system

Abstract

We provide the system. [Solution] Means for transmitting environmental data received from a data collection device to a processing device, A means for analyzing environmental data and weather data received by the processing unit, A means for generating crop cultivation methods based on analysis results, A means of notifying the user terminal of the generated cultivation method, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional cultivation methods in agriculture mainly rely on empirical rules and manual labor, and the accompanying inefficiencies are a major problem. For example, for small and medium-sized farmers and new farmers, it is difficult to collect necessary data and judge optimal cultivation conditions, and optimization of crop growth and harvest is required. Furthermore, the uncertainties associated with climate change directly affect the yield and quality. It is necessary to solve such problems and improve the efficiency and sustainability of agriculture.

Means for Solving the Problems

[0005] This invention provides a system that receives environmental data from a data collection device and analyzes that data along with weather data using a processing device. Based on the analysis results, it generates an optimal cultivation method for crops and notifies the user terminal of this method. This system proposes agricultural actions based on the information received by the user terminal and collects feedback from the user to further accumulate data and improve the accuracy of the analysis. This enables increased agricultural efficiency and optimized harvesting, thereby realizing sustainable agriculture that reduces environmental impact.

[0006] A "data acquisition device" is a device that detects various conditions in the environment and acquires them as digital data.

[0007] "Environmental data" refers to numerical information about the crop's growing environment, such as temperature, humidity, soil nutrient status, and light intensity.

[0008] A "processing device" is a computer device used to analyze received data and process information.

[0009] "Weather data" refers to information about the current climate and future weather forecasts, and is one of the factors that influence crop cultivation.

[0010] "Analysis" is the process of extracting information based on collected data to determine a specific purpose and to derive conclusions or insights.

[0011] "Cultivation methods" refer to the specific procedures and conditions for agricultural work implemented to optimize crop growth and harvest.

[0012] A "user terminal" is an electronic device that receives information from a system and is used by the user to perform operations.

[0013] "Notification" refers to the act or process of informing a user of information or a message.

[0014] "Feedback" is the process of returning user reactions and information to the system, and this data can be used for future analysis and improvement. [Brief explanation of the drawing]

[0015] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when combined with an emotion engine.

Embodiments for Carrying out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0019] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 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.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0027] 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.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] The 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.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention implements a system consisting of a data collection device, a processing device, and a user terminal to improve the efficiency of crop cultivation in agriculture. The server receives environmental data from the data collection device and also acquires weather information from an external weather information provider. This data is analyzed in the processing device within the server, and appropriate cultivation methods for crops are generated.

[0037] This analysis process utilizes machine learning algorithms to analyze the relationships between collected environmental and weather data and identify optimal growing conditions. Cultivation methods are constantly updated as needed, aiming to improve yields and ensure the sustainable use of resources.

[0038] Analysis results from the server are provided to the user's terminal, where the user receives real-time suggestions. For example, specific instructions for agricultural work, such as increasing or decreasing irrigation volume or timing of fertilization under certain weather conditions, are displayed. Based on this, the user takes action using agricultural machinery or manual labor. In addition, the user can provide feedback on the results of their actions to the server via their terminal. This feedback information contributes to further improving the accuracy of the system.

[0039] As a concrete example, consider a farmer in a certain region using this system. The server collects data from the entire field using periodic aerial images taken by drones to understand the current vegetation conditions. If the server's analysis predicts a rise in temperature next week, it sends a notification to the user's terminal saying, "The forecast is for high daytime temperatures. Water in the morning and evening." Based on this information, the user can optimize water use and maintain the health of their crops.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server receives environmental data from the data collection devices. This data includes temperature, humidity, soil nutrient status, and light intensity, collected through drones and IoT sensors.

[0043] Step 2:

[0044] The server retrieves future weather forecasts from weather information providers. This includes data such as temperature, precipitation, and wind speed, which are considered as factors influencing crop cultivation methods.

[0045] Step 3:

[0046] The server preprocesses the received environmental and meteorological data, performing noise reduction and missing value imputation. This creates a dataset suitable for analysis.

[0047] Step 4:

[0048] The server uses machine learning algorithms within the processing unit to analyze the pre-processed data. This allows for an assessment of the current state of the crops and the calculation of optimal cultivation conditions.

[0049] Step 5:

[0050] The server generates specific cultivation methods based on the analysis results. For example, it may suggest irrigation schedules and fertilization timings.

[0051] Step 6:

[0052] The server sends the generated cultivation method to the user's terminal. This allows the user to receive information about the latest agricultural actions.

[0053] Step 7:

[0054] The terminal visualizes the cultivation methods sent by the server and displays suggestions to the user. The user confirms the information in the form of specific instructions.

[0055] Step 8:

[0056] Users perform actual agricultural actions using agricultural machinery and manual labor based on instructions displayed on their devices.

[0057] Step 9:

[0058] Users provide feedback via their devices, detailing the results of their actions and any observations they make, and send this feedback to the server. This feedback is stored on the server and used to improve the system.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] In modern agriculture, rapidly adapting to climate change and environmental shifts and achieving high cultivation efficiency are crucial challenges. Traditional methods involve collecting and analyzing environmental data individually, making immediate decision-making difficult and hindering optimal resource utilization. Furthermore, if the information users receive is not specific, it becomes difficult to translate it into appropriate action. This can ultimately lead to decreased crop yields and wasted resources.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for transmitting environmental data received from a data collection device to an integration device, means for preprocessing the environmental data received by the integration device and weather data acquired from external information, and means for analyzing the preprocessed data using a machine learning algorithm. This enables real-time data integration and analysis, and provides users with concrete and actionable cultivation suggestions, thereby achieving efficient resource utilization and high yields.

[0064] A "data collection device" is a device used to automatically acquire environmental data in agricultural settings and transmit it to a server.

[0065] An "integrated device" is a component for centrally managing and preprocessing collected environmental data and external weather data.

[0066] "Environmental data" refers to information related to the growing environment of crops, including data such as temperature, humidity, and soil conditions.

[0067] "Weather data" refers to information about the weather obtained from external sources, including data such as temperature, precipitation, and wind speed.

[0068] "Preprocessing" refers to a series of processes that format the collected data into an analyzable form, removing outliers and imputing missing values.

[0069] A "machine learning algorithm" is a mathematical method used to learn rules and patterns from data and perform predictions and classifications.

[0070] "Analysis" refers to the process of analyzing data based on pre-processed data to determine the optimal conditions for crop cultivation.

[0071] A "communication terminal" is an electronic device that allows a user to receive notifications from a server and confirm suggested cultivation methods.

[0072] "Feedback" refers to the provision of reports and information that users send to the server regarding the farm work they have performed and the results thereof.

[0073] This invention is a system for improving the efficiency of crop cultivation in the agricultural field. The system mainly consists of a server, terminals, and users.

[0074] The server receives environmental data via data collection devices installed in the fields. These devices measure various data such as temperature, humidity, and soil conditions, and transmit them to the server. The server also obtains weather data from external weather information providers via APIs. Data integration and management take place throughout this process.

[0075] Next, the integration device within the server performs preprocessing on the received environmental data and weather data imported from external sources. This preprocessing includes imputing missing values ​​and removing outliers. The Python library Pandas is used to clean and format the data.

[0076] After the data is preprocessed, the processing unit on the server performs analysis using machine learning algorithms. Libraries such as TENSORFLOW® and Scikit-learn are utilized to analyze the optimal cultivation conditions for crops based on predictive models learned from historical data. For example, if a rise in temperature is predicted for the following week, it may suggest the timing of irrigation.

[0077] The terminal receives analysis results from the server in real time and notifies the user. The terminal plays a role in allowing the user to confirm the suggested cultivation methods and provide actionable information in a timely manner. This enables the user to carry out farm work according to the specified schedule. The terminal also collects feedback from the user and sends it to the server.

[0078] Users adjust agricultural machinery and perform manual farming tasks based on information provided through their devices. For example, based on notifications, users can maintain the health of their crops by irrigating in the early morning and evening if high daytime temperatures are forecast.

[0079] An example of a prompt for a generative AI model is: "Given weather and environmental data as input, suggest the optimal farming plan for next week. For example, include irrigation timing when high temperatures are predicted."

[0080] In this way, this invention aims to achieve efficient resource utilization and high yields in agriculture.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The server receives environmental data from data collection devices. This data includes temperature, humidity, and soil moisture. The received data is stored in a database. At this stage, real-time data collection from the field is mainly performed using IoT devices.

[0084] Step 2:

[0085] The server retrieves the latest weather data from external weather information providers via an API. This data primarily includes temperature, precipitation, and wind speed. The retrieved weather data is integrated with environmental data and managed centrally.

[0086] Step 3:

[0087] The integration system on the server performs preprocessing on environmental and meteorological data. During this preprocessing, outliers are detected and removed, and missing values ​​are imputed, formatting the data for analysis. This process involves cleaning and adjusting the data using the Pandas library.

[0088] Step 4:

[0089] The server's processing unit applies machine learning algorithms to pre-processed data as input. This process uses TensorFlow and Scikit-learn to analyze optimal cultivation conditions based on past trends. The output of this step appears as specific cultivation suggestions.

[0090] Step 5:

[0091] The server sends the generated cultivation suggestions to the communication terminal. The suggestions include specific irrigation timings and fertilization plans, providing detailed information that can be implemented on the user's terminal.

[0092] Step 6:

[0093] The terminal receives suggestions from the server and notifies the user. The user can review the suggestions on the terminal and perform necessary adjustments to agricultural machinery or manual tasks. Through this process, the user can take actions based on recommended cultivation methods.

[0094] Step 7:

[0095] Users provide feedback on the results of their farming activities to the server via their terminals. This feedback includes information on crop growth and yield, which is then used to improve the system's machine learning model.

[0096] This continuous processing flow allows the system to promote efficient resource utilization in agriculture and enable increased yields.

[0097] (Application Example 1)

[0098] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0099] The aim is to support local agricultural activities in sustainable urban development and to realize appropriate cultivation methods and resource optimization. In particular, the need for efficient agricultural activities within cities is increasing amidst the demand for efficient resource use due to climate change and population growth. However, existing technologies have not sufficiently established methods for generating specific indicators of the living environment that contribute to local sustainability and providing them to the entire community.

[0100] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0101] In this invention, the server includes means for transmitting environmental data received from a data collection device to an information processing device; means for the information processing device to analyze the received environmental and meteorological data; means for generating agricultural production methods based on the analysis results; means for notifying an information terminal of the generated cultivation methods; means for generating indicators of the living environment to contribute to regional sustainability; and means for providing the generated indicators to the community. This makes it possible to achieve sustainable agriculture and resource optimization throughout the entire community and to improve environmental awareness throughout the city.

[0102] A "data acquisition device" is a device that acquires environmental data and transmits it to an information processing device.

[0103] An "information processing device" is a device that analyzes received environmental and weather data to generate indicators for appropriate cultivation methods and living environments.

[0104] "Environmental data" refers to data that measures various elements related to agricultural production and living environments, including data such as temperature, humidity, and soil information.

[0105] "Weather data" refers to weather-related data obtained from external weather information providers, including forecast information such as temperature, precipitation, and wind speed.

[0106] "Cultivation methods" are guidelines that indicate the optimal procedures and conditions for growing crops, generated based on analyzed data.

[0107] An "information terminal" is a device used to notify users of generated cultivation methods and indicators of the living environment, and includes smartphones and tablets.

[0108] "Indicators for living environments that contribute to regional sustainability" are evaluation indicators related to daily life and agricultural activities that are created to support the effective use of regional environmental resources and sustainable development.

[0109] A "community" is a group of people who live together in a specific local area, sharing common environments and resources, and cooperating with each other.

[0110] The system realizing this invention consists of a data collection device that acquires environmental data, an information processing device that analyzes the information, and an information terminal that displays the results. The server acquires environmental data such as temperature, humidity, and soil information from the data collection device. In addition, it receives weather data such as temperature, precipitation, and wind speed from an external weather information provider. The information processing device analyzes this data using a machine learning algorithm (e.g., TensorFlow) to generate indicators of the living environment that contribute to optimal cultivation methods and regional sustainability.

[0111] The analyzed cultivation methods are processed on a cloud platform (AWS® or Google® Cloud) and transmitted in real time to information terminals. Users can view this information on their smartphones or tablets and receive specific suggestions to optimize their cultivation activities. User feedback is then sent back to the information processing device, and the system uses this data to further improve its accuracy.

[0112] As a concrete example, a community garden in a certain area is using this system to optimize the harvest schedule for a weekend event. Analysis of the system displays instructions on the terminal such as, "Since high temperatures are forecast for the weekend, concentrate watering in the morning and evening," allowing community members to work more efficiently based on this information. As a result, the harvest yield for the event has increased, and the sustainability of the community has been improved, according to reports.

