System

Abstract

An object of a system according to an embodiment is to allow even an inexperienced person to easily participate in agriculture.SOLUTION: A system includes a prompt analysis unit and a proposal unit. The prompt analysis unit analyzes a prompt input by a user. The suggestion unit provides agricultural knowledge and technique based on the result analyzed by the prompt analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the barrier to entry for inexperienced people in agricultural consulting was high, making it difficult to increase the rate of agricultural participation.

[0005] The system according to the embodiment aims to make it easier for even inexperienced people to enter the agricultural industry. [Means for solving the problem]

[0006] The system according to the embodiment includes a prompt analysis unit and a suggestion unit. The prompt analysis unit analyzes prompts input by a user. The suggestion unit provides agricultural knowledge and techniques based on the results of the analysis by the prompt analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can make it easier for even inexperienced people to enter the agricultural industry. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The agricultural consulting system according to an embodiment of the present invention is a system that uses generative AI to provide agricultural consulting services. This system lowers the barriers to entry for inexperienced people into agriculture and increases the agricultural employment rate. It also frees current agricultural workers from physical labor, allowing them to focus on managing their farms. In this way, the agricultural consulting system can lower the barriers to entry for inexperienced people into agriculture and increase the agricultural employment rate. It can also change the form of agriculture by freeing current agricultural workers from physical labor, allowing them to focus on managing their farms.

[0029] The agricultural consulting system according to the embodiment includes a generation AI, a prompt analysis unit, and a proposal unit. The generation AI uses generation AI technology such as GPT-3 or BERT. The prompt analysis unit analyzes prompts input by a user. For example, it analyzes the content of the prompt using natural language processing technology and extracts keywords. The proposal unit provides agricultural knowledge and techniques based on the results of the analysis by the prompt analysis unit. For example, it provides specific advice on crop cultivation methods and pest control measures. This lowers the barrier to entry for inexperienced people into agriculture and increases the rate of agricultural participation.

[0030] The proposal unit can analyze microclimate data for each region and propose crops and cultivation methods that are optimal for specific microclimates. For example, the generation AI collects and analyzes microclimate data for each region. For example, based on data such as temperature, humidity, and precipitation for a specific region, the proposal unit selects the most optimal crop for that region and proposes specific cultivation methods. The generation AI also identifies the optimal conditions for crop growth based on the microclimate data and proposes a cultivation schedule that matches those conditions. For example, it instructs on the timing of fertilization and irrigation that is optimal for a specific period. Furthermore, the generation AI monitors the microclimate data in real time and adjusts cultivation methods according to changes in weather conditions. For example, it proposes measures to deal with sudden temperature changes. This makes it possible to propose the optimal crops and cultivation methods for each region.

[0031] The proposal unit can monitor the chemical components of the soil in real time and propose methods to automatically replenish necessary nutrients. For example, the generation AI monitors the chemical components of the soil in real time using sensors and identifies necessary nutrients. For example, it detects deficiencies of nitrogen, phosphorus, or potassium and proposes the appropriate type and amount of fertilizer. The generation AI also analyzes soil chemical component data and proposes methods to automatically replenish nutrients necessary for crop growth. For example, it provides a fertilizer application schedule appropriate for a specific time of year. Furthermore, the generation AI tracks changes in the chemical components of the soil in real time and identifies the timing when nutrients need to be replenished. For example, it proposes a fertilization plan according to the crop's growth stage. This allows for appropriate management of soil nutrients.

[0032] The proposal unit can provide consulting services that can be applied to fields other than agriculture. For example, the proposal unit's generation AI provides consulting services related to gardening. For example, it proposes home vegetable garden designs, plant selection, and cultivation methods. The generation AI also provides consulting services related to urban agriculture. For example, it proposes methods for designing and operating rooftop farms and community gardens. The generation AI also provides consulting services that can be applied to fields other than agriculture. For example, it proposes methods for growing ornamental plants and indoor gardening. This makes it possible to provide consulting services that can be applied to fields other than agriculture.

