system

Abstract

The system according to this embodiment aims to analyze and integrate weather data, soil information, and market trends to propose optimal cultivation methods and efficient resource utilization methods. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects weather data, soil information, and market trends. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes optimal cultivation methods and efficient resource utilization methods based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for agricultural workers to efficiently integrate and analyze meteorological data, soil information, and market trends to find an optimal cultivation method and resource utilization method.

[0005] The system according to the embodiment aims to analyze and integrate meteorological data, soil information, and market trends and propose an optimal cultivation method and an efficient resource utilization method.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects weather data, soil information, and market trends. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes optimal cultivation methods and efficient resource utilization methods based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze and integrate weather data, soil information, and market trends to propose optimal cultivation methods and efficient resource utilization methods. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent specifically for the agricultural sector according to the embodiment of the present invention is a system aimed at small to large-scale agricultural businesses, individuals interested in urban agriculture, and local governments. This system analyzes and integrates weather data, soil information, and market trends in real time to propose optimal cultivation methods and efficient resource utilization methods. In Japan, urban agriculture and urban farming are not spreading easily due to problems such as declining yields, decreased profitability, labor shortages, and environmental impact. This system aims to solve these problems by providing knowledge support and minimizing the barriers to entry. Specifically, the AI ​​identifies the causes of vague problems faced by agricultural workers (for example, vague instructions such as "yields have been decreasing recently") and provides concrete solutions. It also utilizes generative AI technology to provide market analysis, forecasts, and training programs on the latest agricultural technologies. For example, the AI ​​agent collects and analyzes data such as weather data, soil information, and market trends in real time. Then, based on the analysis results, it proposes optimal cultivation methods and efficient resource utilization methods. For example, it can propose an appropriate irrigation schedule based on weather data, or propose the optimal amount of fertilizer to use based on soil information. Furthermore, the AI ​​agent can understand vague instructions from farmers and propose concrete solutions. For example, in response to an instruction such as "yields have been decreasing recently," the AI ​​can identify the cause and propose appropriate countermeasures. It also utilizes generative AI technology to perform market analysis and forecasting, providing strategies for maximizing profits. For instance, it can predict market price trends and suggest the appropriate timing for sales. In addition, it provides training programs on the latest agricultural technologies to support the skill development of farmers. In this way, this AI agent solves various challenges in the agricultural sector and provides agricultural solutions that create a sustainable future. As a result, this AI agent specifically for the agricultural sector can quickly and effectively solve problems faced by farmers and realize sustainable agriculture.

[0029] The AI ​​agent for the agricultural sector according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects weather data, soil information, and market trends. Weather data includes, but is not limited to, temperature, precipitation, and wind speed. Soil information includes, but is not limited to, soil pH, nutrient content, and moisture content. Market trends include, but is not limited to, crop supply and demand and price trends. The data collection unit can collect weather data using sensors, for example. The data collection unit can also collect soil information using soil sensors. Furthermore, the data collection unit can collect market trend data via the internet. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, use AI to analyze weather data and predict weather fluctuations. Furthermore, the analysis unit can use AI to analyze soil information and evaluate soil conditions. Furthermore, the analysis unit can use AI to analyze market trend data and predict market trends. The proposal unit proposes optimal cultivation methods and efficient resource utilization methods based on the analysis results obtained by the analysis unit. The suggestion unit can, for example, propose an appropriate irrigation schedule based on weather data. It can also propose the optimal amount of fertilizer to use based on soil information. Furthermore, it can propose strategies for maximizing profits based on market trends. For example, the suggestion unit proposes an appropriate irrigation schedule based on weather data. For example, the suggestion unit proposes the optimal amount of fertilizer to use based on soil information. For example, the suggestion unit proposes strategies for maximizing profits based on market trends. As a result, the AI ​​agent dedicated to the agricultural sector according to this embodiment can analyze and integrate weather data, soil information, and market trends to propose optimal cultivation methods and efficient resource utilization methods.

