system

Abstract

The system according to this embodiment aims to measure the sugar content and color of crops and determine the appropriate harvest time. [Solution] The system according to the embodiment comprises a sensor unit, an analysis unit, and an instruction unit. The sensor unit measures the sugar content and color of the crop. The analysis unit analyzes the data measured by the sensor unit and determines the appropriate harvest time. The instruction unit issues a harvest instruction based on the harvest time determined 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 conventional technology, there was a problem that it was difficult to appropriately determine the harvesting time.

[0005] The system according to the embodiment aims to measure the sugar content and color and taste of crops and determine an appropriate harvesting time.

Means for Solving the Problems

[0006] The system according to the embodiment includes a sensor unit, an analysis unit, and an instruction unit. The sensor unit measures the sugar content and color and taste of crops. The analysis unit analyzes the data measured by the sensor unit and determines an appropriate harvesting time. The instruction unit gives an instruction to harvest based on the harvesting time determined by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can measure the sugar content and color of crops and determine the appropriate harvest time. [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) The harvest support system according to an embodiment of the present invention is a technology that assists in making appropriate decisions about the timing of harvest in agriculture. This harvest support system improves the accuracy and efficiency of harvesting by using sensors and AI to analyze the sugar content and color of crops in real time and instructing the appropriate harvest time. For example, by using a goggle-type device, the harvest support system enables even people with little agricultural experience to distinguish the sugar content and color of crops, and enables the AI ​​to make appropriate harvest decisions. This provides many opportunities to improve productivity and profitability for agricultural producers, new entrants, and agribusinesses. First, the harvest support system uses sensors to measure the sugar content and color of crops and transmits this data to the AI ​​in real time. The AI ​​analyzes the received data and determines the appropriate harvest time. For example, it issues a harvest instruction when the sugar content exceeds a certain standard or when the color is suitable for harvesting. In addition, the harvest support system also uses AI to improve the efficiency of harvesting by size. For example, by sorting crops by size and automating the harvest, it realizes efficient harvesting work. This reduces the labor involved in agriculture and enables sustainable agriculture. Furthermore, the harvest support system also contributes to the training of young farmers. By providing the latest technology and training, we aim to realize efficient and sustainable agriculture and promote the development of young talent who will support the future of agriculture. This technology is expected to contribute to improved agricultural productivity, increased yields, and improved quality, and will have a significant impact on the future development of agriculture. As a result, the harvest support system can help in making appropriate decisions about the timing of harvest in agriculture, improving the accuracy and efficiency of harvesting.

[0029] The harvest support system according to this embodiment comprises a sensor unit, an analysis unit, and an instruction unit. The sensor unit measures the sugar content and color of the crop. For example, the sensor unit can use a near-infrared sensor to measure the sugar content of the crop. The sensor unit can also use a colorimeter to measure the color of the crop. Furthermore, the sensor unit can use multiple sensors in combination to simultaneously measure the sugar content and color of the crop. For example, the sensor unit can combine a near-infrared sensor and a colorimeter to simultaneously measure the sugar content and color of the crop. The analysis unit analyzes the data measured by the sensor unit and determines the appropriate harvest time. The analysis unit can analyze the data transmitted from the sensor unit in real time, for example, using AI. The analysis unit can also predict the harvest time with greater accuracy by referring to past harvest data. Furthermore, the analysis unit can apply different analysis algorithms for each type of crop. For example, the analysis unit can apply an algorithm to predict the harvest time of tomatoes. The instruction unit issues harvest instructions based on the harvest time determined by the analysis unit. The instruction unit can, for example, provide detailed instructions for the harvesting work procedure. Furthermore, the instruction unit can also provide instructions for sorting and packaging crops after harvest. In addition, the instruction unit can provide training programs to support the development of young farmers. For example, the instruction unit can provide step-by-step instructions for the harvesting process. As a result, the harvesting support system according to this embodiment improves the accuracy and efficiency of harvesting by measuring the sugar content and color of the crops and determining the appropriate harvesting time.

[0030] The sensor unit measures the sugar content and color of crops. For example, to measure the sugar content of crops, the sensor unit can use a near-infrared sensor. A near-infrared sensor irradiates the crop with near-infrared light and measures the reflected light, allowing for non-destructive measurement of the sugar content inside the crop. This technology enables accurate sugar content measurement while maintaining crop quality. The sensor unit can also use a colorimeter to measure the color of crops. A colorimeter irradiates light onto the surface of the crop and measures the color of the reflected light, allowing for quantitative evaluation of the crop's color. This enables a visual assessment of the crop's maturity and quality. Furthermore, the sensor unit can use multiple sensors in combination to simultaneously measure the sugar content and color of crops. For example, the sensor unit can combine a near-infrared sensor and a colorimeter to simultaneously measure the sugar content and color of crops. By combining multiple sensors in this way, crop quality can be evaluated from multiple angles, enabling more accurate determination of the harvest time. The sensor unit collects this data in real time and transmits it to the analysis unit. This improves the efficiency and accuracy of the entire harvest support system.

[0031] The analysis unit analyzes data measured by the sensor unit to determine the appropriate harvest time. For example, the analysis unit can use AI to analyze data transmitted from the sensor unit in real time. The AI ​​uses machine learning algorithms to learn from past harvest data and environmental data, and compares it with current data to predict the optimal harvest time. Specifically, the AI ​​analyzes changes in the sugar content and color of crops as time-series data to identify the optimal timing for harvesting. The analysis unit can also refer to past harvest data to predict the harvest time with greater accuracy. For example, by analyzing the relationship between the growth pattern of a specific crop and environmental conditions based on past harvest data and comparing it with current data, the accuracy of harvest time prediction can be improved. Furthermore, the analysis unit can apply different analysis algorithms to each type of crop. For example, the analysis unit can apply an algorithm to predict the harvest time of tomatoes. An AI that has learned the sugar content and color change patterns of tomatoes can predict the optimal harvest time of tomatoes with high accuracy. As a result, the analysis unit can determine the optimal harvest time for each crop, improving the accuracy and efficiency of harvesting.

