system

Abstract

We provide the system. [Solution] A sensor means for detecting the growth status of crops, A generative model means for analyzing collected external environmental information, Information processing means for generating a work plan based on external environment information analyzed by the generation model means, An output interface means for providing the aforementioned work plan to the end user, An integrated management system for comprehensively managing environmental conditions at multiple locations within a city, 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 method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of 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] In the agricultural field, labor shortage and improvement of production efficiency have become urgent issues. In particular, due to aging and lack of successors, the number of agricultural workers has been continuously decreasing, so it is required to improve the current situation where a lot of time and labor are required for crop production management. Also, in order to flexibly respond to changes in the environment and the market and stably produce high-quality crops, optimization of agricultural operations is essential. However, the conventional technology has a problem that it is difficult to analyze crop growth data in real time and take immediate countermeasures.

Means for Solving the Problems

[0005] This invention utilizes sensors for highly accurate monitoring of crop growth and a generative model for analyzing the collected data. This enables the creation of a processor that provides optimal environmental conditions for crop growth and generates specific action plans for agricultural work based on the obtained analysis data. The action plans are communicated to the user through an intuitive and immediate output interface. This makes it possible to achieve sustainable agriculture while increasing the efficiency of agricultural work.

[0006] A "sensor" is a device that detects physical or environmental conditions and outputs them as electrical signals.

[0007] A "generative model" is an algorithm or program used to generate and analyze new data based on a large amount of existing data.

[0008] A "processor" is a central processing unit used for data processing and calculations.

[0009] An "action plan" is a set of specific work instructions and recommended procedures to be implemented based on the results of data analysis.

[0010] An "output interface" is a means or device for communicating system analysis results or notifications to the user.

[0011] A "user" is the entity that operates the system and carries out agricultural work based on the information and instructions provided. [Brief explanation of the drawing]

[0012] [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] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the numbered 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), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention provides a system for optimally managing crop growth in agricultural settings. Specific embodiments are described below.

[0034] Data collection

[0035] The terminal is equipped with sensors to acquire environmental data such as temperature, humidity, light intensity, and soil moisture in order to accurately monitor crop growth. The data transmitted from the sensors is acquired in real time and temporarily stored within the terminal. The stored data is then transmitted to a server via wireless communication. This process makes it possible to regularly and efficiently monitor the environmental conditions of the entire farmland.

[0036] Data Analysis

[0037] The server receives environmental data transmitted from the terminal. The received data is stored in a database and then analyzed by a generative model. The generative model uses machine learning algorithms to predict the growth status of crops and proposes optimal environmental adjustments and farming practices. Based on the analysis results, a plan for fertilization and irrigation suitable for future weather conditions and growth stages is also formulated.

[0038] Feedback and Implementation

[0039] The user receives analysis results and proposed action plans from the server. These are visualized through the terminal's user interface and displayed in a format that is easy for the user to understand. For example, the user may receive specific instructions such as, "Since temperatures will be high for the next three days, watering every morning is recommended." Furthermore, through the interface on the terminal, it is also possible to instruct a robot to perform tasks based on the suggestions received, thereby automating agricultural work.

[0040] This system optimizes farming operations based on scientific data, rather than relying on the experience of farmers. This enables efficient production and supports a stable supply of high-quality agricultural products. Furthermore, by effectively utilizing limited resources, it promotes sustainable agriculture.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The device collects data on temperature, humidity, light intensity, and soil moisture through sensors to understand the surrounding environment of crops. The collected data is temporarily stored within the device.

[0044] Step 2:

[0045] The terminal transmits the stored sensor data to the server via the network. IoT technology is used for transmission, ensuring efficient and secure data transfer through wireless communication.

[0046] Step 3:

[0047] The server receives data sent from the terminal and records it in the database. The received data is immediately analyzed by a generative model and used to calculate crop growth predictions and environmental optimization.

[0048] Step 4:

[0049] The generative model utilizes machine learning algorithms to generate appropriate action plans from the analysis results. For example, it can estimate the timing of irrigation and the appropriate amount of fertilizer.

[0050] Step 5:

[0051] The server sends the generated action plan to the terminal. The terminal converts the received information into a visualized format for the user and displays it on the operation screen.

[0052] Step 6:

[0053] Based on the information presented through the terminal, users decide how to carry out agricultural work. If necessary, they can adopt the presented action plan, send work instructions from the terminal to the automated robot, and perform the actual work.

[0054] (Example 1)

[0055] 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."

[0056] Traditional methods for efficiently managing crop growth lacked flexibility in responding to fluctuations in environmental conditions and often relied heavily on the experience of farmers, making it difficult to perform agricultural tasks at the optimal time. Furthermore, it was challenging to easily develop concrete plans for effectively utilizing limited resources.

[0057] 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.

[0058] In this invention, the server includes detection means for collecting environmental information, analysis means for executing a machine learning algorithm for analyzing the collected environmental information, and planning means for generating a farming plan that corresponds to future weather conditions and crop growth stages based on the information analyzed by the analysis means. This makes it possible to automatically plan the appropriate timing of farming work according to the growth of crops and to make the most of limited resources.

[0059] "Environmental information" refers to data on the natural environment that affects crop growth, such as temperature, humidity, light intensity, and soil moisture.

[0060] "Detection means" refers to a device or group of devices that measures data through sensors in order to collect environmental information.

[0061] "Analysis means" refers to a computer program or system that processes collected environmental information using machine learning algorithms to make predictions about crop growth and the environment.

[0062] "Plan generation means" refers to a system or process that creates schedules and plans for agricultural work based on information obtained by analysis means.

[0063] "Display means" refers to an interface or device for providing and visually presenting agricultural work plans created by the plan generation means to the user.

[0064] This system is designed to efficiently manage crop growth in agricultural settings and generate farming plans based on that growth. It primarily functions through the collaboration of three parties: terminals, servers, and users.

[0065] Hardware and software configuration

[0066] The terminal is equipped with sensors that accurately collect environmental information such as temperature, humidity, light intensity, and soil moisture. This collected data is temporarily stored in the terminal's internal memory. The terminal also has wireless communication capabilities, which allow it to send the collected data to a server.

[0067] The server has the function of receiving environmental information sent from terminals and storing it in a database. Machine learning algorithms are implemented within the server, and generative AI models such as TENSORFLOW® and PyTorch are used to analyze the collected data. As a result, the server automatically generates predictions of crop growth and optimal farming plans.

[0068] Users can view analysis results and work plans provided by the server on the terminal's user interface. This interface is intuitively designed and easy for users to operate. Users can perform specific farming tasks based on the displayed suggestions and, if necessary, assign robots to perform the tasks.

[0069] Examples and prompts for generative AI models

[0070] As a concrete example, consider a situation where a user is growing tomatoes. The device collects environmental information, and the server analyzes that data to generate a plan such as, "High temperatures are expected for the next three days, so we recommend watering every morning." This plan helps the user take timely action.

[0071] An example of a prompt to input into the generating AI model is, "Please suggest the optimal watering schedule for tomatoes based on the weather conditions for the next 7 days." This prompt will prompt the AI ​​model to provide an appropriate watering schedule.

[0072] This system enables users to practice agricultural management based on scientific data, promoting efficient and sustainable agriculture.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The device collects environmental information such as temperature, humidity, light intensity, and soil moisture in real time using sensors. The input is an analog signal from the sensors, which is converted into digital data and temporarily stored in internal memory. This process makes it possible to instantly grasp changes in the environment.

[0076] Step 2:

[0077] The terminal transmits the collected digital data to the server using wireless communication. The input is environmental data stored inside the terminal, which is compressed and converted into packet data format, then output to the server via wireless technology. Once the transmission is successfully completed, an LED lights up on the terminal to confirm this.

[0078] Step 3:

[0079] The server immediately stores data received from the terminal into the database. The input is packet data sent wirelessly, which is then decompressed and converted into a database format. Before storing the data in the database, preprocessing is performed, such as removing duplicate data and filtering outliers.

[0080] Step 4:

[0081] The server analyzes pre-processed environmental data using a generative AI model. The input is a clean dataset, which is passed to the generative AI model to run machine learning algorithms. The output obtained here includes analysis results such as crop growth predictions and optimal irrigation and fertilization plans.

[0082] Step 5:

[0083] The server generates a specific action plan based on the analysis results. The input is the analysis results from the generating AI model, and the plan generation algorithm is executed based on this to create the action plan. For example, an instruction such as "Water with 20 liters of water every morning for the next three days" is generated.

[0084] Step 6:

[0085] The user reviews the action plan provided by the server on the terminal's user interface. The input is the action plan sent from the server, which is visually displayed on the terminal's screen. Based on this, the user can decide whether to perform specific farm tasks or entrust the work to a robot.

[0086] (Application Example 1)

[0087] 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."

[0088] Managing farmland and green spaces in urban areas is challenging due to limited space and diverse environmental conditions. Currently, environmental management is often carried out individually in multiple locations, preventing integrated monitoring and control. This makes it difficult to maintain optimal environmental conditions overall. Furthermore, management relies on individual workers, making efficiency and sustainability difficult.

[0089] 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.

[0090] In this invention, the server includes a sensor means for detecting the growth status of crops, a generative model means for analyzing collected external environmental information, an information processing means for generating a work plan based on the external environmental information analyzed by the generative model means, and an integrated management means for comprehensively managing environmental conditions at multiple locations within a city. This enables efficient and sustainable management of farmland and green spaces within cities.

[0091] A "sensor for detecting the growth status of crops" is a device used to monitor the process of crop growth in agriculture, and functions to acquire environmental data such as temperature, humidity, light intensity, and soil moisture.

[0092] "Generative modeling means for analyzing collected external environmental information" refers to a system that includes machine learning algorithms used to analyze collected data related to agriculture and predict crop growth status and changes in environmental conditions.

[0093] "Information processing means for generating work plans" refers to a digital processor or computing device for creating optimal farm work and environmental adjustment plans based on analysis results.

[0094] "Output interface means" refers to a device that includes a screen or operation panel to display generated information and suggestions to the user in an easy-to-understand manner, and to enable the user to perform the necessary operations.

[0095] "Integrated management means" refers to a system or platform for centrally managing environmental information of multiple agricultural and green spaces in an urban area and providing unified guidelines.

[0096] In order to implement this invention, it is necessary to construct an environmental monitoring system in the agricultural field. The system mainly consists of sensor means, information collection and analysis means, output interface means, and integrated management means.

[0097] The sensor system includes various sensors that measure temperature, humidity, light intensity, and soil moisture in real time to monitor the growth status of crops and green spaces within urban areas. This data is transmitted to a server via wireless communication.

[0098] The server analyzes the collected data using a generative AI model written in Python. This generative AI model utilizes machine learning algorithms to predict crop growth and future environmental changes from environmental data stored in a database (e.g., MySQL®). Based on the analysis information obtained in this way, the information processing system automatically formulates the optimal work plan.

[0099] The output interface includes a smartphone application that allows users to intuitively view real-time information on multiple farmlands and green spaces within the city. The application is built using a framework such as React Native.

[0100] Finally, the integrated management system allows data from multiple locations to be collected and centrally managed, enabling efficient greening across the entire city. For example, this system can be used to comprehensively monitor the humidity and temperature of multiple community gardens and propose an optimal irrigation schedule.

[0101] As an example prompt, by inputting the instruction "Collect data from sensors in the city for May and propose the optimal irrigation schedule" into the generating AI model, specific measures can be obtained.

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The device collects environmental data such as temperature, humidity, light intensity, and soil moisture from various sensors. This data is temporarily stored in the device's memory. The input is real-time environmental data from each sensor, and the output is formatted environmental data. This allows for an accurate understanding of the environmental conditions that affect crop growth.

[0105] Step 2:

[0106] The terminal transmits the collected environmental data to the server via a wireless communication module. The input is the environmental data temporarily stored in the terminal's memory, and the output is the data transmitted to the server. In this step, data is transmitted in real time via long-distance communication.

[0107] Step 3:

[0108] The server stores the received environment data in a database. A database such as MySQL is used to efficiently manage the data. Input is raw data sent from the terminal, and output is structured information stored in the database. Data persistence takes place at this stage.

[0109] Step 4:

[0110] The server analyzes the stored data using a generative AI model. This analysis utilizes machine learning algorithms written in Python. The input is historical and current environmental data obtained from a database, and the output is analysis results regarding crop growth predictions and optimal environmental conditions. The data is processed here by the AI ​​model, generating useful insights.

[0111] Step 5:

[0112] The server generates a work plan using information processing tools based on the analyzed data. Specific farm work suggestions and environmental adjustment plans are calculated. The input is predictive data from an AI model, and the output is the actual work schedule and suggestions. The server then constructs a concrete action plan.

[0113] Step 6:

[0114] Users can view work plans and analysis results on a smartphone app via the output interface. A UI built with React Native supports this. Input is analysis results provided by the server, and output is user-friendly visual information. This makes it easier for users to receive specific instructions.