[0030] The data collection unit collects weather data, soil information, and market trends. Weather data includes, but is not limited to, temperature, precipitation, and wind speed. Specifically, weather data is collected in real time from local weather stations, satellite data, and weather sensors installed on farms. This allows for a detailed understanding of the farm's microclimate. Soil information includes, but is not limited to, soil pH, nutrient content, and moisture content. Soil information is obtained from soil sensors and the results of periodic soil sample analysis. These sensors are installed at different depths of soil, and by collecting information from different layers, a three-dimensional understanding of the soil condition can be obtained. Market trends include, but are not limited to, crop supply and demand and price trends. Market trend data is collected via the internet from agricultural market research reports and exchange databases. This allows the data collection unit to centrally collect diverse agricultural data and update it in real time. Furthermore, the data collection unit stores this data on a cloud server, making it accessible to the analysis and proposal units. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can use AI to analyze weather data and predict weather fluctuations. Specifically, the AI ​​compares past and current weather data to learn patterns of temperature and precipitation fluctuations. This allows for highly accurate predictions of future weather conditions. The analysis unit can also use AI to analyze soil information and evaluate soil conditions. The AI ​​analyzes data such as soil pH, nutrient content, and moisture content to identify optimal soil conditions for crop growth. Furthermore, the analysis unit can use AI to analyze market trend data and predict market trends. The AI ​​compares past and current market data to learn the balance of supply and demand and price fluctuation patterns. This allows for highly accurate predictions of future market trends. Based on these analysis results, the analysis unit supports optimal decision-making in agriculture. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The proposal department proposes optimal cultivation methods and efficient resource utilization based on the analysis results obtained by the analysis department. For example, the proposal department can propose an appropriate irrigation schedule based on meteorological data. Specifically, it calculates the optimal irrigation timing and water volume based on meteorological data, taking into account rainfall forecasts and temperature fluctuations. The proposal department can also propose the optimal amount of fertilizer to use based on soil information. It analyzes the nutrient content and pH of the soil to identify the type and amount of fertilizer necessary for crop growth. Furthermore, the proposal department can propose strategies for maximizing profits based on market trends. It analyzes the balance of supply and demand in the market and price trends to propose the optimal crop selection and harvest time. In this way, the proposal department supports agricultural managers in achieving efficient and profitable agricultural operations. In addition, the proposal department can also propose long-term agricultural plans and risk management strategies based on the analysis results. For example, it assesses the risks to climate change and market fluctuations and provides advice on taking appropriate countermeasures. In this way, the proposal department can support agricultural managers in achieving sustainable and stable agricultural operations.

[0033] The proposal unit can propose an appropriate irrigation schedule based on meteorological data. For example, the proposal unit can propose an irrigation schedule that takes into account soil moisture content based on meteorological data. For example, the proposal unit can also propose an irrigation schedule that corresponds to the growth stage of the crops based on meteorological data. For example, the proposal unit can propose an irrigation schedule based on precipitation forecasts based on meteorological data. As a result, by proposing an appropriate irrigation schedule based on meteorological data, the proposal unit enables efficient irrigation. Some or all of the above processing in the proposal unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the proposal unit can input meteorological data into a generating AI, and the generating AI can generate an irrigation schedule.

[0034] The suggestion unit can propose the optimal amount of fertilizer to use based on soil information. For example, the suggestion unit can propose the amount of fertilizer to use according to the crop's nutritional requirements based on the soil's nutrient content. The suggestion unit can also propose the optimal type and amount of fertilizer to use based on the soil's pH. The suggestion unit can also propose the timing and amount of fertilizer to use based on the soil's moisture content. As a result, by proposing the optimal amount of fertilizer to use based on soil information, the suggestion unit enables efficient fertilizer use. Some or all of the above processing in the suggestion unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the suggestion unit can input soil information into a generating AI, and the generating AI can generate the amount of fertilizer to use.

[0035] The proposal department can identify the cause of vague instructions from farmers and propose concrete solutions. For example, in response to the instruction, "Recently, crop yields have decreased," the proposal department can identify the cause of the decrease in yield and propose appropriate countermeasures. For example, in response to the instruction, "Crop growth is slow," the proposal department can identify the cause of the slow growth and propose appropriate countermeasures. For example, in response to the instruction, "Soil conditions are poor," the proposal department can identify the cause of the deterioration of soil conditions and propose appropriate countermeasures. In this way, the proposal department can quickly resolve problems by identifying the cause of vague instructions from farmers and proposing concrete solutions. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal department can input instructions from farmers into a generative AI, which can identify the cause and generate a solution.

[0036] The proposal department can perform market analysis and forecasting and provide strategies for maximizing profits. For example, the proposal department can predict market price trends and propose appropriate sales timings. For example, the proposal department can analyze market supply and demand and propose optimal crop cultivation plans. For example, the proposal department can analyze the competitive landscape of the market and propose strategies to secure a competitive advantage. In this way, the proposal department can improve profitability by performing market analysis and forecasting and providing strategies for maximizing profits. Some or all of the above processes in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input market data into a generative AI, which can perform market analysis and forecasting and generate strategies.

[0037] The proposal unit can provide training programs on the latest agricultural technologies. For example, the proposal unit can provide a training program on pesticide spraying using drones. The proposal unit can also provide a training program on monitoring technology using IoT sensors. The proposal unit can also provide a training program on smart farming technology. In this way, the proposal unit can improve the skills of agricultural workers by providing training programs on the latest agricultural technologies. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the content of the training program into a generative AI, and the generative AI can generate the training program.

