system

Abstract

We provide the system. [Solution] A means for collecting environmental information obtained from an environmental measuring device, Means for analyzing the aforementioned environmental information and generating optimal control information, A means for controlling automated equipment to perform agricultural work based on the aforementioned optimal control information, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] Agriculture has traditionally relied on the inheritance of techniques based on individual experiences and intuitions, and there is a problem that the standardization and efficiency of techniques are difficult to progress. In particular, as the aging of agricultural workers and the shortage of successors are becoming serious problems, new methods for coping with the reduction of the labor force are required. In addition, in order to realize efficient and sustainable agriculture, it is necessary to perform appropriate cultivation management in a timely manner, but there are limitations to doing this manually.

Means for Solving the Problems

[0005] This invention provides a system that continuously collects environmental information acquired from an environmental measurement device and analyzes the collected data to automatically calculate the optimal conditions for agricultural work. Based on the analysis, this system controls automated equipment using optimal control information, enabling efficient agricultural work. Furthermore, it features an interactive interface that accepts user input and provides feedback to the analysis system, enabling even more accurate and flexible agricultural management. This aims to support the efficiency and automation of agriculture, reducing labor and realizing sustainable agriculture.

[0006] An "environmental measurement device" is a device installed in agricultural fields to measure environmental information such as temperature, humidity, and moisture content, and to collect that data.

[0007] "Environmental information" refers to data that shows the weather conditions and soil conditions of agricultural land, and specifically includes information such as temperature, humidity, moisture content, and light intensity.

[0008] "Analysis means" refers to a system or program that processes collected environmental information and has the function of calculating optimal farming conditions and control parameters.

[0009] "Optimal control information" refers to information generated by analytical means, which includes instructions and settings for automated equipment to efficiently perform agricultural tasks.

[0010] "Automated equipment" refers to devices or robots that perform agricultural tasks automatically, including equipment that can physically carry out tasks such as watering, fertilizing, and harvesting.

[0011] An "interactive interface" is a user interface that allows users to communicate with a system, check its operating status, and input adjustment instructions.

[0012] "Feedback" is the process by which a system adjusts its analysis and operation based on user input and external information, enabling further improvements in accuracy and flexible responses. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a 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.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention aims to digitize and automate agriculture by providing a system that utilizes an environmental measurement device, analysis means, automation equipment, and an interactive interface. This system continuously monitors the agricultural environment, calculates optimal cultivation conditions, and automates agricultural work based on those conditions.

[0035] First, the terminal collects environmental information such as field temperature, humidity, and moisture content through an environmental measurement device. This information is measured by sensors and transmitted to the server as data packets at regular intervals.

[0036] The server stores the received environmental information in a database and processes the data using an analysis tool. The analysis tool uses a machine learning algorithm to calculate optimal agricultural conditions from the collected data. These conditions include, for example, the amount of water needed, the timing of fertilization, and the adjustment of light.

[0037] Based on the calculated optimal conditions, the server generates optimal control information for controlling the automated equipment. This allows the automated equipment, acting as the terminal, to automatically perform tasks such as watering, fertilizing, and harvesting.

[0038] Users can communicate with the system through an interactive interface. This allows users to check the system's operating status and make adjustments as needed. For example, if a rapid response is required to sudden weather changes or anomaly detection, users can issue readjustment instructions through the interface.

[0039] As a concrete example, consider a situation where soil moisture is insufficient. The terminal detects the lack of moisture using a sensor and transmits this information to the server. The server calculates the optimal amount of water to be sprayed through an analysis mechanism and sends instructions to the automated equipment. The terminal (automated equipment) follows these instructions and sprays the appropriate amount of water, supporting the healthy growth of crops. In this way, the present invention provides a comprehensive system for achieving labor efficiency and sustainable agriculture.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The terminal periodically collects data such as temperature, humidity, and moisture content from multiple environmental monitoring devices placed in the field. This data is obtained as precise numerical values ​​through sensors.

[0043] Step 2:

[0044] The terminal organizes the collected data into data packets and sends them to the server. This transmission occurs in real time, ensuring that data is received without delay.

[0045] Step 3:

[0046] The server stores the received data packets in a database for analysis. This allows for the storage of past and present environmental conditions in chronological order, making them available for analysis.

[0047] Step 4:

[0048] The server inputs the stored environmental data into an analysis device and performs the analysis. This analysis device processes the data using machine learning and data analysis algorithms to find the optimal cultivation conditions for the crops.

[0049] Step 5:

[0050] The server generates optimal control information based on the cultivation conditions derived from the analysis. This information includes specific instructions such as the amount of water to be sprayed and the timing of fertilizer application.

[0051] Step 6:

[0052] The server sends the generated optimal control information to the terminal. Based on this information, the terminal controls the automated agricultural equipment and prepares to perform actual farm work.

[0053] Step 7:

[0054] The terminals, following instructions from the server, operate agricultural equipment to perform tasks such as watering with the required amount of water, spreading fertilizer, and automatically removing weeds. This allows agricultural work to proceed more efficiently.

[0055] Step 8:

[0056] Users can check the system's operating status through an interactive interface. If necessary, users can communicate additional instructions or adjustments to the system via the interactive interface.

[0057] Step 9:

[0058] The server receives feedback from user input and incorporates it into its analysis tools, preparing future farming operations for more effective results. This feedback function improves the system's flexibility and accuracy.

[0059] (Example 1)

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

[0061] Modern agriculture requires the rapid collection of environmental information and the efficient control of automated equipment based on that information. Furthermore, to cope with sudden environmental changes, a system with predictive capabilities that reflects user input is necessary. However, current systems have challenges in real-time data analysis and user interface enhancement, and overcoming these problems is essential.

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

[0063] In this invention, the server includes means for aggregating data acquired from a device that records environmental information, means for computationally processing the data and generating optimal management information, and means for managing equipment and executing tasks based on the management information. This enables automated agricultural management and efficient task execution.

[0064] "Environmental information" refers to data such as temperature, humidity, and moisture conditions in agriculture and the natural environment, and includes all information necessary for crop cultivation and management.

[0065] "Means of aggregating data" refers to technologies for efficiently collecting and summarizing environmental information, and includes processes and devices for centrally managing data from sensors.

[0066] "Computational processing" refers to the act of analyzing acquired data and, as necessary, using mathematical and statistical methods to derive meaningful inferences or results from that data.

[0067] "Management information" refers to instructions and policies generated based on the results of data analysis, and includes information for effectively controlling the operation of automated equipment.

[0068] "Interactive means" refers to an interface that allows users to communicate with the system, and includes technologies and devices for effectively receiving user instructions and feedback.

[0069] A "detector" refers to a sensor that measures temperature, humidity, and moisture levels, and is a device installed for the purpose of accurately acquiring environmental information.

[0070] This invention provides a system for efficiently collecting environmental information and automating optimal agricultural operations based on that information. Specific embodiments of the system are shown below.

[0071] The terminal acquires environmental information using multiple sensors placed in the farmland. Specifically, various types of sensors are used to measure temperature, humidity, and moisture levels. This information is aggregated as digital signals and transmitted as data packets using a communication module. Specific hardware examples include the use of Arduino sensors and Raspberry Pi modules.

[0072] The server runs on a cloud platform to analyze these received data packets. Specifically, platforms such as AWS® and Microsoft® Azure® are used. The analysis methods on the server include data processing modules written in Python and machine learning algorithms utilizing TENSORFLOW®. This generates optimal management information from the collected data. This management information includes the required amount of watering and the timing of fertilization.

[0073] Based on the generated management information, the automated equipment, acting as a terminal, performs its tasks. Using a Raspberry Pi or other control modules, it controls motors and valves to perform specified tasks. Examples include automated irrigation systems and automated fertilizer application devices.

[0074] Users communicate with the system using an interactive interface via smartphones or PCs. This interface allows for real-time data visualization and control adjustments, and includes a function that allows users to give instructions to the system using prompts, enabling flexible responses to unexpected environmental changes. For example, a prompt such as, "Please tell me how this agricultural automation system can help grow healthy crops that are lacking water during dry seasons," can be used.

[0075] Thus, this invention solves various problems in agriculture and contributes to the realization of efficient and sustainable agriculture.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The terminal collects environmental information using sensors installed in the field. The input consists of raw data measuring temperature, humidity, and moisture content. These sensors acquire information in real time and convert it into digital signals. The converted digital data is then prepared to be passed to the communication module as data packets. The output is the data packets converted into a communication-ready format.