[0113] An example of a prompt message is: "Based on this week's weather data and historical agricultural data, generate the optimal cultivation plan for the next 7 days. Please specify any points that require particular attention." By giving this instruction to the generating AI model, you can obtain suggestions for appropriate cultivation methods.

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The server receives environmental data acquired from the data collection device. This environmental data includes temperature, humidity, soil information, and other relevant information. This data is converted to an appropriate format and stored in a database for use in subsequent analysis steps.

[0117] Step 2:

[0118] The server accesses external weather information providers to retrieve weather data. This weather data includes temperature, precipitation, wind speed, and other parameters. This data is formatted in the same way as environmental data and stored in a database.

[0119] Step 3:

[0120] The information processing device acquires stored environmental and weather data and analyzes the data using machine learning algorithms. Specifically, it uses TensorFlow to extract data features and predict the optimal cultivation method. In this process, a model is trained based on historical data and applied to the current data to generate accurate suggestions.

[0121] Step 4:

[0122] The server generates cultivation methods and transmits them to information terminals. Users receive this information on their smartphones or tablets and check specific cultivation activities and environmental adjustment instructions in real time. This allows users to effectively manage resources and optimize farm work.

[0123] Step 5:

[0124] Users perform actions through an information terminal and check the results. If necessary, they input the results of the work performed as feedback into the terminal and send it to the server.

[0125] Step 6:

[0126] The server improves the accuracy of future predictions by updating the parameters of its analysis algorithm based on feedback received from users. This allows the generative AI model to propose cultivation methods that contribute to regional sustainability with greater accuracy.

[0127] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0128] This invention provides more personalized agricultural advice by incorporating an emotion engine into an agricultural support system that recognizes and utilizes the user's emotions. The server receives environmental and weather data from a data collection device, and the processing unit analyzes this data. Here, the emotion engine also acquires the user's emotional data and reflects it in cultivation methods and suggestions as needed.

[0129] The emotion engine analyzes the user's emotions from their facial expressions and tone of voice, generating emotion data. This data is sent to the server via the user's device. The server integrates and analyzes the emotion data with other environmental and weather data to generate suggestions for cultivation methods that the user will find more appropriate. These suggestions are notified to the user's device, and the user receives specific actions on their device.

[0130] For example, if the emotion engine detects that a user is overwhelmed, the server will adjust the suggested workload to reduce the user's burden. This makes it possible to create an environment where agricultural activities can be continued without difficulty. Furthermore, if it is determined that the user is satisfied, more proactive advice can be offered.

[0131] Users review suggestions and take agricultural actions via devices such as smartphones and tablets. In addition, users provide feedback on the condition of their crops to the system, and the results are sent to the server. The server updates the system based on the feedback, improving the accuracy of the next analysis. This enables detailed agricultural support that takes into account the user's emotional state.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The terminal transmits environmental data acquired from the data collection device to the server. Simultaneously, the emotion engine analyzes emotional data from the user's facial expressions and voice, and transmits this data to the server.

[0135] Step 2:

[0136] The server preprocesses the received environmental and meteorological data, preparing it for analysis. This includes imputing missing values ​​and denoising the data.

[0137] Step 3:

[0138] The server integrates emotional data with other data and analyzes it in the processing unit. Machine learning algorithms calculate the optimal cultivation method, taking into account the user's emotional state.

[0139] Step 4:

[0140] Based on the analysis results, the server sends the user the optimal cultivation method to their terminal. This includes suggestions that take emotions into consideration.

[0141] Step 5:

[0142] The terminal displays suggestions received from the server to the user. These include specific instructions for agricultural actions that have been adjusted according to the user's emotional state.

[0143] Step 6:

[0144] Users follow the suggestions from their devices and actually carry out agricultural actions. This ensures that the suggested cultivation methods are applied in the field.

[0145] Step 7:

[0146] Users provide feedback through their devices regarding the results of their actions and any changes in their emotions. This feedback, along with emotion data, is sent to the server.

[0147] Step 8:

[0148] The server updates the system's learning model based on feedback information, improving the accuracy and reliability of future suggestions. This process is repeated to optimize individual agricultural support for each user.

[0149] (Example 2)

[0150] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0151] Conventional agricultural support systems often provide uniform cultivation methods and work instructions without considering the user's emotional state, which increases the user's burden. To solve this problem, there is a need for technology that provides personalized advice that takes the user's emotions into account, thereby better supporting the user's agricultural activities.

[0152] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0153] In this invention, the server includes means for transmitting data collected from a data acquisition device to an analysis device, means for integrating and analyzing the environmental data and weather information received by the analysis device, and means for acquiring the user's emotional state using an emotion recognition engine and generating it as data. This makes it possible to propose personalized cultivation methods that reflect the user's emotional state.

[0154] A "data acquisition device" is a device that collects environmental information and related information, and it acquires real-time data using sensors, APIs, etc.

[0155] An "analysis device" is a device that integrates and analyzes received data, and includes a computer or processor for outputting processing results.

[0156] "Environmental data" refers to information related to agriculture, such as temperature, humidity, and soil conditions, and serves as basic data for determining crop cultivation methods.

[0157] "Weather information" refers to data related to weather conditions, including information that shows weather trends based on forecasts and actual measurements.

[0158] An "emotion recognition engine" refers to a program or algorithm that analyzes a user's facial expressions and tone of voice to generate data on their emotional state.

[0159] A "user terminal" is a device used by a user to receive and send information, and includes smartphones, tablets, and other similar devices.

[0160] "Cultivation methods" refer to information that outlines specific procedures and work methods for growing crops efficiently and effectively.

[0161] "Personalized cultivation methods" refer to suggested methods that take into account the user's specific circumstances and emotional state, providing unique guidelines that are adjusted from standard methods.

[0162] One embodiment of this invention is a system that provides personalized advice that takes into account the user's emotions in agricultural support. The system mainly consists of the following elements:

[0163] The server collects environmental and meteorological data through data acquisition devices, which are then integrated and analyzed by analysis devices. This analysis utilizes computer systems and specialized software with advanced data processing capabilities. Furthermore, an emotion recognition engine is used to analyze the user's facial expressions and voice tone, generating emotion data. Smart devices equipped with cameras and microphones are used for emotion recognition.

[0164] Users input their emotional state into the system via information devices such as smartphones and tablets. This information is transmitted to the server in real time and analyzed in combination with other data.

[0165] Based on the analysis results, the server generates personalized cultivation methods tailored to the user. In this process, the generating AI model utilizes prompts to construct advice with unprecedented accuracy. These prompts are used, for example, in the form of "What is the optimal farming task when rain is expected and the user is feeling stressed?"

[0166] The generated cultivation methods and work suggestions are notified to the information terminal and immediately displayed to the user. Based on this, the user carries out specific agricultural activities and provides feedback from the terminal to the server. The server uses this feedback to further refine the accuracy of the model.

[0167] In this way, users can engage in sustainable and less stressful agricultural activities and receive meticulous support that takes their feelings into consideration.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The server receives environmental data from data acquisition devices. This data includes temperature, humidity, and soil conditions, and is collected in real time via sensors and APIs. This received data is temporarily stored on the platform in preparation for subsequent analysis.

[0171] Step 2:

[0172] The server retrieves weather information from another system. Input data includes forecasts and weather history, and data collection is performed via an API. This information, along with environmental data, is stored in a database and prepared for integrated analysis.

[0173] Step 3:

[0174] Users provide emotional data using their smart devices. The input consists of facial expressions and voice tone, which are transmitted to the emotion recognition engine via the camera and microphone. As a result, data indicating the user's emotional state is generated and immediately sent to the server.

[0175] Step 4:

[0176] The server processes integrated environmental data, weather information, and sentiment data using an analysis device. Based on the input data, a generative AI model uses the prompt "What is the optimal cultivation guidance in this situation?" to generate the optimal cultivation method for the user. The output is a personalized cultivation method suggestion.

[0177] Step 5:

[0178] The server notifies the user's information terminal of the generated cultivation method. This output is displayed as an advice message, providing the user with specific work instructions. This allows the user to decide on actions based on the received suggestions.

[0179] Step 6:

[0180] Users perform farm work based on the suggested cultivation methods and input the results and their impressions as feedback into the terminal. This input data, including the condition of the crops and their impressions of the work, is sent to the server.

[0181] Step 7:

[0182] The server receives user feedback and updates the system's analysis algorithm. Based on the input feedback, it improves the accuracy of suggestions for subsequent attempts, resulting in output that provides more user-friendly support.

[0183] (Application Example 2)

[0184] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0185] Agricultural and horticultural activities in urban environments depend on the emotional state and environmental conditions of individual users, requiring efficient support tailored to their specific needs. Conventional systems only provide uniform advice without adequately reflecting the user's emotions and state, resulting in challenges in improving the user experience and increasing work efficiency.

[0186] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0187] In this invention, the server includes means for transmitting environmental data received from a data collection device to a processing device; means for the processing device to generate user emotion data using an emotion analysis device, integrate and analyze it with the received environmental data and weather data; and means for generating a crop cultivation method that takes into account the user's emotional state based on the analysis results and notifying the user terminal. This enables personalized agricultural advice tailored to the user's emotional state.

[0188] A "data collection device" is a device that acquires environmental data and user sentiment data and transmits it to a processing device.

[0189] A "processing device" is a device that analyzes received data and generates cultivation methods tailored to the user's emotional state.

[0190] An "emotion analysis device" is part of a system that analyzes a user's facial expressions, voice tone, etc., and generates emotional data.

[0191] "Environmental data" refers to data such as ambient temperature, humidity, and sunlight necessary for growing crops.

[0192] "Weather data" refers to information about weather conditions such as temperature, precipitation, and wind speed.

[0193] "Integrated analysis" is a process that combines emotional data, environmental data, and weather data for analysis.

[0194] "Cultivation methods" refer to information about the procedures and techniques for growing crops.

[0195] A "user terminal" is a device that notifies the user of the generated cultivation method and receives feedback from the user.

[0196] To implement this invention, a user's smart terminal, a server, a data collection device, and an emotion analysis system are utilized. The server receives environmental data and user emotion data transmitted from the user's smart terminal. The environmental data includes conditions suitable for cultivation, such as temperature and sunlight, and the emotion data is based on facial expressions and tone of voice obtained through the emotion analysis system.

[0197] Specifically, the user's smart device uses its camera and microphone to record the user's facial expressions and voice, and sends this data to an emotion analysis system. The emotion analysis system generates emotion data using, for example, the Google Cloud Vision API or IBM Watson® Tone Analyzer. The server leverages AWS Lambda to integrate and analyze the emotion data with environmental and weather data, and uses a generative AI model to suggest the most appropriate agricultural action for the user's emotions.

[0198] Suggestions for users are communicated via smart devices, providing specific advice such as, "Today's temperature is 25 degrees Celsius and it's sunny. You seem a little tired, so we recommend only gentle watering." Users can adjust their farming activities based on these suggestions. This also helps avoid excessive workloads based on emotional analysis, resulting in more efficient farming.

[0199] To more effectively apply the generative AI model, the following can be used as an example of a prompt: "List quick and easy gardening tasks that are suitable for a user who is feeling stressed. The weather is sunny and the temperature is 20 degrees Celsius." Using such prompts, the system can generate more accurate suggestions.

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The user terminal uses a camera and microphone to capture the user's facial expressions and voice. This data is transmitted in real time to an emotion analysis system. The input is raw image and voice data, and the output is emotional features based on this data. Specifically, the analysis of image data identifies emotions from facial expressions, and the analysis of voice data analyzes tone and pitch to determine the emotional state.

[0203] Step 2:

[0204] The sentiment analysis system uses the Google Cloud Vision API to analyze image data and IBM Watson Tone Analyzer to analyze audio data. This outputs sentiment data that labels the user's current emotional state, such as "stressed" or "satisfied." This sentiment data is then sent to a processing unit.

[0205] Step 3:

[0206] The server receives environmental and weather data collected from sensors, along with sentiment data. This data is integrated and analyzed using AWS Lambda. The inputs are sentiment data, environmental data, and weather data, and the output is the combined analysis results. Specifically, each dataset is normalized and prepared for input into a machine learning model.

[0207] Step 4:

[0208] The server uses a generative AI model to generate agricultural actions based on the analyzed data. The input is the integrated analysis results. The output is a suggestion of specific cultivation methods tailored to the user's emotional state. For example, it might generate a suggestion such as, "The conditions are good today, so try a new soil improvement."

[0209] Step 5:

[0210] The user terminal receives suggestions from the server and delivers them to the user via a notification function. The user checks the notification and takes action based on the suggested action. The input is the suggestion notification from the server, and the output is the user's action. Specifically, information is provided to the user by displaying notifications or pop-ups on the terminal.

[0211] Step 6:

[0212] Users send feedback from their terminal to the server after completing a task. This feedback is used to improve the accuracy of future analyses. The input is user feedback information, and the output is information related to system improvements. Specifically, this involves analyzing the feedback data and updating the machine learning model as needed.

