System

Abstract

An object of a system according to an embodiment is to accurately determine an optimum harvest time of fruit and efficiently harvest the fruit.SOLUTION: A system according to an embodiment includes an imaging and sweetness determination machine, a generation AI, a notifier, an automatic harvesting robot, and a harvesting box. The imaging and sweetness determination machine measures the sugar content of the fruit. The generation AI analyzes the sweetness measured by the imaging and sweetness determination machine, and calculates an optimum harvesting time. The notifying unit notifies the harvesting time calculated by the generation AI. The automatic harvesting robot harvests the fruit based on the harvest time notified by the notification unit. The harvest box stores harvested fruits.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to accurately determine the optimal time to harvest fruit, and there is a risk of harvesting at the wrong time.

[0005] The system according to the embodiment aims to accurately determine the optimum harvesting time for fruits and harvest them efficiently. [Means for solving the problem]

[0006] The system according to the embodiment includes a photography and sweetness judging machine, a generation AI, a notification unit, an automatic harvesting robot, and a harvest box. The photography and sweetness judging machine measures the sugar content of fruit. The generation AI analyzes the sugar content data measured by the photography and sweetness judging machine and calculates the optimal harvest time. The notification unit notifies the harvest time calculated by the generation AI. The automatic harvesting robot harvests fruit based on the harvest time notified by the notification unit. The harvest box stores the harvested fruit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately determine the optimal harvesting time for fruits and harvest them efficiently. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The harvesting system according to an embodiment of the present invention is a system that accurately measures the sugar content of fruit and notifies the user of the optimal harvest time. This allows the harvesting system to accurately measure the sugar content of fruit and notify the user of the optimal harvest time.

[0029] A harvesting system according to an embodiment includes a photography and sweetness judging device, a generation AI, a notification unit, an automatic harvesting robot, and a harvest box. The photography and sweetness judging device measures the sugar content of fruit. For example, the photography and sweetness judging device photographs the surface of fruit and inputs the image data into the generation AI. The generation AI analyzes the image data and determines the sugar content of the fruit. The generation AI determines the sugar content using, for example, a text generation AI (e.g., LLM). The generation AI also predicts the time when the fruit will be at its most delicious, taking into account past data and weather conditions. The notification unit notifies the farmer of the harvest time calculated by the generation AI. For example, the notification unit notifies the farmer via a smartphone app or email. The automatic harvesting robot harvests fruit based on the harvest time notified by the notification unit. For example, the automatic harvesting robot identifies the location of fruit using a camera or sensor and gently picks the fruit using an arm. The harvest box stores the harvested fruit. For example, the harvest box is covered with cushioning material to protect the fruit from impact. As a result, the harvesting system according to the embodiment can accurately measure the sugar content of fruit and notify the optimal harvest time. For example, the sugar content of apples and mandarins can be measured daily, allowing them to be harvested at their most delicious state. Furthermore, by using an automatic harvesting robot, the harvesting process can be made more efficient and the fruit can be harvested without being damaged.

[0030] The photography and sweetness judging machine can simultaneously measure the sugar content, acidity, and moisture content of fruit to evaluate its quality. For example, the photography and sweetness judging machine can simultaneously measure the sugar content, acidity, and moisture content of fruit. For example, the surface of an apple is photographed, and the generated AI analyzes the image data to determine the sugar content, acidity, and moisture content. This allows for a comprehensive quality evaluation of the fruit.

[0031] The photography and sweetness judging device can track the growth process of fruit and monitor the progress of growth in real time. For example, the photography and sweetness judging device can add a function to track the growth process and monitor the growth of fruit in real time. For example, the growth process of apples can be tracked and the progress of growth can be monitored. This allows the growth status of fruit to be known in real time.

[0032] The generative AI can predict harvest times with greater accuracy based on fruit sugar content data, weather data, and soil data. For example, the generative AI predicts harvest times by taking into account fruit sugar content data, weather data, and soil data. For example, it predicts harvest times based on apple sugar content data, weather data, and soil data. This allows for more accurate harvest time predictions.

[0033] The generative AI can compare it with past harvest data, analyze trends in harvest times, and propose long-term harvest plans. For example, the generative AI can compare it with past harvest data, analyze trends in harvest times, and propose long-term harvest plans. For example, it can propose a harvest plan based on past apple harvest data. This makes it possible to propose long-term harvest plans.