[0033] The suggestion unit can provide real-time guidance on how to operate and maintain agricultural machinery. For example, the suggestion unit's generating AI provides real-time guidance on how to operate agricultural machinery. For example, it provides specific explanations of operating procedures for tractors and combine harvesters. The generating AI also provides real-time guidance on how to maintain agricultural machinery. For example, it suggests regular inspection items and the timing of part replacement. Furthermore, the generating AI supports real-time troubleshooting of agricultural machinery. For example, it identifies the cause of machinery failure and suggests repair methods. This allows for real-time guidance on how to operate and maintain agricultural machinery.

[0034] The suggestion unit can track the user's learning progress and provide an individually customized learning plan. In the suggestion unit, for example, the generation AI tracks the user's learning progress and provides an individually customized learning plan. For example, it suggests what content the user should learn next based on their level of understanding. The generation AI also analyzes the user's learning history and provides an individually customized learning plan. For example, it suggests review and application questions based on past learning content. Furthermore, the generation AI monitors the user's learning progress in real time and provides an individually customized learning plan. For example, it suggests a learning schedule that matches the user's pace. This makes it possible to provide a customized learning plan that matches the user's learning progress.

[0035] The suggestion unit can analyze the user's past question history and predict and suggest the knowledge and skills that should be learned next. For example, the generation AI analyzes the user's past question history and predicts and suggests the knowledge and skills that should be learned next. For example, it can suggest new topics related to content asked in the past. The generation AI can also predict the knowledge and skills that should be learned next based on the user's question history and provide an individually customized learning plan. For example, it can suggest learning content based on the user's interests and concerns. Furthermore, the generation AI can analyze the user's past question history in real time and predict and suggest the knowledge and skills that should be learned next. For example, it can provide a step-up plan according to the user's learning progress. This makes it possible to predict and suggest the knowledge and skills that should be learned next based on the user's past question history.

[0036] The suggestion unit can operate an online community related to agriculture and promote knowledge sharing among users. For example, the suggestion unit has a generating AI operate an online community related to agriculture and promote knowledge sharing among users. For example, the generating AI provides a forum or chat function, creating a space where users can exchange questions and opinions. The generating AI also promotes knowledge sharing among users through the online community. For example, the generating AI holds regular webinars and online events, providing lectures and discussions by experts. Furthermore, the generating AI operates an online community related to agriculture and promotes knowledge sharing among users. For example, the generating AI provides a platform where users can post their experiences and success stories and share them with other users. This makes it possible to operate an online community that promotes knowledge sharing among users.

[0037] The suggestion unit can automatically generate video tutorials on agriculture, providing content that is visually easy to learn. For example, the suggestion unit uses a generation AI to automatically generate video tutorials on agriculture, providing content that is visually easy to learn. For example, videos are used to explain how to grow crops and how to operate agricultural machinery. The generation AI also automatically generates video tutorials, providing content that is visually easy for users to learn. For example, basic agricultural knowledge and techniques are explained using animations and live-action footage. The generation AI also automatically generates video tutorials on agriculture, providing content that is visually easy to learn. For example, step-by-step guides and practical demonstrations are provided in video. This makes it possible to provide video tutorials that are visually easy to learn.

[0038] The proposal unit can analyze crop growth data over the long term and propose the optimal cultivation cycle. For example, the generation AI collects and analyzes crop growth data over the long term. For example, it proposes the optimal cultivation cycle based on growth data from the past few years. The generation AI also analyzes crop growth data and proposes the optimal cultivation cycle. For example, it provides a cultivation schedule according to seasonal weather conditions and soil conditions. Furthermore, the generation AI monitors crop growth data over the long term and proposes the optimal cultivation cycle. For example, it instructs the timing of fertilization and irrigation according to the crop's growth stage. In this way, crop growth data can be analyzed over the long term and the optimal cultivation cycle can be proposed.