[0213] 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.

[0214] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0215] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0220] 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.

[0221] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0222] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0223] 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.

[0224] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0225] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0226] The 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.

[0227] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0228] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0229] This invention implements a system consisting of a data collection device, a processing device, and a user terminal to improve the efficiency of crop cultivation in agriculture. The server receives environmental data from the data collection device and also acquires weather information from an external weather information provider. This data is analyzed in the processing device within the server, and appropriate cultivation methods for crops are generated.

[0230] This analysis process utilizes machine learning algorithms to analyze the relationships between collected environmental and weather data and identify optimal growing conditions. Cultivation methods are constantly updated as needed, aiming to improve yields and ensure the sustainable use of resources.

[0231] Analysis results from the server are provided to the user's terminal, where the user receives real-time suggestions. For example, specific instructions for agricultural work, such as increasing or decreasing irrigation volume or timing of fertilization under certain weather conditions, are displayed. Based on this, the user takes action using agricultural machinery or manual labor. In addition, the user can provide feedback on the results of their actions to the server via their terminal. This feedback information contributes to further improving the accuracy of the system.

[0232] As a concrete example, consider a farmer in a certain region using this system. The server collects data from the entire field using periodic aerial images taken by drones to understand the current vegetation conditions. If the server's analysis predicts a rise in temperature next week, it sends a notification to the user's terminal saying, "The forecast is for high daytime temperatures. Water in the morning and evening." Based on this information, the user can optimize water use and maintain the health of their crops.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The server receives environmental data from the data collection devices. This data includes temperature, humidity, soil nutrient status, and light intensity, collected through drones and IoT sensors.

[0236] Step 2:

[0237] The server retrieves future weather forecasts from weather information providers. This includes data such as temperature, precipitation, and wind speed, which are considered as factors influencing crop cultivation methods.

[0238] Step 3:

[0239] The server preprocesses the received environmental and meteorological data, performing noise reduction and missing value imputation. This creates a dataset suitable for analysis.

[0240] Step 4:

[0241] The server uses machine learning algorithms within the processing unit to analyze the pre-processed data. This allows for an assessment of the current state of the crops and the calculation of optimal cultivation conditions.

[0242] Step 5:

[0243] The server generates specific cultivation methods based on the analysis results. For example, it may suggest irrigation schedules and fertilization timings.

[0244] Step 6:

[0245] The server sends the generated cultivation method to the user's terminal. This allows the user to receive information about the latest agricultural actions.

[0246] Step 7:

[0247] The terminal visualizes the cultivation methods sent by the server and displays suggestions to the user. The user confirms the information in the form of specific instructions.

[0248] Step 8:

[0249] Users perform actual agricultural actions using agricultural machinery and manual labor based on instructions displayed on their devices.

[0250] Step 9:

[0251] Users provide feedback via their devices, detailing the results of their actions and any observations they make, and send this feedback to the server. This feedback is stored on the server and used to improve the system.

[0252] (Example 1)

[0253] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0254] In modern agriculture, rapidly adapting to climate change and environmental shifts and achieving high cultivation efficiency are crucial challenges. Traditional methods involve collecting and analyzing environmental data individually, making immediate decision-making difficult and hindering optimal resource utilization. Furthermore, if the information users receive is not specific, it becomes difficult to translate it into appropriate action. This can ultimately lead to decreased crop yields and wasted resources.

[0255] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0256] In this invention, the server includes means for transmitting environmental data received from a data collection device to an integration device, means for preprocessing the environmental data received by the integration device and weather data acquired from external information, and means for analyzing the preprocessed data using a machine learning algorithm. This enables real-time data integration and analysis, and provides users with concrete and actionable cultivation suggestions, thereby achieving efficient resource utilization and high yields.

[0257] A "data collection device" is a device used to automatically acquire environmental data in agricultural settings and transmit it to a server.

[0258] An "integrated device" is a component for centrally managing and preprocessing collected environmental data and external weather data.

[0259] "Environmental data" refers to information related to the growing environment of crops, including data such as temperature, humidity, and soil conditions.

[0260] "Weather data" refers to information about the weather obtained from external sources, including data such as temperature, precipitation, and wind speed.

[0261] "Preprocessing" refers to a series of processes that format the collected data into an analyzable form, removing outliers and imputing missing values.

[0262] A "machine learning algorithm" is a mathematical method used to learn rules and patterns from data and perform predictions and classifications.

[0263] "Analysis" refers to the process of analyzing data based on pre-processed data to determine the optimal conditions for crop cultivation.

[0264] A "communication terminal" is an electronic device that allows a user to receive notifications from a server and confirm suggested cultivation methods.

[0265] "Feedback" refers to the provision of reports and information that users send to the server regarding the farm work they have performed and the results thereof.

[0266] This invention is a system for improving the efficiency of crop cultivation in the agricultural field. The system mainly consists of a server, terminals, and users.

[0267] The server receives environmental data via data collection devices installed in the fields. These devices measure various data such as temperature, humidity, and soil conditions, and transmit them to the server. The server also obtains weather data from external weather information providers via APIs. Data integration and management take place throughout this process.

[0268] Next, the integration device within the server performs preprocessing on the received environmental data and weather data imported from external sources. This preprocessing includes imputing missing values ​​and removing outliers. The Python library Pandas is used to clean and format the data.

[0269] After the data is preprocessed, the processing unit on the server performs analysis using machine learning algorithms. Libraries such as TensorFlow and Scikit-learn are utilized to analyze the optimal growing conditions for crops based on predictive models learned from historical data. For example, if a rise in temperature is predicted for the following week, it may suggest the timing of irrigation.

[0270] The terminal receives analysis results from the server in real time and notifies the user. The terminal plays a role in allowing the user to confirm the suggested cultivation methods and provide actionable information in a timely manner. This enables the user to carry out farm work according to the specified schedule. The terminal also collects feedback from the user and sends it to the server.

[0271] Users adjust agricultural machinery and perform manual farming tasks based on information provided through their devices. For example, based on notifications, users can maintain the health of their crops by irrigating in the early morning and evening if high daytime temperatures are forecast.

[0272] An example of a prompt for a generative AI model is: "Given weather and environmental data as input, suggest the optimal farming plan for next week. For example, include irrigation timing when high temperatures are predicted."

[0273] In this way, this invention aims to achieve efficient resource utilization and high yields in agriculture.

[0274] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0275] Step 1:

[0276] The server receives environmental data from the data collection device. This data includes temperature, humidity, soil moisture, etc. The received data is stored in the database. At this stage, mainly IoT devices are used to collect data from the field in real time.

[0277] Step 2:

[0278] The server obtains the latest weather data from an external weather information provider through the API. The weather data obtained here is mainly air temperature, precipitation, wind speed, etc. The obtained weather data is integrated with the environmental data and managed uniformly.

[0279] Step 3:

[0280] The integration device in the server preprocesses the environmental data and weather data. In the process of preprocessing, detection and removal of abnormal values, and complementation of missing values are performed, and the data is formatted into a state suitable for analysis. It is a process of using the Pandas library to clean and adjust the data.

[0281] Step 4:

[0282] The processing device of the server applies a machine learning algorithm with the preprocessed data as the input. For this processing, TensorFlow or Scikit-learn is used to analyze the optimal cultivation conditions based on past trends. The output of this step appears as a specific cultivation proposal.

[0283] Step 5:

[0284] The server sends the generated cultivation proposal to the communication terminal. The proposal includes specific irrigation timing and fertilization plan, and provides detailed information that can be executed on the user terminal.

[0285] Step 6:

[0286] The terminal receives proposals from the server and notifies the user. The user can check the proposal content on the terminal and perform the necessary adjustments or manual operations on the agricultural machinery. In this process, the user can implement actions based on the recommended cultivation methods.

[0287] Step 7:

[0288] The user feeds back the results of the agricultural operations performed to the server via the terminal. The feedback includes information on the growth status and yield of the crops, and this information is further used to improve the machine learning model of the system.

[0289] This continuous processing flow enables the system to promote efficient resource utilization in agriculture and improve yields.

[0290] (Application Example 1)

[0291] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0292] In sustainable urban development, the aim is to support the agricultural activities of the local community and achieve appropriate cultivation methods and resource optimization. In particular, with the increasing demand for effective use of resources due to climate change and population growth, the need for efficient agricultural activities within the city is growing. However, with the existing technologies, the methods for generating specific living environment indicators that contribute to the sustainability of the region and providing them to the entire community have not been fully established.

[0293] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0294] In this invention, the server includes means for transmitting environmental data received from a data collection device to an information processing device; means for the information processing device to analyze the received environmental and meteorological data; means for generating agricultural production methods based on the analysis results; means for notifying an information terminal of the generated cultivation methods; means for generating indicators of the living environment to contribute to regional sustainability; and means for providing the generated indicators to the community. This makes it possible to achieve sustainable agriculture and resource optimization throughout the entire community and to improve environmental awareness throughout the city.

[0295] A "data acquisition device" is a device that acquires environmental data and transmits it to an information processing device.

[0296] An "information processing device" is a device that analyzes received environmental and weather data to generate indicators for appropriate cultivation methods and living environments.

[0297] "Environmental data" refers to data that measures various elements related to agricultural production and living environments, including data such as temperature, humidity, and soil information.

[0298] "Weather data" refers to weather-related data obtained from external weather information providers, including forecast information such as temperature, precipitation, and wind speed.

[0299] "Cultivation methods" are guidelines that indicate the optimal procedures and conditions for growing crops, generated based on analyzed data.

[0300] An "information terminal" is a device used to notify users of generated cultivation methods and indicators of the living environment, and includes smartphones and tablets.

[0301] "Indicators for living environments that contribute to regional sustainability" are evaluation indicators related to daily life and agricultural activities that are created to support the effective use of regional environmental resources and sustainable development.

[0302] A "community" is a group of people who live together in a specific local area, sharing common environments and resources, and cooperating with each other.

[0303] The system realizing this invention consists of a data collection device that acquires environmental data, an information processing device that analyzes the information, and an information terminal that displays the results. The server acquires environmental data such as temperature, humidity, and soil information from the data collection device. In addition, it receives weather data such as temperature, precipitation, and wind speed from an external weather information provider. The information processing device analyzes this data using a machine learning algorithm (e.g., TensorFlow) to generate indicators of the living environment that contribute to optimal cultivation methods and regional sustainability.

[0304] The analyzed cultivation methods are processed on a cloud platform (AWS or Google Cloud) and transmitted in real time to information terminals. Users can view this information on their smartphones or tablets and receive specific suggestions to optimize their cultivation activities. User feedback is then sent back to the information processing device, and the system uses this data to further improve its accuracy.

[0305] As a concrete example, a community garden in a certain area is using this system to optimize the harvest schedule for a weekend event. Analysis of the system displays instructions on the terminal such as, "Since high temperatures are forecast for the weekend, concentrate watering in the morning and evening," allowing community members to work more efficiently based on this information. As a result, the harvest yield for the event has increased, and the sustainability of the community has been improved, according to reports.

[0306] An example of a prompt message is: "Based on this week's weather data and historical agricultural data, generate the optimal cultivation plan for the next 7 days. Please specify any points that require particular attention." By giving this instruction to the generating AI model, you can obtain suggestions for appropriate cultivation methods.

[0307] The flow of the specific process in Application Example 1 will be described with reference to FIG. 12.

[0308] Step 1:

[0309] The server receives the environmental data acquired from the data collection device. The environmental data includes temperature, humidity, soil information, etc. This data is converted into an appropriate format and stored in the database for use in subsequent analysis steps.

[0310] Step 2:

[0311] The server accesses an external weather information provider to obtain weather data. The weather data includes air temperature, precipitation, wind speed, etc. This data is formatted in the same way as the environmental data and stored in the database.

[0312] Step 3:

[0313] The information processing device retrieves the stored environmental data and weather data and analyzes the data using a machine learning algorithm. Specifically, using TensorFlow, the characteristics of the data are extracted and the optimal cultivation method is predicted. In this process, the model is trained based on past data and applied to the current data to generate accurate proposals.

[0314] Step 4:

[0315] The server transmits the cultivation method it generated to the information terminal. The user receives this information on a smartphone or tablet and can check instructions for specific cultivation activities and environmental adjustments in real time. This enables the user to effectively manage resources and optimize farming operations.

[0316] Step 5:

[0317] The user performs an action through the information terminal and checks the result. If necessary, the result of the work performed is input as feedback to the terminal and transmitted to the server.

[0318] Step 6:

[0319] The server improves the accuracy of future predictions by updating the parameters of its analysis algorithm based on feedback received from users. This allows the generative AI model to propose cultivation methods that contribute to regional sustainability with greater accuracy.