[0034] The notification unit can notify users of the harvest time not only through a smartphone app, but also through a smartwatch and a voice assistant. For example, the notification unit can notify users of the harvest time not only through a smartphone app, but also through a smartwatch and a voice assistant. For example, the notification unit can notify users of the apple harvest time through a smartwatch. This allows notifications of the harvest time to be sent via a variety of devices.

[0035] The notification unit can also include advice on post-harvest storage methods and sales strategies in the harvest time notification. For example, the notification unit includes advice on post-harvest storage methods and sales strategies in the harvest time notification. For example, advice on storage methods and sales strategies is included in the apple harvest time notification. This makes it possible to provide advice on post-harvest storage methods and sales strategies.

[0036] An automatic harvesting robot can recognize the size and shape of fruit and select the appropriate harvesting method. For example, an automatic harvesting robot can add a function to recognize the size and shape of fruit and select the optimal harvesting method. For example, it can recognize the size and shape of apples and select the optimal harvesting method. This allows it to select the optimal harvesting method according to the size and shape of the fruit.

[0037] An automatic harvesting robot can evaluate the ripeness of fruit in real time and harvest at the appropriate time. For example, an automatic harvesting robot can add a function to evaluate the ripeness of fruit in real time and harvest at the optimal time. For example, an automatic harvesting robot can evaluate the ripeness of apples in real time and harvest at the optimal time. This allows harvesting to be done at the optimal time depending on the ripeness of the fruit.

[0038] The automatic harvesting robot can also be applied to harvesting agricultural products other than fruit. For example, the automatic harvesting robot harvests agricultural products other than fruit (e.g., tomatoes and cucumbers). For example, an automatic harvesting robot harvests tomatoes. This makes it possible to apply the automatic harvesting robot to harvesting agricultural products other than fruit.

[0039] The automatic harvesting robot can sort and package the fruits after harvesting. The automatic harvesting robot adds a function to sort and package the fruits after harvesting, for example. For example, it sorts and packages apples after harvesting. This allows the sorting and packaging of fruits after harvesting to be done automatically.

[0040] The harvest box is equipped with sensors that monitor temperature and humidity in real time, allowing it to maintain an appropriate storage environment. For example, an apple harvest box could be equipped with temperature and humidity sensors. This would allow it to maintain an optimal storage environment for the fruit.

[0041] The harvest box has a function to automatically adjust the position of the fruit to prevent it from being damaged. For example, a position adjustment function can be added to the apple harvest box. This allows the fruit to be stored without being damaged.

[0042] The harvest box can also be used to store agricultural products other than fruit. For example, the harvest box stores agricultural products other than fruit (such as tomatoes and cucumbers). For example, tomatoes are stored in the harvest box after harvesting. This makes the harvest box applicable to storing agricultural products other than fruit.

[0043] The Harvest Box is equipped with a function to evaluate the quality of fruit in real time, allowing for quality control. For example, a quality evaluation function can be added to the Harvest Box to evaluate the quality of fruit in real time, allowing for quality control. For example, a quality evaluation function can be added to the Harvest Box for apples. This allows for real-time evaluation and quality control of fruit.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The harvesting system can further include an aroma determination unit that measures the aroma of the fruit. The aroma determination unit analyzes the aroma components of the fruit and determines the intensity and type of the aroma. For example, measuring the aroma of an apple and determining the intensity and type of the aroma can be useful for evaluating the quality of the fruit. The aroma determination unit can also track changes in the fruit's aroma and predict the optimal harvest time with even greater accuracy. This makes it possible to predict the harvest time taking the fruit's aroma into account.

[0046] The harvesting system may further include a color determination unit that measures the color of the fruit. The color determination unit analyzes the color of the fruit and tracks changes in color. For example, by measuring the color of an apple and tracking the color change, the ripeness of the fruit can be evaluated. The color determination unit can also predict the optimal harvest time based on the color change of the fruit. This makes it possible to predict the harvest time taking the color of the fruit into consideration.

[0047] The harvesting system may further include a firmness determination unit that measures the firmness of the fruit. The firmness determination unit measures the firmness of the fruit and tracks changes in the firmness. For example, by measuring the firmness of an apple and tracking changes in the firmness, the ripeness of the fruit can be evaluated. The firmness determination unit can also predict the optimal harvest time based on changes in the fruit's firmness. This makes it possible to predict the harvest time taking the fruit's firmness into consideration.