[0039] The proposal unit can analyze agricultural machinery operation data and propose an optimal usage schedule for the machinery. In the proposal unit, for example, the generation AI collects and analyzes agricultural machinery operation data. For example, it proposes an optimal usage schedule based on the operating time and frequency of use of tractors and combines. The generation AI also analyzes agricultural machinery operation data and proposes an optimal usage schedule for the machinery. For example, it provides information on when machinery should be maintained and how to use it efficiently. Furthermore, the generation AI monitors agricultural machinery operation data in real time and proposes an optimal usage schedule. For example, it adjusts the schedule according to the machinery's operating status. This makes it possible to propose an optimal usage schedule for agricultural machinery.

[0040] The proposal unit can manage the entire agricultural supply chain and propose efficient logistics and sales strategies. For example, the generation AI in the proposal unit manages the entire agricultural supply chain and proposes efficient logistics and sales strategies. For example, it provides an optimal schedule from harvest to shipping. The generation AI also analyzes supply chain data and proposes efficient logistics and sales strategies. For example, it provides inventory management and delivery plans based on demand forecasts. Furthermore, the generation AI monitors the entire agricultural supply chain in real time and proposes efficient logistics and sales strategies. For example, it identifies logistics bottlenecks and proposes improvement measures. This makes it possible to manage the entire agricultural supply chain and propose efficient logistics and sales strategies.

[0041] The proposal unit can provide agricultural regulations and subsidy information to support agricultural management. For example, the generation AI in the proposal unit provides agricultural regulations and subsidy information to support agricultural management. For example, it provides guidance on the latest regulations and subsidy application procedures. The generation AI also analyzes regulations and subsidy information to provide information useful for agricultural management. For example, it proposes subsidies and tax incentives for specific crops. Furthermore, the generation AI updates agricultural regulations and subsidy information in real time to support agricultural management. For example, it notifies users of new regulations and changes to subsidy systems. This allows the generation AI to provide agricultural regulations and subsidy information to support agricultural management.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The proposal unit can provide the latest research results and technical information related to agriculture. For example, the generative AI analyzes the latest agricultural research papers and extracts and provides information useful to users. The generative AI also tracks progress in agricultural technology and introduces new technologies and methods to users. Furthermore, the generative AI collects patent information related to agriculture and suggests new technologies and devices that users can use. This allows users to make their farming more efficient by utilizing the latest research results and technical information.

[0044] The proposal unit can propose agricultural marketing strategies. For example, the generation AI analyzes market data and identifies crops and products that are in high demand. The generation AI also proposes optimal sales channels and pricing based on the user's agricultural production data. Furthermore, the generation AI analyzes consumer preferences and trends and provides marketing strategies that will enable the user to be competitive in the market. This allows the user to implement effective marketing strategies and maximize profits.

[0045] The suggestion unit can provide agricultural educational programs. For example, the generation AI creates an online course for learning basic agricultural knowledge and techniques and provides it to the user. The generation AI also tracks the user's learning progress and provides an individually customized learning plan. Furthermore, the generation AI suggests practical agricultural workshops and field trips, providing the user with an opportunity to learn in an actual agricultural field. This allows the user to effectively acquire agricultural knowledge and techniques.

[0046] The proposal unit can propose risk management measures for agriculture. For example, the generation AI analyzes weather data and proposes measures to address weather risks. The generation AI also provides hedging measures for price fluctuation risks based on market data. Furthermore, the generation AI analyzes agricultural production data and proposes preventive measures for pest and disease risks. This allows users to effectively manage risks in agriculture and maintain stable production.

