the weather. The notification unit can also notify farmers of the results of post-harvest sorting work and provide information on good quality crops and those that are not. Furthermore, the notification unit can issue warnings to farmers when it predicts the occurrence of extreme weather. For example, the notification unit can predict the occurrence of typhoons or heavy rains and notify farmers to take countermeasures in advance. This allows farmers to respond quickly and minimize damage.

[0058] The agricultural support system can also be equipped with a diagnostic unit. The diagnostic unit diagnoses the health of crops based on data obtained from the collection and analysis units. For example, the diagnostic unit can diagnose the risk of pest and disease outbreaks in crops based on weather and soil data. This allows farmers to take early action and maintain crop health. The diagnostic unit can also diagnose crop growth and suggest the supply of necessary nutrients and water. For instance, it can analyze the color and shape of crop leaves to diagnose nutrient and water deficiencies. Furthermore, the diagnostic unit can diagnose the quality of crops after harvest and suggest appropriate storage methods. This allows farmers to maintain crop quality and maximize profits.

[0059] The agricultural support system can also be equipped with an optimization unit. This unit optimizes the entire agricultural process based on data obtained from the data collection and analysis units. For example, the optimization unit can suggest optimal planting and harvesting times based on weather and growth data. This allows farmers to maximize crop growth and increase yields. The optimization unit can also optimize irrigation and fertilization schedules, suggesting efficient use of water resources and fertilizers. Furthermore, the optimization unit can optimize post-harvest sorting, improving the efficiency of separating high-quality crops from lower-quality ones. This allows farmers to utilize labor efficiently and improve work efficiency.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The collection unit collects weather data. The collection unit can collect weather data such as temperature, humidity, and precipitation. For example, the collection unit can collect temperature data using a temperature sensor. It can also collect humidity data using a humidity sensor. It can also collect precipitation data using a precipitation sensor. Step 2: The analysis unit analyzes the weather data collected by the collection unit. The analysis unit analyzes the weather data using, for example, a generating AI. The generating AI takes, for example, temperature data, humidity data, and precipitation data as input and outputs optimal growing conditions. The analysis unit, for example, uses the generating AI to analyze the weather data and provides optimal growing conditions for crops. Step 3: The construction unit constructs the optimal growing environment based on the data analyzed by the analysis unit. The construction unit constructs the optimal growing environment using, for example, a generation AI. The generation AI adjusts conditions such as temperature, humidity, light intensity, and soil pH to provide the optimal growing environment for the crop.

[0062] (Example of form 2) An agricultural support system according to an embodiment of the present invention is a system that utilizes generative AI to construct an optimal growing environment that takes weather into consideration and to optimize post-harvest sorting work. The agricultural support system uses generative AI to analyze weather data and construct an optimal growing environment. For example, it collects weather data such as temperature, humidity, and precipitation, and the generative AI analyzes this data to provide optimal growing conditions for crops. This is expected to improve crop quality and increase yields. Next, the agricultural support system optimizes post-harvest sorting work. The generative AI determines the quality of the harvested crops and performs appropriate sorting. For example, the generative AI uses image recognition technology to analyze the appearance of crops and automatically sorts good quality crops from poor quality crops. This improves the efficiency of sorting work and contributes to solving labor shortages. The agricultural support system enables farmers to solve the problem of labor shortages and operate agriculturally efficiently. By utilizing generative AI, it is possible to improve agricultural productivity by providing an optimal growing environment that takes weather into consideration and automating post-harvest sorting work. As a result, the agricultural support system realizes improved crop quality and increased yields, enabling efficient agricultural operation.

[0063] The agricultural support system according to this embodiment comprises a collection unit, an analysis unit, and a construction unit. The collection unit collects weather data. The collection unit can collect weather data such as temperature, humidity, and precipitation. The collection unit can collect temperature data using a temperature sensor, humidity data using a humidity sensor, and precipitation data using a precipitation sensor. The analysis unit analyzes the weather data collected by the collection unit. The analysis unit analyzes the weather data using a generation AI, for example. The generation AI takes temperature data, humidity data, and precipitation data as input and outputs optimal growth conditions. The analysis unit uses the generation AI to analyze the weather data and provide optimal growth conditions for crops. The construction unit constructs an optimal growth environment based on the data analyzed by the analysis unit. The construction unit uses the generation AI to construct an optimal growth environment, for example. The generation AI adjusts conditions such as temperature, humidity, light intensity, and soil pH to provide an optimal growth environment for crops. As a result, the agricultural support system according to this embodiment can collect and analyze weather data and create an optimal growing environment, which is expected to improve crop quality and increase yields.