[0032] The instruction unit issues harvesting instructions based on the harvest time determined by the analysis unit. The instruction unit can, for example, provide detailed instructions on the harvesting procedure. Specifically, it can specify the start time, work procedures, and the selection of equipment and tools to be used. The instruction unit can also provide instructions for sorting and packaging the harvested crops. For example, by sorting the harvested crops by quality and providing instructions on appropriate packaging methods, the crops can be efficiently shipped while maintaining their quality. Furthermore, the instruction unit can provide training programs to support the development of young farmers. For example, it can provide step-by-step instructions for the harvesting procedure, enabling even inexperienced farmers to harvest accurately and efficiently. The instruction unit can also monitor the progress of the harvest in real time and modify the instructions as needed, further improving the efficiency and accuracy of the harvest. The instruction unit can utilize devices such as smartphones and tablets to quickly and accurately transmit these instructions to farmers. This allows farmers to check the instructions and take appropriate action anytime, anywhere.

[0033] The instruction unit can optimize harvesting by size. For example, the instruction unit can optimize the harvesting procedure by size. It can also optimize the harvesting procedure by quality. Furthermore, it can optimize the harvesting procedure by crop type. For example, the instruction unit can optimize the tomato harvesting procedure by size. By optimizing harvesting by size, the efficiency of the harvesting operation is improved.

[0034] The sensor unit can measure the sugar content and color of crops using a goggle-type device. For example, the sensor unit can measure the sugar content of crops by equipping the goggle-type device with a near-infrared sensor. It can also measure the color of crops by equipping the goggle-type device with a color meter. Furthermore, the sensor unit can simultaneously measure the sugar content and color of crops by equipping the goggle-type device with multiple sensors. For example, the sensor unit can simultaneously measure the sugar content and color of crops by equipping the goggle-type device with a near-infrared sensor and a color meter. This allows for accurate measurement of the sugar content and color of crops using a goggle-type device.

[0035] The analysis unit can analyze data in real time and determine the appropriate harvest time. For example, the analysis unit can use AI to analyze data transmitted from the sensor unit in real time. The analysis unit can update data on a second-by-second basis and perform real-time analysis. Furthermore, the analysis unit can update data on a minute-by-minute basis and perform real-time analysis. In addition, the analysis unit can use AI with high-speed data processing capabilities to analyze data in real time. For example, the analysis unit can use AI with high-speed data processing capabilities to analyze data in real time and determine the appropriate harvest time. This allows for a rapid determination of the appropriate harvest time by analyzing data in real time.

[0036] The instruction unit can provide training programs to support the development of young farmers. For example, the instruction unit can provide young farmers with a training program that instructs them step-by-step on the procedures for harvesting. It can also provide young farmers with a training program that visually illustrates the harvesting procedures, making it easy to understand visually. Furthermore, the instruction unit can provide young farmers with a training program that reduces the burden on workers by providing voice instructions for the harvesting procedures. In this way, by supporting the development of young farmers, it is possible to cultivate human resources who will support the future of agriculture.

[0037] The sensor unit can dynamically change the measurement parameters according to the crop's growth stage. For example, when the crop is in its early growth stage, the sensor unit can focus on measuring soil moisture content and nutrient status. When the crop is in its mid-growth stage, the sensor unit can also measure leaf color and shape to check its health. Furthermore, when the crop is in its late growth stage, the sensor unit can focus on measuring sugar content and color to determine the harvest time. For example, when the crop is in its early growth stage, the sensor unit can focus on measuring soil moisture content and nutrient status. This allows for the collection of appropriate data by changing the measurement parameters according to the crop's growth stage.

[0038] The sensor unit can add soil nutrient status and moisture content to the data it measures. For example, the sensor unit can measure the nitrogen, phosphorus, and potassium content of the soil to understand the nutritional status of crops. It can also measure soil moisture content to determine the timing of irrigation. Furthermore, the sensor unit can measure the soil pH value to maintain an environment suitable for crop growth. For example, the sensor unit can measure the nitrogen, phosphorus, and potassium content of the soil to understand the nutritional status of crops. This allows for an understanding of the crop's growing environment by measuring soil nutrient status and moisture content.

[0039] The sensor unit can be equipped with the ability to detect signs of pests and diseases in crops. For example, if abnormalities are observed on the leaves of a crop, the sensor unit can detect signs of pests and diseases. It can also detect signs of pests and diseases if abnormalities are observed on the stems or roots of the crop. Furthermore, the sensor unit can monitor the overall health of the crop and enable early detection of pests and diseases. For instance, if abnormalities are observed on the leaves of a crop, the sensor unit can detect signs of pests and diseases. This allows for early detection of pests and diseases, thereby maintaining the health of the crop.

[0040] The sensor unit can be equipped with the ability to acquire weather data, which can then be used to determine the optimal harvest time. For example, the sensor unit can acquire temperature and humidity data and use that data to determine the optimal harvest time. It can also acquire precipitation data and use that data to determine the optimal harvest time. Furthermore, the sensor unit can acquire wind speed and wind direction data and use that data to determine the optimal harvest time. By incorporating weather data into the harvest time determination process, a more accurate harvest time can be determined.