[0038] The data collection unit can analyze past collected data and select the optimal collection method. For example, the data collection unit can identify the most effective collection method from past collected data and prioritize its use. The data collection unit can also analyze past collected data to identify areas for improvement in collection methods and optimize them. For example, the data collection unit can analyze patterns in collection methods based on past collected data and create an optimal collection schedule. This improves the efficiency of data collection by allowing the data collection unit to analyze past collected data and select the optimal collection method. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into AI, which can then select the optimal collection method.

[0039] The data collection unit can prioritize the collection of data specific to particular crops or regions during data collection. For example, the data collection unit can prioritize the collection of data related to a particular crop and use it to improve the cultivation of that crop. The data collection unit can also prioritize the collection of data related to a particular region and use it to improve agriculture in that region. The data collection unit can also improve the accuracy of the data by prioritizing the collection of data related to specific crops or regions. As a result, the data collection unit improves the accuracy of the data by prioritizing the collection of data specific to particular crops or regions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data related to a particular crop or region into AI, and the AI ​​can select the data to prioritize the collection of.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant weather data based on the user's current location. For example, the data collection unit can prioritize the collection of highly relevant soil information based on the user's farm location information. For example, the data collection unit can prioritize the collection of highly relevant data based on market trends in the user's region. As a result, the data relevance is improved by the data collection unit prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then select highly relevant data.

[0041] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze users' social media posts and collect relevant weather data. The data collection unit can also collect data on crops of interest from users' social media activity. The data collection unit can also collect relevant market trend data based on users' social media activity. This improves the accuracy of the data by allowing the data collection unit to analyze users' social media activity and collect relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user social media data into AI, which can then select relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can also determine the priority of the analysis based on the importance of the data. For example, the analysis unit can apply multiple analysis methods to perform a detailed analysis on important data. In this way, the analysis unit can perform a detailed analysis on important data by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a weather forecasting algorithm to meteorological data. For example, the analysis unit can apply a soil analysis algorithm to soil information. For example, the analysis unit can apply a market forecasting algorithm to market trend data. By applying different analysis algorithms depending on the data category, the analysis unit can improve the accuracy of the analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can select an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the latest data to provide real-time information. The analysis unit can also analyze long-term trends based on historical data. The analysis unit can also adjust the priority of analysis according to the data collection timing. This allows the analysis unit to prioritize the analysis of the latest information by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI, and the AI ​​can determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to quickly provide important information. The analysis unit can also adjust the order of analysis according to the relevance of the data. For example, the analysis unit can perform a detailed analysis on highly relevant data. In this way, the analysis unit can quickly provide important information by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of analysis.

[0046] The proposal unit can adjust the level of detail of a proposal based on the importance of the data. For example, the proposal unit can provide detailed proposals based on important data and simplified proposals based on less important data. The proposal unit can also prioritize proposals based on the importance of the data. For example, the proposal unit can present multiple options for proposals based on important data. This allows the proposal unit to provide detailed proposals based on important data by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the proposal.

[0047] The proposal unit can apply different proposal algorithms depending on the data category when making a proposal. For example, the proposal unit can apply a weather forecasting algorithm to a proposal based on weather data. For example, the proposal unit can also apply a soil analysis algorithm to a proposal based on soil information. For example, the proposal unit can also apply a market forecasting algorithm to a proposal based on market trend data. By applying different proposal algorithms depending on the data category, the proposal unit can improve the accuracy of its proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the data category into the AI, and the AI ​​can select an appropriate proposal algorithm.

[0048] The proposal department can determine the priority of proposals based on the data collection timing when making a proposal. For example, the proposal department will prioritize proposals based on the latest data. For example, proposals based on historical data can also be made from a long-term perspective. The proposal department can also adjust the priority of proposals according to the data collection timing. This allows the proposal department to prioritize proposals based on the latest information by determining the priority of proposals based on the data collection timing. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the data collection timing into the AI, and the AI ​​can determine the priority of proposals.

[0049] The proposal unit can adjust the order of proposals based on the relevance of the data when making proposals. For example, the proposal unit will prioritize proposals based on highly relevant data. The proposal unit can also adjust the order of proposals according to the relevance of the data. For example, the proposal unit can add detailed explanations to proposals based on highly relevant data. This allows the proposal unit to quickly make proposals based on important information by adjusting the order of proposals based on the relevance of the data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of proposals.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can adjust the frequency of data collection according to the growth stage of a particular crop. For example, it can collect data frequently in the early stages of crop growth and reduce the collection frequency once growth has stabilized. It can also increase the collection frequency again as harvest time approaches. In this way, the data collection unit can efficiently collect data by adjusting the data collection frequency according to the growth stage of the crop.