[0064] The data collection unit collects weather data. For example, it can collect weather data such as temperature, humidity, and precipitation. Specifically, it collects temperature data using temperature sensors. Temperature sensors are installed in various locations on farmland to monitor daily temperature fluctuations in real time. This allows for the immediate detection of sudden temperature changes and abnormally high or low temperatures. Humidity data can also be collected using humidity sensors. Humidity sensors measure soil moisture and air humidity, which are important for crop growth, providing data to determine the appropriate timing for irrigation and ventilation. Precipitation data can also be collected using precipitation sensors. Precipitation sensors accurately measure the presence and amount of rainfall, providing information to prevent flood risks due to excessive rainfall and water shortages due to drought. These sensors transmit data to a central database using wireless communication technology, and the data collection unit manages this data centrally. Furthermore, the data collection unit can also incorporate data from weather satellites and ground-based weather observation stations to collect wide-area weather information. This allows the data collection unit to integrate local and regional weather data, enabling more accurate weather forecasts. By updating this data in real time and providing the latest information at all times, the data collection unit can improve the accuracy and reliability of the entire agricultural support system.

[0065] The analysis unit analyzes the weather data collected by the collection unit. The analysis unit analyzes the weather data using, for example, a generative AI. The generative AI takes, for example, temperature data, humidity data, and precipitation data as input and outputs the optimal growing conditions. Specifically, the generative AI compares past weather data with current weather data to predict the optimal conditions for crop growth. For example, it analyzes temperature data to identify the temperature range suitable for crop growth. It analyzes humidity data to calculate the appropriate timing and amount of irrigation. It analyzes precipitation data to assess the risk of flooding due to excessive rainfall and propose necessary countermeasures. The generative AI integrates this data to provide the optimal conditions for crop growth. Furthermore, the generative AI can dynamically adjust the optimal growing conditions according to the type of crop and its growth stage. For example, young seedlings require high humidity, while mature crops require moderate dryness. Taking such changes in conditions into consideration, the generative AI provides the optimal growing conditions in real time. In addition, the generative AI can respond quickly to extreme weather and unexpected weather fluctuations. For example, if a rapid rise in temperature or an increase in rainfall is predicted, the generating AI will immediately issue a warning and suggest appropriate countermeasures. This allows the analysis unit to quickly and accurately analyze the collected weather data and provide optimal conditions for crop growth.

[0066] The construction unit constructs the optimal growing environment based on data analyzed by the analysis unit. For example, the construction unit uses generative AI to construct the optimal growing environment. The generative AI adjusts conditions such as temperature, humidity, light intensity, and soil pH to provide the optimal growing environment for crops. Specifically, it uses a temperature control system to maintain a temperature suitable for crop growth. For example, it controls heaters and cooling devices in the greenhouse to maintain an appropriate temperature according to temperature fluctuations. It uses a humidity control system to maintain appropriate humidity. For example, it controls humidifiers and dehumidifiers to maintain the humidity necessary for crop growth. It uses a light intensity adjustment system to provide the appropriate light intensity. For example, it controls LED lighting to provide the light intensity necessary for crop growth. Appropriate fertilizers and soil conditioners are used to adjust the soil pH. In this way, the construction unit provides the optimal environment for crop growth, resulting in improved quality and increased yields. Furthermore, the construction unit can automate these adjustments, monitor environmental conditions in real time, and make adjustments as needed. For example, it can automatically adjust temperature, humidity, and light intensity based on data from sensors to maintain the optimal growing environment. Furthermore, the system can immediately issue a warning and propose appropriate countermeasures if abnormal environmental conditions are detected. This allows the system to consistently provide the optimal growing environment, supporting improved crop quality and increased yields.