[0041] The analysis unit can predict harvest times with greater accuracy by referring to past harvest data. For example, the analysis unit can analyze crop growth patterns based on past harvest data. It can also analyze the relationship between weather conditions and harvest time based on past harvest data. Furthermore, the analysis unit can predict the optimal harvest time based on past harvest data. For example, the analysis unit can analyze crop growth patterns based on past harvest data. This improves the accuracy of harvest time predictions by referring to past harvest data.

[0042] The analysis unit can apply different analysis algorithms to each type of crop. For example, it can apply an algorithm to predict the harvest time of tomatoes. It can also apply an algorithm to predict the harvest time of strawberries. Furthermore, it can also apply an algorithm to predict the harvest time of grapes. For example, the analysis unit can apply an algorithm to predict the harvest time of tomatoes. By performing appropriate analysis for each type of crop, the accuracy of determining the harvest time is improved.

[0043] The analysis unit can determine not only the harvest time but also the optimal timing for fertilization and irrigation. For example, the analysis unit can determine the optimal timing for fertilization according to the growth stage of the crop. It can also determine the optimal timing for irrigation according to the moisture level of the crop. Furthermore, the analysis unit can adjust the timing of fertilization and irrigation according to the health of the crop. For example, the analysis unit can determine the optimal timing for fertilization according to the growth stage of the crop. This allows for the optimization of crop growth by determining not only the harvest time but also the timing of fertilization and irrigation.

[0044] The analysis unit can also propose storage and transportation methods after harvest. For example, it can propose the optimal storage method depending on the type of crop. Furthermore, it can propose transportation methods to maintain the freshness of the crop. In addition, it can propose storage and transportation methods to maintain the quality of the crop. For example, it can propose the optimal storage method depending on the type of crop. This allows for the maintenance of crop quality by proposing storage and transportation methods after harvest.

[0045] The instruction unit can provide detailed instructions for harvesting procedures, thereby improving work efficiency. For example, the instruction unit can provide step-by-step instructions for harvesting. It can also visually represent the harvesting procedures for easier understanding. Furthermore, the instruction unit can provide voice instructions for harvesting, reducing the burden on workers. For instance, the instruction unit can provide step-by-step instructions for harvesting. This detailed instruction improves work efficiency.

[0046] The instruction unit can also provide instructions for sorting and packaging harvested crops. For example, it can provide instructions for sorting harvested crops by size. It can also provide instructions for sorting harvested crops by quality. Furthermore, it can provide instructions for properly packaging harvested crops. For example, it can provide instructions for sorting harvested crops by size. By providing instructions for sorting and packaging harvested crops, it is possible to maintain crop quality and enable efficient shipment.

[0047] The instruction unit can monitor the progress of the harvesting work in real time and modify instructions as needed. For example, the instruction unit can monitor the progress of the harvesting work in real time and modify instructions if delays occur. It can also monitor the progress of the harvesting work in real time and optimize instructions when the work is progressing smoothly. Furthermore, the instruction unit can monitor the progress of the harvesting work in real time and adjust instructions to reduce the burden on workers. For example, the instruction unit can monitor the progress of the harvesting work in real time and modify instructions if delays occur. This allows for real-time monitoring of the harvesting work's progress, preventing delays and enabling efficient harvesting.

[0048] The control unit can optimize worker placement and machine usage to improve the efficiency of harvesting operations. For example, the control unit can optimize worker placement to improve the efficiency of harvesting. It can also optimize machine usage to improve the efficiency of harvesting. Furthermore, the control unit can optimize the coordination between workers and machines to improve the efficiency of harvesting. For example, the control unit can optimize worker placement to improve the efficiency of harvesting. This improves the efficiency of harvesting operations by optimizing worker placement and machine usage.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The analysis unit can predict crop growth. For example, it can predict the growth rate of crops and optimize the harvest time. Furthermore, the analysis unit can consider environmental factors affecting crop growth when making growth predictions. In addition, the analysis unit can create a harvest plan based on the crop growth prediction. For example, the analysis unit can predict the growth rate of crops and optimize the harvest time. This makes it possible to optimize the harvest time by predicting crop growth.

[0051] The instruction unit can customize the harvesting procedure. For example, it can customize the procedure according to the worker's experience and skills. It can also customize the procedure according to the work environment and conditions. Furthermore, it can customize the procedure according to the worker's physical condition and fatigue level. By customizing the harvesting procedure, work efficiency can be improved.

[0052] The sensor unit can be equipped with a function to measure the aroma of crops. For example, the sensor unit can use a gas sensor to measure the aroma of crops. It can also use an electronic nose to measure the aroma of crops. Furthermore, the sensor unit can use multiple sensors in combination to measure the aroma of crops. For example, the sensor unit can combine a gas sensor and an electronic nose to measure the aroma of crops. This allows for the determination of the harvest time by measuring the aroma of crops.

[0053] The analysis unit can be equipped with a function to evaluate crop quality. For example, the analysis unit can evaluate not only the sugar content and color of crops, but also their taste and nutritional value. Furthermore, the analysis unit can optimize the harvest time based on the crop quality evaluation. In addition, the analysis unit can propose post-harvest processing methods based on the crop quality evaluation. For example, the analysis unit can evaluate not only the sugar content and color of crops, but also their taste and nutritional value. This makes it possible to optimize the harvest time and propose post-harvest processing methods by evaluating crop quality.