[0047] The proposal unit can propose measures to improve energy efficiency in agriculture. For example, the generation AI analyzes energy consumption data and proposes efficient energy usage methods. The generation AI also supports the introduction of renewable energy, allowing users to reduce energy costs. Furthermore, the generation AI proposes energy-efficient agricultural machinery and equipment, enabling users to achieve sustainable agriculture. This allows users to improve energy efficiency and practice environmentally friendly agriculture.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The prompt analysis unit analyzes the prompt input by the user. For example, it analyzes the content of the prompt using natural language processing technology and extracts keywords. Step 2: The proposal unit provides agricultural knowledge and techniques based on the results of the analysis by the prompt analysis unit. For example, it provides specific advice on crop cultivation methods and pest control.

[0050] (Example 2) The agricultural consulting system according to an embodiment of the present invention is a system that uses generative AI to provide agricultural consulting services. This system lowers the barriers to entry for inexperienced people into agriculture and increases the agricultural employment rate. It also frees current agricultural workers from physical labor, allowing them to focus on managing their farms. In this way, the agricultural consulting system can lower the barriers to entry for inexperienced people into agriculture and increase the agricultural employment rate. It can also change the form of agriculture by freeing current agricultural workers from physical labor, allowing them to focus on managing their farms.

[0051] The agricultural consulting system according to the embodiment includes a generation AI, a prompt analysis unit, and a proposal unit. The generation AI uses generation AI technology such as GPT-3 or BERT. The prompt analysis unit analyzes prompts input by a user. For example, it analyzes the content of the prompt using natural language processing technology and extracts keywords. The proposal unit provides agricultural knowledge and techniques based on the results of the analysis by the prompt analysis unit. For example, it provides specific advice on crop cultivation methods and pest control measures. This lowers the barrier to entry for inexperienced people into agriculture and increases the rate of agricultural participation.

[0052] The proposal unit can analyze microclimate data for each region and propose crops and cultivation methods that are optimal for specific microclimates. For example, the generation AI collects and analyzes microclimate data for each region. For example, based on data such as temperature, humidity, and precipitation for a specific region, the proposal unit selects the most optimal crop for that region and proposes specific cultivation methods. The generation AI also identifies the optimal conditions for crop growth based on the microclimate data and proposes a cultivation schedule that matches those conditions. For example, it instructs on the timing of fertilization and irrigation that is optimal for a specific period. Furthermore, the generation AI monitors the microclimate data in real time and adjusts cultivation methods according to changes in weather conditions. For example, it proposes measures to deal with sudden temperature changes. This makes it possible to propose the optimal crops and cultivation methods for each region.

[0053] The proposal unit can monitor the chemical components of the soil in real time and propose methods to automatically replenish necessary nutrients. For example, the generation AI monitors the chemical components of the soil in real time using sensors and identifies necessary nutrients. For example, it detects deficiencies of nitrogen, phosphorus, or potassium and proposes the appropriate type and amount of fertilizer. The generation AI also analyzes soil chemical component data and proposes methods to automatically replenish nutrients necessary for crop growth. For example, it provides a fertilizer application schedule appropriate for a specific time of year. Furthermore, the generation AI tracks changes in the chemical components of the soil in real time and identifies the timing when nutrients need to be replenished. For example, it proposes a fertilization plan according to the crop's growth stage. This allows for appropriate management of soil nutrients.

[0054] The suggestion unit can use the emotion estimation function to analyze the user's stress level and suggest agricultural activities and crop selections to reduce stress. For example, the generation AI in the suggestion unit analyzes the user's stress level and suggests agricultural activities to reduce stress. For example, it may recommend growing herbs that have a relaxing effect. The emotion estimation function may also be used to monitor the user's stress level in real time and suggest agricultural activities that are effective in reducing stress. For example, it may recommend gardening or light work. Furthermore, the generation AI may analyze the user's stress level and suggest crop selections to reduce stress. For example, it may recommend growing fragrant flowers or herbs. This makes it possible to suggest agricultural activities to reduce the user's stress.