[0048] The harvesting system can further include a nutritional value determination unit that measures the nutritional value of the fruit. The nutritional value determination unit analyzes the nutritional components of the fruit and tracks changes in nutritional value. For example, measuring the nutritional value of an apple and tracking changes in nutritional value can be useful for evaluating the quality of the fruit. The nutritional value determination unit can also predict the optimal harvest time based on changes in the nutritional value of the fruit. This makes it possible to predict the harvest time taking into account the nutritional value of the fruit.

[0049] The harvesting system can further include a temperature determination unit that measures the surface temperature of the fruit. The temperature determination unit measures the surface temperature of the fruit and tracks changes in temperature. For example, by measuring the surface temperature of an apple and tracking the temperature changes, the ripeness of the fruit can be evaluated. The temperature determination unit can also predict the optimal harvest time based on changes in the surface temperature of the fruit. This makes it possible to predict the harvest time taking the surface temperature of the fruit into consideration.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The photography and sweetness judging machine measures the sugar content of fruit. For example, it takes a photo of the surface of the fruit and inputs the image data into the generating AI. Step 2: The AI ​​analyzes the image data provided by the camera and sweetness tester to determine the sugar content of the fruit. It also takes into account past data and weather conditions to predict when the fruit will be at its most delicious. Step 3: The notification unit notifies farmers of the harvest time calculated by the generation AI, for example, via a smartphone app or email. Step 4: The automated harvesting robot harvests the fruit based on the harvest time notified by the notification unit. For example, it uses a camera or sensor to identify the location of the fruit and then uses its arm to gently pick the fruit. Step 5: The harvest box contains the harvested fruit. For example, it is covered with cushioning material to protect the fruit from impacts.

[0052] (Example 2) The harvesting system according to an embodiment of the present invention is a system that accurately measures the sugar content of fruit and notifies the user of the optimal harvest time. This allows the harvesting system to accurately measure the sugar content of fruit and notify the user of the optimal harvest time.

[0053] A harvesting system according to an embodiment includes a photography and sweetness judging device, a generation AI, a notification unit, an automatic harvesting robot, and a harvest box. The photography and sweetness judging device measures the sugar content of fruit. For example, the photography and sweetness judging device photographs the surface of fruit and inputs the image data into the generation AI. The generation AI analyzes the image data and determines the sugar content of the fruit. The generation AI determines the sugar content using, for example, a text generation AI (e.g., LLM). The generation AI also predicts the time when the fruit will be at its most delicious, taking into account past data and weather conditions. The notification unit notifies the farmer of the harvest time calculated by the generation AI. For example, the notification unit notifies the farmer via a smartphone app or email. The automatic harvesting robot harvests fruit based on the harvest time notified by the notification unit. For example, the automatic harvesting robot identifies the location of fruit using a camera or sensor and gently picks the fruit using an arm. The harvest box stores the harvested fruit. For example, the harvest box is covered with cushioning material to protect the fruit from impact. As a result, the harvesting system according to the embodiment can accurately measure the sugar content of fruit and notify the optimal harvest time. For example, the sugar content of apples and mandarins can be measured daily, allowing them to be harvested at their most delicious state. Furthermore, by using an automatic harvesting robot, the harvesting process can be made more efficient and the fruit can be harvested without being damaged.

[0054] The photography and sweetness judging machine can simultaneously measure the sugar content, acidity, and moisture content of fruit to evaluate its quality. For example, the photography and sweetness judging machine can simultaneously measure the sugar content, acidity, and moisture content of fruit. For example, the surface of an apple is photographed, and the generated AI analyzes the image data to determine the sugar content, acidity, and moisture content. This allows for a comprehensive quality evaluation of the fruit.

[0055] The photography and sweetness judging device can track the growth process of fruit and monitor the progress of growth in real time. For example, the photography and sweetness judging device can add a function to track the growth process and monitor the growth of fruit in real time. For example, the growth process of apples can be tracked and the progress of growth can be monitored. This allows the growth status of fruit to be known in real time.

[0056] The photography and sweetness judgment machine can estimate what emotions a consumer will have based on the results of measuring the sugar content of fruit and provide the results as feedback. The photography and sweetness judgment machine can, for example, use an emotion estimation function to predict what emotions a consumer will have based on the results of measuring the sugar content of fruit. For example, the photography and sweetness judgment machine can predict the consumer's emotions based on the results of measuring the sugar content of apples and provide the results as feedback. This makes it possible to predict the consumer's emotions and provide feedback.