[0054] The instruction unit can be enhanced with a function to provide voice instructions for harvesting procedures. For example, by providing voice instructions for harvesting procedures, the instruction unit can reduce the visual burden on the worker. Furthermore, by providing voice instructions, the worker can receive instructions without using their hands. Additionally, by providing voice instructions for harvesting procedures, the instruction unit can help the worker concentrate on the task. For instance, by providing voice instructions for harvesting procedures, the instruction unit can reduce the visual burden on the worker. This, in turn, improves work efficiency.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The sensor unit measures the sugar content and color of the crop. For example, a near-infrared sensor can be used to measure sugar content, and a colorimeter can be used to measure color. Alternatively, a combination of a near-infrared sensor and a colorimeter can be used to measure sugar content and color simultaneously. Step 2: The analysis unit analyzes the data measured by the sensor unit to determine the appropriate harvest time. For example, it is possible to use AI to analyze the data in real time and refer to past harvest data to predict the harvest time with high accuracy. It is also possible to apply different analysis algorithms to each type of crop. Step 3: The instruction unit issues harvesting instructions based on the harvest time determined by the analysis unit. For example, it can provide detailed instructions on the harvesting procedure, as well as instructions on sorting and packaging the harvested crops. It can also provide training programs to support the development of young farmers.

[0057] (Example of form 2) The harvest support system according to an embodiment of the present invention is a technology that assists in making appropriate decisions about the timing of harvest in agriculture. This harvest support system improves the accuracy and efficiency of harvesting by using sensors and AI to analyze the sugar content and color of crops in real time and instructing the appropriate harvest time. For example, by using a goggle-type device, the harvest support system enables even people with little agricultural experience to distinguish the sugar content and color of crops, and enables the AI ​​to make appropriate harvest decisions. This provides many opportunities to improve productivity and profitability for agricultural producers, new entrants, and agribusinesses. First, the harvest support system uses sensors to measure the sugar content and color of crops and transmits this data to the AI ​​in real time. The AI ​​analyzes the received data and determines the appropriate harvest time. For example, it issues a harvest instruction when the sugar content exceeds a certain standard or when the color is suitable for harvesting. In addition, the harvest support system also uses AI to improve the efficiency of harvesting by size. For example, by sorting crops by size and automating the harvest, it realizes efficient harvesting work. This reduces the labor involved in agriculture and enables sustainable agriculture. Furthermore, the harvest support system also contributes to the training of young farmers. By providing the latest technology and training, we aim to realize efficient and sustainable agriculture and promote the development of young talent who will support the future of agriculture. This technology is expected to contribute to improved agricultural productivity, increased yields, and improved quality, and will have a significant impact on the future development of agriculture. As a result, the harvest support system can help in making appropriate decisions about the timing of harvest in agriculture, improving the accuracy and efficiency of harvesting.

[0058] The harvest support system according to this embodiment comprises a sensor unit, an analysis unit, and an instruction unit. The sensor unit measures the sugar content and color of the crop. For example, the sensor unit can use a near-infrared sensor to measure the sugar content of the crop. The sensor unit can also use a colorimeter to measure the color of the crop. Furthermore, the sensor unit can use multiple sensors in combination to simultaneously measure the sugar content and color of the crop. For example, the sensor unit can combine a near-infrared sensor and a colorimeter to simultaneously measure the sugar content and color of the crop. The analysis unit analyzes the data measured by the sensor unit and determines the appropriate harvest time. The analysis unit can analyze the data transmitted from the sensor unit in real time, for example, using AI. The analysis unit can also predict the harvest time with greater accuracy by referring to past harvest data. Furthermore, the analysis unit can apply different analysis algorithms for each type of crop. For example, the analysis unit can apply an algorithm to predict the harvest time of tomatoes. The instruction unit issues harvest instructions based on the harvest time determined by the analysis unit. The instruction unit can, for example, provide detailed instructions for the harvesting work procedure. Furthermore, the instruction unit can also provide instructions for sorting and packaging crops after harvest. In addition, the instruction unit can provide training programs to support the development of young farmers. For example, the instruction unit can provide step-by-step instructions for the harvesting process. As a result, the harvesting support system according to this embodiment improves the accuracy and efficiency of harvesting by measuring the sugar content and color of the crops and determining the appropriate harvesting time.

[0059] The sensor unit measures the sugar content and color of crops. For example, to measure the sugar content of crops, the sensor unit can use a near-infrared sensor. A near-infrared sensor irradiates the crop with near-infrared light and measures the reflected light, allowing for non-destructive measurement of the sugar content inside the crop. This technology enables accurate sugar content measurement while maintaining crop quality. The sensor unit can also use a colorimeter to measure the color of crops. A colorimeter irradiates light onto the surface of the crop and measures the color of the reflected light, allowing for quantitative evaluation of the crop's color. This enables a visual assessment of the crop's maturity and quality. Furthermore, the sensor unit can use multiple sensors in combination to simultaneously measure the sugar content and color of crops. For example, the sensor unit can combine a near-infrared sensor and a colorimeter to simultaneously measure the sugar content and color of crops. By combining multiple sensors in this way, crop quality can be evaluated from multiple angles, enabling more accurate determination of the harvest time. The sensor unit collects this data in real time and transmits it to the analysis unit. This improves the efficiency and accuracy of the entire harvest support system.