[0055] The proposal unit can provide consulting services that can be applied to fields other than agriculture. For example, the proposal unit's generation AI provides consulting services related to gardening. For example, it proposes home vegetable garden designs, plant selection, and cultivation methods. The generation AI also provides consulting services related to urban agriculture. For example, it proposes methods for designing and operating rooftop farms and community gardens. The generation AI also provides consulting services that can be applied to fields other than agriculture. For example, it proposes methods for growing ornamental plants and indoor gardening. This makes it possible to provide consulting services that can be applied to fields other than agriculture.

[0056] The suggestion unit can provide real-time guidance on how to operate and maintain agricultural machinery. For example, the suggestion unit's generating AI provides real-time guidance on how to operate agricultural machinery. For example, it provides specific explanations of operating procedures for tractors and combine harvesters. The generating AI also provides real-time guidance on how to maintain agricultural machinery. For example, it suggests regular inspection items and the timing of part replacement. Furthermore, the generating AI supports real-time troubleshooting of agricultural machinery. For example, it identifies the cause of machinery failure and suggests repair methods. This allows for real-time guidance on how to operate and maintain agricultural machinery.

[0057] The suggestion unit can use the emotion estimation function to analyze the user's emotions toward agricultural activities and suggest activities that will elicit positive emotions. For example, the suggestion unit can use the emotion estimation function to analyze the user's emotions toward agricultural activities and suggest activities that will elicit positive emotions. For example, it can recommend harvest festivals and agricultural experience events. The generation AI can also analyze the user's emotion data and suggest agricultural activities that will elicit positive emotions. For example, it can recommend farm work done with family or friends. Furthermore, the emotion estimation function can be used to monitor the user's emotions toward agricultural activities in real time and suggest activities that will elicit positive emotions. For example, it can recommend relaxing activities or gardening as a hobby. This makes it possible to suggest activities that will elicit positive emotions from the user.

[0058] The suggestion unit can track the user's learning progress and provide an individually customized learning plan. In the suggestion unit, for example, the generation AI tracks the user's learning progress and provides an individually customized learning plan. For example, it suggests what content the user should learn next based on their level of understanding. The generation AI also analyzes the user's learning history and provides an individually customized learning plan. For example, it suggests review and application questions based on past learning content. Furthermore, the generation AI monitors the user's learning progress in real time and provides an individually customized learning plan. For example, it suggests a learning schedule that matches the user's pace. This makes it possible to provide a customized learning plan that matches the user's learning progress.

[0059] The suggestion unit can analyze the user's past question history and predict and suggest the knowledge and skills that should be learned next. For example, the generation AI analyzes the user's past question history and predicts and suggests the knowledge and skills that should be learned next. For example, it can suggest new topics related to content asked in the past. The generation AI can also predict the knowledge and skills that should be learned next based on the user's question history and provide an individually customized learning plan. For example, it can suggest learning content based on the user's interests and concerns. Furthermore, the generation AI can analyze the user's past question history in real time and predict and suggest the knowledge and skills that should be learned next. For example, it can provide a step-up plan according to the user's learning progress. This makes it possible to predict and suggest the knowledge and skills that should be learned next based on the user's past question history.

[0060] The suggestion unit can use the emotion estimation function to suggest motivation improvement measures to increase the user's motivation to learn. For example, the suggestion unit uses the emotion estimation function to suggest motivation improvement measures to increase the user's motivation to learn. For example, it provides words of praise or encouraging messages according to the user's learning progress. In addition, the generation AI analyzes the user's emotion data and suggests motivation improvement measures to increase the user's motivation to learn. For example, it provides a specific action plan for achieving goals. Furthermore, the emotion estimation function is used to monitor the user's motivation to learn in real time and suggest motivation improvement measures. For example, it provides rewards or incentives according to the user's learning progress. In this way, it is possible to suggest motivation improvement measures to increase the user's motivation to learn.