[0320] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0321] This invention provides more personalized agricultural advice by incorporating an emotion engine into an agricultural support system that recognizes and utilizes the user's emotions. The server receives environmental and weather data from a data collection device, and the processing unit analyzes this data. Here, the emotion engine also acquires the user's emotional data and reflects it in cultivation methods and suggestions as needed.

[0322] The emotion engine analyzes the user's emotions from their facial expressions and tone of voice, generating emotion data. This data is sent to the server via the user's device. The server integrates and analyzes the emotion data with other environmental and weather data to generate suggestions for cultivation methods that the user will find more appropriate. These suggestions are notified to the user's device, and the user receives specific actions on their device.

[0323] For example, if the emotion engine detects that a user is overwhelmed, the server will adjust the suggested workload to reduce the user's burden. This makes it possible to create an environment where agricultural activities can be continued without difficulty. Furthermore, if it is determined that the user is satisfied, more proactive advice can be offered.

[0324] Users review suggestions and take agricultural actions via devices such as smartphones and tablets. In addition, users provide feedback on the condition of their crops to the system, and the results are sent to the server. The server updates the system based on the feedback, improving the accuracy of the next analysis. This enables detailed agricultural support that takes into account the user's emotional state.

[0325] The following describes the processing flow.

[0326] Step 1:

[0327] The terminal transmits environmental data acquired from the data collection device to the server. Simultaneously, the emotion engine analyzes emotional data from the user's facial expressions and voice, and transmits this data to the server.

[0328] Step 2:

[0329] The server preprocesses the received environmental and meteorological data, preparing it for analysis. This includes imputing missing values ​​and denoising the data.

[0330] Step 3:

[0331] The server integrates emotional data with other data and analyzes it in the processing unit. Machine learning algorithms calculate the optimal cultivation method, taking into account the user's emotional state.

[0332] Step 4:

[0333] Based on the analysis results, the server sends the user the optimal cultivation method to their terminal. This includes suggestions that take emotions into consideration.

[0334] Step 5:

[0335] The terminal displays suggestions received from the server to the user. These include specific instructions for agricultural actions that have been adjusted according to the user's emotional state.

[0336] Step 6:

[0337] Users follow the suggestions from their devices and actually carry out agricultural actions. This ensures that the suggested cultivation methods are applied in the field.

[0338] Step 7:

[0339] Users provide feedback through their devices regarding the results of their actions and any changes in their emotions. This feedback, along with emotion data, is sent to the server.

[0340] Step 8:

[0341] The server updates the system's learning model based on feedback information, improving the accuracy and reliability of future suggestions. This process is repeated to optimize individual agricultural support for each user.

[0342] (Example 2)

[0343] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0344] Conventional agricultural support systems often provide uniform cultivation methods and work instructions without considering the user's emotional state, which increases the user's burden. To solve this problem, there is a need for technology that provides personalized advice that takes the user's emotions into account, thereby better supporting the user's agricultural activities.

[0345] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0346] In this invention, the server includes means for transmitting data collected from a data acquisition device to an analysis device, means for integrating and analyzing the environmental data and weather information received by the analysis device, and means for acquiring the user's emotional state using an emotion recognition engine and generating it as data. This makes it possible to propose personalized cultivation methods that reflect the user's emotional state.

[0347] A "data acquisition device" is a device that collects environmental information and related information, and it acquires real-time data using sensors, APIs, etc.

[0348] An "analysis device" is a device that integrates and analyzes received data, and includes a computer or processor for outputting processing results.

[0349] "Environmental data" refers to information related to agriculture, such as temperature, humidity, and soil conditions, and serves as basic data for determining crop cultivation methods.

[0350] "Weather information" refers to data related to weather conditions, including information that shows weather trends based on forecasts and actual measurements.

[0351] An "emotion recognition engine" refers to a program or algorithm that analyzes a user's facial expressions and tone of voice to generate data on their emotional state.

[0352] A "user terminal" is a device used by a user to receive and send information, and includes smartphones, tablets, and other similar devices.

[0353] "Cultivation methods" refer to information that outlines specific procedures and work methods for growing crops efficiently and effectively.

[0354] "Personalized cultivation methods" refer to suggested methods that take into account the user's specific circumstances and emotional state, providing unique guidelines that are adjusted from standard methods.

[0355] One embodiment of this invention is a system that provides personalized advice that takes into account the user's emotions in agricultural support. The system mainly consists of the following elements:

[0356] The server collects environmental and meteorological data through data acquisition devices, which are then integrated and analyzed by analysis devices. This analysis utilizes computer systems and specialized software with advanced data processing capabilities. Furthermore, an emotion recognition engine is used to analyze the user's facial expressions and voice tone, generating emotion data. Smart devices equipped with cameras and microphones are used for emotion recognition.

[0357] Users input their emotional state into the system via information devices such as smartphones and tablets. This information is transmitted to the server in real time and analyzed in combination with other data.

[0358] Based on the analysis results, the server generates personalized cultivation methods tailored to the user. In this process, the generating AI model utilizes prompts to construct advice with unprecedented accuracy. These prompts are used, for example, in the form of "What is the optimal farming task when rain is expected and the user is feeling stressed?"

[0359] The generated cultivation methods and work suggestions are notified to the information terminal and immediately displayed to the user. Based on this, the user carries out specific agricultural activities and provides feedback from the terminal to the server. The server uses this feedback to further refine the accuracy of the model.

[0360] In this way, users can engage in sustainable and less stressful agricultural activities and receive meticulous support that takes their feelings into consideration.

[0361] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0362] Step 1:

[0363] The server receives environmental data from data acquisition devices. This data includes temperature, humidity, and soil conditions, and is collected in real time via sensors and APIs. This received data is temporarily stored on the platform in preparation for subsequent analysis.

[0364] Step 2:

[0365] The server retrieves weather information from another system. Input data includes forecasts and weather history, and data collection is performed via an API. This information, along with environmental data, is stored in a database and prepared for integrated analysis.

[0366] Step 3:

[0367] Users provide emotional data using their smart devices. The input consists of facial expressions and voice tone, which are transmitted to the emotion recognition engine via the camera and microphone. As a result, data indicating the user's emotional state is generated and immediately sent to the server.

[0368] Step 4:

[0369] The server processes integrated environmental data, weather information, and sentiment data using an analysis device. Based on the input data, a generative AI model uses the prompt "What is the optimal cultivation guidance in this situation?" to generate the optimal cultivation method for the user. The output is a personalized cultivation method suggestion.

[0370] Step 5:

[0371] The server notifies the user's information terminal of the generated cultivation method. This output is displayed as an advice message, providing the user with specific work instructions. This allows the user to decide on actions based on the received suggestions.

[0372] Step 6:

[0373] Users perform farm work based on the suggested cultivation methods and input the results and their impressions as feedback into the terminal. This input data, including the condition of the crops and their impressions of the work, is sent to the server.

[0374] Step 7:

[0375] The server receives user feedback and updates the system's analysis algorithm. Based on the input feedback, it improves the accuracy of suggestions for subsequent attempts, resulting in output that provides more user-friendly support.

[0376] (Application Example 2)

[0377] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0378] Agricultural and horticultural activities in urban environments depend on the emotional state and environmental conditions of individual users, requiring efficient support tailored to their specific needs. Conventional systems only provide uniform advice without adequately reflecting the user's emotions and state, resulting in challenges in improving the user experience and increasing work efficiency.

[0379] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0380] In this invention, the server includes means for transmitting environmental data received from a data collection device to a processing device; means for the processing device to generate user emotion data using an emotion analysis device, integrate and analyze it with the received environmental data and weather data; and means for generating a crop cultivation method that takes into account the user's emotional state based on the analysis results and notifying the user terminal. This enables personalized agricultural advice tailored to the user's emotional state.

[0381] A "data collection device" is a device that acquires environmental data and user sentiment data and transmits it to a processing device.

[0382] A "processing device" is a device that analyzes received data and generates cultivation methods tailored to the user's emotional state.

[0383] An "emotion analysis device" is part of a system that analyzes a user's facial expressions, voice tone, etc., and generates emotional data.

[0384] "Environmental data" refers to data such as ambient temperature, humidity, and sunlight necessary for growing crops.

[0385] "Weather data" refers to information about weather conditions such as temperature, precipitation, and wind speed.

[0386] "Integrated analysis" is a process that combines emotional data, environmental data, and weather data for analysis.

[0387] "Cultivation methods" refer to information about the procedures and techniques for growing crops.

[0388] A "user terminal" is a device that notifies the user of the generated cultivation method and receives feedback from the user.

[0389] To implement this invention, a user's smart terminal, a server, a data collection device, and an emotion analysis system are utilized. The server receives environmental data and user emotion data transmitted from the user's smart terminal. The environmental data includes conditions suitable for cultivation, such as temperature and sunlight, and the emotion data is based on facial expressions and tone of voice obtained through the emotion analysis system.

[0390] Specifically, the user's smart device uses its camera and microphone to record the user's facial expressions and voice, and sends this data to an emotion analysis system. The emotion analysis system generates emotion data using, for example, the Google Cloud Vision API or IBM Watson Tone Analyzer. A server leverages AWS Lambda to integrate and analyze the emotion data with environmental and weather data, and uses a generative AI model to suggest the most appropriate agricultural action for the user's emotions.

[0391] Suggestions for users are communicated via smart devices, providing specific advice such as, "Today's temperature is 25 degrees Celsius and it's sunny. You seem a little tired, so we recommend only gentle watering." Users can adjust their farming activities based on these suggestions. This also helps avoid excessive workloads based on emotional analysis, resulting in more efficient farming.

[0392] To more effectively apply the generative AI model, the following can be used as an example of a prompt: "List quick and easy gardening tasks that are suitable for a user who is feeling stressed. The weather is sunny and the temperature is 20 degrees Celsius." Using such prompts, the system can generate more accurate suggestions.

[0393] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0394] Step 1:

[0395] The user terminal uses a camera and microphone to capture the user's facial expressions and voice. This data is transmitted in real time to an emotion analysis system. The input is raw image and voice data, and the output is emotional features based on this data. Specifically, the analysis of image data identifies emotions from facial expressions, and the analysis of voice data analyzes tone and pitch to determine the emotional state.

[0396] Step 2:

[0397] The sentiment analysis system uses the Google Cloud Vision API to analyze image data and IBM Watson Tone Analyzer to analyze audio data. This outputs sentiment data that labels the user's current emotional state, such as "stressed" or "satisfied." This sentiment data is then sent to a processing unit.

[0398] Step 3:

[0399] The server receives environmental and weather data collected from sensors, along with sentiment data. This data is integrated and analyzed using AWS Lambda. The inputs are sentiment data, environmental data, and weather data, and the output is the combined analysis results. Specifically, each dataset is normalized and prepared for input into a machine learning model.

[0400] Step 4:

[0401] The server uses a generative AI model to generate agricultural actions based on the analyzed data. The input is the integrated analysis results. The output is a suggestion of specific cultivation methods tailored to the user's emotional state. For example, it might generate a suggestion such as, "The conditions are good today, so try a new soil improvement."

[0402] Step 5:

[0403] The user terminal receives suggestions from the server and delivers them to the user via a notification function. The user checks the notification and takes action based on the suggested action. The input is the suggestion notification from the server, and the output is the user's action. Specifically, information is provided to the user by displaying notifications or pop-ups on the terminal.

[0404] Step 6:

[0405] Users send feedback from their terminal to the server after completing a task. This feedback is used to improve the accuracy of future analyses. The input is user feedback information, and the output is information related to system improvements. Specifically, this involves analyzing the feedback data and updating the machine learning model as needed.

[0406] 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.

[0407] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0408] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0409] [Third Embodiment]

[0410] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0411] 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.

[0412] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0413] 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.

[0414] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0415] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0416] 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.

[0417] 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.

[0418] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0419] The 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.

[0420] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0421] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0422] This invention implements a system consisting of a data collection device, a processing device, and a user terminal to improve the efficiency of crop cultivation in agriculture. The server receives environmental data from the data collection device and also acquires weather information from an external weather information provider. This data is analyzed in the processing device within the server, and appropriate cultivation methods for crops are generated.

[0423] This analysis process utilizes machine learning algorithms to analyze the relationships between collected environmental and weather data and identify optimal growing conditions. Cultivation methods are constantly updated as needed, aiming to improve yields and ensure the sustainable use of resources.

[0424] Analysis results from the server are provided to the user's terminal, where the user receives real-time suggestions. For example, specific instructions for agricultural work, such as increasing or decreasing irrigation volume or timing of fertilization under certain weather conditions, are displayed. Based on this, the user takes action using agricultural machinery or manual labor. In addition, the user can provide feedback on the results of their actions to the server via their terminal. This feedback information contributes to further improving the accuracy of the system.