[0052] The analysis unit can apply different analysis methods depending on the type of data. For example, a weather forecasting algorithm can be applied to meteorological data, and a soil analysis algorithm to soil information. A market forecasting algorithm can also be applied to market trend data. This allows the analysis unit to improve the accuracy of the analysis by applying the most suitable analysis method according to the type of data.

[0053] The suggestion system can customize suggestions based on the user's past behavior history. For example, users who have previously adopted a specific cultivation method can receive priority suggestions related to that method. It can also suggest highly profitable crops based on past market trends. This allows the suggestion system to provide optimal suggestions by customizing them based on the user's past behavior history.

[0054] The data collection unit can adjust the timing of data collection according to the weather conditions of a specific region. For example, it can frequently collect precipitation data during the rainy season and prioritize temperature data collection during the dry season. Furthermore, if extreme weather events are predicted in a particular region, the data collection frequency for that region can be increased. This allows the data collection unit to efficiently collect data by adjusting the timing of data collection according to the weather conditions of a specific region.

[0055] The analysis unit can evaluate the reliability of the analysis according to the data collection source and prioritize the analysis of highly reliable data. For example, it can prioritize the analysis of data collected from highly reliable sensors and use less reliable data supplementarily. Furthermore, the reliability of the analysis results can be improved by integrating and analyzing data from multiple data sources. In this way, the analysis unit can provide highly reliable analysis results by evaluating the reliability of the analysis according to the data collection source.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects weather data, soil information, and market trends. Weather data includes temperature, precipitation, wind speed, etc., while soil information includes soil pH, nutrient content, moisture content, etc. Market trends include crop supply and demand, price trends, etc. The data collection unit can collect weather data using sensors, collect soil information using soil sensors, and collect market trend data via the internet. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can use AI to analyze weather data and predict weather fluctuations. It can also use AI to analyze soil information and evaluate soil conditions. Furthermore, it can use AI to analyze market trend data and predict market trends. Step 3: The proposal department proposes optimal cultivation methods and efficient resource utilization methods based on the analysis results obtained by the analysis department. The proposal department can propose appropriate irrigation schedules based on weather data, optimal fertilizer usage based on soil information, and strategies for maximizing profits based on market trends.

[0058] (Example of form 2) The AI ​​agent specifically for the agricultural sector according to the embodiment of the present invention is a system aimed at small to large-scale agricultural businesses, individuals interested in urban agriculture, and local governments. This system analyzes and integrates weather data, soil information, and market trends in real time to propose optimal cultivation methods and efficient resource utilization methods. In Japan, urban agriculture and urban farming are not spreading easily due to problems such as declining yields, decreased profitability, labor shortages, and environmental impact. This system aims to solve these problems by providing knowledge support and minimizing the barriers to entry. Specifically, the AI ​​identifies the causes of vague problems faced by agricultural workers (for example, vague instructions such as "yields have been decreasing recently") and provides concrete solutions. It also utilizes generative AI technology to provide market analysis, forecasts, and training programs on the latest agricultural technologies. For example, the AI ​​agent collects and analyzes data such as weather data, soil information, and market trends in real time. Then, based on the analysis results, it proposes optimal cultivation methods and efficient resource utilization methods. For example, it can propose an appropriate irrigation schedule based on weather data, or propose the optimal amount of fertilizer to use based on soil information. Furthermore, the AI ​​agent can understand vague instructions from farmers and propose concrete solutions. For example, in response to an instruction such as "yields have been decreasing recently," the AI ​​can identify the cause and propose appropriate countermeasures. It also utilizes generative AI technology to perform market analysis and forecasting, providing strategies for maximizing profits. For instance, it can predict market price trends and suggest the appropriate timing for sales. In addition, it provides training programs on the latest agricultural technologies to support the skill development of farmers. In this way, this AI agent solves various challenges in the agricultural sector and provides agricultural solutions that create a sustainable future. As a result, this AI agent specifically for the agricultural sector can quickly and effectively solve problems faced by farmers and realize sustainable agriculture.

[0059] The AI ​​agent for the agricultural sector according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects weather data, soil information, and market trends. Weather data includes, but is not limited to, temperature, precipitation, and wind speed. Soil information includes, but is not limited to, soil pH, nutrient content, and moisture content. Market trends include, but is not limited to, crop supply and demand and price trends. The data collection unit can collect weather data using sensors, for example. The data collection unit can also collect soil information using soil sensors. Furthermore, the data collection unit can collect market trend data via the internet. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, use AI to analyze weather data and predict weather fluctuations. Furthermore, the analysis unit can use AI to analyze soil information and evaluate soil conditions. Furthermore, the analysis unit can use AI to analyze market trend data and predict market trends. The proposal unit proposes optimal cultivation methods and efficient resource utilization methods based on the analysis results obtained by the analysis unit. The suggestion unit can, for example, propose an appropriate irrigation schedule based on weather data. It can also propose the optimal amount of fertilizer to use based on soil information. Furthermore, it can propose strategies for maximizing profits based on market trends. For example, the suggestion unit proposes an appropriate irrigation schedule based on weather data. For example, the suggestion unit proposes the optimal amount of fertilizer to use based on soil information. For example, the suggestion unit proposes strategies for maximizing profits based on market trends. As a result, the AI ​​agent dedicated to the agricultural sector according to this embodiment can analyze and integrate weather data, soil information, and market trends to propose optimal cultivation methods and efficient resource utilization methods.