[0115] Step 7:

[0116] The integrated management system centrally manages data from various locations within the city and supervises the execution of work plans. Inputs include monitoring data and work plans from each location, while outputs are integrated management reports and improvement suggestions. At this stage, efficient operation is achieved by utilizing data from multiple locations.

[0117] 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.

[0118] This invention incorporates an emotion engine into conventional agricultural assistance systems, enabling flexible responses tailored to the user's emotional state. Specific embodiments are described below.

[0119] Data collection and emotion recognition

[0120] The terminal collects environmental data such as temperature, humidity, light intensity, and soil moisture, which are necessary for managing crop growth, using various sensors. This data is temporarily stored on the terminal and later transmitted to the server. Simultaneously, an emotion engine connected to the terminal analyzes the user's voice and video data to recognize the user's emotional state in real time. The emotion engine uses an emotion analysis algorithm to determine stress levels and satisfaction levels, for example, from the user's tone of voice and facial expressions.

[0121] Data analysis and action plan generation

[0122] The server receives environmental data sent from the terminal, records it in a database, and then analyzes it using a generative model. This model has the ability to predict crop growth and suggest environmental optimizations. Based on the results, the processor generates an action plan, taking into account the output of the emotion engine. For example, if the user is stressed, an action plan to reduce the workload will be suggested.

[0123] feedback

[0124] The generated action plan is sent to the device and presented to the user through the user interface. The device adaptively adjusts the content and tone of the feedback message based on the emotion engine's recognition results. If the user is relaxed, a normal message is displayed; if they are stressed, encouraging messages and simple instructions are provided. This allows the user to perform farm work at a pace that suits their current situation.

[0125] (Specific example)

[0126] When a user asks the device, "What's the soil moisture level today?", the device sends data from its sensors to the server, and the emotion engine analyzes the user's voice to detect "anxiety." The server's analysis results in "The soil is a little dry, so watering in the afternoon is recommended," and the device notifies the user in a friendly manner, "Let's water it a little this afternoon. Don't worry, it will get better soon!"

[0127] In this way, the agricultural assistance system provides optimal information and work support tailored to the user's emotional state, supporting the efficient and sustainable management of crops.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The device collects data from sensors that detect temperature, humidity, light intensity, soil moisture, and other environmental conditions on the farm. This data is stored in the device in real time.

[0131] Step 2:

[0132] The device uses an emotion engine to analyze the user's voice and video data and recognize the user's emotional state. Based on this, the emotion engine determines whether the user is currently feeling stressed or relaxed.

[0133] Step 3:

[0134] The device transmits collected environmental data and user sentiment data to a server via the network. A secure protocol is used for this transmission to maintain the confidentiality and integrity of the data.

[0135] Step 4:

[0136] The server stores the received environmental data in a database and uses generative models to perform analyses for predicting crop growth and adjusting the environment. The results obtained here include specific work instructions such as temperature and humidity adjustments and when to irrigate.

[0137] Step 5:

[0138] The server considers the analysis results and emotional state, and the processor creates an action plan. This plan incorporates flexible content that takes the user's emotions into account. For example, if the user is feeling stressed, it may include suggestions to reduce their workload.

[0139] Step 6:

[0140] When the terminal displays the action plan received from the server in the user interface, it adjusts the tone and content of the feedback message based on the emotion engine's recognition results. A gentle tone and simplified explanations are used to avoid burdening the user.

[0141] Step 7:

[0142] Users review instructions from their terminals and direct automated agricultural robots and other equipment to execute the appropriate commands. This allows users to manage their farms efficiently while reducing emotional burden.

[0143] (Example 2)

[0144] 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".

[0145] While conventional agricultural support systems offered crop management suggestions based on environmental data, they had limitations in providing flexible and individualized responses that took into account the user's emotional state. As a result, they failed to contribute to reducing user stress or promoting efficient farm work.

[0146] 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.

[0147] In this invention, the server includes means for detecting the growth status of crops, an emotion engine means for analyzing the emotional state of the user, and a processing means for generating a plan for agricultural work based on the information analyzed by a generation algorithm. This enables work support, including individualized responses according to the emotional state of the user.

[0148] A "device for detecting the growth status of crops" is a device that collects information related to the environmental conditions surrounding the crops and the growth of the crops themselves.

[0149] A "generative algorithm for analyzing collected information" is a computational method for analyzing collected environmental data and related information to generate information necessary for crop management and growth prediction.

[0150] A "processing device for generating agricultural work plans" is a computing device that generates specific action plans necessary for agricultural work based on analyzed information.

[0151] "Display devices provided to users" refer to devices such as screens or projectors that allow users to visually confirm the generated action plan and related information.

[0152] An "emotion engine that analyzes the user's emotional state" is software or hardware that analyzes data such as the user's voice and facial expressions to identify their emotions in real time.

[0153] "Means for adjusting responses to users" refers to a system that appropriately modifies the messages and action plans provided according to the emotional state of identified users, thereby providing the best possible response to each user.

[0154] This invention is an integrated support system for users to efficiently manage their crops. The following describes in detail how this system is configured and operates.

[0155] The terminal collects environmental data through various sensors installed in the surrounding environment of agricultural crops. This environmental data includes temperature, humidity, light intensity, and soil moisture. For example, a general-purpose sensor is used to measure temperature and humidity, a photosensor to measure light intensity, and a soil sensor to measure soil moisture. The terminal is also equipped with a microphone and camera to acquire user voice and video data.

[0156] The server receives environmental data and user voice information transmitted from the terminal. This dataset is analyzed by a generative algorithm, which is a generative AI model. The generative algorithm is implemented, for example, using a machine learning framework, and creates a crop growth prediction model from the environmental data. Based on the output of this model, the processing unit generates an action plan for farm work.

[0157] Furthermore, the emotion engine installed on the server analyzes audio and video data acquired from the user to identify the user's emotional state in real time. This emotion analysis uses a combination of speech recognition and facial expression recognition systems. This allows the system to recognize whether the user is stressed or relaxed.

[0158] The generated action plan is adjusted according to the user's emotional state. For example, if the user shows signs of stress, the workload included in the action plan is reduced, and an encouraging message is added before it is sent to the device. The user can receive this information visually or audibly through the device's display.

[0159] For example, if a user asks the device, "What's the soil moisture level today?", the device sends data from its sensors to the server. If the emotion engine, which analyzes the user's voice, detects "anxiety," the server generates an action plan such as, "The soil is a little dry, so watering in the afternoon is recommended." The device then notifies the user of this in a friendly message like, "Let's water it a little this afternoon. Don't worry, it will get better soon!"

[0160] A concrete example of a prompt message is one that can be sent to the AI ​​generation model, such as "Create a message that will reassure the user." This prompt will generate an appropriate message that takes the user's emotional state into consideration.

[0161] In this way, the system supports the efficient and sustainable management of crops and can respond flexibly to the user's emotional state.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The terminal collects environmental data using various sensors. Inputs include environmental information obtained from temperature, humidity, light, and soil moisture sensors. The terminal temporarily records this sensor data in digital format and then prepares it for transmission to the server as a data package. This provides data that accurately reflects the current environmental conditions of crops.

[0165] Step 2:

[0166] The device acquires the user's voice and video data. Inputs include audio data from the microphone and video data from the camera. The device records this data in real time and converts it into a format necessary for the emotion engine's analysis. This output is used in the next step as foundational data to identify the user's emotional state.

[0167] Step 3:

[0168] The server processes environmental data and user data received from the terminal. First, a generative algorithm, which is a generative AI model, is executed based on the environmental data to predict crop growth. In this process, the input environmental data is analyzed and an output is generated that identifies the optimal conditions necessary for growth. At the same time, an emotion engine is activated that analyzes the collected user's voice and video, and generates the user's emotional state as an output.

[0169] Step 4:

[0170] The server creates an action plan for farm work based on the generated growth prediction, taking into account the output of the emotion engine. Input includes the analysis results and the user's emotional state obtained from the emotion engine. Based on this, the server outputs a specific plan to improve the efficiency of farm work. For example, if the user is feeling stressed, a plan to reduce the workload will be suggested.

[0171] Step 5:

[0172] The server sends the generated action plan to the terminal. The terminal receives it and prepares to display it in the user interface. The input is the action plan from the server, and the output is information converted into a format that is easily understandable to the user. The terminal displays this information visually and audibly, providing guidance for the user to take appropriate action based on the current situation.

[0173] Step 6:

[0174] Users check the information provided through the terminal's display device and then perform farm work. By checking the information, users can set a work pace that suits their emotional state. As a result, they obtain efficient and low-stress work results. This cycle allows users to perform farm work based on environmental conditions and their own state of mind.

[0175] (Application Example 2)

[0176] 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".

[0177] Traditional farm management systems often provide uniform advice and action plans without considering the user's emotional state, potentially leading to decreased user motivation and increased stress. Furthermore, they often place too much emphasis on collecting environmental data, lacking the flexibility to address individual user situations. This creates a challenge in efficiently and sustainably managing crops.

[0178] 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.

[0179] In this invention, the server includes means for using a device to collect data related to agricultural work, means for using a device to recognize the user's emotional state, and means for using a generative model that performs analysis based on the collected data and emotional data. This makes it possible to provide action plans adapted to the user's emotional state and to provide feedback that responds to those emotions.

[0180] A "device for collecting data related to agricultural work" is a device used to acquire necessary data such as temperature, humidity, light intensity, and soil moisture in an agricultural environment in real time.

[0181] A "device for recognizing user emotions" is a device that analyzes a user's emotional state from information such as voice and facial expressions, and determines their stress levels and satisfaction levels.

[0182] A "generative model" is a model used to predict crop growth and propose environmental optimizations based on collected data.

[0183] A "processor" is a device that generates flexible action plans by taking into account the analysis results obtained from the generative model and the user's emotional state.

[0184] An "output device" is a device that provides the user with a generated action plan and the feedback based on it, either visually or audibly.

[0185] This invention aims to realize a smart farming system that improves the efficiency of agricultural work and reduces the mental burden on users. An embodiment thereof is described below.

[0186] First, the user utilizes a device designed to acquire agricultural data, continuously collecting environmental information such as temperature, humidity, light intensity, and soil moisture. This device accurately acquires data by integrating various sensors and stores it on the device in real time.

[0187] Next, the device that recognizes the user's emotional state analyzes the user's emotions using voice and video input. Here, voice recognition software and image analysis software are used to analyze voice tone and facial expressions to evaluate the user's satisfaction level and stress level. Specifically, this is software that uses emotion analysis algorithms.

[0188] The server uses a generative AI model to analyze collected environmental and sentiment data. This model has the ability to predict crop growth under various conditions and generate optimal action plans accordingly. For example, it analyzes the adaptation of different crop types to different weather conditions and proposes harvest times and growth promotion strategies.

[0189] The generated action plan is fed back to the user through the device. Here, the user interface adaptively adjusts the content and tone of the message according to the user's emotional state. The device features an intuitive interface that prioritizes friendliness and provides information in a way that is easy for the user to understand.

[0190] For example, if a user asks, "How are the crops doing lately?", the device queries the server based on the collected data, and the server analyzes it using a generative AI model to generate advice that also takes into account the user's current emotional state. For instance, the device might display a message like, "It's been dry this week, so water them this evening. Don't worry, it will get better soon!"

[0191] Examples of prompts for a generative AI model are as follows:

[0192] "Generate concise action plans for when users are feeling anxious, and create warm, encouraging messages."

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The terminal collects agricultural environmental data. This device is equipped with sensors to measure temperature, humidity, light intensity, and soil moisture. Data from these sensors is input into the terminal and temporarily stored. During this process, the terminal performs real-time monitoring and organizes and stores the collected numerical data chronologically.

[0196] Step 2:

[0197] To recognize the user's emotional state, the device uses its camera and microphone to collect audio and video data. This data is input into the device, and an emotion analysis algorithm is activated. This algorithm extracts features such as voice tone and facial expressions, and processes the data to identify specific emotions. The determined emotions are output as indicators of stress and satisfaction.

[0198] Step 3:

[0199] The server receives environmental and emotional data transmitted from the terminal. The server uses a generative AI model to analyze this data and predict crop growth. This model learns from historical data and performs data calculations to generate the optimal action plan based on current environmental conditions. The generated action plan is output as specific work instructions and suggestions.

[0200] Step 4:

[0201] The server sends the action plan to the terminal. The terminal displays the action plan to the user. Here, the output interface adjusts the content and tone of the feedback message according to the user's emotional state. For example, a relaxed user is presented with a normal message, while a stressed user is given gentle words of encouragement.

[0202] Step 5:

[0203] The user performs farm work based on the received action plan. Any environmental or emotional changes observed during this process are recorded again on the device. This feedback loop allows the system to continuously update data, contributing to the generation of more precise action plans for the next cycle.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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".