[0061] The suggestion unit can operate an online community related to agriculture and promote knowledge sharing among users. For example, the suggestion unit has a generating AI operate an online community related to agriculture and promote knowledge sharing among users. For example, the generating AI provides a forum or chat function, creating a space where users can exchange questions and opinions. The generating AI also promotes knowledge sharing among users through the online community. For example, the generating AI holds regular webinars and online events, providing lectures and discussions by experts. Furthermore, the generating AI operates an online community related to agriculture and promotes knowledge sharing among users. For example, the generating AI provides a platform where users can post their experiences and success stories and share them with other users. This makes it possible to operate an online community that promotes knowledge sharing among users.

[0062] The suggestion unit can automatically generate video tutorials on agriculture, providing content that is visually easy to learn. For example, the suggestion unit uses a generation AI to automatically generate video tutorials on agriculture, providing content that is visually easy to learn. For example, videos are used to explain how to grow crops and how to operate agricultural machinery. The generation AI also automatically generates video tutorials, providing content that is visually easy for users to learn. For example, basic agricultural knowledge and techniques are explained using animations and live-action footage. The generation AI also automatically generates video tutorials on agriculture, providing content that is visually easy to learn. For example, step-by-step guides and practical demonstrations are provided in video. This makes it possible to provide video tutorials that are visually easy to learn.

[0063] The suggestion unit can use the emotion estimation function to detect in real time any anxiety or doubt the user feels while studying and provide appropriate support. For example, the suggestion unit can use the emotion estimation function to detect in real time any anxiety or doubt the user feels while studying and provide appropriate support. For example, providing an encouraging message or additional explanation when the user feels anxious. Furthermore, the generation AI analyzes the user's emotion data to detect in real time any anxiety or doubt the user feels while studying and provide appropriate support. For example, providing immediate answers to questions and related learning resources. Furthermore, the emotion estimation function can be used to monitor in real time any anxiety or doubt the user feels while studying and provide appropriate support. For example, providing advice or additional learning materials according to the study progress. In this way, the suggestion unit can detect in real time any anxiety or doubt the user feels while studying and provide appropriate support.

[0064] The proposal unit can analyze crop growth data over the long term and propose the optimal cultivation cycle. For example, the generation AI collects and analyzes crop growth data over the long term. For example, it proposes the optimal cultivation cycle based on growth data from the past few years. The generation AI also analyzes crop growth data and proposes the optimal cultivation cycle. For example, it provides a cultivation schedule according to seasonal weather conditions and soil conditions. Furthermore, the generation AI monitors crop growth data over the long term and proposes the optimal cultivation cycle. For example, it instructs the timing of fertilization and irrigation according to the crop's growth stage. In this way, crop growth data can be analyzed over the long term and the optimal cultivation cycle can be proposed.

[0065] The proposal unit can analyze agricultural machinery operation data and propose an optimal usage schedule for the machinery. In the proposal unit, for example, the generation AI collects and analyzes agricultural machinery operation data. For example, it proposes an optimal usage schedule based on the operating time and frequency of use of tractors and combines. The generation AI also analyzes agricultural machinery operation data and proposes an optimal usage schedule for the machinery. For example, it provides information on when machinery should be maintained and how to use it efficiently. Furthermore, the generation AI monitors agricultural machinery operation data in real time and proposes an optimal usage schedule. For example, it adjusts the schedule according to the machinery's operating status. This makes it possible to propose an optimal usage schedule for agricultural machinery.

[0066] The suggestion unit can use the emotion estimation function to analyze the fatigue level of the farmer and suggest the timing and method of rest. For example, the suggestion unit uses the emotion estimation function to analyze the fatigue level of the farmer and suggest the timing and method of rest. For example, it may suggest an appropriate rest period if fatigue is accumulating. In addition, the generation AI analyzes the farmer's emotion data, monitors the fatigue level in real time, and suggests the timing and method of rest. For example, it may provide regular breaks and methods for refreshing. Furthermore, the emotion estimation function may be used to analyze the farmer's fatigue level and suggest the timing and method of rest. For example, it may recommend activities that allow them to relax between work. This makes it possible to analyze the farmer's fatigue level and suggest the appropriate timing and method of rest.