[0067] The judgment unit determines the quality of the harvested crops. The judgment unit determines the quality of the crops using, for example, a generating AI. The generating AI analyzes the appearance of the crops using, for example, image recognition technology and determines the quality. The judgment unit, for example, uses the generating AI to analyze the appearance of the crops and determines which crops are of good quality and which are not. This makes it possible to sort the harvested crops appropriately by determining their quality. Some or all of the above processing in the judgment unit may be performed using, for example, AI, or without AI. For example, the judgment unit can input image data of the harvested crops into the generating AI and have the generating AI perform the quality determination.

[0068] The judgment unit can analyze the appearance of crops using image recognition technology and determine their quality. The judgment unit can, for example, analyze the appearance of crops using a generative AI and determine their quality. The generative AI can, for example, analyze the appearance of crops using image recognition technology and determine which crops are of good quality and which are not. In this way, the quality of crops can be accurately determined by using image recognition technology. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input image data of crops into a generative AI and have the generative AI perform the quality determination.

[0069] The judgment unit includes a sorting unit that automatically sorts crops into good quality and poor quality crops. The sorting unit, for example, uses a generation AI to determine the quality of the crops and sorts them automatically. The generation AI, for example, uses image recognition technology to analyze the appearance of the crops and automatically sorts them into good quality and poor quality crops. This improves the efficiency of the sorting process by automatically sorting good quality and poor quality crops. Some or all of the above-described processes in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input image data of the crops into the generation AI and have the generation AI perform quality determination and sorting.

[0070] The data collection unit can collect weather data such as temperature, humidity, and precipitation. For example, the data collection unit can collect temperature data using a temperature sensor. It can also collect humidity data using a humidity sensor. It can also collect precipitation data using a precipitation sensor. By collecting weather data such as temperature, humidity, and precipitation, detailed weather information can be obtained. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired by the temperature sensor into a generating AI and have the generating AI perform analysis of the temperature data.

[0071] The construction unit can provide optimal growing conditions for crops based on analyzed weather data. For example, the construction unit provides optimal growing conditions using a generation AI. The generation AI adjusts conditions such as temperature, humidity, light intensity, and soil pH to provide the optimal growing environment for crops. By providing optimal growing conditions for crops based on analyzed weather data, it is expected that crop quality will improve and yields will increase. Some or all of the above processing in the construction unit may be performed using AI, for example, or without AI. For example, the construction unit can input analyzed weather data into the generation AI and have the generation AI perform the task of providing optimal growing conditions.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of weather data collection based on the estimated user emotions. For example, the data collection unit estimates the user's emotions using an emotion estimation algorithm. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the data collection unit can collect the user's biometric data (heart rate or skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This enables efficient data collection by adjusting the timing of weather data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data of the user captured by the camera into a generating AI, which can then perform the estimation of the user's emotions.

[0073] The data collection unit can analyze past weather data and select the optimal collection method. For example, the data collection unit can adjust the collection frequency to specific seasons or time periods based on past weather data. For example, the data collection unit can identify periods when extreme weather events are likely to occur from past data and focus data collection during those periods. For example, the data collection unit can analyze past data and develop algorithms to optimize the collection method. This allows the optimal collection method to be selected by analyzing past weather data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past weather data into a generating AI and have the generating AI select the optimal collection method.

[0074] The data collection unit can filter weather data based on regional characteristics. For example, the data collection unit can select the types of data to collect by considering the climate characteristics of each region. The data collection unit can also filter necessary weather data based on the characteristics of agricultural crops in each region. The data collection unit can also improve the accuracy of the collected data by analyzing weather patterns in each region. This makes it possible to collect highly accurate data by filtering weather data based on regional characteristics. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the climate characteristics of each region into a generating AI and have the generating AI execute the filtering criteria.

[0075] The data collection unit can estimate the user's emotions and determine the priority of weather data to collect based on the estimated user emotions. For example, the data collection unit estimates the user's emotions using an emotion estimation algorithm. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the data collection unit can collect the user's biometric data (heart rate or skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This enables efficient data collection by prioritizing weather data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data of the user captured by the camera into a generating AI, which can then perform the estimation of the user's emotions.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information when collecting weather data. For example, the data collection unit can prioritize the collection of weather data for a specific region based on geographical location information. For example, the data collection unit can also prioritize the collection of data that is likely to affect crops by considering geographical location information. For example, the data collection unit can also prioritize the collection of data for areas prone to extreme weather events based on geographical location information. This enables efficient data collection by prioritizing the collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI select highly relevant data.