[0425] As a concrete example, consider a farmer in a certain region using this system. The server collects data from the entire field using periodic aerial images taken by drones to understand the current vegetation conditions. If the server's analysis predicts a rise in temperature next week, it sends a notification to the user's terminal saying, "The forecast is for high daytime temperatures. Water in the morning and evening." Based on this information, the user can optimize water use and maintain the health of their crops.

[0426] The following describes the processing flow.

[0427] Step 1:

[0428] The server receives environmental data from the data collection devices. This data includes temperature, humidity, soil nutrient status, and light intensity, collected through drones and IoT sensors.

[0429] Step 2:

[0430] The server retrieves future weather forecasts from weather information providers. This includes data such as temperature, precipitation, and wind speed, which are considered as factors influencing crop cultivation methods.

[0431] Step 3:

[0432] The server preprocesses the received environmental and meteorological data, performing noise reduction and missing value imputation. This creates a dataset suitable for analysis.

[0433] Step 4:

[0434] The server uses machine learning algorithms within the processing unit to analyze the pre-processed data. This allows for an assessment of the current state of the crops and the calculation of optimal cultivation conditions.

[0435] Step 5:

[0436] The server generates specific cultivation methods based on the analysis results. For example, it may suggest irrigation schedules and fertilization timings.

[0437] Step 6:

[0438] The server sends the generated cultivation method to the user's terminal. This allows the user to receive information about the latest agricultural actions.

[0439] Step 7:

[0440] The terminal visualizes the cultivation methods sent by the server and displays suggestions to the user. The user confirms the information in the form of specific instructions.

[0441] Step 8:

[0442] Users perform actual agricultural actions using agricultural machinery and manual labor based on instructions displayed on their devices.

[0443] Step 9:

[0444] Users provide feedback via their devices, detailing the results of their actions and any observations they make, and send this feedback to the server. This feedback is stored on the server and used to improve the system.

[0445] (Example 1)

[0446] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0447] In modern agriculture, rapidly adapting to climate change and environmental shifts and achieving high cultivation efficiency are crucial challenges. Traditional methods involve collecting and analyzing environmental data individually, making immediate decision-making difficult and hindering optimal resource utilization. Furthermore, if the information users receive is not specific, it becomes difficult to translate it into appropriate action. This can ultimately lead to decreased crop yields and wasted resources.

[0448] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0449] In this invention, the server includes means for transmitting environmental data received from a data collection device to an integration device, means for preprocessing the environmental data received by the integration device and weather data acquired from external information, and means for analyzing the preprocessed data using a machine learning algorithm. This enables real-time data integration and analysis, and provides users with concrete and actionable cultivation suggestions, thereby achieving efficient resource utilization and high yields.

[0450] A "data collection device" is a device used to automatically acquire environmental data in agricultural settings and transmit it to a server.

[0451] An "integrated device" is a component for centrally managing and preprocessing collected environmental data and external weather data.

[0452] "Environmental data" refers to information related to the growing environment of crops, including data such as temperature, humidity, and soil conditions.

[0453] "Weather data" refers to information about the weather obtained from external sources, including data such as temperature, precipitation, and wind speed.

[0454] "Preprocessing" refers to a series of processes that format the collected data into an analyzable form, removing outliers and imputing missing values.

[0455] A "machine learning algorithm" is a mathematical method used to learn rules and patterns from data and perform predictions and classifications.

[0456] "Analysis" refers to the process of analyzing data based on pre-processed data to determine the optimal conditions for crop cultivation.

[0457] A "communication terminal" is an electronic device that allows a user to receive notifications from a server and confirm suggested cultivation methods.

[0458] "Feedback" refers to the provision of reports and information that users send to the server regarding the farm work they have performed and the results thereof.

[0459] This invention is a system for improving the efficiency of crop cultivation in the agricultural field. The system mainly consists of a server, terminals, and users.

[0460] The server receives environmental data via data collection devices installed in the fields. These devices measure various data such as temperature, humidity, and soil conditions, and transmit them to the server. The server also obtains weather data from external weather information providers via APIs. Data integration and management take place throughout this process.

[0461] Next, the integration device within the server performs preprocessing on the received environmental data and weather data imported from external sources. This preprocessing includes imputing missing values ​​and removing outliers. The Python library Pandas is used to clean and format the data.

[0462] After the data is preprocessed, the processing unit on the server performs analysis using machine learning algorithms. Libraries such as TensorFlow and Scikit-learn are utilized to analyze the optimal growing conditions for crops based on predictive models learned from historical data. For example, if a rise in temperature is predicted for the following week, it may suggest the timing of irrigation.

[0463] The terminal receives analysis results from the server in real time and notifies the user. The terminal plays a role in allowing the user to confirm the suggested cultivation methods and provide actionable information in a timely manner. This enables the user to carry out farm work according to the specified schedule. The terminal also collects feedback from the user and sends it to the server.

[0464] Users adjust agricultural machinery and perform manual farming tasks based on information provided through their devices. For example, based on notifications, users can maintain the health of their crops by irrigating in the early morning and evening if high daytime temperatures are forecast.

[0465] An example of a prompt for a generative AI model is: "Given weather and environmental data as input, suggest the optimal farming plan for next week. For example, include irrigation timing when high temperatures are predicted."

[0466] In this way, this invention aims to achieve efficient resource utilization and high yields in agriculture.

[0467] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0468] Step 1:

[0469] The server receives environmental data from data collection devices. This data includes temperature, humidity, and soil moisture. The received data is stored in a database. At this stage, real-time data collection from the field is mainly performed using IoT devices.

[0470] Step 2:

[0471] The server retrieves the latest weather data from external weather information providers via an API. This data primarily includes temperature, precipitation, and wind speed. The retrieved weather data is integrated with environmental data and managed centrally.

[0472] Step 3:

[0473] The integration system on the server performs preprocessing on environmental and meteorological data. During this preprocessing, outliers are detected and removed, and missing values ​​are imputed, formatting the data for analysis. This process involves cleaning and adjusting the data using the Pandas library.

[0474] Step 4:

[0475] The server's processing unit applies machine learning algorithms to pre-processed data as input. This process uses TensorFlow and Scikit-learn to analyze optimal cultivation conditions based on past trends. The output of this step appears as specific cultivation suggestions.

[0476] Step 5:

[0477] The server sends the generated cultivation suggestions to the communication terminal. The suggestions include specific irrigation timings and fertilization plans, providing detailed information that can be implemented on the user's terminal.

[0478] Step 6:

[0479] The terminal receives suggestions from the server and notifies the user. The user can review the suggestions on the terminal and perform necessary adjustments to agricultural machinery or manual tasks. Through this process, the user can take actions based on recommended cultivation methods.

[0480] Step 7:

[0481] Users provide feedback on the results of their farming activities to the server via their terminals. This feedback includes information on crop growth and yield, which is then used to improve the system's machine learning model.

[0482] This continuous processing flow allows the system to promote efficient resource utilization in agriculture and enable increased yields.

[0483] (Application Example 1)

[0484] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0485] The aim is to support local agricultural activities in sustainable urban development and to realize appropriate cultivation methods and resource optimization. In particular, the need for efficient agricultural activities within cities is increasing amidst the demand for efficient resource use due to climate change and population growth. However, existing technologies have not sufficiently established methods for generating specific indicators of the living environment that contribute to local sustainability and providing them to the entire community.

[0486] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0487] In this invention, the server includes means for transmitting environmental data received from a data collection device to an information processing device; means for the information processing device to analyze the received environmental and meteorological data; means for generating agricultural production methods based on the analysis results; means for notifying an information terminal of the generated cultivation methods; means for generating indicators of the living environment to contribute to regional sustainability; and means for providing the generated indicators to the community. This makes it possible to achieve sustainable agriculture and resource optimization throughout the entire community and to improve environmental awareness throughout the city.

[0488] A "data acquisition device" is a device that acquires environmental data and transmits it to an information processing device.

[0489] An "information processing device" is a device that analyzes received environmental and weather data to generate indicators for appropriate cultivation methods and living environments.

[0490] "Environmental data" refers to data that measures various elements related to agricultural production and living environments, including data such as temperature, humidity, and soil information.

[0491] "Weather data" refers to weather-related data obtained from external weather information providers, including forecast information such as temperature, precipitation, and wind speed.

[0492] "Cultivation methods" are guidelines that indicate the optimal procedures and conditions for growing crops, generated based on analyzed data.

[0493] An "information terminal" is a device used to notify users of generated cultivation methods and indicators of the living environment, and includes smartphones and tablets.

[0494] "Indicators for living environments that contribute to regional sustainability" are evaluation indicators related to daily life and agricultural activities that are created to support the effective use of regional environmental resources and sustainable development.

[0495] A "community" is a group of people who live together in a specific local area, sharing common environments and resources, and cooperating with each other.

[0496] The system realizing this invention consists of a data collection device that acquires environmental data, an information processing device that analyzes the information, and an information terminal that displays the results. The server acquires environmental data such as temperature, humidity, and soil information from the data collection device. In addition, it receives weather data such as temperature, precipitation, and wind speed from an external weather information provider. The information processing device analyzes this data using a machine learning algorithm (e.g., TensorFlow) to generate indicators of the living environment that contribute to optimal cultivation methods and regional sustainability.

[0497] The analyzed cultivation methods are processed on a cloud platform (AWS or Google Cloud) and transmitted in real time to information terminals. Users can view this information on their smartphones or tablets and receive specific suggestions to optimize their cultivation activities. User feedback is then sent back to the information processing device, and the system uses this data to further improve its accuracy.

[0498] As a concrete example, a community garden in a certain area is using this system to optimize the harvest schedule for a weekend event. Analysis of the system displays instructions on the terminal such as, "Since high temperatures are forecast for the weekend, concentrate watering in the morning and evening," allowing community members to work more efficiently based on this information. As a result, the harvest yield for the event has increased, and the sustainability of the community has been improved, according to reports.

[0499] An example of a prompt message is: "Based on this week's weather data and historical agricultural data, generate the optimal cultivation plan for the next 7 days. Please specify any points that require particular attention." By giving this instruction to the generating AI model, you can obtain suggestions for appropriate cultivation methods.

[0500] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0501] Step 1:

[0502] The server receives environmental data acquired from the data collection device. This environmental data includes temperature, humidity, soil information, and other relevant information. This data is converted to an appropriate format and stored in a database for use in subsequent analysis steps.

[0503] Step 2:

[0504] The server accesses external weather information providers to retrieve weather data. This weather data includes temperature, precipitation, wind speed, and other parameters. This data is formatted in the same way as environmental data and stored in a database.

[0505] Step 3:

[0506] The information processing device acquires stored environmental and weather data and analyzes the data using machine learning algorithms. Specifically, it uses TensorFlow to extract data features and predict the optimal cultivation method. In this process, a model is trained based on historical data and applied to the current data to generate accurate suggestions.

[0507] Step 4:

[0508] The server generates cultivation methods and transmits them to information terminals. Users receive this information on their smartphones or tablets and check specific cultivation activities and environmental adjustment instructions in real time. This allows users to effectively manage resources and optimize farm work.

[0509] Step 5:

[0510] Users perform actions through an information terminal and check the results. If necessary, they input the results of the work performed as feedback into the terminal and send it to the server.

[0511] Step 6:

[0512] The server improves the accuracy of future predictions by updating the parameters of its analysis algorithm based on feedback received from users. This allows the generative AI model to propose cultivation methods that contribute to regional sustainability with greater accuracy.

[0513] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0514] This invention provides more personalized agricultural advice by incorporating an emotion engine into an agricultural support system that recognizes and utilizes the user's emotions. The server receives environmental and weather data from a data collection device, and the processing unit analyzes this data. Here, the emotion engine also acquires the user's emotional data and reflects it in cultivation methods and suggestions as needed.

[0515] The emotion engine analyzes the user's emotions from their facial expressions and tone of voice, generating emotion data. This data is sent to the server via the user's device. The server integrates and analyzes the emotion data with other environmental and weather data to generate suggestions for cultivation methods that the user will find more appropriate. These suggestions are notified to the user's device, and the user receives specific actions on their device.

[0516] For example, if the emotion engine detects that a user is overwhelmed, the server will adjust the suggested workload to reduce the user's burden. This makes it possible to create an environment where agricultural activities can be continued without difficulty. Furthermore, if it is determined that the user is satisfied, more proactive advice can be offered.

[0517] Users review suggestions and take agricultural actions via devices such as smartphones and tablets. In addition, users provide feedback on the condition of their crops to the system, and the results are sent to the server. The server updates the system based on the feedback, improving the accuracy of the next analysis. This enables detailed agricultural support that takes into account the user's emotional state.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] The terminal transmits environmental data acquired from the data collection device to the server. Simultaneously, the emotion engine analyzes emotional data from the user's facial expressions and voice, and transmits this data to the server.

[0521] Step 2:

[0522] The server preprocesses the received environmental and meteorological data, preparing it for analysis. This includes imputing missing values ​​and denoising the data.