[0057] The generative AI can predict harvest times with greater accuracy based on fruit sugar content data, weather data, and soil data. For example, the generative AI predicts harvest times by taking into account fruit sugar content data, weather data, and soil data. For example, it predicts harvest times based on apple sugar content data, weather data, and soil data. This allows for more accurate harvest time predictions.

[0058] The generative AI can compare it with past harvest data, analyze trends in harvest times, and propose long-term harvest plans. For example, the generative AI can compare it with past harvest data, analyze trends in harvest times, and propose long-term harvest plans. For example, it can propose a harvest plan based on past apple harvest data. This makes it possible to propose long-term harvest plans.

[0059] The generation AI can use the emotion estimation function to analyze farmers' emotional reactions to harvest time notifications and improve the notification method. For example, the generation AI can use the emotion estimation function to analyze farmers' emotional reactions to harvest time notifications and optimize the notification method. For example, the generation AI can analyze farmers' emotional reactions to apple harvest time notifications and optimize the notification method. This makes it possible to optimize the notification method.

[0060] The notification unit can notify users of the harvest time not only through a smartphone app, but also through a smartwatch and a voice assistant. For example, the notification unit can notify users of the harvest time not only through a smartphone app, but also through a smartwatch and a voice assistant. For example, the notification unit can notify users of the apple harvest time through a smartwatch. This allows notifications of the harvest time to be sent via a variety of devices.

[0061] The notification unit can also include advice on post-harvest storage methods and sales strategies in the harvest time notification. For example, the notification unit includes advice on post-harvest storage methods and sales strategies in the harvest time notification. For example, advice on storage methods and sales strategies is included in the apple harvest time notification. This makes it possible to provide advice on post-harvest storage methods and sales strategies.

[0062] The notification unit can use the emotion estimation function to analyze the consumer's emotional response to the harvest time notification and improve a marketing strategy for the consumer. For example, the notification unit can use the emotion estimation function to analyze the consumer's emotional response to the harvest time notification and optimize a marketing strategy. For example, the notification unit can analyze the consumer's emotional response to the apple harvest time notification and optimize a marketing strategy. This makes it possible to optimize a marketing strategy for the consumer.

[0063] An automatic harvesting robot can recognize the size and shape of fruit and select the appropriate harvesting method. For example, an automatic harvesting robot can add a function to recognize the size and shape of fruit and select the optimal harvesting method. For example, it can recognize the size and shape of apples and select the optimal harvesting method. This allows it to select the optimal harvesting method according to the size and shape of the fruit.

[0064] An automatic harvesting robot can evaluate the ripeness of fruit in real time and harvest at the appropriate time. For example, an automatic harvesting robot can add a function to evaluate the ripeness of fruit in real time and harvest at the optimal time. For example, an automatic harvesting robot can evaluate the ripeness of apples in real time and harvest at the optimal time. This allows harvesting to be done at the optimal time depending on the ripeness of the fruit.

[0065] The automatic harvesting robot can use the emotion estimation function to monitor the emotions of farmers during harvesting work and provide feedback to improve work efficiency. The automatic harvesting robot can, for example, use the emotion estimation function to monitor the emotions of farmers during harvesting work and provide feedback to improve work efficiency. For example, the automatic harvesting robot can monitor the emotions of farmers during apple harvesting work and provide feedback. This makes it possible to monitor the emotions of farmers during harvesting work and provide feedback to improve work efficiency.

[0066] The automatic harvesting robot can also be applied to harvesting agricultural products other than fruit. For example, the automatic harvesting robot harvests agricultural products other than fruit (e.g., tomatoes and cucumbers). For example, an automatic harvesting robot harvests tomatoes. This makes it possible to apply the automatic harvesting robot to harvesting agricultural products other than fruit.

[0067] The automatic harvesting robot can sort and package the fruits after harvesting. The automatic harvesting robot adds a function to sort and package the fruits after harvesting, for example. For example, it sorts and packages apples after harvesting. This allows the sorting and packaging of fruits after harvesting to be done automatically.