[0077] The data collection unit can analyze social media activity and collect relevant data when collecting weather data. For example, the data collection unit can analyze posts on social media and collect weather-related information. For example, the data collection unit can also collect weather data for a specific region based on hashtags on social media. For example, the data collection unit can analyze images and videos on social media and collect weather-related data. This enables efficient data collection by analyzing social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media post data into a generating AI and have the generating AI perform the collection of relevant data.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using an emotion estimation algorithm. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit can collect the user's biometric data (heart rate or skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. By adjusting the presentation of the analysis based on the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI, allowing the AI ​​to estimate the user's emotions.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the weather data during the analysis. For example, the analysis unit can perform a detailed analysis on important weather data. For example, the analysis unit can perform a simplified analysis on less important weather data. The analysis unit can also optimally allocate analysis resources according to the importance of the weather data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the weather data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the weather data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of weather data during analysis. For example, the analysis unit applies a specific algorithm to temperature data. For example, the analysis unit can apply a different algorithm to precipitation data. For example, the analysis unit can apply yet another algorithm to humidity data. By applying different analysis algorithms depending on the category of weather data, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of weather data into a generating AI and have the generating AI select the analysis algorithm to apply.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using an emotion estimation algorithm. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit can collect the user's biometric data (heart rate or skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. By adjusting the length of the analysis based on the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI, allowing the AI ​​to estimate the user's emotions.

[0082] The analysis unit can determine the priority of analysis based on the timing of weather data collection during the analysis. For example, the analysis unit may prioritize the analysis of data collected recently. The analysis unit may also lower the priority of analysis for older data collected at a later date. The analysis unit may also optimize the allocation of analysis resources based on the collection timing. This enables efficient analysis by determining the priority of analysis based on the timing of weather data collection. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of weather data collection into a generating AI and have the generating AI determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the weather data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may also optimally allocate analysis resources based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the weather data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the weather data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0084] The construction unit can estimate the user's emotions and adjust the method of constructing the growth environment based on the estimated user emotions. For example, the construction unit estimates the user's emotions using an emotion estimation algorithm. For example, the construction unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The construction unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the construction unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This makes it possible to construct a growth environment that is easy for the user to understand by adjusting the method of constructing the growth environment based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the construction unit may be performed using AI, for example, or without AI. For example, the development unit can input user image data captured by a camera into a generation AI, and have the generation AI perform the estimation of the user's emotions.

[0085] The construction unit can analyze past growth environment data during construction to select the optimal construction method. For example, the construction unit selects the optimal construction method based on past growth environment data. The construction unit can also identify periods when abnormal weather is likely to occur from past data and select a construction method suitable for those periods. The construction unit can also analyze past data and develop algorithms to optimize construction methods. This allows the optimal construction method to be selected by analyzing past growth environment data. Some or all of the above processes in the construction unit may be performed using AI, for example, or without AI. For example, the construction unit can input past growth environment data into a generating AI and have the generating AI select the optimal construction method.

[0086] The construction unit can customize the growing environment based on regional characteristics during construction. For example, the construction unit can customize the growing environment by considering regional climatic characteristics. The construction unit can also customize the growing environment based on regional crop characteristics. The construction unit can also optimize the growing environment by analyzing regional weather patterns. This allows for the provision of an optimal growing environment by customizing the growing environment based on regional characteristics. Some or all of the above processes in the construction unit may be performed using AI, for example, or without AI. For example, the construction unit can input regional climatic characteristics into a generating AI and have the generating AI perform the customization of the growing environment.

[0087] The construction unit can estimate the user's emotions and determine the priority of the growth environment based on the estimated user emotions. For example, the construction unit estimates the user's emotions using an emotion estimation algorithm. For example, the construction unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The construction unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the construction unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This enables the efficient construction of the growth environment by determining the priority of the growth environment based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the construction unit may be performed using AI, for example, or without AI. For example, the development unit can input user image data captured by a camera into a generation AI, and have the generation AI perform the estimation of the user's emotions.