[0060] The data collection unit collects weather data, soil information, and market trends. Weather data includes, but is not limited to, temperature, precipitation, and wind speed. Specifically, weather data is collected in real time from local weather stations, satellite data, and weather sensors installed on farms. This allows for a detailed understanding of the farm's microclimate. Soil information includes, but is not limited to, soil pH, nutrient content, and moisture content. Soil information is obtained from soil sensors and the results of periodic soil sample analysis. These sensors are installed at different depths of soil, and by collecting information from different layers, a three-dimensional understanding of the soil condition can be obtained. Market trends include, but are not limited to, crop supply and demand and price trends. Market trend data is collected via the internet from agricultural market research reports and exchange databases. This allows the data collection unit to centrally collect diverse agricultural data and update it in real time. Furthermore, the data collection unit stores this data on a cloud server, making it accessible to the analysis and proposal units. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0061] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can use AI to analyze weather data and predict weather fluctuations. Specifically, the AI ​​compares past and current weather data to learn patterns of temperature and precipitation fluctuations. This allows for highly accurate predictions of future weather conditions. The analysis unit can also use AI to analyze soil information and evaluate soil conditions. The AI ​​analyzes data such as soil pH, nutrient content, and moisture content to identify optimal soil conditions for crop growth. Furthermore, the analysis unit can use AI to analyze market trend data and predict market trends. The AI ​​compares past and current market data to learn the balance of supply and demand and price fluctuation patterns. This allows for highly accurate predictions of future market trends. Based on these analysis results, the analysis unit supports optimal decision-making in agriculture. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0062] The proposal department proposes optimal cultivation methods and efficient resource utilization based on the analysis results obtained by the analysis department. For example, the proposal department can propose an appropriate irrigation schedule based on meteorological data. Specifically, it calculates the optimal irrigation timing and water volume based on meteorological data, taking into account rainfall forecasts and temperature fluctuations. The proposal department can also propose the optimal amount of fertilizer to use based on soil information. It analyzes the nutrient content and pH of the soil to identify the type and amount of fertilizer necessary for crop growth. Furthermore, the proposal department can propose strategies for maximizing profits based on market trends. It analyzes the balance of supply and demand in the market and price trends to propose the optimal crop selection and harvest time. In this way, the proposal department supports agricultural managers in achieving efficient and profitable agricultural operations. In addition, the proposal department can also propose long-term agricultural plans and risk management strategies based on the analysis results. For example, it assesses the risks to climate change and market fluctuations and provides advice on taking appropriate countermeasures. In this way, the proposal department can support agricultural managers in achieving sustainable and stable agricultural operations.

[0063] The proposal unit can propose an appropriate irrigation schedule based on meteorological data. For example, the proposal unit can propose an irrigation schedule that takes into account soil moisture content based on meteorological data. For example, the proposal unit can also propose an irrigation schedule that corresponds to the growth stage of the crops based on meteorological data. For example, the proposal unit can propose an irrigation schedule based on precipitation forecasts based on meteorological data. As a result, by proposing an appropriate irrigation schedule based on meteorological data, the proposal unit enables efficient irrigation. Some or all of the above processing in the proposal unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the proposal unit can input meteorological data into a generating AI, and the generating AI can generate an irrigation schedule.

[0064] The suggestion unit can propose the optimal amount of fertilizer to use based on soil information. For example, the suggestion unit can propose the amount of fertilizer to use according to the crop's nutritional requirements based on the soil's nutrient content. The suggestion unit can also propose the optimal type and amount of fertilizer to use based on the soil's pH. The suggestion unit can also propose the timing and amount of fertilizer to use based on the soil's moisture content. As a result, by proposing the optimal amount of fertilizer to use based on soil information, the suggestion unit enables efficient fertilizer use. Some or all of the above processing in the suggestion unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the suggestion unit can input soil information into a generating AI, and the generating AI can generate the amount of fertilizer to use.