[0079] Step 2:

[0080] The terminal sends prepared data packets to the server at regular intervals. The input here is the converted data packets. The data is uploaded to the cloud using Wi-Fi or LPWAN technology. The output is environment information stored in a database on the cloud.

[0081] Step 3:

[0082] The server analyzes the received data. The input is environmental information stored in a database. A data processing module using Python cleanses the data, and a machine learning algorithm (using TensorFlow) calculates the optimal farming conditions. The output is a set of instructions documented as optimal management information.

[0083] Step 4:

[0084] The server sends control signals to the automated devices (terminals) based on the generated management information. The input is the optimal management information. The control signals are sent to the automated devices equipped with Raspberry Pi and interpreted as actual work commands. The output is the specific signals necessary for starting and adjusting the operation of the devices.

[0085] Step 5:

[0086] The automated equipment, acting as the terminal, receives control signals and performs agricultural tasks. The input is the specific control signal sent from the server. Motors and valves are activated, for example, to spray a specified amount of water or supply the necessary fertilizer. The output is the actual physical agricultural work that is performed.

[0087] Step 6:

[0088] The user monitors the system status through the interface and adjusts the system's operation using prompt messages as needed. Inputs are the system's current operating status and user instructions. When the user changes settings via a smartphone app and sends a prompt message, the changes are reflected in the system. Outputs are the adjusted new operating schedule and commands.

[0089] (Application Example 1)

[0090] 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."

[0091] Modern agriculture demands proper management of environmental information and automation of operations. However, the challenge lies in the lack of concrete means to achieve efficient and sustainable agriculture, as it is not easy to collect data in real time that is tailored to the specific conditions of individual farms and community gardens, and to implement optimal control and provide feedback to users based on that data.

[0092] 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.

[0093] In this invention, the server includes a function for accumulating environmental data obtained from an environmental measurement device, a function for analyzing the environmental data and generating appropriate control data, and a function for controlling machinery and performing work based on the appropriate control data. This enables real-time collection and analysis of environmental data, as well as easy-to-understand feedback to the user.

[0094] An "environmental measurement device" is a device used to measure physical environmental information in farms and community gardens, and includes sensors for acquiring data such as temperature, humidity, and moisture content.

[0095] "Environmental data" refers to information that indicates the environmental conditions of a specific location, such as temperature, humidity, and moisture content, collected by environmental measurement devices.

[0096] The "data aggregation function" refers to the ability to centrally collect and store multiple environmental data acquired from environmental measurement devices.

[0097] "Analysis means" refers to the ability to process collected environmental data and derive control data that is optimal for a specific purpose.

[0098] "Optimal control data" refers to control information generated to perform optimal operations and management based on analyzed environmental data.

[0099] The "function of controlling machinery to perform tasks" refers to the ability to operate automated equipment according to generated appropriate control data and perform necessary tasks (e.g., watering or fertilizing).

[0100] "User feedback" refers to the transmission of information that returns analysis results, machine control status, and environmental changes to the user, enabling them to operate the machine and check its status.

[0101] To implement this invention, a system consisting of an environmental measurement device, a server for data analysis, and a terminal capable of interacting with the user is required. Specifically, the invention can be implemented using the following components and procedures.

[0102] Environmental measurement device

[0103] This device is used to measure the physical conditions of agricultural environments. Equipped with sensors, it collects environmental data such as temperature, humidity, and moisture content. This device is installed in managed areas such as farms and community gardens.

[0104] server

[0105] The server collects environmental data transmitted from environmental measurement devices and analyzes it using machine learning algorithms. It generates appropriate control data from the analysis results, which is then used to control machinery and perform agricultural tasks. Furthermore, the server provides feedback to the user by transmitting the analysis results to a terminal.

[0106] terminal

[0107] This terminal provides an interface with the user. It receives feedback from the server and displays it to the user. The user can check the progress of their work through the interface and send feedback to the server as needed.

[0108] Specific example

[0109] For example, if you are growing tomatoes in a community garden, and an environmental monitoring device detects a decrease in moisture levels, this data is sent to a server where the optimal watering amount is calculated. This information is then sent to your device, and you can check it through an application. This allows you to adjust the operation as needed.

[0110] Example of a prompt

[0111] "As the manager of the tomato garden, given the high temperatures and lack of moisture, what watering rate should I set?"

[0112] In this way, we provide a system that enables efficient and automated agriculture tailored to the environment.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] Environmental measurement devices use sensors to measure environmental data such as temperature, humidity, and moisture content. This measured data is generated as data packets in a digital format.

[0116] Input: Environmental data measured by sensors

[0117] Output: Digital data packets

[0118] Step 2:

[0119] The terminal receives data packets transmitted from the environmental measurement device. The received data is then transferred to the server using a secure communication protocol.

[0120] Input: Data packets from environmental measuring device

[0121] Output: Transfer of data packets to the server

[0122] Step 3:

[0123] The server analyzes the received data packets. Machine learning algorithms running on the server process environmental data and calculate optimal agricultural conditions. The results of this calculation are generated as appropriate control data.

[0124] Input: Environmental data transferred from the terminal

[0125] Output: Optimal control data indicating optimal agricultural conditions

[0126] Step 4:

[0127] The server sends the generated appropriate control data to the machine. This allows the machine to automatically perform tasks based on the specified agricultural conditions and initiate necessary actions (e.g., watering or fertilizing).

[0128] Input: Optimal control data

[0129] Output: Machine control instructions and their execution results

[0130] Step 5:

[0131] The server sends appropriate control data and its execution results as feedback to the terminal.

[0132] Input: Appropriate control data and its execution results

[0133] Output: Feedback data to the terminal

[0134] Step 6:

[0135] The terminal displays feedback data received from the server to the user. The user reviews the displayed information and, if necessary, sends feedback to the server through the interface. Further adjustments are made based on this interaction.

[0136] Input: Feedback data from the server

[0137] Output: Data displayed to the user and feedback data from the user.

[0138] 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.

[0139] This invention is a system aimed at improving the efficiency and automation of agriculture. In addition to functions that collect and analyze environmental information and control automated equipment to perform agricultural work, it is equipped with an emotion engine that recognizes the user's emotions. This emotion engine has the ability to understand the user's emotional state and adjust the system's response, thereby improving the user experience.

[0140] Specifically, the terminal first collects temperature, humidity, moisture content, and other data from sensors placed to measure the agricultural environment. This data is sent to a server and stored in a database. The server analyzes the received environmental data and calculates the optimal cultivation conditions. Machine learning and data analysis algorithms are used for this analysis.

[0141] Next, the server generates optimal control information based on the analysis results and sends instructions to the automated equipment, which acts as the terminal. The automated equipment, upon receiving the instructions, automatically performs agricultural tasks such as watering, fertilizing, and harvesting at the specified locations.

[0142] The emotion engine recognizes the user's emotional state from their voice and facial expressions when they interact with the interactive interface. Users can make adjustment requests to the system through the interface as needed, and their emotional state is analyzed at that time. For example, if a user is feeling stressed, the system adjusts the interface's response to a more supportive tone to reduce the user's burden.

[0143] Furthermore, the identified emotional state is reflected in the system's feedback process, allowing the analysis means to flexibly adjust the conditions and plans for farm work. For example, if the user's emotional state is anxious, the server can simplify the farm work plan and reduce the tasks performed by the terminal. In this way, the present invention realizes flexible control in response to the user's emotions, providing a comfortable and effective farming experience.

[0144] The following describes the processing flow.

[0145] Step 1:

[0146] The terminal periodically collects environmental information such as temperature, humidity, and moisture content from environmental monitoring devices placed in the field. Accurate measurement of this data enhances the reliability of subsequent processing.

[0147] Step 2:

[0148] The terminal converts the collected environmental data into data packets and sends them to the server. Transmission occurs in real time, ensuring a smooth data flow.

[0149] Step 3:

[0150] The server stores the received data packets in a database for analysis. This storage process enables the management of time-series data, allowing for advanced analysis that integrates past and present data.

[0151] Step 4:

[0152] The server inputs environmental information stored in the database into the analysis device and begins data analysis. The analysis device uses machine learning algorithms to calculate the optimal cultivation conditions. These analysis results include appropriate watering amounts and fertilizer application timings.