[0088] The construction unit can select the optimal growing environment by considering geographical location information during construction. For example, the construction unit can select a growing environment suitable for a specific region based on geographical location information. For example, the construction unit can also select the optimal growing environment for a crop by considering geographical location information. For example, the construction unit can also select a growing environment suitable for a region prone to extreme weather events based on geographical location information. By selecting the optimal growing environment by considering geographical location information, it becomes possible to construct an efficient growing environment. Some or all of the above-described processes in the construction unit may be performed using AI, for example, or without using AI. For example, the construction unit can input geographical location information into a generating AI and have the generating AI perform the selection of the optimal growing environment.

[0089] The construction unit can analyze social media activity during construction and propose methods for creating a suitable growing environment. For example, the construction unit can analyze posts on social media and collect information related to the growing environment. For example, the construction unit can also propose a growing environment suitable for a specific region based on hashtags on social media. For example, the construction unit can analyze images and videos on social media and propose methods related to the growing environment. This enables the efficient construction of a growing environment by analyzing social media activity and proposing methods for creating a suitable growing environment. Some or all of the above-described processes in the construction unit may be performed using AI, for example, or without AI. For example, the construction unit can input social media post data into a generating AI and have the generating AI execute proposals for growing environment methods.

[0090] The judgment unit can estimate the user's emotions and adjust the quality judgment criteria based on the estimated user emotions. For example, the judgment unit estimates the user's emotions using an emotion estimation algorithm. For example, the judgment unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The judgment unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the judgment unit can collect the user's biometric data (heart rate or skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This enables efficient quality judgment by adjusting the quality judgment criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input image data of the user captured by the camera into the generating AI, and have the generating AI perform the estimation of the user's emotions.

[0091] The judgment unit can optimize its judgment algorithm by referring to past quality data during the judgment process. For example, the judgment unit optimizes the judgment algorithm based on past quality data. The judgment unit can also identify periods when abnormal quality is likely to occur from past data and optimize the judgment algorithm to suit those periods. For example, the judgment unit can analyze past data and develop an algorithm to optimize the judgment algorithm. This enables efficient quality judgment by optimizing the judgment algorithm by referring to past quality data. Some or all of the above processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input past quality data into a generating AI and have the generating AI perform the optimization of the judgment algorithm.

[0092] The judgment unit can estimate the user's emotions and adjust the frequency of quality judgments based on the estimated user emotions. For example, the judgment unit estimates the user's emotions using an emotion estimation algorithm. For example, the judgment unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The judgment unit can also record the user's voice and estimate the emotions using voice analysis technology. The judgment unit can also collect the user's biometric data (heart rate or skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This enables efficient quality judgment by adjusting the frequency of quality judgments based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input image data of the user captured by the camera into the generating AI, and have the generating AI perform the estimation of the user's emotions.

[0093] The judgment unit can weight quality data based on harvest time during the judgment process. For example, the judgment unit can prioritize weighting data with an approaching harvest time. For example, the judgment unit can also lower the weighting of data with a distant harvest time. For example, the judgment unit can also optimally adjust the weighting of quality data based on harvest time. This enables efficient quality judgment by weighting quality data based on harvest time. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input quality data based on harvest time into a generating AI and have the generating AI perform the weighting adjustment.

[0094] The sorting unit can estimate the user's emotions and adjust the sorting method based on the estimated emotions. For example, the sorting unit can estimate the user's emotions using an emotion estimation algorithm. For example, the sorting unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The sorting unit can also record the user's voice and estimate the emotions using voice analysis technology. The sorting unit can also collect the user's biometric data (heart rate or skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows for efficient sorting by adjusting the sorting method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input image data of the user captured by a camera into a generating AI, which can then perform the estimation of the user's emotions.