[0220] This invention provides a system for optimally managing crop growth in agricultural settings. Specific embodiments are described below.

[0221] Data collection

[0222] The terminal is equipped with sensors to acquire environmental data such as temperature, humidity, light intensity, and soil moisture in order to accurately monitor crop growth. The data transmitted from the sensors is acquired in real time and temporarily stored within the terminal. The stored data is then transmitted to a server via wireless communication. This process makes it possible to regularly and efficiently monitor the environmental conditions of the entire farmland.

[0223] Data Analysis

[0224] The server receives environmental data transmitted from the terminal. The received data is stored in a database and then analyzed by a generative model. The generative model uses machine learning algorithms to predict the growth status of crops and proposes optimal environmental adjustments and farming practices. Based on the analysis results, a plan for fertilization and irrigation suitable for future weather conditions and growth stages is also formulated.

[0225] Feedback and Implementation

[0226] The user receives analysis results and proposed action plans from the server. These are visualized through the terminal's user interface and displayed in a format that is easy for the user to understand. For example, the user may receive specific instructions such as, "Since temperatures will be high for the next three days, watering every morning is recommended." Furthermore, through the interface on the terminal, it is also possible to instruct a robot to perform tasks based on the suggestions received, thereby automating agricultural work.

[0227] This system optimizes farming operations based on scientific data, rather than relying on the experience of farmers. This enables efficient production and supports a stable supply of high-quality agricultural products. Furthermore, by effectively utilizing limited resources, it promotes sustainable agriculture.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] The device collects data on temperature, humidity, light intensity, and soil moisture through sensors to understand the surrounding environment of crops. The collected data is temporarily stored within the device.

[0231] Step 2:

[0232] The terminal transmits the stored sensor data to the server via the network. IoT technology is used for transmission, ensuring efficient and secure data transfer through wireless communication.

[0233] Step 3:

[0234] The server receives data sent from the terminal and records it in the database. The received data is immediately analyzed by a generative model and used to calculate crop growth predictions and environmental optimization.

[0235] Step 4:

[0236] The generative model utilizes machine learning algorithms to generate appropriate action plans from the analysis results. For example, it can estimate the timing of irrigation and the appropriate amount of fertilizer.

[0237] Step 5:

[0238] The server sends the generated action plan to the terminal. The terminal converts the received information into a visualized format for the user and displays it on the operation screen.

[0239] Step 6:

[0240] Based on the information presented through the terminal, users decide how to carry out agricultural work. If necessary, they can adopt the presented action plan, send work instructions from the terminal to the automated robot, and perform the actual work.

[0241] (Example 1)

[0242] 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."

[0243] Traditional methods for efficiently managing crop growth lacked flexibility in responding to fluctuations in environmental conditions and often relied heavily on the experience of farmers, making it difficult to perform agricultural tasks at the optimal time. Furthermore, it was challenging to easily develop concrete plans for effectively utilizing limited resources.

[0244] 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.

[0245] In this invention, the server includes detection means for collecting environmental information, analysis means for executing a machine learning algorithm for analyzing the collected environmental information, and planning means for generating a farming plan that corresponds to future weather conditions and crop growth stages based on the information analyzed by the analysis means. This makes it possible to automatically plan the appropriate timing of farming work according to the growth of crops and to make the most of limited resources.

[0246] "Environmental information" refers to data on the natural environment that affects crop growth, such as temperature, humidity, light intensity, and soil moisture.

[0247] "Detection means" refers to a device or group of devices that measures data through sensors in order to collect environmental information.

[0248] "Analysis means" refers to a computer program or system that processes collected environmental information using machine learning algorithms to make predictions about crop growth and the environment.

[0249] "Plan generation means" refers to a system or process that creates schedules and plans for agricultural work based on information obtained by analysis means.

[0250] "Display means" refers to an interface or device for providing and visually presenting agricultural work plans created by the plan generation means to the user.

[0251] This system is designed to efficiently manage crop growth in agricultural settings and generate farming plans based on that growth. It primarily functions through the collaboration of three parties: terminals, servers, and users.

[0252] Hardware and software configuration

[0253] The terminal is equipped with sensors that accurately collect environmental information such as temperature, humidity, light intensity, and soil moisture. This collected data is temporarily stored in the terminal's internal memory. The terminal also has wireless communication capabilities, which allow it to send the collected data to a server.

[0254] The server has the function of receiving environmental information sent from terminals and storing it in a database. Machine learning algorithms are implemented within the server, and generative AI models such as TensorFlow and PyTorch are used to analyze the collected data. As a result, the server automatically generates predictions of crop growth and optimal farming plans.

[0255] Users can view analysis results and work plans provided by the server on the terminal's user interface. This interface is intuitively designed and easy for users to operate. Users can perform specific farming tasks based on the displayed suggestions and, if necessary, assign robots to perform the tasks.

[0256] Examples and prompts for generative AI models

[0257] As a concrete example, consider a situation where a user is growing tomatoes. The device collects environmental information, and the server analyzes that data to generate a plan such as, "High temperatures are expected for the next three days, so we recommend watering every morning." This plan helps the user take timely action.

[0258] An example of a prompt to input into the generating AI model is, "Please suggest the optimal watering schedule for tomatoes based on the weather conditions for the next 7 days." This prompt will prompt the AI ​​model to provide an appropriate watering schedule.

[0259] This system enables users to practice agricultural management based on scientific data, promoting efficient and sustainable agriculture.

[0260] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0261] Step 1:

[0262] The device collects environmental information such as temperature, humidity, light intensity, and soil moisture in real time using sensors. The input is an analog signal from the sensors, which is converted into digital data and temporarily stored in internal memory. This process makes it possible to instantly grasp changes in the environment.

[0263] Step 2:

[0264] The terminal transmits the collected digital data to the server using wireless communication. The input is environmental data stored inside the terminal, which is compressed and converted into packet data format, then output to the server via wireless technology. Once the transmission is successfully completed, an LED lights up on the terminal to confirm this.

[0265] Step 3:

[0266] The server immediately stores data received from the terminal into the database. The input is packet data sent wirelessly, which is then decompressed and converted into a database format. Before storing the data in the database, preprocessing is performed, such as removing duplicate data and filtering outliers.

[0267] Step 4:

[0268] The server analyzes pre-processed environmental data using a generative AI model. The input is a clean dataset, which is passed to the generative AI model to run machine learning algorithms. The output obtained here includes analysis results such as crop growth predictions and optimal irrigation and fertilization plans.

[0269] Step 5:

[0270] The server generates a specific action plan based on the analysis results. The input is the analysis results from the generating AI model, and the plan generation algorithm is executed based on this to create the action plan. For example, an instruction such as "Water with 20 liters of water every morning for the next three days" is generated.

[0271] Step 6:

[0272] The user reviews the action plan provided by the server on the terminal's user interface. The input is the action plan sent from the server, which is visually displayed on the terminal's screen. Based on this, the user can decide whether to perform specific farm tasks or entrust the work to a robot.

[0273] (Application Example 1)

[0274] 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 glasses 214 will be referred to as the "terminal."

[0275] Managing farmland and green spaces in urban areas is challenging due to limited space and diverse environmental conditions. Currently, environmental management is often carried out individually in multiple locations, preventing integrated monitoring and control. This makes it difficult to maintain optimal environmental conditions overall. Furthermore, management relies on individual workers, making efficiency and sustainability difficult.

[0276] 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.

[0277] In this invention, the server includes a sensor means for detecting the growth status of crops, a generative model means for analyzing collected external environmental information, an information processing means for generating a work plan based on the external environmental information analyzed by the generative model means, and an integrated management means for comprehensively managing environmental conditions at multiple locations within a city. This enables efficient and sustainable management of farmland and green spaces within cities.

[0278] A "sensor for detecting the growth status of crops" is a device used to monitor the process of crop growth in agriculture, and functions to acquire environmental data such as temperature, humidity, light intensity, and soil moisture.

[0279] "Generative modeling means for analyzing collected external environmental information" refers to a system that includes machine learning algorithms used to analyze collected data related to agriculture and predict crop growth status and changes in environmental conditions.

[0280] "Information processing means for generating work plans" refers to a digital processor or computing device for creating optimal farm work and environmental adjustment plans based on analysis results.

[0281] "Output interface means" refers to a device that includes a screen or operation panel to display generated information and suggestions to the user in an easy-to-understand manner, and to enable the user to perform the necessary operations.

[0282] "Integrated management means" refers to a system or platform for centrally managing environmental information of multiple agricultural and green spaces in an urban area and providing unified guidelines.

[0283] In order to implement this invention, it is necessary to construct an environmental monitoring system in the agricultural field. The system mainly consists of sensor means, information collection and analysis means, output interface means, and integrated management means.

[0284] The sensor means includes various sensors that measure temperature, humidity, light intensity, and soil moisture in real time to monitor the growth status of crops and green spaces within the city. This data is transmitted to the server via wireless communication.

[0285] The server analyzes the collected data using a generative AI model written in Python. The generative AI model utilizes machine learning algorithms to predict the growth status of crops and future environmental changes from the environmental data stored in a database (e.g., MySQL). Based on the analysis information thus obtained, the information processing means automatically formulates an optimal work plan.

[0286] The output interface means includes a smartphone application through which users can intuitively view the information of multiple farmlands and green spaces within the city in real time. The application is constructed using a framework such as React Native.

[0287] Finally, the integrated management means aggregates and centrally manages the data at multiple locations, enabling efficient implementation of greening across the entire city. For example, using this system, the humidity and temperature of multiple community gardens can be monitored integratively, and an optimal irrigation schedule can be proposed.

[0288] As a prompt example, by inputting an instruction such as "Collect the data for May from the sensors within the city and propose an optimal irrigation schedule" into the generative AI model, specific measures can be obtained.

[0289] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0290] Step 1:

[0291] The device collects environmental data such as temperature, humidity, light intensity, and soil moisture from various sensors. This data is temporarily stored in the device's memory. The input is real-time environmental data from each sensor, and the output is formatted environmental data. This allows for an accurate understanding of the environmental conditions that affect crop growth.

[0292] Step 2:

[0293] The terminal transmits the collected environmental data to the server via a wireless communication module. The input is the environmental data temporarily stored in the terminal's memory, and the output is the data transmitted to the server. In this step, data is transmitted in real time via long-distance communication.

[0294] Step 3:

[0295] The server stores the received environment data in a database. A database such as MySQL is used to efficiently manage the data. Input is raw data sent from the terminal, and output is structured information stored in the database. Data persistence takes place at this stage.

[0296] Step 4:

[0297] The server analyzes the stored data using a generative AI model. This analysis utilizes machine learning algorithms written in Python. The input is historical and current environmental data obtained from a database, and the output is analysis results regarding crop growth predictions and optimal environmental conditions. The data is processed here by the AI ​​model, generating useful insights.

[0298] Step 5:

[0299] The server generates a work plan using information processing tools based on the analyzed data. Specific farm work suggestions and environmental adjustment plans are calculated. The input is predictive data from an AI model, and the output is the actual work schedule and suggestions. The server then constructs a concrete action plan.

[0300] Step 6:

[0301] Users can view work plans and analysis results on a smartphone app via the output interface. A UI built with React Native supports this. Input is analysis results provided by the server, and output is user-friendly visual information. This makes it easier for users to receive specific instructions.

[0302] Step 7:

[0303] The integrated management system centrally manages data from various locations within the city and supervises the execution of work plans. Inputs include monitoring data and work plans from each location, while outputs are integrated management reports and improvement suggestions. At this stage, efficient operation is achieved by utilizing data from multiple locations.

[0304] 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.

[0305] This invention incorporates an emotion engine into conventional agricultural assistance systems, enabling flexible responses tailored to the user's emotional state. Specific embodiments are described below.

[0306] Data collection and emotion recognition

[0307] The terminal collects environmental data such as temperature, humidity, light intensity, and soil moisture, which are necessary for managing crop growth, using various sensors. This data is temporarily stored on the terminal and later transmitted to the server. Simultaneously, an emotion engine connected to the terminal analyzes the user's voice and video data to recognize the user's emotional state in real time. The emotion engine uses an emotion analysis algorithm to determine stress levels and satisfaction levels, for example, from the user's tone of voice and facial expressions.

[0308] Data analysis and action plan generation

[0309] The server receives the environmental data sent from the terminal, records it in the database, and then analyzes it using a generation model. This model has the ability to predict the growth of crops and propose environmental optimization. Based on the results, the processor generates an action plan, and at this time, the output of the emotion engine is also considered. For example, if the user is feeling stressed, an action plan to reduce the workload is proposed.

[0310] Feedback

[0311] The generated action plan is sent to the terminal and presented to the user through the user interface. The terminal adaptively adjusts the content and tone of the feedback message according to the recognition result of the emotion engine. If the user is in a relaxed state, a normal message is displayed, and if the user is feeling stressed, simple instructions are provided along with an encouraging message. This enables the user to perform farming operations at a pace suitable for their current situation.