[0065] The proposal department can identify the cause of vague instructions from farmers and propose concrete solutions. For example, in response to the instruction, "Recently, crop yields have decreased," the proposal department can identify the cause of the decrease in yield and propose appropriate countermeasures. For example, in response to the instruction, "Crop growth is slow," the proposal department can identify the cause of the slow growth and propose appropriate countermeasures. For example, in response to the instruction, "Soil conditions are poor," the proposal department can identify the cause of the deterioration of soil conditions and propose appropriate countermeasures. In this way, the proposal department can quickly resolve problems by identifying the cause of vague instructions from farmers and proposing concrete solutions. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal department can input instructions from farmers into a generative AI, which can identify the cause and generate a solution.

[0066] The proposal department can perform market analysis and forecasting and provide strategies for maximizing profits. For example, the proposal department can predict market price trends and propose appropriate sales timings. For example, the proposal department can analyze market supply and demand and propose optimal crop cultivation plans. For example, the proposal department can analyze the competitive landscape of the market and propose strategies to secure a competitive advantage. In this way, the proposal department can improve profitability by performing market analysis and forecasting and providing strategies for maximizing profits. Some or all of the above processes in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input market data into a generative AI, which can perform market analysis and forecasting and generate strategies.

[0067] The proposal unit can provide training programs on the latest agricultural technologies. For example, the proposal unit can provide a training program on pesticide spraying using drones. The proposal unit can also provide a training program on monitoring technology using IoT sensors. The proposal unit can also provide a training program on smart farming technology. In this way, the proposal unit can improve the skills of agricultural workers by providing training programs on the latest agricultural technologies. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the content of the training program into a generative AI, and the generative AI can generate the training program.

[0068] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection and collect more detailed data. For example, if the user is in a hurry, the data collection unit can prioritize collecting only important data. In this way, the data collection unit can reduce the user's burden by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's facial expression data into a generative AI, which can estimate emotions and adjust the timing of data collection.

[0069] The data collection unit can analyze past collected data and select the optimal collection method. For example, the data collection unit can identify the most effective collection method from past collected data and prioritize its use. The data collection unit can also analyze past collected data to identify areas for improvement in collection methods and optimize them. For example, the data collection unit can analyze patterns in collection methods based on past collected data and create an optimal collection schedule. This improves the efficiency of data collection by allowing the data collection unit to analyze past collected data and select the optimal collection method. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into AI, which can then select the optimal collection method.

[0070] The data collection unit can prioritize the collection of data specific to particular crops or regions during data collection. For example, the data collection unit can prioritize the collection of data related to a particular crop and use it to improve the cultivation of that crop. The data collection unit can also prioritize the collection of data related to a particular region and use it to improve agriculture in that region. The data collection unit can also improve the accuracy of the data by prioritizing the collection of data related to specific crops or regions. As a result, the data collection unit improves the accuracy of the data by prioritizing the collection of data specific to particular crops or regions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data related to a particular crop or region into AI, and the AI ​​can select the data to prioritize the collection of.

[0071] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting only important data. If the user is relaxed, the data collection unit may prioritize collecting detailed data. If the user is in a hurry, the data collection unit may prioritize collecting data that can be collected quickly. In this way, the data collection unit can prioritize collecting important data by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user facial expression data into a generative AI, which can estimate emotions and determine the priority of data to collect.

[0072] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant weather data based on the user's current location. For example, the data collection unit can prioritize the collection of highly relevant soil information based on the user's farm location information. For example, the data collection unit can prioritize the collection of highly relevant data based on market trends in the user's region. As a result, the data relevance is improved by the data collection unit prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then select highly relevant data.

[0073] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze users' social media posts and collect relevant weather data. The data collection unit can also collect data on crops of interest from users' social media activity. The data collection unit can also collect relevant market trend data based on users' social media activity. This improves the accuracy of the data by allowing the data collection unit to analyze users' social media activity and collect relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user social media data into AI, which can then select relevant data.

[0074] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can also provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI, which can estimate emotions and adjust the presentation of the analysis.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can also determine the priority of the analysis based on the importance of the data. For example, the analysis unit can apply multiple analysis methods to perform a detailed analysis on important data. In this way, the analysis unit can perform a detailed analysis on important data by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a weather forecasting algorithm to meteorological data. For example, the analysis unit can apply a soil analysis algorithm to soil information. For example, the analysis unit can apply a market forecasting algorithm to market trend data. By applying different analysis algorithms depending on the data category, the analysis unit can improve the accuracy of the analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can select an appropriate analysis algorithm.

[0077] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can provide a short analysis result that can be quickly understood. In this way, the analysis unit can provide an analysis result of an appropriate length for the user by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI, which can estimate emotions and adjust the length of the analysis.