[0153] Step 5:

[0154] The server generates specific instructions for automated equipment based on the optimal control information generated by the analysis system. These instructions are sent to the terminal and reflected in the actual farm work plan.

[0155] Step 6:

[0156] The terminal operates automated equipment according to instructions from the server, automatically performing tasks such as watering, fertilizing, and harvesting on-site. This significantly reduces the workload on human labor.

[0157] Step 7:

[0158] Users interact with the system through an interactive interface and check its operating status. When users give adjustment instructions to the interface, the emotion engine recognizes the user's emotions through voice and video.

[0159] Step 8:

[0160] The server incorporates the user's emotional state, identified by the emotion engine, into its analysis and adjusts the interactive interface's responses as needed. For example, if the user's emotions are unstable, the interface's responses are changed to be calmer and more reassuring.

[0161] Step 9:

[0162] The server incorporates the emotional state obtained from the analysis into the farm work plan, flexibly changing the conditions and content of the farm work as needed. For example, if the user's emotional state is high stress, the server reduces the user's burden by simplifying tasks or adjusting priorities.

[0163] Step 10:

[0164] Based on new instructions from the server, the terminal readjusts automated equipment under an optimized plan and continuously performs farm work. This process allows the system to adapt to real-time environmental changes as well as the user's emotional state, always providing optimal agricultural management.

[0165] (Example 2)

[0166] 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".

[0167] While conventional agricultural systems could control automated equipment based on environmental information, they lacked the ability to adjust responses to consider the user's emotional state, limiting the potential for improving the user experience. Furthermore, there was a need for flexible adjustments to agricultural operations in response to changes in environmental conditions.

[0168] 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.

[0169] In this invention, the server includes means for acquiring environmental information, means for analyzing the environmental information and calculating optimal conditions, means for controlling the behavioral device to perform tasks based on the optimal conditions, and means for recognizing the user's emotional state and adjusting the response. This enables flexible work control and response adjustment that takes the user's emotional state into consideration, improving the user experience and enabling efficient agricultural work.

[0170] "Environmental information" refers to information that indicates environmental conditions in agriculture, and includes data such as temperature, humidity, and moisture content.

[0171] "Optimal conditions" refer to the environment and work schedule that are most suitable for plant growth and agricultural work, and are calculated based on the analysis of environmental information.

[0172] An "action device" is a machine or device that automatically performs agricultural tasks, such as watering, fertilizing, and harvesting, according to instructions.

[0173] "User emotional state" refers to information that indicates the user's psychological and emotional state, and is recognized through interactions via the interface.

[0174] "Means of adjusting responses" refer to functions that modify the content and tone of system responses based on the user's emotional state to improve the user experience.

[0175] "Analysis methods" refer to computational methods and algorithms used to analyze collected environmental information and derive optimal conditions.

[0176] This invention is a system aimed at improving efficiency and automation in agriculture. It collects and analyzes environmental information to transmit optimal control instructions to equipment and execute tasks. It also has a function to recognize user emotions and adjust responses accordingly.

[0177] The terminal first collects environmental information using various sensors. Specifically, sensors that measure temperature, humidity, and moisture content are used. This makes it possible to understand the current environmental conditions in the field in real time.

[0178] Once environmental information is acquired, the server receives it and stores it in a database. This provides the basis for comparison and analysis with past data while maintaining data integrity.

[0179] Next, the server processes the data using analytical tools. This analysis utilizes machine learning algorithms and data analysis software to derive optimal conditions. These optimal conditions include specific farming plans, such as the amount of water to irrigate and the timing of fertilizer application, tailored to the current environment.

[0180] Subsequently, the server generates control information based on optimal conditions and transmits control signals to the operational device acting as a terminal. The operational device autonomously performs agricultural tasks according to the received information. For example, if the soil moisture content is low, it automatically sprinkles a specified amount of water.

[0181] Furthermore, as users interact with the system through the interactive interface, the system recognizes the user's emotional state. When the user's psychological state (e.g., stress, anxiety) is observed, the server adjusts its response based on that analysis to provide a more empathetic interface experience.

[0182] For example, when a user is worried about insufficient sunlight, the system can automatically suggest a farming plan to alleviate that anxiety. An example of a prompt message for a generative AI model is as follows:

[0183] Prompt example: "If the user is feeling anxious about the current farming situation, suggest how to adjust the work plan. Also, generate a more supportive interface response."

[0184] In this way, agricultural systems aim to improve the user experience and enable effective farming operations through efficient data processing and a highly responsive user interface.

[0185] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0186] Step 1:

[0187] The terminal collects environmental information through various sensors placed in the agricultural environment. Inputs include temperature, humidity, and moisture content. By aggregating this sensor data, the terminal obtains the current environmental conditions of the field. Specifically, the sensors detect data in real time and transmit it to the server using a secure communication protocol.

[0188] Step 2:

[0189] The server stores environmental information received from the terminals in a database. The input for this step is raw data transmitted from each sensor. By being stored in the database, it serves as fundamental information necessary for comparison and analysis with past data. Specifically, the server verifies data integrity and saves it appropriately to avoid data loss.

[0190] Step 3:

[0191] The server processes the stored environmental information for analysis. In this step, the input is historical and current environmental data stored in the database. Machine learning algorithms and statistical methods are used to process the data and calculate the optimal environmental conditions. The output is the optimal cultivation conditions at that time, and this information is used to guide the next action. Specifically, pattern recognition and predictive analysis are performed using data analysis software.

[0192] Step 4:

[0193] The server generates specific action instructions based on the analysis results and sends them to the terminal. These instructions include optimal environmental conditions as input and generate control signals as output. A specific action might be to instruct the action device to perform additional watering if the specified amount of moisture is insufficient.

[0194] Step 5:

[0195] The terminal receives instructions from the server, and the action device automatically performs agricultural tasks. The input for this step is the control signal provided by the server, and the output is the actual execution of agricultural work. Specific actions include a watering device operating to spray a specified amount of water, or a fertilizer spreader accurately distributing fertilizer.

[0196] Step 6:

[0197] The user interacts with the system through an interactive interface, and the emotion engine analyzes the user's voice and facial expressions. The input for this step is the user's interaction data (voice, facial expressions), and the output is the estimated emotional state. Specifically, information acquired by the camera and microphone is processed using voice and image analysis technology to understand the user's emotions.

[0198] Step 7:

[0199] The server adjusts the system's response content and tone according to the recognized user's emotions and reflects this in the user interface. The input for this step is the analyzed emotional state, and the output is the optimized user response. Specific actions include adjusting the display of more supportive messages or providing simpler operation guides.

[0200] (Application Example 2)

[0201] 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".

[0202] This invention aims to solve the problem of providing a more comfortable and effective work environment by improving the efficiency of automated work based on environmental information, as well as enabling system adjustments to match the user's emotional state. In particular, it focuses on reducing user stress and fatigue by making full use of emotion recognition technology.

[0203] 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.

[0204] In this invention, the server includes means for collecting ambient information acquired from an environmental detection device, means for analyzing the ambient information and generating optimal operation information, means for controlling an automated device to perform tasks based on the optimal operation information, and means for recognizing the user's emotional state using an emotion recognition engine and adjusting the system's response. This enables flexible adjustment of the workload according to the user's emotional state.

[0205] An "environmental detection device" is a device used to measure the physical conditions of the surroundings. Specifically, it includes sensors that measure temperature, humidity, moisture content, etc.

[0206] "Surrounding information" refers to information about the surrounding physical conditions acquired by environmental detection devices. This includes temperature, humidity, moisture content, etc.

[0207] "Optimal operation information" refers to instruction information generated based on analyzed peripheral information to optimize the operation of automated equipment.

[0208] An "automation device" is a machine or device that performs tasks through artificial control. It is used to automate agricultural work and factory operations.

[0209] An "emotion recognition engine" is a technology that detects and determines the user's emotional state. It analyzes emotions based on facial expressions and changes in voice.

[0210] A "user" is an individual or group that operates or adjusts the system through its interface.

[0211] An "interactive interface" is a means of communication that allows a user to interact directly with a system. It can utilize voice and screen displays.

[0212] "Workload" refers to the mental or physical burden that an employer bears when performing a task.

[0213] The system for carrying out this invention mainly consists of an environmental detection device, a server, an automation device, an emotion recognition engine, and an interactive interface.