[0312] (Specific example)

[0313] When the user asks the terminal "How's the soil moisture today?", the terminal sends the data from the sensor to the server, and the emotion engine analyzes the user's voice and detects "uneasiness". The result analyzed by the server is "The soil is slightly dry, so watering is recommended in the afternoon", and the terminal notifies the user in an amiable form such as "Let's do a little watering in the afternoon. Don't worry, it will surely improve soon!".

[0314] In this way, the agricultural assistance system provides optimal information and work support according to the user's emotional state, and supports the efficient and sustainable management of crops.

[0315] The following describes the processing flow.

[0316] Step 1:

[0317] The device collects data from sensors that detect temperature, humidity, light intensity, soil moisture, and other environmental conditions on the farm. This data is stored in the device in real time.

[0318] Step 2:

[0319] The device uses an emotion engine to analyze the user's voice and video data and recognize the user's emotional state. Based on this, the emotion engine determines whether the user is currently feeling stressed or relaxed.

[0320] Step 3:

[0321] The device transmits collected environmental data and user sentiment data to a server via the network. A secure protocol is used for this transmission to maintain the confidentiality and integrity of the data.

[0322] Step 4:

[0323] The server stores the received environmental data in a database and uses generative models to perform analyses for predicting crop growth and adjusting the environment. The results obtained here include specific work instructions such as temperature and humidity adjustments and when to irrigate.

[0324] Step 5:

[0325] The server considers the analysis results and emotional state, and the processor creates an action plan. This plan incorporates flexible content that takes the user's emotions into account. For example, if the user is feeling stressed, it may include suggestions to reduce their workload.

[0326] Step 6:

[0327] When the terminal displays the action plan received from the server in the user interface, it adjusts the tone and content of the feedback message based on the emotion engine's recognition results. A gentle tone and simplified explanations are used to avoid burdening the user.

[0328] Step 7:

[0329] Users review instructions from their terminals and direct automated agricultural robots and other equipment to execute the appropriate commands. This allows users to manage their farms efficiently while reducing emotional burden.

[0330] (Example 2)

[0331] 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".

[0332] While conventional agricultural support systems offered crop management suggestions based on environmental data, they had limitations in providing flexible and individualized responses that took into account the user's emotional state. As a result, they failed to contribute to reducing user stress or promoting efficient farm work.

[0333] 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.

[0334] In this invention, the server includes means for detecting the growth status of crops, an emotion engine means for analyzing the emotional state of the user, and a processing means for generating a plan for agricultural work based on the information analyzed by a generation algorithm. This enables work support, including individualized responses according to the emotional state of the user.

[0335] A "device for detecting the growth status of crops" is a device that collects information related to the environmental conditions surrounding the crops and the growth of the crops themselves.

[0336] A "generative algorithm for analyzing collected information" is a computational method for analyzing collected environmental data and related information to generate information necessary for crop management and growth prediction.

[0337] A "processing device for generating agricultural work plans" is a computing device that generates specific action plans necessary for agricultural work based on analyzed information.

[0338] "Display devices provided to users" refer to devices such as screens or projectors that allow users to visually confirm the generated action plan and related information.

[0339] An "emotion engine that analyzes the user's emotional state" is software or hardware that analyzes data such as the user's voice and facial expressions to identify their emotions in real time.

[0340] "Means for adjusting responses to users" refers to a system that appropriately modifies the messages and action plans provided according to the emotional state of identified users, thereby providing the best possible response to each user.

[0341] This invention is an integrated support system for users to efficiently manage their crops. The following describes in detail how this system is configured and operates.

[0342] The terminal collects environmental data through various sensors installed in the surrounding environment of agricultural crops. This environmental data includes temperature, humidity, light intensity, and soil moisture. For example, a general-purpose sensor is used to measure temperature and humidity, a photosensor to measure light intensity, and a soil sensor to measure soil moisture. The terminal is also equipped with a microphone and camera to acquire user voice and video data.

[0343] The server receives environmental data and user voice information transmitted from the terminal. This dataset is analyzed by a generative algorithm, which is a generative AI model. The generative algorithm is implemented, for example, using a machine learning framework, and creates a crop growth prediction model from the environmental data. Based on the output of this model, the processing unit generates an action plan for farm work.

[0344] Furthermore, the emotion engine installed on the server analyzes audio and video data acquired from the user to identify the user's emotional state in real time. This emotion analysis uses a combination of speech recognition and facial expression recognition systems. This allows the system to recognize whether the user is stressed or relaxed.

[0345] The generated action plan is adjusted according to the user's emotional state. For example, if the user shows signs of stress, the workload included in the action plan is reduced, and an encouraging message is added before it is sent to the device. The user can receive this information visually or audibly through the device's display.

[0346] For example, if a user asks the device, "What's the soil moisture level today?", the device sends data from its sensors to the server. If the emotion engine, which analyzes the user's voice, detects "anxiety," the server generates an action plan such as, "The soil is a little dry, so watering in the afternoon is recommended." The device then notifies the user of this in a friendly message like, "Let's water it a little this afternoon. Don't worry, it will get better soon!"

[0347] A concrete example of a prompt message is one that can be sent to the AI ​​generation model, such as "Create a message that will reassure the user." This prompt will generate an appropriate message that takes the user's emotional state into consideration.

[0348] In this way, the system supports the efficient and sustainable management of crops and can respond flexibly to the user's emotional state.

[0349] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0350] Step 1:

[0351] The terminal collects environmental data using various sensors. Inputs include environmental information obtained from temperature, humidity, light, and soil moisture sensors. The terminal temporarily records this sensor data in digital format and then prepares it for transmission to the server as a data package. This provides data that accurately reflects the current environmental conditions of crops.

[0352] Step 2:

[0353] The device acquires the user's voice and video data. Inputs include audio data from the microphone and video data from the camera. The device records this data in real time and converts it into a format necessary for the emotion engine's analysis. This output is used in the next step as foundational data to identify the user's emotional state.

[0354] Step 3:

[0355] The server processes environmental data and user data received from the terminal. First, a generative algorithm, which is a generative AI model, is executed based on the environmental data to predict crop growth. In this process, the input environmental data is analyzed and an output is generated that identifies the optimal conditions necessary for growth. At the same time, an emotion engine is activated that analyzes the collected user's voice and video, and generates the user's emotional state as an output.

[0356] Step 4:

[0357] The server creates an action plan for farm work based on the generated growth prediction, taking into account the output of the emotion engine. Input includes the analysis results and the user's emotional state obtained from the emotion engine. Based on this, the server outputs a specific plan to improve the efficiency of farm work. For example, if the user is feeling stressed, a plan to reduce the workload will be suggested.

[0358] Step 5:

[0359] The server sends the generated action plan to the terminal. The terminal receives it and prepares to display it in the user interface. The input is the action plan from the server, and the output is information converted into a format that is easily understandable to the user. The terminal displays this information visually and audibly, providing guidance for the user to take appropriate action based on the current situation.

[0360] Step 6:

[0361] Users check the information provided through the terminal's display device and then perform farm work. By checking the information, users can set a work pace that suits their emotional state. As a result, they obtain efficient and low-stress work results. This cycle allows users to perform farm work based on environmental conditions and their own state of mind.

[0362] (Application Example 2)

[0363] 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."

[0364] Traditional farm management systems often provide uniform advice and action plans without considering the user's emotional state, potentially leading to decreased user motivation and increased stress. Furthermore, they often place too much emphasis on collecting environmental data, lacking the flexibility to address individual user situations. This creates a challenge in efficiently and sustainably managing crops.

[0365] 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.

[0366] In this invention, the server includes means for using a device to collect data related to agricultural work, means for using a device to recognize the user's emotional state, and means for using a generative model that performs analysis based on the collected data and emotional data. This makes it possible to provide action plans adapted to the user's emotional state and to provide feedback that responds to those emotions.

[0367] A "device for collecting data related to agricultural work" is a device used to acquire necessary data such as temperature, humidity, light intensity, and soil moisture in an agricultural environment in real time.

[0368] A "device for recognizing user emotions" is a device that analyzes a user's emotional state from information such as voice and facial expressions, and determines their stress levels and satisfaction levels.

[0369] A "generative model" is a model used to predict crop growth and propose environmental optimizations based on collected data.

[0370] A "processor" is a device that generates flexible action plans by taking into account the analysis results obtained from the generative model and the user's emotional state.

[0371] An "output device" is a device that provides the user with a generated action plan and the feedback based on it, either visually or audibly.

[0372] This invention aims to realize a smart farming system that improves the efficiency of agricultural work and reduces the mental burden on users. An embodiment thereof is described below.

[0373] First, the user utilizes a device designed to acquire agricultural data, continuously collecting environmental information such as temperature, humidity, light intensity, and soil moisture. This device accurately acquires data by integrating various sensors and stores it on the device in real time.

[0374] Next, the device that recognizes the user's emotional state analyzes the user's emotions using voice and video input. Here, voice recognition software and image analysis software are used to analyze voice tone and facial expressions to evaluate the user's satisfaction level and stress level. Specifically, this is software that uses emotion analysis algorithms.

[0375] The server uses a generative AI model to analyze collected environmental and sentiment data. This model has the ability to predict crop growth under various conditions and generate optimal action plans accordingly. For example, it analyzes the adaptation of different crop types to different weather conditions and proposes harvest times and growth promotion strategies.

[0376] The generated action plan is fed back to the user through the device. Here, the user interface adaptively adjusts the content and tone of the message according to the user's emotional state. The device features an intuitive interface that prioritizes friendliness and provides information in a way that is easy for the user to understand.

[0377] For example, if a user asks, "How are the crops doing lately?", the device queries the server based on the collected data, and the server analyzes it using a generative AI model to generate advice that also takes into account the user's current emotional state. For instance, the device might display a message like, "It's been dry this week, so water them this evening. Don't worry, it will get better soon!"

[0378] Examples of prompts for a generative AI model are as follows:

[0379] "Generate concise action plans for when users are feeling anxious, and create warm, encouraging messages."

[0380] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0381] Step 1:

[0382] The terminal collects agricultural environmental data. This device is equipped with sensors to measure temperature, humidity, light intensity, and soil moisture. Data from these sensors is input into the terminal and temporarily stored. During this process, the terminal performs real-time monitoring and organizes and stores the collected numerical data chronologically.

[0383] Step 2:

[0384] To recognize the user's emotional state, the device uses its camera and microphone to collect audio and video data. This data is input into the device, and an emotion analysis algorithm is activated. This algorithm extracts features such as voice tone and facial expressions, and processes the data to identify specific emotions. The determined emotions are output as indicators of stress and satisfaction.

[0385] Step 3:

[0386] The server receives environmental and emotional data transmitted from the terminal. The server uses a generative AI model to analyze this data and predict crop growth. This model learns from historical data and performs data calculations to generate the optimal action plan based on current environmental conditions. The generated action plan is output as specific work instructions and suggestions.

[0387] Step 4:

[0388] The server sends the action plan to the terminal. The terminal displays the action plan to the user. Here, the output interface adjusts the content and tone of the feedback message according to the user's emotional state. For example, a relaxed user is presented with a normal message, while a stressed user is given gentle words of encouragement.

[0389] Step 5:

[0390] The user performs farm work based on the received action plan. Any environmental or emotional changes observed during this process are recorded again on the device. This feedback loop allows the system to continuously update data, contributing to the generation of more precise action plans for the next cycle.

[0391] 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.

[0392] 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.

[0393] 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.

[0394] [Third Embodiment]

[0395] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0396] 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.

[0397] 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).

[0398] 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.

[0399] 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.

[0400] 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).

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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".

[0407] This invention provides a system for optimally managing crop growth in agricultural settings. Specific embodiments are described below.

[0408] Data collection

[0409] The terminal is equipped with sensors to acquire environmental data such as temperature, humidity, light intensity, and soil moisture in order to accurately monitor crop growth. The data transmitted from the sensors is acquired in real time and temporarily stored within the terminal. The stored data is then transmitted to a server via wireless communication. This process makes it possible to regularly and efficiently monitor the environmental conditions of the entire farmland.

[0410] Data Analysis

[0411] The server receives environmental data transmitted from the terminal. The received data is stored in a database and then analyzed by a generative model. The generative model uses machine learning algorithms to predict the growth status of crops and proposes optimal environmental adjustments and farming practices. Based on the analysis results, a plan for fertilization and irrigation suitable for future weather conditions and growth stages is also formulated.

[0412] Feedback and Implementation

[0413] The user receives analysis results and proposed action plans from the server. These are visualized through the terminal's user interface and displayed in a format that is easy for the user to understand. For example, the user may receive specific instructions such as, "Since temperatures will be high for the next three days, watering every morning is recommended." Furthermore, through the interface on the terminal, it is also possible to instruct a robot to perform tasks based on the suggestions received, thereby automating agricultural work.