[0068] The automatic harvesting robot can use the emotion estimation function to analyze consumers' emotional reactions to the harvesting robot's operations and improve promotional strategies for consumers. The automatic harvesting robot, for example, uses the emotion estimation function to analyze consumers' emotional reactions to the harvesting robot's operations and optimizes promotional strategies. For example, the automatic harvesting robot analyzes consumers' emotional reactions to the operations of an apple harvesting robot and optimizes promotional strategies. This makes it possible to analyze consumers' emotional reactions to the harvesting robot's operations and optimize promotional strategies.

[0069] The harvest box is equipped with sensors that monitor temperature and humidity in real time, allowing it to maintain an appropriate storage environment. For example, an apple harvest box could be equipped with temperature and humidity sensors. This would allow it to maintain an optimal storage environment for the fruit.

[0070] The harvest box has a function to automatically adjust the position of the fruit to prevent it from being damaged. For example, a position adjustment function can be added to the apple harvest box. This allows the fruit to be stored without being damaged.

[0071] Harvest Box can use the emotion estimation function to analyze consumers' emotional responses to the design and functions of the Harvest Box and improve consumer satisfaction. Harvest Box, for example, can use the emotion estimation function to analyze consumers' emotional responses to the design and functions of the Harvest Box and improve consumer satisfaction. For example, the emotional responses to the design of an apple Harvest Box can be analyzed and satisfaction improved. This makes it possible to analyze consumers' emotional responses to the design and functions of the Harvest Box and improve satisfaction.

[0072] The harvest box can also be used to store agricultural products other than fruit. For example, the harvest box stores agricultural products other than fruit (such as tomatoes and cucumbers). For example, tomatoes are stored in the harvest box after harvesting. This makes the harvest box applicable to storing agricultural products other than fruit.

[0073] The Harvest Box is equipped with a function to evaluate the quality of fruit in real time, allowing for quality control. For example, a quality evaluation function can be added to the Harvest Box to evaluate the quality of fruit in real time, allowing for quality control. For example, a quality evaluation function can be added to the Harvest Box for apples. This allows for real-time evaluation and quality control of fruit.

[0074] The Harvest Box can use the emotion estimation function to analyze the emotional reactions of farmers to the use of the Harvest Box and make improvements to improve the usability. For example, the Harvest Box can use the emotion estimation function to analyze the emotional reactions of farmers to the use of the Harvest Box and make improvements to improve the usability. For example, the emotional reactions to the use of the apple Harvest Box can be analyzed and improvements can be made. This allows improvements to be made to improve the usability of the Harvest Box.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The harvesting system can further include an aroma determination unit that measures the aroma of the fruit. The aroma determination unit analyzes the aroma components of the fruit and determines the intensity and type of the aroma. For example, measuring the aroma of an apple and determining the intensity and type of the aroma can be useful for evaluating the quality of the fruit. The aroma determination unit can also track changes in the fruit's aroma and predict the optimal harvest time with even greater accuracy. This makes it possible to predict the harvest time taking the fruit's aroma into account.

[0077] The harvesting system may further include a color determination unit that measures the color of the fruit. The color determination unit analyzes the color of the fruit and tracks changes in color. For example, by measuring the color of an apple and tracking the color change, the ripeness of the fruit can be evaluated. The color determination unit can also predict the optimal harvest time based on the color change of the fruit. This makes it possible to predict the harvest time taking the color of the fruit into consideration.

[0078] The harvesting system may further include a firmness determination unit that measures the firmness of the fruit. The firmness determination unit measures the firmness of the fruit and tracks changes in the firmness. For example, by measuring the firmness of an apple and tracking changes in the firmness, the ripeness of the fruit can be evaluated. The firmness determination unit can also predict the optimal harvest time based on changes in the fruit's firmness. This makes it possible to predict the harvest time taking the fruit's firmness into consideration.

[0079] The harvesting system can further include a nutritional value determination unit that measures the nutritional value of the fruit. The nutritional value determination unit analyzes the nutritional components of the fruit and tracks changes in nutritional value. For example, measuring the nutritional value of an apple and tracking changes in nutritional value can be useful for evaluating the quality of the fruit. The nutritional value determination unit can also predict the optimal harvest time based on changes in the nutritional value of the fruit. This makes it possible to predict the harvest time taking into account the nutritional value of the fruit.