[0214] The server first acquires ambient information from an environmental detection device. This device includes a temperature sensor, a humidity sensor, and a moisture content sensor, which accurately measure the state of the surrounding environment. The acquired ambient information is transmitted to the server and stored in a database. The server then analyzes the data using machine learning algorithms to generate optimal operational information.

[0215] The server controls automated equipment based on analyzed optimal operation information. For example, it can adjust the movement of robots in a factory to improve work efficiency. Automated equipment performs tasks by executing specified actions.

[0216] Next, the user communicates with the system through an interactive interface. Through this interface, the user's voice and facial expressions are analyzed by an emotion recognition engine to recognize the user's emotional state. If the user is experiencing stress, the server detects this and adjusts the system's response and the operation of automated devices. This reduces the user's mental and physical burden, providing a comfortable working environment.

[0217] For example, if a factory worker is under excessive stress, this system can analyze their emotional state and automatically adjust the robot's workload. This balances the workload by reducing the workload of one staff member and having other staff members or machines take over some of it.

[0218] An example of a prompt to input into the generating AI model might be, "How can we optimize the robot's movements based on environmental data and staff emotional states within the factory?" This allows the system to acquire information for appropriate analysis and optimization, enabling effective control.

[0219] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0220] Step 1:

[0221] The system collects ambient information from environmental detection devices. The server acquires sensor data such as temperature, humidity, and moisture content from these devices. This data represents the physical conditions of the surroundings and serves as input to the server. The acquired data is temporarily stored within the server.

[0222] Step 2:

[0223] The system analyzes surrounding information and generates optimal operating information. The server uses machine learning algorithms to analyze the collected data. This process calculates the optimal operating conditions and parameters. Specifically, statistical analysis and pattern recognition are performed based on the data. The optimal operating information is generated as output and used to control the automated equipment.

[0224] Step 3:

[0225] The system controls automated equipment to perform tasks. Based on optimal operational information, the server sends commands to the automated equipment. This allows automated equipment, such as robots, to operate according to the environment. Specific examples of such operations include starting and stopping tasks at the appropriate time, and adjusting the movement of transport equipment.

[0226] Step 4:

[0227] The system recognizes the user's emotional state. As the user interacts with the system through an interactive interface, the server uses an emotion recognition engine to analyze voice and facial expression data. The user's voice and camera images are used as input, and the results of the emotional state analysis are output.

[0228] Step 5:

[0229] The system adjusts its response based on the user's emotional state. The server adjusts the system's response and operation based on analysis results from the emotion recognition engine. For example, if a user is experiencing stress, the automated device will receive instructions to reduce their workload. This reduces the user's burden and allows the work to proceed more smoothly.

[0230] Step 6:

[0231] The system performs analysis using prompts. The server uses a generated AI model as needed, taking prompt sentences as input to enhance the system's analysis and suggestions. Examples of input prompt sentences include, "How can we optimize robot movements based on environmental data and staff emotional states within the factory?" This generates specific and effective analysis results, leading to the optimization of the entire system.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] [Second Embodiment]

[0236] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0237] 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.

[0238] 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).

[0239] 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.

[0240] 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.

[0241] 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).

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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".

[0248] This invention aims to digitize and automate agriculture by providing a system that utilizes an environmental measurement device, analysis means, automation equipment, and an interactive interface. This system continuously monitors the agricultural environment, calculates optimal cultivation conditions, and automates agricultural work based on those conditions.

[0249] First, the terminal collects environmental information such as field temperature, humidity, and moisture content through an environmental measurement device. This information is measured by sensors and transmitted to the server as data packets at regular intervals.

[0250] The server stores the received environmental information in a database and processes the data using an analysis tool. The analysis tool uses a machine learning algorithm to calculate optimal agricultural conditions from the collected data. These conditions include, for example, the amount of water needed, the timing of fertilization, and the adjustment of light.

[0251] Based on the calculated optimal conditions, the server generates optimal control information for controlling the automated equipment. This allows the automated equipment, acting as the terminal, to automatically perform tasks such as watering, fertilizing, and harvesting.

[0252] Users can communicate with the system through an interactive interface. This allows users to check the system's operating status and make adjustments as needed. For example, if a rapid response is required to sudden weather changes or anomaly detection, users can issue readjustment instructions through the interface.

[0253] As a concrete example, consider a situation where soil moisture is insufficient. The terminal detects the lack of moisture using a sensor and transmits this information to the server. The server calculates the optimal amount of water to be sprayed through an analysis mechanism and sends instructions to the automated equipment. The terminal (automated equipment) follows these instructions and sprays the appropriate amount of water, supporting the healthy growth of crops. In this way, the present invention provides a comprehensive system for achieving labor efficiency and sustainable agriculture.

[0254] The following describes the processing flow.

[0255] Step 1:

[0256] The terminal periodically collects data such as temperature, humidity, and moisture content from multiple environmental monitoring devices placed in the field. This data is obtained as precise numerical values ​​through sensors.

[0257] Step 2:

[0258] The terminal organizes the collected data into data packets and sends them to the server. This transmission occurs in real time, ensuring that data is received without delay.

[0259] Step 3:

[0260] The server stores the received data packets in a database for analysis. This allows for the storage of past and present environmental conditions in chronological order, making them available for analysis.

[0261] Step 4:

[0262] The server inputs the stored environmental data into an analysis device and performs the analysis. This analysis device processes the data using machine learning and data analysis algorithms to find the optimal cultivation conditions for the crops.

[0263] Step 5:

[0264] The server generates optimal control information based on the cultivation conditions derived from the analysis. This information includes specific instructions such as the amount of water to be sprayed and the timing of fertilizer application.

[0265] Step 6:

[0266] The server sends the generated optimal control information to the terminal. Based on this information, the terminal controls the automated agricultural equipment and prepares to perform actual farm work.

[0267] Step 7:

[0268] The terminals, following instructions from the server, operate agricultural equipment to perform tasks such as watering with the required amount of water, spreading fertilizer, and automatically removing weeds. This allows agricultural work to proceed more efficiently.

[0269] Step 8:

[0270] Users can check the system's operating status through an interactive interface. If necessary, users can communicate additional instructions or adjustments to the system via the interactive interface.

[0271] Step 9:

[0272] The server receives feedback from user input and incorporates it into its analysis tools, preparing future farming operations for more effective results. This feedback function improves the system's flexibility and accuracy.

[0273] (Example 1)

[0274] 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."

[0275] Modern agriculture requires the rapid collection of environmental information and the efficient control of automated equipment based on that information. Furthermore, to cope with sudden environmental changes, a system with predictive capabilities that reflects user input is necessary. However, current systems have challenges in real-time data analysis and user interface enhancement, and overcoming these problems is essential.

[0276] 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.

[0277] In this invention, the server includes means for aggregating data acquired from a device that records environmental information, means for computationally processing the data and generating optimal management information, and means for managing equipment and executing tasks based on the management information. This enables automated agricultural management and efficient task execution.

[0278] "Environmental information" refers to data such as temperature, humidity, and moisture conditions in agriculture and the natural environment, and includes all information necessary for crop cultivation and management.

[0279] The "means for aggregating data" refers to a technology for efficiently collecting and aggregating environmental information, and includes processes and devices for centrally managing data from sensors.

[0280] "Computational processing" refers to the act of analyzing the acquired data and using mathematical and statistical methods as necessary to derive meaningful inferences and results from the data.

[0281] "Management information" refers to instructions and guidelines generated based on the results of data analysis, and includes information for effectively controlling the operation of automated devices.

[0282] The "interactive means" refers to an interface through which a user can communicate with the system, and includes technologies and devices for effectively receiving user instructions and feedback.

[0283] The "detector" refers to a sensor for measuring temperature, humidity, and moisture conditions, and is a device installed for the purpose of accurately acquiring environmental information.

[0284] This invention provides a system for efficiently collecting environmental information and automating optimal agricultural operations based thereon. Specific embodiments of the system are shown below.

[0285] The terminal acquires environmental information using a plurality of sensors arranged on agricultural land. Specifically, various types of sensors for measuring temperature, humidity, and moisture conditions are used. This information is aggregated as a digital signal and transmitted as a data packet using a communication module. As a specific hardware example, it is conceivable to use an Arduino sensor or a Raspberry Pi module.

[0286] The server operates on a cloud platform to analyze the received data packets. Specifically, platforms such as AWS or Microsoft Azure are used. The analysis means on the server includes data processing modules written in Python and machine learning algorithms leveraging TensorFlow. This generates optimal management information from the collected data. The management information includes the required amount of watering, the timing of fertilization, etc.