[0060] The analysis unit analyzes data measured by the sensor unit to determine the appropriate harvest time. For example, the analysis unit can use AI to analyze data transmitted from the sensor unit in real time. The AI ​​uses machine learning algorithms to learn from past harvest data and environmental data, and compares it with current data to predict the optimal harvest time. Specifically, the AI ​​analyzes changes in the sugar content and color of crops as time-series data to identify the optimal timing for harvesting. The analysis unit can also refer to past harvest data to predict the harvest time with greater accuracy. For example, by analyzing the relationship between the growth pattern of a specific crop and environmental conditions based on past harvest data and comparing it with current data, the accuracy of harvest time prediction can be improved. Furthermore, the analysis unit can apply different analysis algorithms to each type of crop. For example, the analysis unit can apply an algorithm to predict the harvest time of tomatoes. An AI that has learned the sugar content and color change patterns of tomatoes can predict the optimal harvest time of tomatoes with high accuracy. As a result, the analysis unit can determine the optimal harvest time for each crop, improving the accuracy and efficiency of harvesting.

[0061] The instruction unit issues harvesting instructions based on the harvest time determined by the analysis unit. The instruction unit can, for example, provide detailed instructions on the harvesting procedure. Specifically, it can specify the start time, work procedures, and the selection of equipment and tools to be used. The instruction unit can also provide instructions for sorting and packaging the harvested crops. For example, by sorting the harvested crops by quality and providing instructions on appropriate packaging methods, the crops can be efficiently shipped while maintaining their quality. Furthermore, the instruction unit can provide training programs to support the development of young farmers. For example, it can provide step-by-step instructions for the harvesting procedure, enabling even inexperienced farmers to harvest accurately and efficiently. The instruction unit can also monitor the progress of the harvest in real time and modify the instructions as needed, further improving the efficiency and accuracy of the harvest. The instruction unit can utilize devices such as smartphones and tablets to quickly and accurately transmit these instructions to farmers. This allows farmers to check the instructions and take appropriate action anytime, anywhere.

[0062] The instruction unit can optimize harvesting by size. For example, the instruction unit can optimize the harvesting procedure by size. It can also optimize the harvesting procedure by quality. Furthermore, it can optimize the harvesting procedure by crop type. For example, the instruction unit can optimize the tomato harvesting procedure by size. By optimizing harvesting by size, the efficiency of the harvesting operation is improved.

[0063] The sensor unit can measure the sugar content and color of crops using a goggle-type device. For example, the sensor unit can measure the sugar content of crops by equipping the goggle-type device with a near-infrared sensor. It can also measure the color of crops by equipping the goggle-type device with a color meter. Furthermore, the sensor unit can simultaneously measure the sugar content and color of crops by equipping the goggle-type device with multiple sensors. For example, the sensor unit can simultaneously measure the sugar content and color of crops by equipping the goggle-type device with a near-infrared sensor and a color meter. This allows for accurate measurement of the sugar content and color of crops using a goggle-type device.

[0064] The analysis unit can analyze data in real time and determine the appropriate harvest time. For example, the analysis unit can use AI to analyze data transmitted from the sensor unit in real time. The analysis unit can update data on a second-by-second basis and perform real-time analysis. Furthermore, the analysis unit can update data on a minute-by-minute basis and perform real-time analysis. In addition, the analysis unit can use AI with high-speed data processing capabilities to analyze data in real time. For example, the analysis unit can use AI with high-speed data processing capabilities to analyze data in real time and determine the appropriate harvest time. This allows for a rapid determination of the appropriate harvest time by analyzing data in real time.

[0065] The instruction unit can provide training programs to support the development of young farmers. For example, the instruction unit can provide young farmers with a training program that instructs them step-by-step on the procedures for harvesting. It can also provide young farmers with a training program that visually illustrates the harvesting procedures, making it easy to understand visually. Furthermore, the instruction unit can provide young farmers with a training program that reduces the burden on workers by providing voice instructions for the harvesting procedures. In this way, by supporting the development of young farmers, it is possible to cultivate human resources who will support the future of agriculture.

[0066] The sensor unit can estimate the user's emotions and adjust the measurement frequency based on the estimated emotions. For example, if the user is stressed, the sensor unit can reduce the measurement frequency to alleviate the user's burden. Conversely, if the user is relaxed, the sensor unit can increase the measurement frequency to collect more detailed data. Furthermore, if the user is in a hurry, the sensor unit can optimize the measurement frequency to quickly collect data. For example, if the user is stressed, the sensor unit can reduce the measurement frequency to alleviate the user's burden. In this way, the user's burden can be reduced by adjusting the measurement frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0067] The sensor unit can dynamically change the measurement parameters according to the crop's growth stage. For example, when the crop is in its early growth stage, the sensor unit can focus on measuring soil moisture content and nutrient status. When the crop is in its mid-growth stage, the sensor unit can also measure leaf color and shape to check its health. Furthermore, when the crop is in its late growth stage, the sensor unit can focus on measuring sugar content and color to determine the harvest time. For example, when the crop is in its early growth stage, the sensor unit can focus on measuring soil moisture content and nutrient status. This allows for the collection of appropriate data by changing the measurement parameters according to the crop's growth stage.

[0068] The sensor unit can add soil nutrient status and moisture content to the data it measures. For example, the sensor unit can measure the nitrogen, phosphorus, and potassium content of the soil to understand the nutritional status of crops. It can also measure soil moisture content to determine the timing of irrigation. Furthermore, the sensor unit can measure the soil pH value to maintain an environment suitable for crop growth. For example, the sensor unit can measure the nitrogen, phosphorus, and potassium content of the soil to understand the nutritional status of crops. This allows for an understanding of the crop's growing environment by measuring soil nutrient status and moisture content.