[0414] This system optimizes farming operations based on scientific data, rather than relying on the experience of farmers. This enables efficient production and supports a stable supply of high-quality agricultural products. Furthermore, by effectively utilizing limited resources, it promotes sustainable agriculture.

[0415] The following describes the processing flow.

[0416] Step 1:

[0417] The device collects data on temperature, humidity, light intensity, and soil moisture through sensors to understand the surrounding environment of crops. The collected data is temporarily stored within the device.

[0418] Step 2:

[0419] The terminal transmits the stored sensor data to the server via the network. IoT technology is used for transmission, ensuring efficient and secure data transfer through wireless communication.

[0420] Step 3:

[0421] The server receives data sent from the terminal and records it in the database. The received data is immediately analyzed by a generative model and used to calculate crop growth predictions and environmental optimization.

[0422] Step 4:

[0423] The generative model utilizes machine learning algorithms to generate appropriate action plans from the analysis results. For example, it can estimate the timing of irrigation and the appropriate amount of fertilizer.

[0424] Step 5:

[0425] The server sends the generated action plan to the terminal. The terminal converts the received information into a visualized format for the user and displays it on the operation screen.

[0426] Step 6:

[0427] Based on the information presented through the terminal, users decide how to carry out agricultural work. If necessary, they can adopt the presented action plan, send work instructions from the terminal to the automated robot, and perform the actual work.

[0428] (Example 1)

[0429] 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."

[0430] Traditional methods for efficiently managing crop growth lacked flexibility in responding to fluctuations in environmental conditions and often relied heavily on the experience of farmers, making it difficult to perform agricultural tasks at the optimal time. Furthermore, it was challenging to easily develop concrete plans for effectively utilizing limited resources.

[0431] 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.

[0432] In this invention, the server includes detection means for collecting environmental information, analysis means for executing a machine learning algorithm for analyzing the collected environmental information, and planning means for generating a farming plan that corresponds to future weather conditions and crop growth stages based on the information analyzed by the analysis means. This makes it possible to automatically plan the appropriate timing of farming work according to the growth of crops and to make the most of limited resources.

[0433] "Environmental information" refers to data on the natural environment that affects crop growth, such as temperature, humidity, light intensity, and soil moisture.

[0434] "Detection means" refers to a device or group of devices that measures data through sensors in order to collect environmental information.

[0435] "Analysis means" refers to a computer program or system that processes collected environmental information using machine learning algorithms to make predictions about crop growth and the environment.

[0436] "Plan generation means" refers to a system or process that creates schedules and plans for agricultural work based on information obtained by analysis means.

[0437] "Display means" refers to an interface or device for providing and visually presenting agricultural work plans created by the plan generation means to the user.

[0438] This system is designed to efficiently manage crop growth in agricultural settings and generate farming plans based on that growth. It primarily functions through the collaboration of three parties: terminals, servers, and users.

[0439] Hardware and software configuration

[0440] The terminal is equipped with sensors that accurately collect environmental information such as temperature, humidity, light intensity, and soil moisture. This collected data is temporarily stored in the terminal's internal memory. The terminal also has wireless communication capabilities, which allow it to send the collected data to a server.

[0441] The server has the function of receiving environmental information sent from terminals and storing it in a database. Machine learning algorithms are implemented within the server, and generative AI models such as TensorFlow and PyTorch are used to analyze the collected data. As a result, the server automatically generates predictions of crop growth and optimal farming plans.

[0442] Users can view analysis results and work plans provided by the server on the terminal's user interface. This interface is intuitively designed and easy for users to operate. Users can perform specific farming tasks based on the displayed suggestions and, if necessary, assign robots to perform the tasks.

[0443] Examples and prompts for generative AI models

[0444] As a concrete example, consider a situation where a user is growing tomatoes. The device collects environmental information, and the server analyzes that data to generate a plan such as, "High temperatures are expected for the next three days, so we recommend watering every morning." This plan helps the user take timely action.

[0445] An example of a prompt to input into the generating AI model is, "Please suggest the optimal watering schedule for tomatoes based on the weather conditions for the next 7 days." This prompt will prompt the AI ​​model to provide an appropriate watering schedule.

[0446] This system enables users to practice agricultural management based on scientific data, promoting efficient and sustainable agriculture.

[0447] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0448] Step 1:

[0449] The device collects environmental information such as temperature, humidity, light intensity, and soil moisture in real time using sensors. The input is an analog signal from the sensors, which is converted into digital data and temporarily stored in internal memory. This process makes it possible to instantly grasp changes in the environment.

[0450] Step 2:

[0451] The terminal transmits the collected digital data to the server using wireless communication. The input is environmental data stored inside the terminal, which is compressed and converted into packet data format, then output to the server via wireless technology. Once the transmission is successfully completed, an LED lights up on the terminal to confirm this.

[0452] Step 3:

[0453] The server immediately stores data received from the terminal into the database. The input is packet data sent wirelessly, which is then decompressed and converted into a database format. Before storing the data in the database, preprocessing is performed, such as removing duplicate data and filtering outliers.

[0454] Step 4:

[0455] The server analyzes pre-processed environmental data using a generative AI model. The input is a clean dataset, which is passed to the generative AI model to run machine learning algorithms. The output obtained here includes analysis results such as crop growth predictions and optimal irrigation and fertilization plans.

[0456] Step 5:

[0457] The server generates a specific action plan based on the analysis results. The input is the analysis results from the generating AI model, and the plan generation algorithm is executed based on this to create the action plan. For example, an instruction such as "Water with 20 liters of water every morning for the next three days" is generated.

[0458] Step 6:

[0459] The user reviews the action plan provided by the server on the terminal's user interface. The input is the action plan sent from the server, which is visually displayed on the terminal's screen. Based on this, the user can decide whether to perform specific farm tasks or entrust the work to a robot.

[0460] (Application Example 1)

[0461] 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."

[0462] Managing farmland and green spaces in urban areas is challenging due to limited space and diverse environmental conditions. Currently, environmental management is often carried out individually in multiple locations, preventing integrated monitoring and control. This makes it difficult to maintain optimal environmental conditions overall. Furthermore, management relies on individual workers, making efficiency and sustainability difficult.

[0463] 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.

[0464] In this invention, the server includes a sensor means for detecting the growth status of crops, a generative model means for analyzing collected external environmental information, an information processing means for generating a work plan based on the external environmental information analyzed by the generative model means, and an integrated management means for comprehensively managing environmental conditions at multiple locations within a city. This enables efficient and sustainable management of farmland and green spaces within cities.

[0465] A "sensor for detecting the growth status of crops" is a device used to monitor the process of crop growth in agriculture, and functions to acquire environmental data such as temperature, humidity, light intensity, and soil moisture.

[0466] "Generative modeling means for analyzing collected external environmental information" refers to a system that includes machine learning algorithms used to analyze collected data related to agriculture and predict crop growth status and changes in environmental conditions.

[0467] "Information processing means for generating work plans" refers to a digital processor or computing device for creating optimal farm work and environmental adjustment plans based on analysis results.

[0468] "Output interface means" refers to a device that includes a screen or operation panel to display generated information and suggestions to the user in an easy-to-understand manner, and to enable the user to perform the necessary operations.

[0469] "Integrated management means" refers to a system or platform for centrally managing environmental information of multiple agricultural and green spaces in an urban area and providing unified guidelines.

[0470] In order to implement this invention, it is necessary to construct an environmental monitoring system in the agricultural field. The system mainly consists of sensor means, information collection and analysis means, output interface means, and integrated management means.

[0471] The sensor system includes various sensors that measure temperature, humidity, light intensity, and soil moisture in real time to monitor the growth status of crops and green spaces within urban areas. This data is transmitted to a server via wireless communication.

[0472] The server analyzes the collected data using a generative AI model written in Python. This generative AI model employs machine learning algorithms to predict crop growth and future environmental changes from environmental data stored in a database (e.g., MySQL). Based on the analysis information obtained in this way, the information processing system automatically formulates the optimal work plan.

[0473] The output interface includes a smartphone application that allows users to intuitively view real-time information on multiple farmlands and green spaces within the city. The application is built using a framework such as React Native.

[0474] Finally, the integrated management system allows data from multiple locations to be collected and centrally managed, enabling efficient greening across the entire city. For example, this system can be used to comprehensively monitor the humidity and temperature of multiple community gardens and propose an optimal irrigation schedule.

[0475] As an example prompt, by inputting the instruction "Collect data from sensors in the city for May and propose the optimal irrigation schedule" into the generating AI model, specific measures can be obtained.

[0476] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0477] Step 1:

[0478] The device collects environmental data such as temperature, humidity, light intensity, and soil moisture from various sensors. This data is temporarily stored in the device's memory. The input is real-time environmental data from each sensor, and the output is formatted environmental data. This allows for an accurate understanding of the environmental conditions that affect crop growth.

[0479] Step 2:

[0480] The terminal transmits the collected environmental data to the server via a wireless communication module. The input is the environmental data temporarily stored in the terminal's memory, and the output is the data transmitted to the server. In this step, data is transmitted in real time via long-distance communication.

[0481] Step 3:

[0482] The server stores the received environment data in a database. A database such as MySQL is used to efficiently manage the data. Input is raw data sent from the terminal, and output is structured information stored in the database. Data persistence takes place at this stage.

[0483] Step 4:

[0484] The server analyzes the stored data using a generative AI model. This analysis utilizes machine learning algorithms written in Python. The input is historical and current environmental data obtained from a database, and the output is analysis results regarding crop growth predictions and optimal environmental conditions. The data is processed here by the AI ​​model, generating useful insights.

[0485] Step 5:

[0486] The server generates a work plan using information processing tools based on the analyzed data. Specific farm work suggestions and environmental adjustment plans are calculated. The input is predictive data from an AI model, and the output is the actual work schedule and suggestions. The server then constructs a concrete action plan.

[0487] Step 6:

[0488] Users can view work plans and analysis results on a smartphone app via the output interface. A UI built with React Native supports this. Input is analysis results provided by the server, and output is user-friendly visual information. This makes it easier for users to receive specific instructions.

[0489] Step 7:

[0490] The integrated management system centrally manages data from various locations within the city and supervises the execution of work plans. Inputs include monitoring data and work plans from each location, while outputs are integrated management reports and improvement suggestions. At this stage, efficient operation is achieved by utilizing data from multiple locations.

[0491] 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.

[0492] This invention incorporates an emotion engine into conventional agricultural assistance systems, enabling flexible responses tailored to the user's emotional state. Specific embodiments are described below.

[0493] Data collection and emotion recognition

[0494] The terminal collects environmental data such as temperature, humidity, light intensity, and soil moisture, which are necessary for managing crop growth, using various sensors. This data is temporarily stored on the terminal and later transmitted to the server. Simultaneously, an emotion engine connected to the terminal analyzes the user's voice and video data to recognize the user's emotional state in real time. The emotion engine uses an emotion analysis algorithm to determine stress levels and satisfaction levels, for example, from the user's tone of voice and facial expressions.

[0495] Data analysis and action plan generation

[0496] The server receives environmental data sent from the terminal, records it in a database, and then analyzes it using a generative model. This model has the ability to predict crop growth and suggest environmental optimizations. Based on the results, the processor generates an action plan, taking into account the output of the emotion engine. For example, if the user is stressed, an action plan to reduce the workload will be suggested.

[0497] feedback

[0498] The generated action plan is sent to the device and presented to the user through the user interface. The device adaptively adjusts the content and tone of the feedback message based on the emotion engine's recognition results. If the user is relaxed, a normal message is displayed; if they are stressed, encouraging messages and simple instructions are provided. This allows the user to perform farm work at a pace that suits their current situation.

[0499] (Specific example)

[0500] When a user asks the device, "What's the soil moisture level today?", the device sends data from its sensors to the server, and the emotion engine analyzes the user's voice to detect "anxiety." The server's analysis results in "The soil is a little dry, so watering in the afternoon is recommended," and the device notifies the user in a friendly manner, "Let's water it a little this afternoon. Don't worry, it will get better soon!"

[0501] In this way, the agricultural assistance system provides optimal information and work support tailored to the user's emotional state, supporting the efficient and sustainable management of crops.

[0502] The following describes the processing flow.

[0503] Step 1:

[0504] The device collects data from sensors that detect temperature, humidity, light intensity, soil moisture, and other environmental conditions on the farm. This data is stored in the device in real time.

[0505] Step 2:

[0506] The device uses an emotion engine to analyze the user's voice and video data and recognize the user's emotional state. Based on this, the emotion engine determines whether the user is currently feeling stressed or relaxed.

[0507] Step 3:

[0508] The device transmits collected environmental data and user sentiment data to a server via the network. A secure protocol is used for this transmission to maintain the confidentiality and integrity of the data.