[0287] Based on the generated management information, the automated device, which is a terminal, executes operations. Using a Raspberry Pi or other control modules, it controls motors and valves to perform the specified operations. Specifically, an automatic irrigation system or an automatic fertilization device can be considered.

[0288] The user communicates with the system using an interactive interface via a smartphone or a PC. This interface enables real-time data visualization and control adjustment, and has a function that allows the user to give instructions to the system using prompt sentences so as to flexibly respond to unexpected environmental changes. For example, a prompt such as "Please tell me how to grow healthy crops with insufficient moisture during the dry season by this agricultural automation system." can be used.

[0289] In this way, the present invention solves various problems in agriculture and contributes to the realization of efficient and sustainable agriculture.

[0290] The flow of specific processing in Example 1 will be described using FIG. 11.

[0291] Step 1:

[0292] The terminal collects environmental information using sensors installed in the field. The input consists of raw data measuring temperature, humidity, and moisture content. These sensors acquire information in real time and convert it into digital signals. The converted digital data is then prepared to be passed to the communication module as data packets. The output is the data packets converted into a communication-ready format.

[0293] Step 2:

[0294] The terminal sends prepared data packets to the server at regular intervals. The input here is the converted data packets. The data is uploaded to the cloud using Wi-Fi or LPWAN technology. The output is environment information stored in a database on the cloud.

[0295] Step 3:

[0296] The server analyzes the received data. The input is environmental information stored in a database. A data processing module using Python cleanses the data, and a machine learning algorithm (using TensorFlow) calculates the optimal farming conditions. The output is a set of instructions documented as optimal management information.

[0297] Step 4:

[0298] The server sends control signals to the automated devices (terminals) based on the generated management information. The input is the optimal management information. The control signals are sent to the automated devices equipped with Raspberry Pi and interpreted as actual work commands. The output is the specific signals necessary for starting and adjusting the operation of the devices.

[0299] Step 5:

[0300] The automated equipment, which is the terminal, receives a control signal and executes farming operations. The input is the specific control signal transmitted from the server. Motors and valves operate to sprinkle a specified amount of water or supply the necessary fertilizer. The output is the actual physical farming operations.

[0301] Step 6:

[0302] The user monitors the system status through the interface and adjusts the system operation using prompt messages as needed. The input is the current operation status of the system and the user's instructions. When the user changes the settings on the smartphone app and sends a prompt message, it is reflected in the system. The output is the new operation schedule and commands after adjustment.

[0303] (Application Example 1)

[0304] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0305] In modern agriculture, appropriate management of environmental information and automation of operations are required. However, there is a problem that specific means for realizing efficient and sustainable agriculture are lacking because it is not easy to collect data in real time according to the situations of individual farms or community gardens and to provide optimal control and feedback to users based on that data.

[0306] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0307] In this invention, the server includes a function of integrating environmental data obtained from an environmental measurement device, a function of analyzing the environmental data and generating appropriate control data, and a function of controlling a machine based on the appropriate control data to perform operations. Thereby, real-time collection and analysis of environmental data, and furthermore, easy-to-understand feedback to the user become possible.

[0308] An "environmental measurement device" is a device used to measure physical environmental information in farms and community gardens, and includes sensors for acquiring data such as temperature, humidity, and moisture content.

[0309] "Environmental data" refers to information that indicates the environmental conditions of a specific location, such as temperature, humidity, and moisture content, collected by environmental measurement devices.

[0310] The "data aggregation function" refers to the ability to centrally collect and store multiple environmental data acquired from environmental measurement devices.

[0311] "Analysis means" refers to the ability to process collected environmental data and derive control data that is optimal for a specific purpose.

[0312] "Optimal control data" refers to control information generated to perform optimal operations and management based on analyzed environmental data.

[0313] The "function of controlling machinery to perform tasks" refers to the ability to operate automated equipment according to generated appropriate control data and perform necessary tasks (e.g., watering or fertilizing).

[0314] "User feedback" refers to the transmission of information that returns analysis results, machine control status, and environmental changes to the user, enabling them to operate the machine and check its status.

[0315] To implement this invention, a system consisting of an environmental measurement device, a server for data analysis, and a terminal capable of interacting with the user is required. Specifically, the invention can be implemented using the following components and procedures.

[0316] Environmental measurement device

[0317] This device is used to measure the physical conditions of agricultural environments. Equipped with sensors, it collects environmental data such as temperature, humidity, and moisture content. This device is installed in managed areas such as farms and community gardens.

[0318] server

[0319] The server collects environmental data transmitted from environmental measurement devices and analyzes it using machine learning algorithms. It generates appropriate control data from the analysis results, which is then used to control machinery and perform agricultural tasks. Furthermore, the server provides feedback to the user by transmitting the analysis results to a terminal.

[0320] terminal

[0321] This terminal provides an interface with the user. It receives feedback from the server and displays it to the user. The user can check the progress of their work through the interface and send feedback to the server as needed.

[0322] Specific example

[0323] For example, if you are growing tomatoes in a community garden, and an environmental monitoring device detects a decrease in moisture levels, this data is sent to a server where the optimal watering amount is calculated. This information is then sent to your device, and you can check it through an application. This allows you to adjust the operation as needed.

[0324] Example of a prompt

[0325] "As the manager of the tomato garden, given the high temperatures and lack of moisture, what watering rate should I set?"

[0326] In this way, we provide a system that enables efficient and automated agriculture tailored to the environment.

[0327] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0328] Step 1:

[0329] Environmental measurement devices use sensors to measure environmental data such as temperature, humidity, and moisture content. This measured data is generated as data packets in a digital format.

[0330] Input: Environmental data measured by sensors

[0331] Output: Digital data packets

[0332] Step 2:

[0333] The terminal receives data packets transmitted from the environmental measurement device. The received data is then transferred to the server using a secure communication protocol.

[0334] Input: Data packets from environmental measuring device

[0335] Output: Transfer of data packets to the server

[0336] Step 3:

[0337] The server analyzes the received data packets. Machine learning algorithms running on the server process environmental data and calculate optimal agricultural conditions. The results of this calculation are generated as appropriate control data.

[0338] Input: Environmental data transferred from the terminal

[0339] Output: Optimal control data indicating optimal agricultural conditions

[0340] Step 4:

[0341] The server sends the generated appropriate control data to the machine. This allows the machine to automatically perform tasks based on the specified agricultural conditions and initiate necessary actions (e.g., watering or fertilizing).

[0342] Input: Optimal control data

[0343] Output: Machine control instructions and their execution results

[0344] Step 5:

[0345] The server sends appropriate control data and its execution results as feedback to the terminal.

[0346] Input: Appropriate control data and its execution results

[0347] Output: Feedback data to the terminal

[0348] Step 6:

[0349] The terminal displays feedback data received from the server to the user. The user reviews the displayed information and, if necessary, sends feedback to the server through the interface. Further adjustments are made based on this interaction.

[0350] Input: Feedback data from the server

[0351] Output: Data displayed to the user and feedback data from the user.

[0352] 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.

[0353] This invention is a system aimed at improving the efficiency and automation of agriculture. In addition to functions that collect and analyze environmental information and control automated equipment to perform agricultural work, it is equipped with an emotion engine that recognizes the user's emotions. This emotion engine has the ability to understand the user's emotional state and adjust the system's response, thereby improving the user experience.

[0354] Specifically, the terminal first collects temperature, humidity, moisture content, and other data from sensors placed to measure the agricultural environment. This data is sent to a server and stored in a database. The server analyzes the received environmental data and calculates the optimal cultivation conditions. Machine learning and data analysis algorithms are used for this analysis.

[0355] Next, the server generates optimal control information based on the analysis results and sends instructions to the automated equipment, which acts as the terminal. The automated equipment, upon receiving the instructions, automatically performs agricultural tasks such as watering, fertilizing, and harvesting at the specified locations.

[0356] The emotion engine recognizes the user's emotional state from their voice and facial expressions when they interact with the interactive interface. Users can make adjustment requests to the system through the interface as needed, and their emotional state is analyzed at that time. For example, if a user is feeling stressed, the system adjusts the interface's response to a more supportive tone to reduce the user's burden.