[0523] Step 3:

[0524] The server integrates emotional data with other data and analyzes it in the processing unit. Machine learning algorithms calculate the optimal cultivation method, taking into account the user's emotional state.

[0525] Step 4:

[0526] Based on the analysis results, the server sends the user the optimal cultivation method to their terminal. This includes suggestions that take emotions into consideration.

[0527] Step 5:

[0528] The terminal displays suggestions received from the server to the user. These include specific instructions for agricultural actions that have been adjusted according to the user's emotional state.

[0529] Step 6:

[0530] Users follow the suggestions from their devices and actually carry out agricultural actions. This ensures that the suggested cultivation methods are applied in the field.

[0531] Step 7:

[0532] Users provide feedback through their devices regarding the results of their actions and any changes in their emotions. This feedback, along with emotion data, is sent to the server.

[0533] Step 8:

[0534] The server updates the system's learning model based on feedback information, improving the accuracy and reliability of future suggestions. This process is repeated to optimize individual agricultural support for each user.

[0535] (Example 2)

[0536] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0537] Conventional agricultural support systems often provide uniform cultivation methods and work instructions without considering the user's emotional state, which increases the user's burden. To solve this problem, there is a need for technology that provides personalized advice that takes the user's emotions into account, thereby better supporting the user's agricultural activities.

[0538] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0539] In this invention, the server includes means for transmitting data collected from a data acquisition device to an analysis device, means for integrating and analyzing the environmental data and weather information received by the analysis device, and means for acquiring the user's emotional state using an emotion recognition engine and generating it as data. This makes it possible to propose personalized cultivation methods that reflect the user's emotional state.

[0540] A "data acquisition device" is a device that collects environmental information and related information, and it acquires real-time data using sensors, APIs, etc.

[0541] An "analysis device" is a device that integrates and analyzes received data, and includes a computer or processor for outputting processing results.

[0542] "Environmental data" refers to information related to agriculture, such as temperature, humidity, and soil conditions, and serves as basic data for determining crop cultivation methods.

[0543] "Weather information" refers to data related to weather conditions, including information that shows weather trends based on forecasts and actual measurements.

[0544] An "emotion recognition engine" refers to a program or algorithm that analyzes a user's facial expressions and tone of voice to generate data on their emotional state.

[0545] A "user terminal" is a device used by a user to receive and send information, and includes smartphones, tablets, and other similar devices.

[0546] "Cultivation methods" refer to information that outlines specific procedures and work methods for growing crops efficiently and effectively.

[0547] "Personalized cultivation methods" refer to suggested methods that take into account the user's specific circumstances and emotional state, providing unique guidelines that are adjusted from standard methods.

[0548] One embodiment of this invention is a system that provides personalized advice that takes into account the user's emotions in agricultural support. The system mainly consists of the following elements:

[0549] The server collects environmental and meteorological data through data acquisition devices, which are then integrated and analyzed by analysis devices. This analysis utilizes computer systems and specialized software with advanced data processing capabilities. Furthermore, an emotion recognition engine is used to analyze the user's facial expressions and voice tone, generating emotion data. Smart devices equipped with cameras and microphones are used for emotion recognition.

[0550] Users input their emotional state into the system via information devices such as smartphones and tablets. This information is transmitted to the server in real time and analyzed in combination with other data.

[0551] Based on the analysis results, the server generates personalized cultivation methods tailored to the user. In this process, the generating AI model utilizes prompts to construct advice with unprecedented accuracy. These prompts are used, for example, in the form of "What is the optimal farming task when rain is expected and the user is feeling stressed?"

[0552] The generated cultivation methods and work suggestions are notified to the information terminal and immediately displayed to the user. Based on this, the user carries out specific agricultural activities and provides feedback from the terminal to the server. The server uses this feedback to further refine the accuracy of the model.

[0553] In this way, users can engage in sustainable and less stressful agricultural activities and receive meticulous support that takes their feelings into consideration.

[0554] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0555] Step 1:

[0556] The server receives environmental data from data acquisition devices. This data includes temperature, humidity, and soil conditions, and is collected in real time via sensors and APIs. This received data is temporarily stored on the platform in preparation for subsequent analysis.

[0557] Step 2:

[0558] The server retrieves weather information from another system. Input data includes forecasts and weather history, and data collection is performed via an API. This information, along with environmental data, is stored in a database and prepared for integrated analysis.

[0559] Step 3:

[0560] Users provide emotional data using their smart devices. The input consists of facial expressions and voice tone, which are transmitted to the emotion recognition engine via the camera and microphone. As a result, data indicating the user's emotional state is generated and immediately sent to the server.

[0561] Step 4:

[0562] The server processes integrated environmental data, weather information, and sentiment data using an analysis device. Based on the input data, a generative AI model uses the prompt "What is the optimal cultivation guidance in this situation?" to generate the optimal cultivation method for the user. The output is a personalized cultivation method suggestion.

[0563] Step 5:

[0564] The server notifies the user's information terminal of the generated cultivation method. This output is displayed as an advice message, providing the user with specific work instructions. This allows the user to decide on actions based on the received suggestions.

[0565] Step 6:

[0566] Users perform farm work based on the suggested cultivation methods and input the results and their impressions as feedback into the terminal. This input data, including the condition of the crops and their impressions of the work, is sent to the server.

[0567] Step 7:

[0568] The server receives user feedback and updates the system's analysis algorithm. Based on the input feedback, it improves the accuracy of suggestions for subsequent attempts, resulting in output that provides more user-friendly support.

[0569] (Application Example 2)

[0570] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0571] Agricultural and horticultural activities in urban environments depend on the emotional state and environmental conditions of individual users, requiring efficient support tailored to their specific needs. Conventional systems only provide uniform advice without adequately reflecting the user's emotions and state, resulting in challenges in improving the user experience and increasing work efficiency.

[0572] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0573] In this invention, the server includes means for transmitting environmental data received from a data collection device to a processing device; means for the processing device to generate user emotion data using an emotion analysis device, integrate and analyze it with the received environmental data and weather data; and means for generating a crop cultivation method that takes into account the user's emotional state based on the analysis results and notifying the user terminal. This enables personalized agricultural advice tailored to the user's emotional state.

[0574] A "data collection device" is a device that acquires environmental data and user sentiment data and transmits it to a processing device.

[0575] A "processing device" is a device that analyzes received data and generates cultivation methods tailored to the user's emotional state.

[0576] An "emotion analysis device" is part of a system that analyzes a user's facial expressions, voice tone, etc., and generates emotional data.

[0577] "Environmental data" refers to data such as ambient temperature, humidity, and sunlight necessary for growing crops.

[0578] "Weather data" refers to information about weather conditions such as temperature, precipitation, and wind speed.

[0579] "Integrated analysis" is a process that combines emotional data, environmental data, and weather data for analysis.

[0580] "Cultivation methods" refer to information about the procedures and techniques for growing crops.

[0581] A "user terminal" is a device that notifies the user of the generated cultivation method and receives feedback from the user.

[0582] To implement this invention, a user's smart terminal, a server, a data collection device, and an emotion analysis system are utilized. The server receives environmental data and user emotion data transmitted from the user's smart terminal. The environmental data includes conditions suitable for cultivation, such as temperature and sunlight, and the emotion data is based on facial expressions and tone of voice obtained through the emotion analysis system.

[0583] Specifically, the user's smart device uses its camera and microphone to record the user's facial expressions and voice, and sends this data to an emotion analysis system. The emotion analysis system generates emotion data using, for example, the Google Cloud Vision API or IBM Watson Tone Analyzer. A server leverages AWS Lambda to integrate and analyze the emotion data with environmental and weather data, and uses a generative AI model to suggest the most appropriate agricultural action for the user's emotions.

[0584] Suggestions for users are communicated via smart devices, providing specific advice such as, "Today's temperature is 25 degrees Celsius and it's sunny. You seem a little tired, so we recommend only gentle watering." Users can adjust their farming activities based on these suggestions. This also helps avoid excessive workloads based on emotional analysis, resulting in more efficient farming.

[0585] To more effectively apply the generative AI model, the following can be used as an example of a prompt: "List quick and easy gardening tasks that are suitable for a user who is feeling stressed. The weather is sunny and the temperature is 20 degrees Celsius." Using such prompts, the system can generate more accurate suggestions.

[0586] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0587] Step 1:

[0588] The user terminal uses a camera and microphone to capture the user's facial expressions and voice. This data is transmitted in real time to an emotion analysis system. The input is raw image and voice data, and the output is emotional features based on this data. Specifically, the analysis of image data identifies emotions from facial expressions, and the analysis of voice data analyzes tone and pitch to determine the emotional state.

[0589] Step 2:

[0590] The sentiment analysis system uses the Google Cloud Vision API to analyze image data and IBM Watson Tone Analyzer to analyze audio data. This outputs sentiment data that labels the user's current emotional state, such as "stressed" or "satisfied." This sentiment data is then sent to a processing unit.

[0591] Step 3:

[0592] The server receives environmental and weather data collected from sensors, along with sentiment data. This data is integrated and analyzed using AWS Lambda. The inputs are sentiment data, environmental data, and weather data, and the output is the combined analysis results. Specifically, each dataset is normalized and prepared for input into a machine learning model.

[0593] Step 4:

[0594] The server uses a generative AI model to generate agricultural actions based on the analyzed data. The input is the integrated analysis results. The output is a suggestion of specific cultivation methods tailored to the user's emotional state. For example, it might generate a suggestion such as, "The conditions are good today, so try a new soil improvement."

[0595] Step 5:

[0596] The user terminal receives suggestions from the server and delivers them to the user via a notification function. The user checks the notification and takes action based on the suggested action. The input is the suggestion notification from the server, and the output is the user's action. Specifically, information is provided to the user by displaying notifications or pop-ups on the terminal.

[0597] Step 6:

[0598] Users send feedback from their terminal to the server after completing a task. This feedback is used to improve the accuracy of future analyses. The input is user feedback information, and the output is information related to system improvements. Specifically, this involves analyzing the feedback data and updating the machine learning model as needed.

[0599] 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.

[0600] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0601] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0602] [Fourth Embodiment]

[0603] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0604] 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.

[0605] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0606] 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.

[0607] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0608] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0609] 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.

[0610] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0611] 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.

[0612] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0613] The 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.

[0614] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0615] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0616] This invention implements a system consisting of a data collection device, a processing device, and a user terminal to improve the efficiency of crop cultivation in agriculture. The server receives environmental data from the data collection device and also acquires weather information from an external weather information provider. This data is analyzed in the processing device within the server, and appropriate cultivation methods for crops are generated.

[0617] This analysis process utilizes machine learning algorithms to analyze the relationships between collected environmental and weather data and identify optimal growing conditions. Cultivation methods are constantly updated as needed, aiming to improve yields and ensure the sustainable use of resources.

[0618] Analysis results from the server are provided to the user's terminal, where the user receives real-time suggestions. For example, specific instructions for agricultural work, such as increasing or decreasing irrigation volume or timing of fertilization under certain weather conditions, are displayed. Based on this, the user takes action using agricultural machinery or manual labor. In addition, the user can provide feedback on the results of their actions to the server via their terminal. This feedback information contributes to further improving the accuracy of the system.

[0619] As a concrete example, consider a farmer in a certain region using this system. The server collects data from the entire field using periodic aerial images taken by drones to understand the current vegetation conditions. If the server's analysis predicts a rise in temperature next week, it sends a notification to the user's terminal saying, "The forecast is for high daytime temperatures. Water in the morning and evening." Based on this information, the user can optimize water use and maintain the health of their crops.

[0620] The following describes the processing flow.

[0621] Step 1:

[0622] The server receives environmental data from the data collection devices. This data includes temperature, humidity, soil nutrient status, and light intensity, collected through drones and IoT sensors.

[0623] Step 2:

[0624] The server retrieves future weather forecasts from weather information providers. This includes data such as temperature, precipitation, and wind speed, which are considered as factors influencing crop cultivation methods.

[0625] Step 3:

[0626] The server preprocesses the received environmental and meteorological data, performing noise reduction and missing value imputation. This creates a dataset suitable for analysis.

[0627] Step 4:

[0628] The server uses machine learning algorithms within the processing unit to analyze the pre-processed data. This allows for an assessment of the current state of the crops and the calculation of optimal cultivation conditions.

[0629] Step 5:

[0630] The server generates specific cultivation methods based on the analysis results. For example, it may suggest irrigation schedules and fertilization timings.

[0631] Step 6:

[0632] The server sends the generated cultivation method to the user's terminal. This allows the user to receive information about the latest agricultural actions.

[0633] Step 7:

[0634] The terminal visualizes the cultivation methods sent by the server and displays suggestions to the user. The user confirms the information in the form of specific instructions.