[0080] The harvesting system can further include a temperature determination unit that measures the surface temperature of the fruit. The temperature determination unit measures the surface temperature of the fruit and tracks changes in temperature. For example, by measuring the surface temperature of an apple and tracking the temperature changes, the ripeness of the fruit can be evaluated. The temperature determination unit can also predict the optimal harvest time based on changes in the surface temperature of the fruit. This makes it possible to predict the harvest time taking the surface temperature of the fruit into consideration.

[0081] The harvesting system can also estimate what emotions consumers will feel based on the sugar content measurement results of the fruit and provide the results as feedback. For example, the harvesting system can predict consumer emotions based on the sugar content measurement results of apples and provide the results as feedback. This makes it possible to predict consumer emotions and provide feedback.

[0082] The harvesting system can further estimate the consumer's emotions based on the results of measuring the fruit's acidity and provide the results as feedback. For example, the harvesting system can predict the consumer's emotions based on the results of measuring the acidity of apples and provide the results as feedback. This makes it possible to predict the consumer's emotions and provide feedback.

[0083] The harvesting system can further estimate the consumer's emotions based on the measurement results of the fruit's moisture content and provide the results as feedback. For example, the harvesting system can predict the consumer's emotions based on the measurement results of the moisture content of apples and provide the results as feedback. This makes it possible to predict the consumer's emotions and provide feedback.

[0084] The harvesting system can also track the growth process of the fruit, predict what emotions consumers will feel based on that data, and provide the results as feedback. For example, the harvesting system can track the growth process of apples, predict what emotions consumers will feel based on that data, and provide the results as feedback. This allows the system to predict consumer emotions and provide feedback.

[0085] The harvesting system can also estimate what emotions consumers will feel based on the results of measuring the fruit's aroma and provide the results as feedback. For example, the harvesting system can predict consumers' emotions based on the results of measuring the apple's aroma and provide the results as feedback. This makes it possible to predict consumers' emotions and provide feedback.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The photography and sweetness judging machine measures the sugar content of fruit. For example, it takes a photo of the surface of the fruit and inputs the image data into the generating AI. Step 2: The AI ​​analyzes the image data provided by the camera and sweetness tester to determine the sugar content of the fruit. It also takes into account past data and weather conditions to predict when the fruit will be at its most delicious. Step 3: The notification unit notifies farmers of the harvest time calculated by the generation AI, for example, via a smartphone app or email. Step 4: The automated harvesting robot harvests the fruit based on the harvest time notified by the notification unit. For example, it uses a camera or sensor to identify the location of the fruit and then uses its arm to gently pick the fruit. Step 5: The harvest box contains the harvested fruit. For example, it is covered with cushioning material to protect the fruit from impacts.

[0088] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0089] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 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.

[0093] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0094] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0095] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0096] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0097] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0098] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0099] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0102] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0129] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0138] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0139] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0140] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0141] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0142] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0143] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0144] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0145] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0146] 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.

[0147] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0148] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0149] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0150] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0151] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0152] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0153] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0154] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0155] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A photography and sweetness judging machine that measures the sugar content of fruits, A generation AI that analyzes the sugar content data measured by the photography and sweetness judgment device and calculates the optimal harvest time; a notification unit that notifies the harvest time calculated by the generation AI; an automatic harvesting robot that harvests fruit based on the harvest time notified by the notification unit; A harvest box for storing harvested fruits. A system characterized by:

2. The photography and sweetness judgment machine is The sugar content, acidity, and moisture content of the fruit are measured simultaneously to evaluate its quality.

2. The system of claim 1.

3. The generated AI is Based on the sugar content data of the fruit, weather data, and soil data, more accurate harvest time predictions are made.

2. The system of claim 1.

4. The notification unit The harvest time notification will be sent not only via a smartphone app, but also via a smartwatch and voice assistant.

2. The system of claim 1.

5. The automatic harvesting robot is Recognize the size and shape of the fruit and select the appropriate harvesting method 2. The system of claim 1.

6. The harvest box is Equipped with sensors that monitor temperature and humidity in real time to maintain an appropriate storage environment 2. The system of claim 1.

7. The photography and sweetness judgment machine is Based on the sugar content measurement results of the fruit, the emotions felt by the consumer are predicted and the results are provided as feedback.

2. The system of claim 1.

8. The generated AI is Using emotion estimation function, we analyze farmers' emotional responses to the harvest notification and improve the notification method.

2. The system of claim 1.

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