[0509] Step 4:

[0510] The server stores the received environmental data in a database and uses generative models to perform analyses for predicting crop growth and adjusting the environment. The results obtained here include specific work instructions such as temperature and humidity adjustments and when to irrigate.

[0511] Step 5:

[0512] The server considers the analysis results and emotional state, and the processor creates an action plan. This plan incorporates flexible content that takes the user's emotions into account. For example, if the user is feeling stressed, it may include suggestions to reduce their workload.

[0513] Step 6:

[0514] When the terminal displays the action plan received from the server in the user interface, it adjusts the tone and content of the feedback message based on the emotion engine's recognition results. A gentle tone and simplified explanations are used to avoid burdening the user.

[0515] Step 7:

[0516] Users review instructions from their terminals and direct automated agricultural robots and other equipment to execute the appropriate commands. This allows users to manage their farms efficiently while reducing emotional burden.

[0517] (Example 2)

[0518] 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."

[0519] While conventional agricultural support systems offered crop management suggestions based on environmental data, they had limitations in providing flexible and individualized responses that took into account the user's emotional state. As a result, they failed to contribute to reducing user stress or promoting efficient farm work.

[0520] 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.

[0521] In this invention, the server includes means for detecting the growth status of crops, an emotion engine means for analyzing the emotional state of the user, and a processing means for generating a plan for agricultural work based on the information analyzed by a generation algorithm. This enables work support, including individualized responses according to the emotional state of the user.

[0522] A "device for detecting the growth status of crops" is a device that collects information related to the environmental conditions surrounding the crops and the growth of the crops themselves.

[0523] A "generative algorithm for analyzing collected information" is a computational method for analyzing collected environmental data and related information to generate information necessary for crop management and growth prediction.

[0524] A "processing device for generating agricultural work plans" is a computing device that generates specific action plans necessary for agricultural work based on analyzed information.

[0525] "Display devices provided to users" refer to devices such as screens or projectors that allow users to visually confirm the generated action plan and related information.

[0526] An "emotion engine that analyzes the user's emotional state" is software or hardware that analyzes data such as the user's voice and facial expressions to identify their emotions in real time.

[0527] "Means for adjusting responses to users" refers to a system that appropriately modifies the messages and action plans provided according to the emotional state of identified users, thereby providing the best possible response to each user.

[0528] This invention is an integrated support system for users to efficiently manage their crops. The following describes in detail how this system is configured and operates.

[0529] The terminal collects environmental data through various sensors installed in the surrounding environment of agricultural crops. This environmental data includes temperature, humidity, light intensity, and soil moisture. For example, a general-purpose sensor is used to measure temperature and humidity, a photosensor to measure light intensity, and a soil sensor to measure soil moisture. The terminal is also equipped with a microphone and camera to acquire user voice and video data.

[0530] The server receives environmental data and user voice information transmitted from the terminal. This dataset is analyzed by a generative algorithm, which is a generative AI model. The generative algorithm is implemented, for example, using a machine learning framework, and creates a crop growth prediction model from the environmental data. Based on the output of this model, the processing unit generates an action plan for farm work.

[0531] Furthermore, the emotion engine installed on the server analyzes audio and video data acquired from the user to identify the user's emotional state in real time. This emotion analysis uses a combination of speech recognition and facial expression recognition systems. This allows the system to recognize whether the user is stressed or relaxed.

[0532] The generated action plan is adjusted according to the user's emotional state. For example, if the user shows signs of stress, the workload included in the action plan is reduced, and an encouraging message is added before it is sent to the device. The user can receive this information visually or audibly through the device's display.

[0533] For example, if a user asks the device, "What's the soil moisture level today?", the device sends data from its sensors to the server. If the emotion engine, which analyzes the user's voice, detects "anxiety," the server generates an action plan such as, "The soil is a little dry, so watering in the afternoon is recommended." The device then notifies the user of this in a friendly message like, "Let's water it a little this afternoon. Don't worry, it will get better soon!"

[0534] A concrete example of a prompt message is one that can be sent to the AI ​​generation model, such as "Create a message that will reassure the user." This prompt will generate an appropriate message that takes the user's emotional state into consideration.

[0535] In this way, the system supports the efficient and sustainable management of crops and can respond flexibly to the user's emotional state.

[0536] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0537] Step 1:

[0538] The terminal collects environmental data using various sensors. Inputs include environmental information obtained from temperature, humidity, light, and soil moisture sensors. The terminal temporarily records this sensor data in digital format and then prepares it for transmission to the server as a data package. This provides data that accurately reflects the current environmental conditions of crops.

[0539] Step 2:

[0540] The device acquires the user's voice and video data. Inputs include audio data from the microphone and video data from the camera. The device records this data in real time and converts it into a format necessary for the emotion engine's analysis. This output is used in the next step as foundational data to identify the user's emotional state.

[0541] Step 3:

[0542] The server processes environmental data and user data received from the terminal. First, a generative algorithm, which is a generative AI model, is executed based on the environmental data to predict crop growth. In this process, the input environmental data is analyzed and an output is generated that identifies the optimal conditions necessary for growth. At the same time, an emotion engine is activated that analyzes the collected user's voice and video, and generates the user's emotional state as an output.

[0543] Step 4:

[0544] The server creates an action plan for farm work based on the generated growth prediction, taking into account the output of the emotion engine. Input includes the analysis results and the user's emotional state obtained from the emotion engine. Based on this, the server outputs a specific plan to improve the efficiency of farm work. For example, if the user is feeling stressed, a plan to reduce the workload will be suggested.

[0545] Step 5:

[0546] The server sends the generated action plan to the terminal. The terminal receives it and prepares to display it in the user interface. The input is the action plan from the server, and the output is information converted into a format that is easily understandable to the user. The terminal displays this information visually and audibly, providing guidance for the user to take appropriate action based on the current situation.

[0547] Step 6:

[0548] Users check the information provided through the terminal's display device and then perform farm work. By checking the information, users can set a work pace that suits their emotional state. As a result, they obtain efficient and low-stress work results. This cycle allows users to perform farm work based on environmental conditions and their own state of mind.

[0549] (Application Example 2)

[0550] 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."

[0551] Traditional farm management systems often provide uniform advice and action plans without considering the user's emotional state, potentially leading to decreased user motivation and increased stress. Furthermore, they often place too much emphasis on collecting environmental data, lacking the flexibility to address individual user situations. This creates a challenge in efficiently and sustainably managing crops.

[0552] 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.

[0553] In this invention, the server includes means for using a device to collect data related to agricultural work, means for using a device to recognize the user's emotional state, and means for using a generative model that performs analysis based on the collected data and emotional data. This makes it possible to provide action plans adapted to the user's emotional state and to provide feedback that responds to those emotions.

[0554] A "device for collecting data related to agricultural work" is a device used to acquire necessary data such as temperature, humidity, light intensity, and soil moisture in an agricultural environment in real time.

[0555] A "device for recognizing user emotions" is a device that analyzes a user's emotional state from information such as voice and facial expressions, and determines their stress levels and satisfaction levels.

[0556] A "generative model" is a model used to predict crop growth and propose environmental optimizations based on collected data.

[0557] A "processor" is a device that generates flexible action plans by taking into account the analysis results obtained from the generative model and the user's emotional state.

[0558] An "output device" is a device that provides the user with a generated action plan and the feedback based on it, either visually or audibly.

[0559] This invention aims to realize a smart farming system that improves the efficiency of agricultural work and reduces the mental burden on users. An embodiment thereof is described below.

[0560] First, the user utilizes a device designed to acquire agricultural data, continuously collecting environmental information such as temperature, humidity, light intensity, and soil moisture. This device accurately acquires data by integrating various sensors and stores it on the device in real time.

[0561] Next, the device that recognizes the user's emotional state analyzes the user's emotions using voice and video input. Here, voice recognition software and image analysis software are used to analyze voice tone and facial expressions to evaluate the user's satisfaction level and stress level. Specifically, this is software that uses emotion analysis algorithms.

[0562] The server uses a generative AI model to analyze collected environmental and sentiment data. This model has the ability to predict crop growth under various conditions and generate optimal action plans accordingly. For example, it analyzes the adaptation of different crop types to different weather conditions and proposes harvest times and growth promotion strategies.

[0563] The generated action plan is fed back to the user through the device. Here, the user interface adaptively adjusts the content and tone of the message according to the user's emotional state. The device features an intuitive interface that prioritizes friendliness and provides information in a way that is easy for the user to understand.

[0564] For example, if a user asks, "How are the crops doing lately?", the device queries the server based on the collected data, and the server analyzes it using a generative AI model to generate advice that also takes into account the user's current emotional state. For instance, the device might display a message like, "It's been dry this week, so water them this evening. Don't worry, it will get better soon!"

[0565] Examples of prompts for a generative AI model are as follows:

[0566] "Generate concise action plans for when users are feeling anxious, and create warm, encouraging messages."

[0567] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0568] Step 1:

[0569] The terminal collects agricultural environmental data. This device is equipped with sensors to measure temperature, humidity, light intensity, and soil moisture. Data from these sensors is input into the terminal and temporarily stored. During this process, the terminal performs real-time monitoring and organizes and stores the collected numerical data chronologically.

[0570] Step 2:

[0571] To recognize the user's emotional state, the device uses its camera and microphone to collect audio and video data. This data is input into the device, and an emotion analysis algorithm is activated. This algorithm extracts features such as voice tone and facial expressions, and processes the data to identify specific emotions. The determined emotions are output as indicators of stress and satisfaction.

[0572] Step 3:

[0573] The server receives environmental and emotional data transmitted from the terminal. The server uses a generative AI model to analyze this data and predict crop growth. This model learns from historical data and performs data calculations to generate the optimal action plan based on current environmental conditions. The generated action plan is output as specific work instructions and suggestions.

[0574] Step 4:

[0575] The server sends the action plan to the terminal. The terminal displays the action plan to the user. Here, the output interface adjusts the content and tone of the feedback message according to the user's emotional state. For example, a relaxed user is presented with a normal message, while a stressed user is given gentle words of encouragement.

[0576] Step 5:

[0577] The user performs farm work based on the received action plan. Any environmental or emotional changes observed during this process are recorded again on the device. This feedback loop allows the system to continuously update data, contributing to the generation of more precise action plans for the next cycle.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] [Fourth Embodiment]

[0582] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0583] 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.

[0584] 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).

[0585] 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.

[0586] 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.

[0587] 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).

[0588] 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.

[0589] 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.

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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".

[0595] This invention provides a system for optimally managing crop growth in agricultural settings. Specific embodiments are described below.

[0596] Data collection

[0597] The terminal is equipped with sensors to acquire environmental data such as temperature, humidity, light intensity, and soil moisture in order to accurately monitor crop growth. The data transmitted from the sensors is acquired in real time and temporarily stored within the terminal. The stored data is then transmitted to a server via wireless communication. This process makes it possible to regularly and efficiently monitor the environmental conditions of the entire farmland.

[0598] Data Analysis

[0599] The server receives environmental data transmitted from the terminal. The received data is stored in a database and then analyzed by a generative model. The generative model uses machine learning algorithms to predict the growth status of crops and proposes optimal environmental adjustments and farming practices. Based on the analysis results, a plan for fertilization and irrigation suitable for future weather conditions and growth stages is also formulated.

[0600] Feedback and Implementation

[0601] The user receives analysis results and proposed action plans from the server. These are visualized through the terminal's user interface and displayed in a format that is easy for the user to understand. For example, the user may receive specific instructions such as, "Since temperatures will be high for the next three days, watering every morning is recommended." Furthermore, through the interface on the terminal, it is also possible to instruct a robot to perform tasks based on the suggestions received, thereby automating agricultural work.

[0602] This system optimizes farming operations based on scientific data, rather than relying on the experience of farmers. This enables efficient production and supports a stable supply of high-quality agricultural products. Furthermore, by effectively utilizing limited resources, it promotes sustainable agriculture.

[0603] The following describes the processing flow.

[0604] Step 1:

[0605] The device collects data on temperature, humidity, light intensity, and soil moisture through sensors to understand the surrounding environment of crops. The collected data is temporarily stored within the device.

[0606] Step 2:

[0607] The terminal transmits the stored sensor data to the server via the network. IoT technology is used for transmission, ensuring efficient and secure data transfer through wireless communication.

[0608] Step 3:

[0609] The server receives data sent from the terminal and records it in the database. The received data is immediately analyzed by a generative model and used to calculate crop growth predictions and environmental optimization.

[0610] Step 4:

[0611] The generative model utilizes machine learning algorithms to generate appropriate action plans from the analysis results. For example, it can estimate the timing of irrigation and the appropriate amount of fertilizer.

[0612] Step 5:

[0613] The server sends the generated action plan to the terminal. The terminal converts the received information into a visualized format for the user and displays it on the operation screen.