[0635] Step 8:

[0636] Users perform actual agricultural actions using agricultural machinery and manual labor based on instructions displayed on their devices.

[0637] Step 9:

[0638] Users provide feedback via their devices, detailing the results of their actions and any observations they make, and send this feedback to the server. This feedback is stored on the server and used to improve the system.

[0639] (Example 1)

[0640] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0641] In modern agriculture, rapidly adapting to climate change and environmental shifts and achieving high cultivation efficiency are crucial challenges. Traditional methods involve collecting and analyzing environmental data individually, making immediate decision-making difficult and hindering optimal resource utilization. Furthermore, if the information users receive is not specific, it becomes difficult to translate it into appropriate action. This can ultimately lead to decreased crop yields and wasted resources.

[0642] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0643] In this invention, the server includes means for transmitting environmental data received from a data collection device to an integration device, means for preprocessing the environmental data received by the integration device and weather data acquired from external information, and means for analyzing the preprocessed data using a machine learning algorithm. This enables real-time data integration and analysis, and provides users with concrete and actionable cultivation suggestions, thereby achieving efficient resource utilization and high yields.

[0644] A "data collection device" is a device used to automatically acquire environmental data in agricultural settings and transmit it to a server.

[0645] An "integrated device" is a component for centrally managing and preprocessing collected environmental data and external weather data.

[0646] "Environmental data" refers to information related to the growing environment of crops, including data such as temperature, humidity, and soil conditions.

[0647] "Weather data" refers to information about the weather obtained from external sources, including data such as temperature, precipitation, and wind speed.

[0648] "Preprocessing" refers to a series of processes that format the collected data into an analyzable form, removing outliers and imputing missing values.

[0649] A "machine learning algorithm" is a mathematical method used to learn rules and patterns from data and perform predictions and classifications.

[0650] "Analysis" refers to the process of analyzing data based on pre-processed data to determine the optimal conditions for crop cultivation.

[0651] A "communication terminal" is an electronic device that allows a user to receive notifications from a server and confirm suggested cultivation methods.

[0652] "Feedback" refers to the provision of reports and information that users send to the server regarding the farm work they have performed and the results thereof.

[0653] This invention is a system for improving the efficiency of crop cultivation in the agricultural field. The system mainly consists of a server, terminals, and users.

[0654] The server receives environmental data via data collection devices installed in the fields. These devices measure various data such as temperature, humidity, and soil conditions, and transmit them to the server. The server also obtains weather data from external weather information providers via APIs. Data integration and management take place throughout this process.

[0655] Next, the integration device within the server performs preprocessing on the received environmental data and weather data imported from external sources. This preprocessing includes imputing missing values ​​and removing outliers. The Python library Pandas is used to clean and format the data.

[0656] After the data is preprocessed, the processing unit on the server performs analysis using machine learning algorithms. Libraries such as TensorFlow and Scikit-learn are utilized to analyze the optimal growing conditions for crops based on predictive models learned from historical data. For example, if a rise in temperature is predicted for the following week, it may suggest the timing of irrigation.

[0657] The terminal receives analysis results from the server in real time and notifies the user. The terminal plays a role in allowing the user to confirm the suggested cultivation methods and provide actionable information in a timely manner. This enables the user to carry out farm work according to the specified schedule. The terminal also collects feedback from the user and sends it to the server.

[0658] Users adjust agricultural machinery and perform manual farming tasks based on information provided through their devices. For example, based on notifications, users can maintain the health of their crops by irrigating in the early morning and evening if high daytime temperatures are forecast.

[0659] An example of a prompt for a generative AI model is: "Given weather and environmental data as input, suggest the optimal farming plan for next week. For example, include irrigation timing when high temperatures are predicted."

[0660] In this way, this invention aims to achieve efficient resource utilization and high yields in agriculture.

[0661] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0662] Step 1:

[0663] The server receives environmental data from data collection devices. This data includes temperature, humidity, and soil moisture. The received data is stored in a database. At this stage, real-time data collection from the field is mainly performed using IoT devices.

[0664] Step 2:

[0665] The server retrieves the latest weather data from external weather information providers via an API. This data primarily includes temperature, precipitation, and wind speed. The retrieved weather data is integrated with environmental data and managed centrally.

[0666] Step 3:

[0667] The integration system on the server performs preprocessing on environmental and meteorological data. During this preprocessing, outliers are detected and removed, and missing values ​​are imputed, formatting the data for analysis. This process involves cleaning and adjusting the data using the Pandas library.

[0668] Step 4:

[0669] The server's processing unit applies machine learning algorithms to pre-processed data as input. This process uses TensorFlow and Scikit-learn to analyze optimal cultivation conditions based on past trends. The output of this step appears as specific cultivation suggestions.

[0670] Step 5:

[0671] The server sends the generated cultivation suggestions to the communication terminal. The suggestions include specific irrigation timings and fertilization plans, providing detailed information that can be implemented on the user's terminal.

[0672] Step 6:

[0673] The terminal receives suggestions from the server and notifies the user. The user can review the suggestions on the terminal and perform necessary adjustments to agricultural machinery or manual tasks. Through this process, the user can take actions based on recommended cultivation methods.

[0674] Step 7:

[0675] Users provide feedback on the results of their farming activities to the server via their terminals. This feedback includes information on crop growth and yield, which is then used to improve the system's machine learning model.

[0676] This continuous processing flow allows the system to promote efficient resource utilization in agriculture and enable increased yields.

[0677] (Application Example 1)

[0678] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0679] The aim is to support local agricultural activities in sustainable urban development and to realize appropriate cultivation methods and resource optimization. In particular, the need for efficient agricultural activities within cities is increasing amidst the demand for efficient resource use due to climate change and population growth. However, existing technologies have not sufficiently established methods for generating specific indicators of the living environment that contribute to local sustainability and providing them to the entire community.

[0680] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0681] In this invention, the server includes means for transmitting environmental data received from a data collection device to an information processing device; means for the information processing device to analyze the received environmental and meteorological data; means for generating agricultural production methods based on the analysis results; means for notifying an information terminal of the generated cultivation methods; means for generating indicators of the living environment to contribute to regional sustainability; and means for providing the generated indicators to the community. This makes it possible to achieve sustainable agriculture and resource optimization throughout the entire community and to improve environmental awareness throughout the city.

[0682] A "data acquisition device" is a device that acquires environmental data and transmits it to an information processing device.

[0683] An "information processing device" is a device that analyzes received environmental and weather data to generate indicators for appropriate cultivation methods and living environments.

[0684] "Environmental data" refers to data that measures various elements related to agricultural production and living environments, including data such as temperature, humidity, and soil information.

[0685] "Weather data" refers to weather-related data obtained from external weather information providers, including forecast information such as temperature, precipitation, and wind speed.

[0686] "Cultivation methods" are guidelines that indicate the optimal procedures and conditions for growing crops, generated based on analyzed data.

[0687] An "information terminal" is a device used to notify users of generated cultivation methods and indicators of the living environment, and includes smartphones and tablets.

[0688] "Indicators for living environments that contribute to regional sustainability" are evaluation indicators related to daily life and agricultural activities that are created to support the effective use of regional environmental resources and sustainable development.

[0689] A "community" is a group of people who live together in a specific local area, sharing common environments and resources, and cooperating with each other.

[0690] The system realizing this invention consists of a data collection device that acquires environmental data, an information processing device that analyzes the information, and an information terminal that displays the results. The server acquires environmental data such as temperature, humidity, and soil information from the data collection device. In addition, it receives weather data such as temperature, precipitation, and wind speed from an external weather information provider. The information processing device analyzes this data using a machine learning algorithm (e.g., TensorFlow) to generate indicators of the living environment that contribute to optimal cultivation methods and regional sustainability.

[0691] The analyzed cultivation methods are processed on a cloud platform (AWS or Google Cloud) and transmitted in real time to information terminals. Users can view this information on their smartphones or tablets and receive specific suggestions to optimize their cultivation activities. User feedback is then sent back to the information processing device, and the system uses this data to further improve its accuracy.

[0692] As a concrete example, a community garden in a certain area is using this system to optimize the harvest schedule for a weekend event. Analysis of the system displays instructions on the terminal such as, "Since high temperatures are forecast for the weekend, concentrate watering in the morning and evening," allowing community members to work more efficiently based on this information. As a result, the harvest yield for the event has increased, and the sustainability of the community has been improved, according to reports.

[0693] An example of a prompt message is: "Based on this week's weather data and historical agricultural data, generate the optimal cultivation plan for the next 7 days. Please specify any points that require particular attention." By giving this instruction to the generating AI model, you can obtain suggestions for appropriate cultivation methods.

[0694] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0695] Step 1:

[0696] The server receives environmental data acquired from the data collection device. This environmental data includes temperature, humidity, soil information, and other relevant information. This data is converted to an appropriate format and stored in a database for use in subsequent analysis steps.

[0697] Step 2:

[0698] The server accesses external weather information providers to retrieve weather data. This weather data includes temperature, precipitation, wind speed, and other parameters. This data is formatted in the same way as environmental data and stored in a database.

[0699] Step 3:

[0700] The information processing device acquires stored environmental and weather data and analyzes the data using machine learning algorithms. Specifically, it uses TensorFlow to extract data features and predict the optimal cultivation method. In this process, a model is trained based on historical data and applied to the current data to generate accurate suggestions.

[0701] Step 4:

[0702] The server generates cultivation methods and transmits them to information terminals. Users receive this information on their smartphones or tablets and check specific cultivation activities and environmental adjustment instructions in real time. This allows users to effectively manage resources and optimize farm work.

[0703] Step 5:

[0704] Users perform actions through an information terminal and check the results. If necessary, they input the results of the work performed as feedback into the terminal and send it to the server.

[0705] Step 6:

[0706] The server improves the accuracy of future predictions by updating the parameters of its analysis algorithm based on feedback received from users. This allows the generative AI model to propose cultivation methods that contribute to regional sustainability with greater accuracy.

[0707] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0708] This invention provides more personalized agricultural advice by incorporating an emotion engine into an agricultural support system that recognizes and utilizes the user's emotions. The server receives environmental and weather data from a data collection device, and the processing unit analyzes this data. Here, the emotion engine also acquires the user's emotional data and reflects it in cultivation methods and suggestions as needed.

[0709] The emotion engine analyzes the user's emotions from their facial expressions and tone of voice, generating emotion data. This data is sent to the server via the user's device. The server integrates and analyzes the emotion data with other environmental and weather data to generate suggestions for cultivation methods that the user will find more appropriate. These suggestions are notified to the user's device, and the user receives specific actions on their device.

[0710] For example, if the emotion engine detects that a user is overwhelmed, the server will adjust the suggested workload to reduce the user's burden. This makes it possible to create an environment where agricultural activities can be continued without difficulty. Furthermore, if it is determined that the user is satisfied, more proactive advice can be offered.

[0711] Users review suggestions and take agricultural actions via devices such as smartphones and tablets. In addition, users provide feedback on the condition of their crops to the system, and the results are sent to the server. The server updates the system based on the feedback, improving the accuracy of the next analysis. This enables detailed agricultural support that takes into account the user's emotional state.

[0712] The following describes the processing flow.

[0713] Step 1:

[0714] The terminal transmits environmental data acquired from the data collection device to the server. Simultaneously, the emotion engine analyzes emotional data from the user's facial expressions and voice, and transmits this data to the server.

[0715] Step 2:

[0716] The server preprocesses the received environmental and meteorological data, preparing it for analysis. This includes imputing missing values ​​and denoising the data.

[0717] Step 3:

[0718] The server integrates emotional data with other data and analyzes it in the processing unit. Machine learning algorithms calculate the optimal cultivation method, taking into account the user's emotional state.

[0719] Step 4:

[0720] Based on the analysis results, the server sends the user the optimal cultivation method to their terminal. This includes suggestions that take emotions into consideration.

[0721] Step 5:

[0722] The terminal displays suggestions received from the server to the user. These include specific instructions for agricultural actions that have been adjusted according to the user's emotional state.

[0723] Step 6:

[0724] Users follow the suggestions from their devices and actually carry out agricultural actions. This ensures that the suggested cultivation methods are applied in the field.

[0725] Step 7:

[0726] Users provide feedback through their devices regarding the results of their actions and any changes in their emotions. This feedback, along with emotion data, is sent to the server.

[0727] Step 8:

[0728] The server updates the system's learning model based on feedback information, improving the accuracy and reliability of future suggestions. This process is repeated to optimize individual agricultural support for each user.

[0729] (Example 2)

[0730] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0731] Conventional agricultural support systems often provide uniform cultivation methods and work instructions without considering the user's emotional state, which increases the user's burden. To solve this problem, there is a need for technology that provides personalized advice that takes the user's emotions into account, thereby better supporting the user's agricultural activities.