[0069] The sensor unit can estimate the user's emotions and adjust the display method of the sensor's measurement results based on the estimated user emotions. For example, if the user is tense, the sensor unit can provide a simple and highly visible display method. If the user is relaxed, the sensor unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the sensor unit can provide a concise display method. For example, if the user is tense, the sensor unit can provide a simple and highly visible display method. By adjusting the display method according to the user's emotions, the information becomes easier 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0070] The sensor unit can be equipped with the ability to detect signs of pests and diseases in crops. For example, if abnormalities are observed on the leaves of a crop, the sensor unit can detect signs of pests and diseases. It can also detect signs of pests and diseases if abnormalities are observed on the stems or roots of the crop. Furthermore, the sensor unit can monitor the overall health of the crop and enable early detection of pests and diseases. For instance, if abnormalities are observed on the leaves of a crop, the sensor unit can detect signs of pests and diseases. This allows for early detection of pests and diseases, thereby maintaining the health of the crop.

[0071] The sensor unit can be equipped with the ability to acquire weather data, which can then be used to determine the optimal harvest time. For example, the sensor unit can acquire temperature and humidity data and use that data to determine the optimal harvest time. It can also acquire precipitation data and use that data to determine the optimal harvest time. Furthermore, the sensor unit can acquire wind speed and wind direction data and use that data to determine the optimal harvest time. By incorporating weather data into the harvest time determination process, a more accurate harvest time can be determined.

[0072] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can present simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also present detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can present concise analysis results. For example, if the user is nervous, the analysis unit can present simple and easy-to-understand analysis results. By adjusting the presentation method of the analysis results according to the user's emotions, the information becomes easier 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The analysis unit can predict harvest times with greater accuracy by referring to past harvest data. For example, the analysis unit can analyze crop growth patterns based on past harvest data. It can also analyze the relationship between weather conditions and harvest time based on past harvest data. Furthermore, the analysis unit can predict the optimal harvest time based on past harvest data. For example, the analysis unit can analyze crop growth patterns based on past harvest data. This improves the accuracy of harvest time predictions by referring to past harvest data.

[0074] The analysis unit can apply different analysis algorithms to each type of crop. For example, it can apply an algorithm to predict the harvest time of tomatoes. It can also apply an algorithm to predict the harvest time of strawberries. Furthermore, it can also apply an algorithm to predict the harvest time of grapes. For example, the analysis unit can apply an algorithm to predict the harvest time of tomatoes. By performing appropriate analysis for each type of crop, the accuracy of determining the harvest time is improved.

[0075] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can prioritize presenting important analysis results. If the user is relaxed, the analysis unit can also present detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can prioritize presenting concise analysis results. For example, if the user is nervous, the analysis unit can prioritize presenting important analysis results. This allows for the prioritization of important information by determining the priority of analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The analysis unit can determine not only the harvest time but also the optimal timing for fertilization and irrigation. For example, the analysis unit can determine the optimal timing for fertilization according to the growth stage of the crop. It can also determine the optimal timing for irrigation according to the moisture level of the crop. Furthermore, the analysis unit can adjust the timing of fertilization and irrigation according to the health of the crop. For example, the analysis unit can determine the optimal timing for fertilization according to the growth stage of the crop. This allows for the optimization of crop growth by determining not only the harvest time but also the timing of fertilization and irrigation.

[0077] The analysis unit can also propose storage and transportation methods after harvest. For example, it can propose the optimal storage method depending on the type of crop. Furthermore, it can propose transportation methods to maintain the freshness of the crop. In addition, it can propose storage and transportation methods to maintain the quality of the crop. For example, it can propose the optimal storage method depending on the type of crop. This allows for the maintenance of crop quality by proposing storage and transportation methods after harvest.

[0078] The instruction unit can estimate the user's emotions and adjust the timing of harvest instructions based on the estimated emotions. For example, if the user is stressed, the instruction unit can delay the timing of harvest instructions. Conversely, if the user is relaxed, the instruction unit can advance the timing of harvest instructions. Furthermore, if the user is in a hurry, the instruction unit can optimize the timing of harvest instructions. For example, if the user is stressed, the instruction unit can delay the timing of harvest instructions. This reduces the user's burden by adjusting the timing of harvest instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The instruction unit can provide detailed instructions for harvesting procedures, thereby improving work efficiency. For example, the instruction unit can provide step-by-step instructions for harvesting. It can also visually represent the harvesting procedures for easier understanding. Furthermore, the instruction unit can provide voice instructions for harvesting, reducing the burden on workers. For instance, the instruction unit can provide step-by-step instructions for harvesting. This detailed instruction improves work efficiency.

[0080] The instruction unit can also provide instructions for sorting and packaging harvested crops. For example, it can provide instructions for sorting harvested crops by size. It can also provide instructions for sorting harvested crops by quality. Furthermore, it can provide instructions for properly packaging harvested crops. For example, it can provide instructions for sorting harvested crops by size. By providing instructions for sorting and packaging harvested crops, it is possible to maintain crop quality and enable efficient shipment.

[0081] The instruction unit can estimate the user's emotions and customize the harvesting instructions based on those emotions. For example, if the user is nervous, the instruction unit can provide simple and clear harvesting instructions. If the user is relaxed, the instruction unit can provide detailed harvesting instructions. Furthermore, if the user is in a hurry, the instruction unit can provide concise harvesting instructions. For example, if the user is nervous, the instruction unit can provide simple and clear harvesting instructions. By customizing the harvesting instructions according to the user's emotions, the user can perform the harvesting work more efficiently. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The instruction unit can monitor the progress of the harvesting work in real time and modify instructions as needed. For example, the instruction unit can monitor the progress of the harvesting work in real time and modify instructions if delays occur. It can also monitor the progress of the harvesting work in real time and optimize instructions when the work is progressing smoothly. Furthermore, the instruction unit can monitor the progress of the harvesting work in real time and adjust instructions to reduce the burden on workers. For example, the instruction unit can monitor the progress of the harvesting work in real time and modify instructions if delays occur. This allows for real-time monitoring of the harvesting work's progress, preventing delays and enabling efficient harvesting.