[0614] Step 6:

[0615] Based on the information presented through the terminal, users decide how to carry out agricultural work. If necessary, they can adopt the presented action plan, send work instructions from the terminal to the automated robot, and perform the actual work.

[0616] (Example 1)

[0617] 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".

[0618] Traditional methods for efficiently managing crop growth lacked flexibility in responding to fluctuations in environmental conditions and often relied heavily on the experience of farmers, making it difficult to perform agricultural tasks at the optimal time. Furthermore, it was challenging to easily develop concrete plans for effectively utilizing limited resources.

[0619] 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.

[0620] In this invention, the server includes detection means for collecting environmental information, analysis means for executing a machine learning algorithm for analyzing the collected environmental information, and planning means for generating a farming plan that corresponds to future weather conditions and crop growth stages based on the information analyzed by the analysis means. This makes it possible to automatically plan the appropriate timing of farming work according to the growth of crops and to make the most of limited resources.

[0621] "Environmental information" refers to data on the natural environment that affects crop growth, such as temperature, humidity, light intensity, and soil moisture.

[0622] "Detection means" refers to a device or group of devices that measures data through sensors in order to collect environmental information.

[0623] "Analysis means" refers to a computer program or system that processes collected environmental information using machine learning algorithms to make predictions about crop growth and the environment.

[0624] "Plan generation means" refers to a system or process that creates schedules and plans for agricultural work based on information obtained by analysis means.

[0625] "Display means" refers to an interface or device for providing and visually presenting agricultural work plans created by the plan generation means to the user.

[0626] This system is designed to efficiently manage crop growth in agricultural settings and generate farming plans based on that growth. It primarily functions through the collaboration of three parties: terminals, servers, and users.

[0627] Hardware and software configuration

[0628] The terminal is equipped with sensors that accurately collect environmental information such as temperature, humidity, light intensity, and soil moisture. This collected data is temporarily stored in the terminal's internal memory. The terminal also has wireless communication capabilities, which allow it to send the collected data to a server.

[0629] The server has the function of receiving environmental information sent from terminals and storing it in a database. Machine learning algorithms are implemented within the server, and generative AI models such as TensorFlow and PyTorch are used to analyze the collected data. As a result, the server automatically generates predictions of crop growth and optimal farming plans.

[0630] Users can view analysis results and work plans provided by the server on the terminal's user interface. This interface is intuitively designed and easy for users to operate. Users can perform specific farming tasks based on the displayed suggestions and, if necessary, assign robots to perform the tasks.

[0631] Examples and prompts for generative AI models

[0632] As a concrete example, consider a situation where a user is growing tomatoes. The device collects environmental information, and the server analyzes that data to generate a plan such as, "High temperatures are expected for the next three days, so we recommend watering every morning." This plan helps the user take timely action.

[0633] An example of a prompt to input into the generating AI model is, "Please suggest the optimal watering schedule for tomatoes based on the weather conditions for the next 7 days." This prompt will prompt the AI ​​model to provide an appropriate watering schedule.

[0634] This system enables users to practice agricultural management based on scientific data, promoting efficient and sustainable agriculture.

[0635] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0636] Step 1:

[0637] The device collects environmental information such as temperature, humidity, light intensity, and soil moisture in real time using sensors. The input is an analog signal from the sensors, which is converted into digital data and temporarily stored in internal memory. This process makes it possible to instantly grasp changes in the environment.

[0638] Step 2:

[0639] The terminal transmits the collected digital data to the server using wireless communication. The input is environmental data stored inside the terminal, which is compressed and converted into packet data format, then output to the server via wireless technology. Once the transmission is successfully completed, an LED lights up on the terminal to confirm this.

[0640] Step 3:

[0641] The server immediately stores data received from the terminal into the database. The input is packet data sent wirelessly, which is then decompressed and converted into a database format. Before storing the data in the database, preprocessing is performed, such as removing duplicate data and filtering outliers.

[0642] Step 4:

[0643] The server analyzes pre-processed environmental data using a generative AI model. The input is a clean dataset, which is passed to the generative AI model to run machine learning algorithms. The output obtained here includes analysis results such as crop growth predictions and optimal irrigation and fertilization plans.

[0644] Step 5:

[0645] The server generates a specific action plan based on the analysis results. The input is the analysis results from the generating AI model, and the plan generation algorithm is executed based on this to create the action plan. For example, an instruction such as "Water with 20 liters of water every morning for the next three days" is generated.

[0646] Step 6:

[0647] The user reviews the action plan provided by the server on the terminal's user interface. The input is the action plan sent from the server, which is visually displayed on the terminal's screen. Based on this, the user can decide whether to perform specific farm tasks or entrust the work to a robot.

[0648] (Application Example 1)

[0649] 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".

[0650] Managing farmland and green spaces in urban areas is challenging due to limited space and diverse environmental conditions. Currently, environmental management is often carried out individually in multiple locations, preventing integrated monitoring and control. This makes it difficult to maintain optimal environmental conditions overall. Furthermore, management relies on individual workers, making efficiency and sustainability difficult.

[0651] 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.

[0652] In this invention, the server includes a sensor means for detecting the growth status of crops, a generative model means for analyzing collected external environmental information, an information processing means for generating a work plan based on the external environmental information analyzed by the generative model means, and an integrated management means for comprehensively managing environmental conditions at multiple locations within a city. This enables efficient and sustainable management of farmland and green spaces within cities.

[0653] A "sensor for detecting the growth status of crops" is a device used to monitor the process of crop growth in agriculture, and functions to acquire environmental data such as temperature, humidity, light intensity, and soil moisture.

[0654] "Generative modeling means for analyzing collected external environmental information" refers to a system that includes machine learning algorithms used to analyze collected data related to agriculture and predict crop growth status and changes in environmental conditions.

[0655] "Information processing means for generating work plans" refers to a digital processor or computing device for creating optimal farm work and environmental adjustment plans based on analysis results.

[0656] "Output interface means" refers to a device that includes a screen or operation panel to display generated information and suggestions to the user in an easy-to-understand manner, and to enable the user to perform the necessary operations.

[0657] "Integrated management means" refers to a system or platform for centrally managing environmental information of multiple agricultural and green spaces in an urban area and providing unified guidelines.

[0658] In order to implement this invention, it is necessary to construct an environmental monitoring system in the agricultural field. The system mainly consists of sensor means, information collection and analysis means, output interface means, and integrated management means.

[0659] The sensor system includes various sensors that measure temperature, humidity, light intensity, and soil moisture in real time to monitor the growth status of crops and green spaces within urban areas. This data is transmitted to a server via wireless communication.

[0660] The server analyzes the collected data using a generative AI model written in Python. This generative AI model employs machine learning algorithms to predict crop growth and future environmental changes from environmental data stored in a database (e.g., MySQL). Based on the analysis information obtained in this way, the information processing system automatically formulates the optimal work plan.

[0661] The output interface includes a smartphone application that allows users to intuitively view real-time information on multiple farmlands and green spaces within the city. The application is built using a framework such as React Native.

[0662] Finally, the integrated management system allows data from multiple locations to be collected and centrally managed, enabling efficient greening across the entire city. For example, this system can be used to comprehensively monitor the humidity and temperature of multiple community gardens and propose an optimal irrigation schedule.

[0663] As an example prompt, by inputting the instruction "Collect data from sensors in the city for May and propose the optimal irrigation schedule" into the generating AI model, specific measures can be obtained.

[0664] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0665] Step 1:

[0666] The device collects environmental data such as temperature, humidity, light intensity, and soil moisture from various sensors. This data is temporarily stored in the device's memory. The input is real-time environmental data from each sensor, and the output is formatted environmental data. This allows for an accurate understanding of the environmental conditions that affect crop growth.

[0667] Step 2:

[0668] The terminal transmits the collected environmental data to the server via a wireless communication module. The input is the environmental data temporarily stored in the terminal's memory, and the output is the data transmitted to the server. In this step, data is transmitted in real time via long-distance communication.

[0669] Step 3:

[0670] The server stores the received environment data in a database. A database such as MySQL is used to efficiently manage the data. Input is raw data sent from the terminal, and output is structured information stored in the database. Data persistence takes place at this stage.

[0671] Step 4:

[0672] The server analyzes the stored data using a generative AI model. This analysis utilizes machine learning algorithms written in Python. The input is historical and current environmental data obtained from a database, and the output is analysis results regarding crop growth predictions and optimal environmental conditions. The data is processed here by the AI ​​model, generating useful insights.

[0673] Step 5:

[0674] The server generates a work plan using information processing tools based on the analyzed data. Specific farm work suggestions and environmental adjustment plans are calculated. The input is predictive data from an AI model, and the output is the actual work schedule and suggestions. The server then constructs a concrete action plan.

[0675] Step 6:

[0676] Users can view work plans and analysis results on a smartphone app via the output interface. A UI built with React Native supports this. Input is analysis results provided by the server, and output is user-friendly visual information. This makes it easier for users to receive specific instructions.

[0677] Step 7:

[0678] The integrated management system centrally manages data from various locations within the city and supervises the execution of work plans. Inputs include monitoring data and work plans from each location, while outputs are integrated management reports and improvement suggestions. At this stage, efficient operation is achieved by utilizing data from multiple locations.

[0679] 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.

[0680] This invention incorporates an emotion engine into conventional agricultural assistance systems, enabling flexible responses tailored to the user's emotional state. Specific embodiments are described below.

[0681] Data collection and emotion recognition

[0682] The terminal collects environmental data such as temperature, humidity, light intensity, and soil moisture, which are necessary for managing crop growth, using various sensors. This data is temporarily stored on the terminal and later transmitted to the server. Simultaneously, an emotion engine connected to the terminal analyzes the user's voice and video data to recognize the user's emotional state in real time. The emotion engine uses an emotion analysis algorithm to determine stress levels and satisfaction levels, for example, from the user's tone of voice and facial expressions.

[0683] Data analysis and action plan generation

[0684] The server receives environmental data sent from the terminal, records it in a database, and then analyzes it using a generative model. This model has the ability to predict crop growth and suggest environmental optimizations. Based on the results, the processor generates an action plan, taking into account the output of the emotion engine. For example, if the user is stressed, an action plan to reduce the workload will be suggested.

[0685] feedback

[0686] The generated action plan is sent to the device and presented to the user through the user interface. The device adaptively adjusts the content and tone of the feedback message based on the emotion engine's recognition results. If the user is relaxed, a normal message is displayed; if they are stressed, encouraging messages and simple instructions are provided. This allows the user to perform farm work at a pace that suits their current situation.

[0687] (Specific example)

[0688] When a user asks the device, "What's the soil moisture level today?", the device sends data from its sensors to the server, and the emotion engine analyzes the user's voice to detect "anxiety." The server's analysis results in "The soil is a little dry, so watering in the afternoon is recommended," and the device notifies the user in a friendly manner, "Let's water it a little this afternoon. Don't worry, it will get better soon!"

[0689] In this way, the agricultural assistance system provides optimal information and work support tailored to the user's emotional state, supporting the efficient and sustainable management of crops.

[0690] The following describes the processing flow.

[0691] Step 1:

[0692] The device collects data from sensors that detect temperature, humidity, light intensity, soil moisture, and other environmental conditions on the farm. This data is stored in the device in real time.

[0693] Step 2:

[0694] The device uses an emotion engine to analyze the user's voice and video data and recognize the user's emotional state. Based on this, the emotion engine determines whether the user is currently feeling stressed or relaxed.

[0695] Step 3:

[0696] The device transmits collected environmental data and user sentiment data to a server via the network. A secure protocol is used for this transmission to maintain the confidentiality and integrity of the data.

[0697] Step 4:

[0698] The server stores the received environmental data in a database and uses generative models to perform analyses for predicting crop growth and adjusting the environment. The results obtained here include specific work instructions such as temperature and humidity adjustments and when to irrigate.

[0699] Step 5:

[0700] The server considers the analysis results and emotional state, and the processor creates an action plan. This plan incorporates flexible content that takes the user's emotions into account. For example, if the user is feeling stressed, it may include suggestions to reduce their workload.

[0701] Step 6:

[0702] When the terminal displays the action plan received from the server in the user interface, it adjusts the tone and content of the feedback message based on the emotion engine's recognition results. A gentle tone and simplified explanations are used to avoid burdening the user.

[0703] Step 7:

[0704] Users review instructions from their terminals and direct automated agricultural robots and other equipment to execute the appropriate commands. This allows users to manage their farms efficiently while reducing emotional burden.

[0705] (Example 2)

[0706] 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".

[0707] While conventional agricultural support systems offered crop management suggestions based on environmental data, they had limitations in providing flexible and individualized responses that took into account the user's emotional state. As a result, they failed to contribute to reducing user stress or promoting efficient farm work.

[0708] 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.

[0709] In this invention, the server includes means for detecting the growth status of crops, an emotion engine means for analyzing the emotional state of the user, and a processing means for generating a plan for agricultural work based on the information analyzed by a generation algorithm. This enables work support, including individualized responses according to the emotional state of the user.