[0732] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0733] In this invention, the server includes means for transmitting data collected from a data acquisition device to an analysis device, means for integrating and analyzing the environmental data and weather information received by the analysis device, and means for acquiring the user's emotional state using an emotion recognition engine and generating it as data. This makes it possible to propose personalized cultivation methods that reflect the user's emotional state.

[0734] A "data acquisition device" is a device that collects environmental information and related information, and it acquires real-time data using sensors, APIs, etc.

[0735] An "analysis device" is a device that integrates and analyzes received data, and includes a computer or processor for outputting processing results.

[0736] "Environmental data" refers to information related to agriculture, such as temperature, humidity, and soil conditions, and serves as basic data for determining crop cultivation methods.

[0737] "Weather information" refers to data related to weather conditions, including information that shows weather trends based on forecasts and actual measurements.

[0738] An "emotion recognition engine" refers to a program or algorithm that analyzes a user's facial expressions and tone of voice to generate data on their emotional state.

[0739] A "user terminal" is a device used by a user to receive and send information, and includes smartphones, tablets, and other similar devices.

[0740] "Cultivation methods" refer to information that outlines specific procedures and work methods for growing crops efficiently and effectively.

[0741] "Personalized cultivation methods" refer to suggested methods that take into account the user's specific circumstances and emotional state, providing unique guidelines that are adjusted from standard methods.

[0742] One embodiment of this invention is a system that provides personalized advice that takes into account the user's emotions in agricultural support. The system mainly consists of the following elements:

[0743] The server collects environmental and meteorological data through data acquisition devices, which are then integrated and analyzed by analysis devices. This analysis utilizes computer systems and specialized software with advanced data processing capabilities. Furthermore, an emotion recognition engine is used to analyze the user's facial expressions and voice tone, generating emotion data. Smart devices equipped with cameras and microphones are used for emotion recognition.

[0744] Users input their emotional state into the system via information devices such as smartphones and tablets. This information is transmitted to the server in real time and analyzed in combination with other data.

[0745] Based on the analysis results, the server generates personalized cultivation methods tailored to the user. In this process, the generating AI model utilizes prompts to construct advice with unprecedented accuracy. These prompts are used, for example, in the form of "What is the optimal farming task when rain is expected and the user is feeling stressed?"

[0746] The generated cultivation methods and work suggestions are notified to the information terminal and immediately displayed to the user. Based on this, the user carries out specific agricultural activities and provides feedback from the terminal to the server. The server uses this feedback to further refine the accuracy of the model.

[0747] In this way, users can engage in sustainable and less stressful agricultural activities and receive meticulous support that takes their feelings into consideration.

[0748] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0749] Step 1:

[0750] The server receives environmental data from data acquisition devices. This data includes temperature, humidity, and soil conditions, and is collected in real time via sensors and APIs. This received data is temporarily stored on the platform in preparation for subsequent analysis.

[0751] Step 2:

[0752] The server retrieves weather information from another system. Input data includes forecasts and weather history, and data collection is performed via an API. This information, along with environmental data, is stored in a database and prepared for integrated analysis.

[0753] Step 3:

[0754] Users provide emotional data using their smart devices. The input consists of facial expressions and voice tone, which are transmitted to the emotion recognition engine via the camera and microphone. As a result, data indicating the user's emotional state is generated and immediately sent to the server.

[0755] Step 4:

[0756] The server processes integrated environmental data, weather information, and sentiment data using an analysis device. Based on the input data, a generative AI model uses the prompt "What is the optimal cultivation guidance in this situation?" to generate the optimal cultivation method for the user. The output is a personalized cultivation method suggestion.

[0757] Step 5:

[0758] The server notifies the user's information terminal of the generated cultivation method. This output is displayed as an advice message, providing the user with specific work instructions. This allows the user to decide on actions based on the received suggestions.

[0759] Step 6:

[0760] Users perform farm work based on the suggested cultivation methods and input the results and their impressions as feedback into the terminal. This input data, including the condition of the crops and their impressions of the work, is sent to the server.

[0761] Step 7:

[0762] The server receives user feedback and updates the system's analysis algorithm. Based on the input feedback, it improves the accuracy of suggestions for subsequent attempts, resulting in output that provides more user-friendly support.

[0763] (Application Example 2)

[0764] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0765] Agricultural and horticultural activities in urban environments depend on the emotional state and environmental conditions of individual users, requiring efficient support tailored to their specific needs. Conventional systems only provide uniform advice without adequately reflecting the user's emotions and state, resulting in challenges in improving the user experience and increasing work efficiency.

[0766] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0767] In this invention, the server includes means for transmitting environmental data received from a data collection device to a processing device; means for the processing device to generate user emotion data using an emotion analysis device, integrate and analyze it with the received environmental data and weather data; and means for generating a crop cultivation method that takes into account the user's emotional state based on the analysis results and notifying the user terminal. This enables personalized agricultural advice tailored to the user's emotional state.

[0768] A "data collection device" is a device that acquires environmental data and user sentiment data and transmits it to a processing device.

[0769] A "processing device" is a device that analyzes received data and generates cultivation methods tailored to the user's emotional state.

[0770] An "emotion analysis device" is part of a system that analyzes a user's facial expressions, voice tone, etc., and generates emotional data.

[0771] "Environmental data" refers to data such as ambient temperature, humidity, and sunlight necessary for growing crops.

[0772] "Weather data" refers to information about weather conditions such as temperature, precipitation, and wind speed.

[0773] "Integrated analysis" is a process that combines emotional data, environmental data, and weather data for analysis.

[0774] "Cultivation methods" refer to information about the procedures and techniques for growing crops.

[0775] A "user terminal" is a device that notifies the user of the generated cultivation method and receives feedback from the user.

[0776] To implement this invention, a user's smart terminal, a server, a data collection device, and an emotion analysis system are utilized. The server receives environmental data and user emotion data transmitted from the user's smart terminal. The environmental data includes conditions suitable for cultivation, such as temperature and sunlight, and the emotion data is based on facial expressions and tone of voice obtained through the emotion analysis system.

[0777] Specifically, the user's smart device uses its camera and microphone to record the user's facial expressions and voice, and sends this data to an emotion analysis system. The emotion analysis system generates emotion data using, for example, the Google Cloud Vision API or IBM Watson Tone Analyzer. A server leverages AWS Lambda to integrate and analyze the emotion data with environmental and weather data, and uses a generative AI model to suggest the most appropriate agricultural action for the user's emotions.

[0778] Suggestions for users are communicated via smart devices, providing specific advice such as, "Today's temperature is 25 degrees Celsius and it's sunny. You seem a little tired, so we recommend only gentle watering." Users can adjust their farming activities based on these suggestions. This also helps avoid excessive workloads based on emotional analysis, resulting in more efficient farming.

[0779] To more effectively apply the generative AI model, the following can be used as an example of a prompt: "List quick and easy gardening tasks that are suitable for a user who is feeling stressed. The weather is sunny and the temperature is 20 degrees Celsius." Using such prompts, the system can generate more accurate suggestions.

[0780] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0781] Step 1:

[0782] The user terminal uses a camera and microphone to capture the user's facial expressions and voice. This data is transmitted in real time to an emotion analysis system. The input is raw image and voice data, and the output is emotional features based on this data. Specifically, the analysis of image data identifies emotions from facial expressions, and the analysis of voice data analyzes tone and pitch to determine the emotional state.

[0783] Step 2:

[0784] The sentiment analysis system uses the Google Cloud Vision API to analyze image data and IBM Watson Tone Analyzer to analyze audio data. This outputs sentiment data that labels the user's current emotional state, such as "stressed" or "satisfied." This sentiment data is then sent to a processing unit.

[0785] Step 3:

[0786] The server receives environmental and weather data collected from sensors, along with sentiment data. This data is integrated and analyzed using AWS Lambda. The inputs are sentiment data, environmental data, and weather data, and the output is the combined analysis results. Specifically, each dataset is normalized and prepared for input into a machine learning model.

[0787] Step 4:

[0788] The server uses a generative AI model to generate agricultural actions based on the analyzed data. The input is the integrated analysis results. The output is a suggestion of specific cultivation methods tailored to the user's emotional state. For example, it might generate a suggestion such as, "The conditions are good today, so try a new soil improvement."

[0789] Step 5:

[0790] The user terminal receives suggestions from the server and delivers them to the user via a notification function. The user checks the notification and takes action based on the suggested action. The input is the suggestion notification from the server, and the output is the user's action. Specifically, information is provided to the user by displaying notifications or pop-ups on the terminal.

[0791] Step 6:

[0792] Users send feedback from their terminal to the server after completing a task. This feedback is used to improve the accuracy of future analyses. The input is user feedback information, and the output is information related to system improvements. Specifically, this involves analyzing the feedback data and updating the machine learning model as needed.

[0793] 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.

[0794] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0795] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0796] 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.

[0797] Figure 9 shows an 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.

[0798] 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.

[0799] 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.

[0800] 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, motorcycles, etc., 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, for example, based 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.

[0801] 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."

[0802] 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.

[0803] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0804] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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 the like 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.

[0813] 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.

[0814] The following is further disclosed regarding the embodiments described above.

[0815] (Claim 1)

[0816] Means for transmitting environmental data received from a data collection device to a processing device,

[0817] A means for analyzing environmental data and weather data received by the processing unit,

[0818] A means for generating crop cultivation methods based on analysis results,

[0819] A means of notifying the user terminal of the generated cultivation method,

[0820] A system that includes this.

[0821] (Claim 2)

[0822] The system according to claim 1, which proposes an action based on a cultivation method received by a user terminal.

[0823] (Claim 3)

[0824] The system according to claim 1, wherein a user terminal receives feedback from the user and transmits it to a processing unit.

[0825] "Example 1"

[0826] (Claim 1)

[0827] Means for transmitting environmental data received from a data collection device to an integration device,

[0828] Means for preprocessing weather data obtained from environmental data and external information received by the integrated device,

[0829] A means of analyzing preprocessed data using a machine learning algorithm,

[0830] A means for optimizing crop cultivation conditions and generating cultivation methods based on analysis results,

[0831] A means of notifying a communication terminal of the generated cultivation method,

[0832] A system that includes this.

[0833] (Claim 2)

[0834] The system according to claim 1, which makes work suggestions based on cultivation methods received by a communication terminal.

[0835] (Claim 3)

[0836] The system according to claim 1, wherein a communication terminal receives feedback from an operator and transmits it to an integrated device.

[0837] "Application Example 1"

[0838] (Claim 1)

[0839] Means for transmitting environmental data received from a data collection device to an information processing device,

[0840] A means for analyzing environmental data and weather data received by an information processing device,

[0841] A means for generating agricultural production methods based on analysis results,

[0842] A means of notifying an information terminal of the generated cultivation method,

[0843] A means of generating indicators of the living environment that contribute to the sustainability of the region,

[0844] Means of providing the generated metrics to the community,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, which proposes an operation based on a cultivation method received by an information terminal.

[0848] (Claim 3)

[0849] The system according to claim 1, wherein an information terminal receives feedback from a user and transmits it to an information processing device.

[0850] "Example 2 of combining an emotion engine"

[0851] (Claim 1)

[0852] A means for transmitting data collected from a data acquisition device to an analysis device,

[0853] A means for integrating and analyzing environmental data and weather information received by the analysis device,

[0854] A means of acquiring a user's emotional state using an emotion recognition engine and generating it as data,

[0855] A means of generating personalized cultivation methods by combining emotional data with analysis results,

[0856] A means of notifying an information terminal of the generated cultivation method,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, which recommends executable tasks based on the cultivation method received by the information terminal.

[0860] (Claim 3)

[0861] The system according to claim 1, wherein an information terminal receives evaluation information from a user and transmits it to an analysis device.

[0862] "Application example 2 when combining with an emotional engine"

[0863] (Claim 1)

[0864] Means for transmitting environmental data received from a data collection device to a processing device,

[0865] A means for analyzing environmental data and weather data received by the processing unit,

[0866] A means by which an emotion analysis device generates user emotion data,

[0867] A method for integrating and analyzing emotional data, environmental data, and weather data,

[0868] A means for generating a crop cultivation method that corresponds to the user's emotional state based on the analysis results,

[0869] A means of notifying the user terminal of the generated cultivation method,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, which proposes an action that takes into account the user's emotional state based on the cultivation method received by the user terminal.

[0873] (Claim 3)

[0874] The system according to claim 1, wherein a user terminal receives emotion-based feedback from the user and transmits it to a processing unit. [Explanation of symbols]

[0875] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for transmitting environmental data received from a data collection device to a processing device, A means for analyzing environmental data and weather data received by the processing unit, A means for generating crop cultivation methods based on analysis results, A means of notifying the user terminal of the generated cultivation method, A system that includes this.

2. The system according to claim 1, which proposes an action based on the cultivation method received by the user terminal.

3. The system according to claim 1, wherein the user terminal receives feedback from the user and transmits it to the processing unit.

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