[0357] Furthermore, the identified emotional state is reflected in the system's feedback process, allowing the analysis means to flexibly adjust the conditions and plans for farm work. For example, if the user's emotional state is anxious, the server can simplify the farm work plan and reduce the tasks performed by the terminal. In this way, the present invention realizes flexible control in response to the user's emotions, providing a comfortable and effective farming experience.

[0358] The following describes the processing flow.

[0359] Step 1:

[0360] The terminal periodically collects environmental information such as temperature, humidity, and moisture content from environmental monitoring devices placed in the field. Accurate measurement of this data enhances the reliability of subsequent processing.

[0361] Step 2:

[0362] The terminal converts the collected environmental data into data packets and sends them to the server. Transmission occurs in real time, ensuring a smooth data flow.

[0363] Step 3:

[0364] The server stores the received data packets in a database for analysis. This storage process enables the management of time-series data, allowing for advanced analysis that integrates past and present data.

[0365] Step 4:

[0366] The server inputs environmental information stored in the database into the analysis device and begins data analysis. The analysis device uses machine learning algorithms to calculate the optimal cultivation conditions. These analysis results include appropriate watering amounts and fertilizer application timings.

[0367] Step 5:

[0368] The server generates specific instructions for automated equipment based on the optimal control information generated by the analysis system. These instructions are sent to the terminal and reflected in the actual farm work plan.

[0369] Step 6:

[0370] The terminal operates automated equipment according to instructions from the server, automatically performing tasks such as watering, fertilizing, and harvesting on-site. This significantly reduces the workload on human labor.

[0371] Step 7:

[0372] Users interact with the system through an interactive interface and check its operating status. When users give adjustment instructions to the interface, the emotion engine recognizes the user's emotions through voice and video.

[0373] Step 8:

[0374] The server incorporates the user's emotional state, identified by the emotion engine, into its analysis and adjusts the interactive interface's responses as needed. For example, if the user's emotions are unstable, the interface's responses are changed to be calmer and more reassuring.

[0375] Step 9:

[0376] The server incorporates the emotional state obtained from the analysis into the farm work plan, flexibly changing the conditions and content of the farm work as needed. For example, if the user's emotional state is high stress, the server reduces the user's burden by simplifying tasks or adjusting priorities.

[0377] Step 10:

[0378] Based on new instructions from the server, the terminal readjusts automated equipment under an optimized plan and continuously performs farm work. This process allows the system to adapt to real-time environmental changes as well as the user's emotional state, always providing optimal agricultural management.

[0379] (Example 2)

[0380] 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".

[0381] While conventional agricultural systems could control automated equipment based on environmental information, they lacked the ability to adjust responses to consider the user's emotional state, limiting the potential for improving the user experience. Furthermore, there was a need for flexible adjustments to agricultural operations in response to changes in environmental conditions.

[0382] 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.

[0383] In this invention, the server includes means for acquiring environmental information, means for analyzing the environmental information and calculating optimal conditions, means for controlling the behavioral device to perform tasks based on the optimal conditions, and means for recognizing the user's emotional state and adjusting the response. This enables flexible work control and response adjustment that takes the user's emotional state into consideration, improving the user experience and enabling efficient agricultural work.

[0384] "Environmental information" refers to information that indicates environmental conditions in agriculture, and includes data such as temperature, humidity, and moisture content.

[0385] "Optimal conditions" refer to the environment and work schedule that are most suitable for plant growth and agricultural work, and are calculated based on the analysis of environmental information.

[0386] An "action device" is a machine or device that automatically performs agricultural tasks, such as watering, fertilizing, and harvesting, according to instructions.

[0387] "User emotional state" refers to information that indicates the user's psychological and emotional state, and is recognized through interactions via the interface.

[0388] "Means of adjusting responses" refer to functions that modify the content and tone of system responses based on the user's emotional state to improve the user experience.

[0389] "Analysis methods" refer to computational methods and algorithms used to analyze collected environmental information and derive optimal conditions.

[0390] This invention is a system aimed at improving efficiency and automation in agriculture. It collects and analyzes environmental information to transmit optimal control instructions to equipment and execute tasks. It also has a function to recognize user emotions and adjust responses accordingly.

[0391] The terminal first collects environmental information using various sensors. Specifically, sensors that measure temperature, humidity, and moisture content are used. This makes it possible to understand the current environmental conditions in the field in real time.

[0392] Once environmental information is acquired, the server receives it and stores it in a database. This provides the basis for comparison and analysis with past data while maintaining data integrity.

[0393] Next, the server processes the data using analytical tools. This analysis utilizes machine learning algorithms and data analysis software to derive optimal conditions. These optimal conditions include specific farming plans, such as the amount of water to irrigate and the timing of fertilizer application, tailored to the current environment.

[0394] Subsequently, the server generates control information based on optimal conditions and transmits control signals to the operational device acting as a terminal. The operational device autonomously performs agricultural tasks according to the received information. For example, if the soil moisture content is low, it automatically sprinkles a specified amount of water.

[0395] Furthermore, as users interact with the system through the interactive interface, the system recognizes the user's emotional state. When the user's psychological state (e.g., stress, anxiety) is observed, the server adjusts its response based on that analysis to provide a more empathetic interface experience.

[0396] For example, when a user is worried about insufficient sunlight, the system can automatically suggest a farming plan to alleviate that anxiety. An example of a prompt message for a generative AI model is as follows:

[0397] Prompt example: "If the user is feeling anxious about the current farming situation, suggest how to adjust the work plan. Also, generate a more supportive interface response."

[0398] In this way, agricultural systems aim to improve the user experience and enable effective farming operations through efficient data processing and a highly responsive user interface.

[0399] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0400] Step 1:

[0401] The terminal collects environmental information through various sensors placed in the agricultural environment. Inputs include temperature, humidity, and moisture content. By aggregating this sensor data, the terminal obtains the current environmental conditions of the field. Specifically, the sensors detect data in real time and transmit it to the server using a secure communication protocol.

[0402] Step 2:

[0403] The server stores environmental information received from the terminals in a database. The input for this step is raw data transmitted from each sensor. By being stored in the database, it serves as fundamental information necessary for comparison and analysis with past data. Specifically, the server verifies data integrity and saves it appropriately to avoid data loss.

[0404] Step 3:

[0405] The server processes the stored environmental information for analysis. In this step, the input is historical and current environmental data stored in the database. Machine learning algorithms and statistical methods are used to process the data and calculate the optimal environmental conditions. The output is the optimal cultivation conditions at that time, and this information is used to guide the next action. Specifically, pattern recognition and predictive analysis are performed using data analysis software.

[0406] Step 4:

[0407] The server generates specific action instructions based on the analysis results and sends them to the terminal. These instructions include optimal environmental conditions as input and generate control signals as output. A specific action might be to instruct the action device to perform additional watering if the specified amount of moisture is insufficient.

[0408] Step 5:

[0409] The terminal receives instructions from the server, and the action device automatically performs agricultural tasks. The input for this step is the control signal provided by the server, and the output is the actual execution of agricultural work. Specific actions include a watering device operating to spray a specified amount of water, or a fertilizer spreader accurately distributing fertilizer.

[0410] Step 6:

[0411] The user interacts with the system through an interactive interface, and the emotion engine analyzes the user's voice and facial expressions. The input for this step is the user's interaction data (voice, facial expressions), and the output is the estimated emotional state. Specifically, information acquired by the camera and microphone is processed using voice and image analysis technology to understand the user's emotions.

[0412] Step 7:

[0413] The server adjusts the system's response content and tone according to the recognized user's emotions and reflects this in the user interface. The input for this step is the analyzed emotional state, and the output is the optimized user response. Specific actions include adjusting the display of more supportive messages or providing simpler operation guides.

[0414] (Application Example 2)

[0415] 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."

[0416] This invention aims to solve the problem of providing a more comfortable and effective work environment by improving the efficiency of automated work based on environmental information, as well as enabling system adjustments to match the user's emotional state. In particular, it focuses on reducing user stress and fatigue by making full use of emotion recognition technology.

[0417] 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.

[0418] In this invention, the server includes means for collecting ambient information acquired from an environmental detection device, means for analyzing the ambient information and generating optimal operation information, means for controlling an automated device to perform tasks based on the optimal operation information, and means for recognizing the user's emotional state using an emotion recognition engine and adjusting the system's response. This enables flexible adjustment of the workload according to the user's emotional state.

[0419] An "environmental detection device" is a device used to measure the physical conditions of the surroundings. Specifically, it includes sensors that measure temperature, humidity, moisture content, etc.