[0078] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the latest data to provide real-time information. The analysis unit can also analyze long-term trends based on historical data. The analysis unit can also adjust the priority of analysis according to the data collection timing. This allows the analysis unit to prioritize the analysis of the latest information by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI, and the AI ​​can determine the priority of analysis.

[0079] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to quickly provide important information. The analysis unit can also adjust the order of analysis according to the relevance of the data. For example, the analysis unit can perform a detailed analysis on highly relevant data. In this way, the analysis unit can quickly provide important information by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of analysis.

[0080] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can present simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can present detailed suggestions. If the user is in a hurry, the suggestion unit can present concise suggestions. In this way, the suggestion unit can provide suggestions that are easy for the user to understand by adjusting the way it presents suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI, which can estimate emotions and adjust the way it presents suggestions.

[0081] The proposal unit can adjust the level of detail of a proposal based on the importance of the data. For example, the proposal unit can provide detailed proposals based on important data and simplified proposals based on less important data. The proposal unit can also prioritize proposals based on the importance of the data. For example, the proposal unit can present multiple options for proposals based on important data. This allows the proposal unit to provide detailed proposals based on important data by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the proposal.

[0082] The proposal unit can apply different proposal algorithms depending on the data category when making a proposal. For example, the proposal unit can apply a weather forecasting algorithm to a proposal based on weather data. For example, the proposal unit can also apply a soil analysis algorithm to a proposal based on soil information. For example, the proposal unit can also apply a market forecasting algorithm to a proposal based on market trend data. By applying different proposal algorithms depending on the data category, the proposal unit can improve the accuracy of its proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the data category into the AI, and the AI ​​can select an appropriate proposal algorithm.

[0083] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will provide short, concise suggestions. If the user is relaxed, the suggestion unit may provide detailed suggestions. If the user is in a hurry, the suggestion unit may provide short suggestions that can be quickly understood. In this way, the suggestion unit can provide suggestions of an appropriate length for the user by adjusting the length of the suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user facial expression data into a generative AI, which can estimate emotions and adjust the length of the suggestions.

[0084] The proposal department can determine the priority of proposals based on the data collection timing when making a proposal. For example, the proposal department will prioritize proposals based on the latest data. For example, proposals based on historical data can also be made from a long-term perspective. The proposal department can also adjust the priority of proposals according to the data collection timing. This allows the proposal department to prioritize proposals based on the latest information by determining the priority of proposals based on the data collection timing. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the data collection timing into the AI, and the AI ​​can determine the priority of proposals.

[0085] The proposal unit can adjust the order of proposals based on the relevance of the data when making proposals. For example, the proposal unit will prioritize proposals based on highly relevant data. The proposal unit can also adjust the order of proposals according to the relevance of the data. For example, the proposal unit can add detailed explanations to proposals based on highly relevant data. This allows the proposal unit to quickly make proposals based on important information by adjusting the order of proposals based on the relevance of the data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of proposals.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The analysis unit can estimate the user's emotions and adjust the analysis priorities based on those emotions. For example, if the user is stressed, the analysis unit can prioritize analyzing important data and provide results quickly. If the user is relaxed, the analysis unit can perform a detailed analysis and provide comprehensive results. Furthermore, if the user is in a hurry, the analysis unit can quickly provide concise analysis results. In this way, the analysis unit can provide the user with the most optimal analysis results by adjusting the analysis priorities based on the user's emotions.

[0088] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the suggestion function can reduce the frequency of suggestions to lessen the user's burden. Conversely, if the user is relaxed, the suggestion function can increase the frequency of suggestions and provide more detailed suggestions. Furthermore, if the user is in a hurry, the suggestion function can prioritize only the most important suggestions. In this way, the suggestion function can provide the most suitable suggestions for the user by adjusting the timing of suggestions based on the user's emotions.

[0089] The data collection unit can estimate the user's emotions and adjust the data collection method based on those emotions. For example, if the user is stressed, the unit can reduce the frequency of data collection to lessen the user's burden. Conversely, if the user is relaxed, the unit can increase the frequency of data collection and collect more detailed data. Furthermore, if the user is in a hurry, the unit can prioritize collecting only the most important data. In this way, the data collection unit can optimize data collection for the user by adjusting the data collection method based on their emotions.

[0090] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on those emotions. For example, if the user is stressed, the analysis unit can provide a concise and to-the-point analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can provide a short analysis for quick understanding. In this way, the analysis unit can provide the optimal analysis results for the user by adjusting the depth of the analysis based on the user's emotions.

[0091] The suggestion function can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is stressed, the suggestion function can provide simple and easy-to-understand suggestions. If the user is relaxed, the suggestion function can provide more detailed suggestions. Furthermore, if the user is in a hurry, the suggestion function can quickly provide concise suggestions. In this way, the suggestion function can provide the most suitable suggestions for the user by adjusting the content of its suggestions based on the user's emotions.