[0067] The proposal unit can manage the entire agricultural supply chain and propose efficient logistics and sales strategies. For example, the generation AI in the proposal unit manages the entire agricultural supply chain and proposes efficient logistics and sales strategies. For example, it provides an optimal schedule from harvest to shipping. The generation AI also analyzes supply chain data and proposes efficient logistics and sales strategies. For example, it provides inventory management and delivery plans based on demand forecasts. Furthermore, the generation AI monitors the entire agricultural supply chain in real time and proposes efficient logistics and sales strategies. For example, it identifies logistics bottlenecks and proposes improvement measures. This makes it possible to manage the entire agricultural supply chain and propose efficient logistics and sales strategies.

[0068] The proposal unit can provide agricultural regulations and subsidy information to support agricultural management. For example, the generation AI in the proposal unit provides agricultural regulations and subsidy information to support agricultural management. For example, it provides guidance on the latest regulations and subsidy application procedures. The generation AI also analyzes regulations and subsidy information to provide information useful for agricultural management. For example, it proposes subsidies and tax incentives for specific crops. Furthermore, the generation AI updates agricultural regulations and subsidy information in real time to support agricultural management. For example, it notifies users of new regulations and changes to subsidy systems. This allows the generation AI to provide agricultural regulations and subsidy information to support agricultural management.

[0069] The suggestion unit can use the emotion estimation function to suggest team building activities to maintain the motivation of farmers. For example, the suggestion unit uses the emotion estimation function to suggest team building activities to maintain the motivation of farmers. For example, regular team meetings and collaborative work are recommended. In addition, the generation AI analyzes the emotion data of farmers and suggests team building activities to maintain motivation. For example, activities that promote communication within the team are provided. Furthermore, the emotion estimation function is used to monitor the motivation of farmers in real time and suggest team building activities. For example, workshops and events to strengthen teamwork are recommended. This makes it possible to suggest team building activities to maintain the motivation of farmers.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The proposal unit can provide the latest research results and technical information related to agriculture. For example, the generative AI analyzes the latest agricultural research papers and extracts and provides information useful to users. The generative AI also tracks progress in agricultural technology and introduces new technologies and methods to users. Furthermore, the generative AI collects patent information related to agriculture and suggests new technologies and devices that users can use. This allows users to make their farming more efficient by utilizing the latest research results and technical information.

[0072] The suggestion unit uses the emotion estimation function to analyze the user's learning style and suggest the optimal learning method. For example, the generation AI analyzes the user's emotional data and provides visual content to users who find visual learning effective. The generation AI also provides an individually customized learning plan based on the user's learning history. Furthermore, the emotion estimation function monitors the stress and fatigue the user feels while studying in real time and suggests appropriate breaks and ways to refresh. This maximizes the user's learning efficiency.

[0073] The proposal unit can propose agricultural marketing strategies. For example, the generation AI analyzes market data and identifies crops and products that are in high demand. The generation AI also proposes optimal sales channels and pricing based on the user's agricultural production data. Furthermore, the generation AI analyzes consumer preferences and trends and provides marketing strategies that will enable the user to be competitive in the market. This allows the user to implement effective marketing strategies and maximize profits.

[0074] The suggestion unit can use the emotion estimation function to analyze the user's health condition and provide advice for maintaining health. For example, the generation AI analyzes the user's emotional data and suggests appropriate rest and exercise if stress or fatigue accumulates. The generation AI also provides a nutritionally balanced meal plan based on the user's health data. Furthermore, the emotion estimation function can be used to monitor the user's health condition in real time and provide advice for maintaining health. This allows users to perform agricultural activities while maintaining their health.