[0420] "Surrounding information" refers to information about the surrounding physical conditions acquired by environmental detection devices. This includes temperature, humidity, moisture content, etc.

[0421] "Optimal operation information" refers to instruction information generated based on analyzed peripheral information to optimize the operation of automated equipment.

[0422] An "automation device" is a machine or device that performs tasks through artificial control. It is used to automate agricultural work and factory operations.

[0423] An "emotion recognition engine" is a technology that detects and determines the user's emotional state. It analyzes emotions based on facial expressions and changes in voice.

[0424] A "user" is an individual or group that operates or adjusts the system through its interface.

[0425] An "interactive interface" is a means of communication that allows a user to interact directly with a system. It can utilize voice and screen displays.

[0426] "Workload" refers to the mental or physical burden that an employer bears when performing a task.

[0427] The system for carrying out this invention mainly consists of an environmental detection device, a server, an automation device, an emotion recognition engine, and an interactive interface.

[0428] The server first acquires ambient information from an environmental detection device. This device includes a temperature sensor, a humidity sensor, and a moisture content sensor, which accurately measure the state of the surrounding environment. The acquired ambient information is transmitted to the server and stored in a database. The server then analyzes the data using machine learning algorithms to generate optimal operational information.

[0429] The server controls automated equipment based on analyzed optimal operation information. For example, it can adjust the movement of robots in a factory to improve work efficiency. Automated equipment performs tasks by executing specified actions.

[0430] Next, the user communicates with the system through an interactive interface. Through this interface, the user's voice and facial expressions are analyzed by an emotion recognition engine to recognize the user's emotional state. If the user is experiencing stress, the server detects this and adjusts the system's response and the operation of automated devices. This reduces the user's mental and physical burden, providing a comfortable working environment.

[0431] For example, if a factory worker is under excessive stress, this system can analyze their emotional state and automatically adjust the robot's workload. This balances the workload by reducing the workload of one staff member and having other staff members or machines take over some of it.

[0432] An example of a prompt to input into the generating AI model might be, "How can we optimize the robot's movements based on environmental data and staff emotional states within the factory?" This allows the system to acquire information for appropriate analysis and optimization, enabling effective control.

[0433] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0434] Step 1:

[0435] The system collects ambient information from environmental detection devices. The server acquires sensor data such as temperature, humidity, and moisture content from these devices. This data represents the physical conditions of the surroundings and serves as input to the server. The acquired data is temporarily stored within the server.

[0436] Step 2:

[0437] The system analyzes surrounding information and generates optimal operating information. The server uses machine learning algorithms to analyze the collected data. This process calculates the optimal operating conditions and parameters. Specifically, statistical analysis and pattern recognition are performed based on the data. The optimal operating information is generated as output and used to control the automated equipment.

[0438] Step 3:

[0439] The system controls automated equipment to perform tasks. Based on optimal operational information, the server sends commands to the automated equipment. This allows automated equipment, such as robots, to operate according to the environment. Specific examples of such operations include starting and stopping tasks at the appropriate time, and adjusting the movement of transport equipment.

[0440] Step 4:

[0441] The system recognizes the user's emotional state. As the user interacts with the system through an interactive interface, the server uses an emotion recognition engine to analyze voice and facial expression data. The user's voice and camera images are used as input, and the results of the emotional state analysis are output.

[0442] Step 5:

[0443] The system adjusts its response based on the user's emotional state. The server adjusts the system's response and operation based on analysis results from the emotion recognition engine. For example, if a user is experiencing stress, the automated device will receive instructions to reduce their workload. This reduces the user's burden and allows the work to proceed more smoothly.

[0444] Step 6:

[0445] The system performs analysis using prompts. The server uses a generated AI model as needed, taking prompt sentences as input to enhance the system's analysis and suggestions. Examples of input prompt sentences include, "How can we optimize robot movements based on environmental data and staff emotional states within the factory?" This generates specific and effective analysis results, leading to the optimization of the entire system.

[0446] 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.

[0447] 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.

[0448] 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.

[0449] [Third Embodiment]

[0450] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0451] 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.

[0452] 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).

[0453] 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.

[0454] 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.

[0455] 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).

[0456] 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.

[0457] 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.

[0458] 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.

[0459] 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.

[0460] 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.

[0461] 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".

[0462] This invention aims to digitize and automate agriculture by providing a system that utilizes an environmental measurement device, analysis means, automation equipment, and an interactive interface. This system continuously monitors the agricultural environment, calculates optimal cultivation conditions, and automates agricultural work based on those conditions.

[0463] First, the terminal collects environmental information such as field temperature, humidity, and moisture content through an environmental measurement device. This information is measured by sensors and transmitted to the server as data packets at regular intervals.

[0464] The server stores the received environmental information in a database and processes the data using an analysis tool. The analysis tool uses a machine learning algorithm to calculate optimal agricultural conditions from the collected data. These conditions include, for example, the amount of water needed, the timing of fertilization, and the adjustment of light.

[0465] Based on the calculated optimal conditions, the server generates optimal control information for controlling the automated equipment. This allows the automated equipment, acting as the terminal, to automatically perform tasks such as watering, fertilizing, and harvesting.

[0466] Users can communicate with the system through an interactive interface. This allows users to check the system's operating status and make adjustments as needed. For example, if a rapid response is required to sudden weather changes or anomaly detection, users can issue readjustment instructions through the interface.

[0467] As a concrete example, consider a situation where soil moisture is insufficient. The terminal detects the lack of moisture using a sensor and transmits this information to the server. The server calculates the optimal amount of water to be sprayed through an analysis mechanism and sends instructions to the automated equipment. The terminal (automated equipment) follows these instructions and sprays the appropriate amount of water, supporting the healthy growth of crops. In this way, the present invention provides a comprehensive system for achieving labor efficiency and sustainable agriculture.

[0468] The following describes the processing flow.

[0469] Step 1:

[0470] The terminal periodically collects data such as temperature, humidity, and moisture content from multiple environmental monitoring devices placed in the field. This data is obtained as precise numerical values ​​through sensors.

[0471] Step 2:

[0472] The terminal organizes the collected data into data packets and sends them to the server. This transmission occurs in real time, ensuring that data is received without delay.

[0473] Step 3:

[0474] The server stores the received data packets in a database for analysis. This allows for the storage of past and present environmental conditions in chronological order, making them available for analysis.

[0475] Step 4:

[0476] The server inputs the stored environmental data into an analysis device and performs the analysis. This analysis device processes the data using machine learning and data analysis algorithms to find the optimal cultivation conditions for the crops.

[0477] Step 5:

[0478] The server generates optimal control information based on the cultivation conditions derived from the analysis. This information includes specific instructions such as the amount of water to be sprayed and the timing of fertilizer application.

[0479] Step 6:

[0480] The server sends the generated optimal control information to the terminal. Based on this information, the terminal controls the automated agricultural equipment and prepares to perform actual farm work.

[0481] Step 7:

[0482] The terminals, following instructions from the server, operate agricultural equipment to perform tasks such as watering with the required amount of water, spreading fertilizer, and automatically removing weeds. This allows agricultural work to proceed more efficiently.

[0483] Step 8:

[0484] Users can check the system's operating status through an interactive interface. If necessary, users can communicate additional instructions or adjustments to the system via the interactive interface.

[0485] Step 9:

[0486] The server receives feedback from user input and incorporates it into its analysis tools, preparing future farming operations for more effective results. This feedback function improves the system's flexibility and accuracy.

[0487] (Example 1)

[0488] 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."

[0489] Modern agriculture requires the rapid collection of environmental information and the efficient control of automated equipment based on that information. Furthermore, to cope with sudden environmental changes, a system with predictive capabilities that reflects user input is necessary. However, current systems have challenges in real-time data analysis and user interface enhancement, and overcoming these problems is essential.

[0490] 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.

[0491] In this invention, the server includes means for aggregating data acquired from a device that records environmental information, means for computationally processing the data and generating optimal management information, and means for managing equipment and executing tasks based on the management information. This enables automated agricultural management and efficient task execution.

[0492] "Environmental information" refers to data such as temperature, humidity, and moisture conditions in agriculture and the natural environment, and includes all information necessary for crop cultivation and management.

[0493] "Means of aggregating data" refers to technologies for efficiently collecting and summarizing environmental information, and includes processes and devices for centrally managing data from sensors.