[0710] A "device for detecting the growth status of crops" is a device that collects information related to the environmental conditions surrounding the crops and the growth of the crops themselves.

[0711] A "generative algorithm for analyzing collected information" is a computational method for analyzing collected environmental data and related information to generate information necessary for crop management and growth prediction.

[0712] A "processing device for generating agricultural work plans" is a computing device that generates specific action plans necessary for agricultural work based on analyzed information.

[0713] "Display devices provided to users" refer to devices such as screens or projectors that allow users to visually confirm the generated action plan and related information.

[0714] An "emotion engine that analyzes the user's emotional state" is software or hardware that analyzes data such as the user's voice and facial expressions to identify their emotions in real time.

[0715] "Means for adjusting responses to users" refers to a system that appropriately modifies the messages and action plans provided according to the emotional state of identified users, thereby providing the best possible response to each user.

[0716] This invention is an integrated support system for users to efficiently manage their crops. The following describes in detail how this system is configured and operates.

[0717] The terminal collects environmental data through various sensors installed in the surrounding environment of agricultural crops. This environmental data includes temperature, humidity, light intensity, and soil moisture. For example, a general-purpose sensor is used to measure temperature and humidity, a photosensor to measure light intensity, and a soil sensor to measure soil moisture. The terminal is also equipped with a microphone and camera to acquire user voice and video data.

[0718] The server receives environmental data and user voice information transmitted from the terminal. This dataset is analyzed by a generative algorithm, which is a generative AI model. The generative algorithm is implemented, for example, using a machine learning framework, and creates a crop growth prediction model from the environmental data. Based on the output of this model, the processing unit generates an action plan for farm work.

[0719] Furthermore, the emotion engine installed on the server analyzes audio and video data acquired from the user to identify the user's emotional state in real time. This emotion analysis uses a combination of speech recognition and facial expression recognition systems. This allows the system to recognize whether the user is stressed or relaxed.

[0720] The generated action plan is adjusted according to the user's emotional state. For example, if the user shows signs of stress, the workload included in the action plan is reduced, and an encouraging message is added before it is sent to the device. The user can receive this information visually or audibly through the device's display.

[0721] For example, if a user asks the device, "What's the soil moisture level today?", the device sends data from its sensors to the server. If the emotion engine, which analyzes the user's voice, detects "anxiety," the server generates an action plan such as, "The soil is a little dry, so watering in the afternoon is recommended." The device then notifies the user of this in a friendly message like, "Let's water it a little this afternoon. Don't worry, it will get better soon!"

[0722] A concrete example of a prompt message is one that can be sent to the AI ​​generation model, such as "Create a message that will reassure the user." This prompt will generate an appropriate message that takes the user's emotional state into consideration.

[0723] In this way, the system supports the efficient and sustainable management of crops and can respond flexibly to the user's emotional state.

[0724] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0725] Step 1:

[0726] The terminal collects environmental data using various sensors. Inputs include environmental information obtained from temperature, humidity, light, and soil moisture sensors. The terminal temporarily records this sensor data in digital format and then prepares it for transmission to the server as a data package. This provides data that accurately reflects the current environmental conditions of crops.

[0727] Step 2:

[0728] The device acquires the user's voice and video data. Inputs include audio data from the microphone and video data from the camera. The device records this data in real time and converts it into a format necessary for the emotion engine's analysis. This output is used in the next step as foundational data to identify the user's emotional state.

[0729] Step 3:

[0730] The server processes environmental data and user data received from the terminal. First, a generative algorithm, which is a generative AI model, is executed based on the environmental data to predict crop growth. In this process, the input environmental data is analyzed and an output is generated that identifies the optimal conditions necessary for growth. At the same time, an emotion engine is activated that analyzes the collected user's voice and video, and generates the user's emotional state as an output.

[0731] Step 4:

[0732] The server creates an action plan for farm work based on the generated growth prediction, taking into account the output of the emotion engine. Input includes the analysis results and the user's emotional state obtained from the emotion engine. Based on this, the server outputs a specific plan to improve the efficiency of farm work. For example, if the user is feeling stressed, a plan to reduce the workload will be suggested.

[0733] Step 5:

[0734] The server sends the generated action plan to the terminal. The terminal receives it and prepares to display it in the user interface. The input is the action plan from the server, and the output is information converted into a format that is easily understandable to the user. The terminal displays this information visually and audibly, providing guidance for the user to take appropriate action based on the current situation.

[0735] Step 6:

[0736] Users check the information provided through the terminal's display device and then perform farm work. By checking the information, users can set a work pace that suits their emotional state. As a result, they obtain efficient and low-stress work results. This cycle allows users to perform farm work based on environmental conditions and their own state of mind.

[0737] (Application Example 2)

[0738] 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".

[0739] Traditional farm management systems often provide uniform advice and action plans without considering the user's emotional state, potentially leading to decreased user motivation and increased stress. Furthermore, they often place too much emphasis on collecting environmental data, lacking the flexibility to address individual user situations. This creates a challenge in efficiently and sustainably managing crops.

[0740] 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.

[0741] In this invention, the server includes means for using a device to collect data related to agricultural work, means for using a device to recognize the user's emotional state, and means for using a generative model that performs analysis based on the collected data and emotional data. This makes it possible to provide action plans adapted to the user's emotional state and to provide feedback that responds to those emotions.

[0742] A "device for collecting data related to agricultural work" is a device used to acquire necessary data such as temperature, humidity, light intensity, and soil moisture in an agricultural environment in real time.

[0743] A "device for recognizing user emotions" is a device that analyzes a user's emotional state from information such as voice and facial expressions, and determines their stress levels and satisfaction levels.

[0744] A "generative model" is a model used to predict crop growth and propose environmental optimizations based on collected data.

[0745] A "processor" is a device that generates flexible action plans by taking into account the analysis results obtained from the generative model and the user's emotional state.

[0746] An "output device" is a device that provides the user with a generated action plan and the feedback based on it, either visually or audibly.

[0747] This invention aims to realize a smart farming system that improves the efficiency of agricultural work and reduces the mental burden on users. An embodiment thereof is described below.

[0748] First, the user utilizes a device designed to acquire agricultural data, continuously collecting environmental information such as temperature, humidity, light intensity, and soil moisture. This device accurately acquires data by integrating various sensors and stores it on the device in real time.

[0749] Next, the device that recognizes the user's emotional state analyzes the user's emotions using voice and video input. Here, voice recognition software and image analysis software are used to analyze voice tone and facial expressions to evaluate the user's satisfaction level and stress level. Specifically, this is software that uses emotion analysis algorithms.

[0750] The server uses a generative AI model to analyze collected environmental and sentiment data. This model has the ability to predict crop growth under various conditions and generate optimal action plans accordingly. For example, it analyzes the adaptation of different crop types to different weather conditions and proposes harvest times and growth promotion strategies.

[0751] The generated action plan is fed back to the user through the device. Here, the user interface adaptively adjusts the content and tone of the message according to the user's emotional state. The device features an intuitive interface that prioritizes friendliness and provides information in a way that is easy for the user to understand.

[0752] For example, if a user asks, "How are the crops doing lately?", the device queries the server based on the collected data, and the server analyzes it using a generative AI model to generate advice that also takes into account the user's current emotional state. For instance, the device might display a message like, "It's been dry this week, so water them this evening. Don't worry, it will get better soon!"

[0753] Examples of prompts for a generative AI model are as follows:

[0754] "Generate concise action plans for when users are feeling anxious, and create warm, encouraging messages."

[0755] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0756] Step 1:

[0757] The terminal collects agricultural environmental data. This device is equipped with sensors to measure temperature, humidity, light intensity, and soil moisture. Data from these sensors is input into the terminal and temporarily stored. During this process, the terminal performs real-time monitoring and organizes and stores the collected numerical data chronologically.

[0758] Step 2:

[0759] To recognize the user's emotional state, the device uses its camera and microphone to collect audio and video data. This data is input into the device, and an emotion analysis algorithm is activated. This algorithm extracts features such as voice tone and facial expressions, and processes the data to identify specific emotions. The determined emotions are output as indicators of stress and satisfaction.

[0760] Step 3:

[0761] The server receives environmental and emotional data transmitted from the terminal. The server uses a generative AI model to analyze this data and predict crop growth. This model learns from historical data and performs data calculations to generate the optimal action plan based on current environmental conditions. The generated action plan is output as specific work instructions and suggestions.

[0762] Step 4:

[0763] The server sends the action plan to the terminal. The terminal displays the action plan to the user. Here, the output interface adjusts the content and tone of the feedback message according to the user's emotional state. For example, a relaxed user is presented with a normal message, while a stressed user is given gentle words of encouragement.

[0764] Step 5:

[0765] The user performs farm work based on the received action plan. Any environmental or emotional changes observed during this process are recorded again on the device. This feedback loop allows the system to continuously update data, contributing to the generation of more precise action plans for the next cycle.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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."

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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 as being incorporated by reference.

[0787] The following is further disclosed regarding the embodiments described above.

[0788] (Claim 1)

[0789] A sensor that detects the growth status of crops,

[0790] A generative model for analyzing the collected data,

[0791] A processor that generates an action plan for agricultural work based on data analyzed by the aforementioned generation model,

[0792] An output interface that provides the aforementioned action plan to the user,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, wherein the sensor is configured to detect temperature, humidity, light intensity, and soil moisture in order to collect environmental data.

[0796] (Claim 3)

[0797] The system according to claim 1, wherein the output interface includes a user interface designed to allow a user to intuitively view and manipulate information.

[0798] "Example 1"

[0799] (Claim 1)

[0800] A detection means for collecting environmental information,

[0801] An analysis means for executing a machine learning algorithm to analyze collected environmental information,

[0802] A plan generation means generates an agricultural work plan corresponding to future weather conditions and crop growth stages based on the information analyzed by the aforementioned analysis means,

[0803] A display means for providing the aforementioned farm work plan to the user,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, wherein the detection means has functions for measuring temperature, humidity, light intensity, and soil moisture.

[0807] (Claim 3)

[0808] The system according to claim 1, wherein the display means includes a user interface designed to be intuitively understandable and operable by the user.

[0809] "Application Example 1"

[0810] (Claim 1)

[0811] A sensor means for detecting the growth status of crops,

[0812] A generative model means for analyzing collected external environmental information,

[0813] Information processing means for generating a work plan based on external environment information analyzed by the generation model means,

[0814] An output interface means for providing the aforementioned work plan to the end user,

[0815] An integrated management system for comprehensively managing environmental conditions at multiple locations within a city,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, wherein the sensor means is configured to detect temperature, humidity, light intensity, and soil moisture in order to collect environmental data.

[0819] (Claim 3)

[0820] The system according to claim 1, wherein the output interface means includes a user interface designed to allow an end user to intuitively view and manipulate information.

[0821] "Example 2 of combining an emotion engine"

[0822] (Claim 1)

[0823] A device for detecting the growth status of crops,

[0824] A generation algorithm for analyzing the collected information,

[0825] A processing device that generates a plan for agricultural work based on information analyzed by the generation algorithm,

[0826] A display device that provides the aforementioned plan to the user,

[0827] An emotion engine that analyzes the emotional state of users,

[0828] A means for adjusting the response to the user based on the output of the emotion engine,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, wherein the device is configured to detect temperature, humidity, light intensity, and soil moisture in order to collect environmental information.

[0832] (Claim 3)

[0833] The system according to claim 1, wherein the display device includes a user interface designed to allow users to intuitively view and operate information.

[0834] "Application example 2 when combining with an emotional engine"

[0835] (Claim 1)

[0836] A device for collecting data related to agricultural work,

[0837] A device for recognizing user emotions,

[0838] A generative model that performs analysis based on the data and sentiment data collected by the aforementioned device,

[0839] A processor that generates flexible action plans related to farm work based on data analyzed by the generative model and the user's emotional state,

[0840] An output device that provides the user with feedback corresponding to the aforementioned action plan and emotions,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, wherein the device is configured to sense temperature, humidity, light intensity, and soil moisture.

[0844] (Claim 3)

[0845] The system according to claim 1, wherein the output device includes an intuitive interface that presents information by adjusting the content and tone of the message in response to the user's emotional state. [Explanation of Symbols]

[0846] 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. A sensor means for detecting the growth status of crops, A generative model means for analyzing collected external environmental information, Information processing means for generating a work plan based on external environment information analyzed by the generation model means, An output interface means for providing the aforementioned work plan to the end user, An integrated management system for comprehensively managing environmental conditions at multiple locations within a city, A system that includes this.

2. The system according to claim 1, wherein the sensor means is configured to detect temperature, humidity, light intensity, and soil moisture in order to collect environmental data.

3. The system according to claim 1, wherein the output interface means includes a user interface designed to allow an end user to intuitively view and manipulate information.

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