[0095] The sorting unit can optimize its sorting algorithm by referring to past sorting data during the sorting process. For example, the sorting unit can optimize its sorting algorithm based on past sorting data. The sorting unit can also identify periods when abnormal sorting is likely to occur from past data and optimize a sorting algorithm suitable for those periods. The sorting unit can also analyze past data and develop an algorithm to optimize the sorting algorithm. This enables efficient sorting by optimizing the sorting algorithm by referring to past sorting data. Some or all of the above processes in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input past sorting data into a generating AI and have the generating AI perform the optimization of the sorting algorithm.

[0096] The sorting unit can estimate the user's emotions and determine sorting priorities based on the estimated emotions. For example, the sorting unit can estimate the user's emotions using an emotion estimation algorithm. For example, the sorting unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The sorting unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the sorting unit can collect the user's biometric data (heart rate or skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This enables efficient sorting by determining sorting priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input image data of the user captured by a camera into a generating AI, which can then perform the estimation of the user's emotions.

[0097] The sorting unit can select the optimal sorting method while considering geographical location information. For example, the sorting unit can select a sorting method suitable for a specific region based on geographical location information. For example, the sorting unit can also select the optimal sorting method for agricultural products while considering geographical location information. For example, the sorting unit can also select a sorting method suitable for areas prone to extreme weather events based on geographical location information. By selecting the optimal sorting method while considering geographical location information, efficient sorting becomes possible. Some or all of the above-described processes in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input geographical location information into a generating AI and have the generating AI select the optimal sorting method.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The agricultural support system can also be equipped with a forecasting unit. The forecasting unit predicts future weather based on weather data obtained from the data collection unit. For example, the forecasting unit can combine past and current weather data to predict the weather for the next week. This allows farmers to create appropriate farming plans based on future weather. The forecasting unit can also predict the occurrence of extreme weather events and issue warnings to farmers. For example, the forecasting unit can predict the occurrence of typhoons or heavy rains and notify farmers to take countermeasures in advance. Furthermore, the forecasting unit can also propose an optimal irrigation schedule based on weather forecasts. This allows farmers to use water resources efficiently and optimize crop growth.

[0100] The agricultural support system can also be equipped with a learning unit. This unit automatically learns agricultural knowledge based on data obtained from the data collection and analysis units. For example, the learning unit can combine historical weather data with crop growth data to learn optimal growing conditions. This allows farmers to provide more accurate growing conditions. The learning unit can also learn data related to post-harvest sorting to improve sorting accuracy. For example, it can automatically update the criteria for distinguishing good and bad quality crops based on past sorting data. Furthermore, the learning unit can continuously improve the overall system performance based on feedback from farmers. This ensures that the agricultural support system always incorporates the latest knowledge and technology, providing optimal support to farmers.

[0101] The agricultural support system can also be equipped with a notification unit. The notification unit provides appropriate notifications to farmers based on information obtained from the analysis and judgment units. For example, the notification unit can notify farmers of the appropriate timing for farm work based on the results of weather data analysis. This allows farmers to perform appropriate farm work according to the weather. The notification unit can also notify farmers of the results of post-harvest sorting work and provide information on good quality crops and those that are not. Furthermore, the notification unit can issue warnings to farmers when it predicts the occurrence of extreme weather. For example, the notification unit can predict the occurrence of typhoons or heavy rains and notify farmers to take countermeasures in advance. This allows farmers to respond quickly and minimize damage.

[0102] The agricultural support system can also be equipped with a diagnostic unit. The diagnostic unit diagnoses the health of crops based on data obtained from the collection and analysis units. For example, the diagnostic unit can diagnose the risk of pest and disease outbreaks in crops based on weather and soil data. This allows farmers to take early action and maintain crop health. The diagnostic unit can also diagnose crop growth and suggest the supply of necessary nutrients and water. For instance, it can analyze the color and shape of crop leaves to diagnose nutrient and water deficiencies. Furthermore, the diagnostic unit can diagnose the quality of crops after harvest and suggest appropriate storage methods. This allows farmers to maintain crop quality and maximize profits.

[0103] The agricultural support system can also be equipped with an optimization unit. This unit optimizes the entire agricultural process based on data obtained from the data collection and analysis units. For example, the optimization unit can suggest optimal planting and harvesting times based on weather and growth data. This allows farmers to maximize crop growth and increase yields. The optimization unit can also optimize irrigation and fertilization schedules, suggesting efficient use of water resources and fertilizers. Furthermore, the optimization unit can optimize post-harvest sorting, improving the efficiency of separating high-quality crops from lower-quality ones. This allows farmers to utilize labor efficiently and improve work efficiency.