[0092] The data collection unit can adjust the frequency of data collection according to the growth stage of a particular crop. For example, it can collect data frequently in the early stages of crop growth and reduce the collection frequency once growth has stabilized. It can also increase the collection frequency again as harvest time approaches. In this way, the data collection unit can efficiently collect data by adjusting the data collection frequency according to the growth stage of the crop.

[0093] The analysis unit can apply different analysis methods depending on the type of data. For example, a weather forecasting algorithm can be applied to meteorological data, and a soil analysis algorithm to soil information. A market forecasting algorithm can also be applied to market trend data. This allows the analysis unit to improve the accuracy of the analysis by applying the most suitable analysis method according to the type of data.

[0094] The suggestion system can customize suggestions based on the user's past behavior history. For example, users who have previously adopted a specific cultivation method can receive priority suggestions related to that method. It can also suggest highly profitable crops based on past market trends. This allows the suggestion system to provide optimal suggestions by customizing them based on the user's past behavior history.

[0095] The data collection unit can adjust the timing of data collection according to the weather conditions of a specific region. For example, it can frequently collect precipitation data during the rainy season and prioritize temperature data collection during the dry season. Furthermore, if extreme weather events are predicted in a particular region, the data collection frequency for that region can be increased. This allows the data collection unit to efficiently collect data by adjusting the timing of data collection according to the weather conditions of a specific region.

[0096] The analysis unit can evaluate the reliability of the analysis according to the data collection source and prioritize the analysis of highly reliable data. For example, it can prioritize the analysis of data collected from highly reliable sensors and use less reliable data supplementarily. Furthermore, the reliability of the analysis results can be improved by integrating and analyzing data from multiple data sources. In this way, the analysis unit can provide highly reliable analysis results by evaluating the reliability of the analysis according to the data collection source.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection unit collects weather data, soil information, and market trends. Weather data includes temperature, precipitation, wind speed, etc., while soil information includes soil pH, nutrient content, moisture content, etc. Market trends include crop supply and demand, price trends, etc. The data collection unit can collect weather data using sensors, collect soil information using soil sensors, and collect market trend data via the internet. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can use AI to analyze weather data and predict weather fluctuations. It can also use AI to analyze soil information and evaluate soil conditions. Furthermore, it can use AI to analyze market trend data and predict market trends. Step 3: The proposal department proposes optimal cultivation methods and efficient resource utilization methods based on the analysis results obtained by the analysis department. The proposal department can propose appropriate irrigation schedules based on weather data, optimal fertilizer usage based on soil information, and strategies for maximizing profits based on market trends.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit can collect weather data, soil information, and market trends using the sensors and internet connection of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to predict weather fluctuations, soil conditions, and market trends. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes optimal cultivation methods and efficient resource utilization methods based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit can collect weather data, soil information, and market trends using the sensors and internet connection of the smart glasses 214. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and predicts weather fluctuations, soil conditions, and market trends. The proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which proposes optimal cultivation methods and efficient resource utilization methods based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit can collect weather data, soil information, and market trends using the sensors and internet connection of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and predicts weather fluctuations, soil conditions, and market trends. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, which proposes optimal cultivation methods and efficient resource utilization methods based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit can collect weather data, soil information, and market trends using the sensors and internet connection of the robot 414. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and predicts weather fluctuations, soil conditions, and market trends. The proposal unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, which proposes optimal cultivation methods and efficient resource utilization methods based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) The data collection department collects weather data, soil information, and market trends, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal cultivation method and efficient resource utilization method, Equipped with A system characterized by the following features. (Note 2) The aforementioned proposal section is, We propose an appropriate irrigation schedule based on weather data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose the optimal amount of fertilizer to use based on soil information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Identify the root cause of ambiguous instructions from agricultural workers and propose concrete solutions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We conduct market analysis and forecasting, and provide strategies to maximize profits. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We offer training programs on the latest agricultural technologies. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past collected data and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, prioritize the collection of data specific to particular crops or regions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects weather data, soil information, and market trends, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal cultivation method and efficient resource utilization method, Equipped with A system characterized by the following features.

2. The aforementioned proposal section is, We propose an appropriate irrigation schedule based on weather data. The system according to feature 1.

3. The aforementioned proposal section is, We propose the optimal amount of fertilizer to use based on soil information. The system according to feature 1.

4. The aforementioned proposal section is, Identify the root cause of ambiguous instructions from agricultural workers and propose concrete solutions. The system according to feature 1.

5. The aforementioned proposal section is, We conduct market analysis and forecasting, and provide strategies to maximize profits. The system according to feature 1.

6. The aforementioned proposal section is, We offer training programs on the latest agricultural technologies. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

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

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