[0075] The suggestion unit can provide agricultural educational programs. For example, the generation AI creates an online course for learning basic agricultural knowledge and techniques and provides it to the user. The generation AI also tracks the user's learning progress and provides an individually customized learning plan. Furthermore, the generation AI suggests practical agricultural workshops and field trips, providing the user with an opportunity to learn in an actual agricultural field. This allows the user to effectively acquire agricultural knowledge and techniques.

[0076] The suggestion unit can use the emotion estimation function to introduce gamification elements to maintain user motivation. For example, the generation AI analyzes the user's emotional data and awards points and badges according to the progress of learning and work. The generation AI also suggests goal setting and challenges that will give the user a sense of accomplishment based on the user's emotional data. Furthermore, the emotion estimation function can monitor the user's motivation in real time and provide appropriate feedback and rewards. This allows users to continue their farming activities while having fun learning.

[0077] The proposal unit can propose risk management measures for agriculture. For example, the generation AI analyzes weather data and proposes measures to address weather risks. The generation AI also provides hedging measures for price fluctuation risks based on market data. Furthermore, the generation AI analyzes agricultural production data and proposes preventive measures for pest and disease risks. This allows users to effectively manage risks in agriculture and maintain stable production.

[0078] The suggestion unit can use the emotion estimation function to analyze the user's communication style and suggest effective communication methods. For example, the generation AI analyzes the user's emotional data and suggests appropriate tones and wording during conversations. The generation AI also provides effective feedback methods and approaches based on the user's communication history. Furthermore, the emotion estimation function can monitor the user's communication style in real time and provide appropriate advice. This allows users to smoothly advance their agricultural activities through effective communication.

[0079] The proposal unit can propose measures to improve energy efficiency in agriculture. For example, the generation AI analyzes energy consumption data and proposes efficient energy usage methods. The generation AI also supports the introduction of renewable energy, allowing users to reduce energy costs. Furthermore, the generation AI proposes energy-efficient agricultural machinery and equipment, enabling users to achieve sustainable agriculture. This allows users to improve energy efficiency and practice environmentally friendly agriculture.

[0080] The suggestion unit can use the emotion estimation function to suggest activities to bring out the user's creativity. For example, the generative AI analyzes the user's emotion data and suggests brainstorming sessions to generate creative ideas. The generative AI also provides arts and crafts activities to stimulate creativity based on the user's emotion data. Furthermore, the emotion estimation function monitors the user's creativity in real time and suggests appropriate activities. This allows users to incorporate a creative approach into their farming activities.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The prompt analysis unit analyzes the prompt input by the user. For example, it analyzes the content of the prompt using natural language processing technology and extracts keywords. Step 2: The proposal unit provides agricultural knowledge and techniques based on the results of the analysis by the prompt analysis unit. For example, it provides specific advice on crop cultivation methods and pest control.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] 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.

[0098] 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.

[0099] 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 AI 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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 AI 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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 AI 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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, in order to avoid confusion and to 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.

[0149] 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]

[0150] 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 system for agricultural consulting using generative AI, a prompt analysis unit that analyzes a prompt input by a user; a suggestion unit that provides agricultural knowledge and techniques based on the results of the analysis by the prompt analysis unit. A system characterized by:

2. The proposal unit Analyzing microclimate data for each region and proposing crops and cultivation methods that are best suited to specific microclimates 2. The system of claim 1.

3. The proposal unit Real-time monitoring of soil chemical composition and proposals for automatic replenishment of necessary nutrients 2. The system of claim 1.

4. The proposal unit Analyzes the user's stress level and suggests agricultural activities and crop selections to reduce stress 2. The system of claim 1.

5. The proposal unit Providing consulting services that can be applied to fields other than agriculture 2. The system of claim 1.

6. The proposal unit Providing real-time instruction on how to operate and maintain agricultural machinery 2. The system of claim 1.

7. The proposal unit Analyzes users' feelings toward agricultural activities and suggests activities that elicit positive emotions 2. The system of claim 1.

8. The proposal unit Track your progress and provide personalized learning plans 2. The system of claim 1.

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