[0104] The agricultural support system can further monitor farmers' stress levels using emotion estimation capabilities. For example, the data collection unit can analyze farmers' facial expressions and voice to estimate their stress levels. This allows the system to understand the farmers' mental health and provide necessary support. The analysis unit can also adjust work schedules based on the farmers' stress levels. For instance, if stress levels are high, it can suggest reducing the workload. Furthermore, the notification unit can provide farmers with advice on relaxation methods and stress relief. This helps farmers maintain their mental health and perform agricultural work efficiently.

[0105] The agricultural support system can further improve farmers' motivation by utilizing emotion estimation capabilities. For example, the data collection unit can analyze farmers' facial expressions and voices to estimate their motivation levels. This allows the system to understand farmers' motivation and provide appropriate support. The analysis unit can also adjust work tasks based on the farmers' motivation levels. For instance, if motivation is low, simpler tasks can be prioritized. Furthermore, the notification unit can provide farmers with encouraging messages and success stories. This helps farmers maintain motivation and perform agricultural work efficiently.

[0106] The agricultural support system can further utilize emotion estimation capabilities to provide customized advice based on the farmer's emotions. For example, the data collection unit can analyze the farmer's facial expressions and voice to estimate their current emotional state. This allows for the provision of appropriate advice tailored to the farmer's feelings. The analysis unit can also adjust the content and method of advice based on the farmer's emotional state. For instance, if a farmer is feeling stressed, the system can provide advice on relaxation techniques and stress relief. Furthermore, the notification unit can provide farmers with customized advice based on their emotions. This allows farmers to receive appropriate support tailored to their feelings and perform agricultural work more efficiently.

[0107] The agricultural support system can further facilitate communication based on farmers' emotions by utilizing emotion estimation capabilities. For example, the data collection unit can analyze farmers' facial expressions and voices to estimate their current emotional state. This facilitates appropriate communication tailored to the farmer's emotions. The analysis unit can also adjust the content and method of communication based on the farmer's emotional state. For instance, if a farmer is feeling anxious, the system can suggest reassuring communication. Furthermore, the notification unit can provide farmers with advice on emotion-based communication. This allows farmers to communicate appropriately according to their emotions and perform agricultural work efficiently.

[0108] The agricultural support system can further utilize emotion estimation capabilities to provide feedback based on the farmer's emotions. For example, the data collection unit can analyze the farmer's facial expressions and voice to estimate their current emotional state. This allows for the provision of appropriate feedback tailored to the farmer's feelings. The analysis unit can also adjust the content and method of feedback based on the farmer's emotional state. For instance, if the farmer is feeling joyful, positive feedback can be provided. Furthermore, the notification unit can provide emotion-based feedback to the farmer. This allows the farmer to receive appropriate feedback according to their emotions, enabling them to perform agricultural work more efficiently.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The collection unit collects weather data. The collection unit can collect weather data such as temperature, humidity, and precipitation. For example, the collection unit can collect temperature data using a temperature sensor. It can also collect humidity data using a humidity sensor. It can also collect precipitation data using a precipitation sensor. Step 2: The analysis unit analyzes the weather data collected by the collection unit. The analysis unit analyzes the weather data using, for example, a generating AI. The generating AI takes, for example, temperature data, humidity data, and precipitation data as input and outputs optimal growing conditions. The analysis unit, for example, uses the generating AI to analyze the weather data and provides optimal growing conditions for crops. Step 3: The construction unit constructs the optimal growing environment based on the data analyzed by the analysis unit. The construction unit constructs the optimal growing environment using, for example, a generation AI. The generation AI adjusts conditions such as temperature, humidity, light intensity, and soil pH to provide the optimal growing environment for the crop.