[0083] The control unit can optimize worker placement and machine usage to improve the efficiency of harvesting operations. For example, the control unit can optimize worker placement to improve the efficiency of harvesting. It can also optimize machine usage to improve the efficiency of harvesting. Furthermore, the control unit can optimize the coordination between workers and machines to improve the efficiency of harvesting. For example, the control unit can optimize worker placement to improve the efficiency of harvesting. This improves the efficiency of harvesting operations by optimizing worker placement and machine usage.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The analysis unit can predict crop growth. For example, it can predict the growth rate of crops and optimize the harvest time. Furthermore, the analysis unit can consider environmental factors affecting crop growth when making growth predictions. In addition, the analysis unit can create a harvest plan based on the crop growth prediction. For example, the analysis unit can predict the growth rate of crops and optimize the harvest time. This makes it possible to optimize the harvest time by predicting crop growth.

[0086] The instruction unit can customize the harvesting procedure. For example, it can customize the procedure according to the worker's experience and skills. It can also customize the procedure according to the work environment and conditions. Furthermore, it can customize the procedure according to the worker's physical condition and fatigue level. By customizing the harvesting procedure, work efficiency can be improved.

[0087] The sensor unit can be equipped with a function to measure the aroma of crops. For example, the sensor unit can use a gas sensor to measure the aroma of crops. It can also use an electronic nose to measure the aroma of crops. Furthermore, the sensor unit can use multiple sensors in combination to measure the aroma of crops. For example, the sensor unit can combine a gas sensor and an electronic nose to measure the aroma of crops. This allows for the determination of the harvest time by measuring the aroma of crops.

[0088] The analysis unit can be equipped with a function to evaluate crop quality. For example, the analysis unit can evaluate not only the sugar content and color of crops, but also their taste and nutritional value. Furthermore, the analysis unit can optimize the harvest time based on the crop quality evaluation. In addition, the analysis unit can propose post-harvest processing methods based on the crop quality evaluation. For example, the analysis unit can evaluate not only the sugar content and color of crops, but also their taste and nutritional value. This makes it possible to optimize the harvest time and propose post-harvest processing methods by evaluating crop quality.

[0089] The instruction unit can be enhanced with a function to provide voice instructions for harvesting procedures. For example, by providing voice instructions for harvesting procedures, the instruction unit can reduce the visual burden on the worker. Furthermore, by providing voice instructions, the worker can receive instructions without using their hands. Additionally, by providing voice instructions for harvesting procedures, the instruction unit can help the worker concentrate on the task. For instance, by providing voice instructions for harvesting procedures, the instruction unit can reduce the visual burden on the worker. This, in turn, improves work efficiency.

[0090] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can present simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can present detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can present concise analysis results. For example, if the user is nervous, the analysis unit can present simple and easy-to-understand analysis results. By adjusting the presentation method of analysis results according to the user's emotions, the information becomes easier for the user to understand.

[0091] The instruction unit can estimate the user's emotions and adjust the timing of harvest instructions based on those emotions. For example, if the user is stressed, the instruction unit can delay the timing of harvest instructions. Conversely, if the user is relaxed, the instruction unit can advance the timing of harvest instructions. Furthermore, if the user is in a hurry, the instruction unit can optimize the timing of harvest instructions. For example, if the user is stressed, the instruction unit can delay the timing of harvest instructions. By adjusting the timing of harvest instructions according to the user's emotions, the burden on the user can be reduced.

[0092] The sensor unit can estimate the user's emotions and adjust the measurement frequency based on the estimated emotions. For example, if the user is stressed, the sensor unit can reduce the measurement frequency to alleviate the user's burden. Conversely, if the user is relaxed, the sensor unit can increase the measurement frequency to collect more detailed data. Furthermore, if the user is in a hurry, the sensor unit can optimize the measurement frequency to quickly collect data. For example, if the user is stressed, the sensor unit can reduce the measurement frequency to alleviate the user's burden. In this way, the user's burden can be reduced by adjusting the measurement frequency according to the user's emotions.

[0093] The instruction unit can estimate the user's emotions and customize the harvesting instructions based on those emotions. For example, if the user is nervous, the instruction unit can provide simple and clear harvesting instructions. If the user is relaxed, the instruction unit can provide detailed harvesting instructions. Furthermore, if the user is in a hurry, the instruction unit can provide concise harvesting instructions. For example, if the user is nervous, the instruction unit can provide simple and clear harvesting instructions. By customizing the harvesting instructions according to the user's emotions, the user can perform the harvesting work more efficiently.