[0111] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, construction unit, and judgment unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects weather data using the sensors of the smart device 14 and transmits it to the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the weather data using generating AI. The construction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and constructs the optimal growing environment based on the analysis results. The judgment unit acquires images of harvested crops using the camera 42 of the smart device 14 and determines the quality by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 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.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, construction unit, and judgment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects weather data using the sensors of the smart glasses 214 and transmits it to the data processing unit 12. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the weather data using generating AI. The construction unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and constructs the optimal growing environment based on the analysis results. The judgment unit acquires images of harvested crops using the camera 42 of the smart glasses 214 and determines the quality using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, construction unit, and judgment unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects weather data using the sensors of the headset terminal 314 and transmits it to the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the weather data using a generating AI. The construction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and constructs the optimal growing environment based on the analysis results. The judgment unit acquires images of harvested crops using the camera 42 of the headset terminal 314 and determines the quality using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 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.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, construction unit, and judgment unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects weather data using the sensors of the robot 414 and transmits it to the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the weather data using a generating AI. The construction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and constructs the optimal growing environment based on the analysis results. The judgment unit acquires images of harvested crops using the camera 42 of the robot 414 and determines the quality by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) The collection unit collects weather data, An analysis unit analyzes the weather data collected by the aforementioned collection unit, The system includes a construction unit that constructs an optimal growth environment based on the data analyzed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) It includes a determination unit for determining the quality of harvested crops. The system described in Appendix 1, characterized by the features described herein. (Note 3) The determination unit, Image recognition technology is used to analyze the appearance of crops and determine their quality. The system described in Appendix 2, characterized by the features described herein. (Note 4) The determination unit, It features a sorting unit that automatically separates high-quality crops from lower-quality ones. The system described in Appendix 2, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect weather data such as temperature, humidity, and precipitation. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned construction unit is Based on analyzed weather data, it provides optimal growing conditions for crops. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of weather data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past weather data and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting weather data, filtering is performed based on regional characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of weather data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting weather data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting weather data, analyze social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the weather data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of weather data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the weather data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the weather data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned construction unit is The system estimates the user's emotions and adjusts the method of constructing the growth environment based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned construction unit is During the construction phase, past growth environment data is analyzed to select the optimal construction method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned construction unit is During construction, the growing environment is customized based on the characteristics of each region. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned construction unit is It estimates the user's emotions and determines the priority of the growing environment based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned construction unit is During construction, the optimal growing environment is selected considering geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned construction unit is During the development phase, we analyze social media activity and propose methods for creating a suitable environment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The determination unit, We estimate user emotions and adjust quality assessment criteria based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The determination unit, During the evaluation process, the evaluation algorithm is optimized by referring to past quality data. The system described in Appendix 2, characterized by the features described herein. (Note 27) The determination unit, The system estimates user sentiment and adjusts the frequency of quality assessments based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 28) The determination unit, During the evaluation process, quality data is weighted based on the harvest time. The system described in Appendix 2, characterized by the features described herein. (Note 29) The sorting unit is, It estimates the user's emotions and adjusts the sorting method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The sorting unit is, During sorting, the sorting algorithm is optimized by referring to past sorting data. The system described in Appendix 2, characterized by the features described herein. (Note 31) The sorting unit is, It estimates the user's emotions and determines the sorting priority based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The sorting unit is, When sorting, the optimal sorting method is selected, taking geographical location information into consideration. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection unit collects weather data, An analysis unit analyzes the weather data collected by the aforementioned collection unit, The system includes a construction unit that constructs an optimal growth environment based on the data analyzed by the aforementioned analysis unit. A system characterized by the following features.

2. It includes a determination unit for determining the quality of harvested crops. The system according to feature 1.

3. The determination unit, Image recognition technology is used to analyze the appearance of crops and determine their quality. The system according to feature 2.

4. The determination unit, It features a sorting unit that automatically separates high-quality crops from lower-quality ones. The system according to feature 2.

5. The aforementioned collection unit is Collect weather data such as temperature, humidity, and precipitation. The system according to feature 1.

6. The aforementioned construction unit is Based on analyzed weather data, it provides optimal growing conditions for crops. The system according to feature 1.

7. The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of weather data collection based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned collection unit is Analyze past weather data and select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting weather data, filtering is performed based on regional characteristics. The system according to feature 1.

Images