[0094] The analysis unit can estimate the user's emotions and prioritize the analysis results based on those emotions. For example, if the user is nervous, the analysis unit can prioritize presenting important analysis results. Conversely, if the user is relaxed, the analysis unit can present detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can prioritize presenting concise analysis results. In this way, by prioritizing analysis results according to the user's emotions, important information can be presented preferentially.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The sensor unit measures the sugar content and color of the crop. For example, a near-infrared sensor can be used to measure sugar content, and a colorimeter can be used to measure color. Alternatively, a combination of a near-infrared sensor and a colorimeter can be used to measure sugar content and color simultaneously. Step 2: The analysis unit analyzes the data measured by the sensor unit to determine the appropriate harvest time. For example, it is possible to use AI to analyze the data in real time and refer to past harvest data to predict the harvest time with high accuracy. It is also possible to apply different analysis algorithms to each type of crop. Step 3: The instruction unit issues harvesting instructions based on the harvest time determined by the analysis unit. For example, it can provide detailed instructions on the harvesting procedure, as well as instructions on sorting and packaging the harvested crops. It can also provide training programs to support the development of young farmers.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] Each of the multiple elements described above, including the sensor unit, analysis unit, and instruction unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the sensor unit measures the sugar content and color of crops using the camera 42 and near-infrared sensor of the smart device 14. The analysis unit is implemented in real time by the identification processing unit 290 of the data processing unit 12, and analyzes the data transmitted from the sensor unit to determine the appropriate harvest time. The instruction unit is implemented in real time by the control unit 46A of the smart device 14, and issues instructions for the harvesting procedure and for sorting and packaging the harvested crops. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the sensor unit, analysis unit, and instruction unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the sensor unit measures the sugar content and color of crops using the camera 42 and near-infrared sensor of the smart glasses 214. The analysis unit is implemented in, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the data transmitted from the sensor unit in real time and determines the appropriate harvest time. The instruction unit is implemented in, for example, the control unit 46A of the smart glasses 214, which gives instructions for the harvesting procedure and for sorting and packaging the harvested crops. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the sensor unit, analysis unit, and instruction unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the sensor unit measures the sugar content and color of crops using the camera 42 and near-infrared sensor of the headset terminal 314. The analysis unit is implemented in real time by the identification processing unit 290 of the data processing unit 12, which analyzes the data transmitted from the sensor unit and determines the appropriate harvest time. The instruction unit is implemented in real time by the control unit 46A of the headset terminal 314, which issues instructions for the harvesting procedure and for sorting and packaging the harvested crops. 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.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the sensor unit, analysis unit, and instruction unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the sensor unit measures the sugar content and color of crops using the camera 42 and near-infrared sensor of the robot 414. The analysis unit is implemented in, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the data transmitted from the sensor unit in real time and determines the appropriate harvest time. The instruction unit is implemented in, for example, the control unit 46A of the robot 414, which gives instructions for the harvesting procedure and for sorting and packaging the harvested crops. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (Note 1) A sensor unit that measures the sugar content and color of crops, An analysis unit analyzes the data measured by the aforementioned sensor unit and determines the appropriate harvest time, The system includes an instruction unit that issues harvesting instructions based on the harvesting time determined by the analysis unit. A system characterized by the following features. (Note 2) The indicator unit is, Implementing efficiency improvements based on harvesting standards. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned sensor unit is Goggle-type devices are used to measure the sugar content and color of crops. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Analyze data in real time to determine the appropriate harvest time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The indicator unit is, We provide training programs to support the development of young farmers. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned sensor unit is The system estimates the user's emotions and adjusts the sensor's measurement frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned sensor unit is The sensor's measurement parameters are dynamically changed according to the crop's growth stage. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned sensor unit is Add soil nutrient status and moisture content to the data being measured. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned sensor unit is The system estimates the user's emotions and adjusts how the sensor's measurement results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned sensor unit is Add a function to detect signs of crop diseases and pests. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned sensor unit is We will add a function to acquire weather data and use it to determine the optimal harvest time. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the presentation method of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, By referring to past harvest data, we can predict harvest times with greater accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Apply different analysis algorithms to each type of crop. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, This involves determining not only the harvest time, but also the optimal timing for fertilization and irrigation. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, We also offer suggestions on post-harvest storage and transportation methods. The system described in Appendix 1, characterized by the features described herein. (Note 18) The indicator unit is, The system estimates the user's emotions and adjusts the timing of harvest instructions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The indicator unit is, Detailed instructions on harvesting procedures improve work efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 20) The indicator unit is, They also provide instructions for sorting and packaging the harvested crops. The system described in Appendix 1, characterized by the features described herein. (Note 21) The indicator unit is, It estimates the user's emotions and customizes the harvest instructions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The indicator unit is, Monitor the progress of the harvesting work in real time and adjust instructions as needed. The system described in Appendix 1, characterized by the features described herein. (Note 23) The indicator unit is, To improve the efficiency of harvesting operations, optimize the placement of workers and the use of machinery. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0169] 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. A sensor unit that measures the sugar content and color of crops, An analysis unit analyzes the data measured by the aforementioned sensor unit and determines the appropriate harvest time, The system includes an instruction unit that issues harvesting instructions based on the harvesting time determined by the analysis unit. A system characterized by the following features.

2. The indicator unit is, Implementing efficiency improvements based on harvesting standards. The system according to feature 1.

3. The aforementioned sensor unit is Goggle-type devices are used to measure the sugar content and color of crops. The system according to feature 1.

4. The aforementioned analysis unit, Analyze data in real time to determine the appropriate harvest time. The system according to feature 1.

5. The indicator unit is, We provide training programs to support the development of young farmers. The system according to feature 1.

6. The aforementioned sensor unit is The system estimates the user's emotions and adjusts the sensor's measurement frequency based on the estimated emotions. The system according to feature 1.

7. The aforementioned sensor unit is The sensor's measurement parameters are dynamically changed according to the crop's growth stage. The system according to feature 1.

8. The aforementioned sensor unit is Add soil nutrient status and moisture content to the data being measured. The system according to feature 1.

9. The aforementioned sensor unit is The system estimates the user's emotions and adjusts how the sensor's measurement results are displayed based on the estimated emotions. The system according to feature 1.

10. The aforementioned sensor unit is Add a function to detect signs of crop diseases and pests. The system according to feature 1.

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