[0494] "Computational processing" refers to the act of analyzing acquired data and, as necessary, using mathematical and statistical methods to derive meaningful inferences or results from that data.

[0495] "Management information" refers to instructions and policies generated based on the results of data analysis, and includes information for effectively controlling the operation of automated equipment.

[0496] "Interactive means" refers to an interface that allows users to communicate with the system, and includes technologies and devices for effectively receiving user instructions and feedback.

[0497] A "detector" refers to a sensor that measures temperature, humidity, and moisture levels, and is a device installed for the purpose of accurately acquiring environmental information.

[0498] This invention provides a system for efficiently collecting environmental information and automating optimal agricultural operations based on that information. Specific embodiments of the system are shown below.

[0499] The terminal acquires environmental information using multiple sensors placed in the farmland. Specifically, various types of sensors are used to measure temperature, humidity, and moisture levels. This information is aggregated as digital signals and transmitted as data packets using a communication module. Specific hardware examples include the use of Arduino sensors and Raspberry Pi modules.

[0500] The server runs on a cloud platform to analyze the received data packets. Specifically, platforms such as AWS and Microsoft Azure are used. The analysis methods on the server include data processing modules written in Python and machine learning algorithms utilizing TensorFlow. This generates optimal management information from the collected data. This management information includes the required amount of watering and the timing of fertilization.

[0501] Based on the generated management information, the automated equipment, acting as a terminal, performs its tasks. Using a Raspberry Pi or other control modules, it controls motors and valves to perform specified tasks. Examples include automated irrigation systems and automated fertilizer application devices.

[0502] Users communicate with the system using an interactive interface via smartphones or PCs. This interface allows for real-time data visualization and control adjustments, and includes a function that allows users to give instructions to the system using prompts, enabling flexible responses to unexpected environmental changes. For example, a prompt such as, "Please tell me how this agricultural automation system can help grow healthy crops that are lacking water during dry seasons," can be used.

[0503] Thus, this invention solves various problems in agriculture and contributes to the realization of efficient and sustainable agriculture.

[0504] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0505] Step 1:

[0506] The terminal collects environmental information using sensors installed in the field. The input consists of raw data measuring temperature, humidity, and moisture content. These sensors acquire information in real time and convert it into digital signals. The converted digital data is then prepared to be passed to the communication module as data packets. The output is the data packets converted into a communication-ready format.

[0507] Step 2:

[0508] The terminal sends prepared data packets to the server at regular intervals. The input here is the converted data packets. The data is uploaded to the cloud using Wi-Fi or LPWAN technology. The output is environment information stored in a database on the cloud.

[0509] Step 3:

[0510] The server analyzes the received data. The input is environmental information stored in a database. A data processing module using Python cleanses the data, and a machine learning algorithm (using TensorFlow) calculates the optimal farming conditions. The output is a set of instructions documented as optimal management information.

[0511] Step 4:

[0512] The server sends control signals to the automated devices (terminals) based on the generated management information. The input is the optimal management information. The control signals are sent to the automated devices equipped with Raspberry Pi and interpreted as actual work commands. The output is the specific signals necessary for starting and adjusting the operation of the devices.

[0513] Step 5:

[0514] The automated equipment, acting as the terminal, receives control signals and performs agricultural tasks. The input is the specific control signal sent from the server. Motors and valves are activated, for example, to spray a specified amount of water or supply the necessary fertilizer. The output is the actual physical agricultural work that is performed.

[0515] Step 6:

[0516] The user monitors the system status through the interface and adjusts the system's operation using prompt messages as needed. Inputs are the system's current operating status and user instructions. When the user changes settings via a smartphone app and sends a prompt message, the changes are reflected in the system. Outputs are the adjusted new operating schedule and commands.

[0517] (Application Example 1)

[0518] 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."

[0519] Modern agriculture demands proper management of environmental information and automation of operations. However, the challenge lies in the lack of concrete means to achieve efficient and sustainable agriculture, as it is not easy to collect data in real time that is tailored to the specific conditions of individual farms and community gardens, and to implement optimal control and provide feedback to users based on that data.

[0520] 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.

[0521] In this invention, the server includes a function for accumulating environmental data obtained from an environmental measurement device, a function for analyzing the environmental data and generating appropriate control data, and a function for controlling machinery and performing work based on the appropriate control data. This enables real-time collection and analysis of environmental data, as well as easy-to-understand feedback to the user.

[0522] An "environmental measurement device" is a device used to measure physical environmental information in farms and community gardens, and includes sensors for acquiring data such as temperature, humidity, and moisture content.

[0523] "Environmental data" refers to information that indicates the environmental conditions of a specific location, such as temperature, humidity, and moisture content, collected by environmental measurement devices.

[0524] The "data aggregation function" refers to the ability to centrally collect and store multiple environmental data acquired from environmental measurement devices.

[0525] "Analysis means" refers to the ability to process collected environmental data and derive control data that is optimal for a specific purpose.

[0526] "Optimal control data" refers to control information generated to perform optimal operations and management based on analyzed environmental data.

[0527] The "function of controlling machinery to perform tasks" refers to the ability to operate automated equipment according to generated appropriate control data and perform necessary tasks (e.g., watering or fertilizing).

[0528] "User feedback" refers to the transmission of information that returns analysis results, machine control status, and environmental changes to the user, enabling them to operate the machine and check its status.

[0529] To implement this invention, a system consisting of an environmental measurement device, a server for data analysis, and a terminal capable of interacting with the user is required. Specifically, the invention can be implemented using the following components and procedures.

[0530] Environmental measurement device

[0531] This device is used to measure the physical conditions of agricultural environments. Equipped with sensors, it collects environmental data such as temperature, humidity, and moisture content. This device is installed in managed areas such as farms and community gardens.

[0532] server

[0533] The server collects environmental data transmitted from environmental measurement devices and analyzes it using machine learning algorithms. It generates appropriate control data from the analysis results, which is then used to control machinery and perform agricultural tasks. Furthermore, the server provides feedback to the user by transmitting the analysis results to a terminal.

[0534] terminal

[0535] This terminal provides an interface with the user. It receives feedback from the server and displays it to the user. The user can check the progress of their work through the interface and send feedback to the server as needed.

[0536] Specific example

[0537] For example, if you are growing tomatoes in a community garden, and an environmental monitoring device detects a decrease in moisture levels, this data is sent to a server where the optimal watering amount is calculated. This information is then sent to your device, and you can check it through an application. This allows you to adjust the operation as needed.

[0538] Example of a prompt

[0539] "As the manager of the tomato garden, given the high temperatures and lack of moisture, what watering rate should I set?"

[0540] In this way, we provide a system that enables efficient and automated agriculture tailored to the environment.

[0541] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0542] Step 1:

[0543] Environmental measurement devices use sensors to measure environmental data such as temperature, humidity, and moisture content. This measured data is generated as data packets in a digital format.

[0544] Input: Environmental data measured by sensors

[0545] Output: Digital data packets

[0546] Step 2:

[0547] The terminal receives data packets transmitted from the environmental measurement device. The received data is then transferred to the server using a secure communication protocol.

[0548] Input: Data packets from environmental measuring device

[0549] Output: Transfer of data packets to the server

[0550] Step 3:

[0551] The server analyzes the received data packets. Machine learning algorithms running on the server process environmental data and calculate optimal agricultural conditions. The results of this calculation are generated as appropriate control data.

[0552] Input: Environmental data transferred from the terminal

[0553] Output: Optimal control data indicating optimal agricultural conditions

[0554] Step 4:

[0555] The server sends the generated appropriate control data to the machine. This allows the machine to automatically perform tasks based on the specified agricultural conditions and initiate necessary actions (e.g., watering or fertilizing).

[0556] Input: Optimal control data

[0557] Output: Machine control instructions and their execution results

[0558] Step 5:

[0559] The server sends appropriate control data and its execution results as feedback to the terminal.

[0560] Input: Appropriate control data and its execution results

[0561] Output: Feedback data to the terminal

[0562] Step 6:

[0563] The terminal displays feedback data received from the server to the user. The user reviews the displayed information and, if necessary, sends feedback to the server through the interface. Further adjustments are made based on this interaction.

[0564] Input: Feedback data from the server

[0565] Output: Data displayed to the user and feedback data from the user.

[0566] 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.

[0567] This invention is a system aimed at improving the efficiency and automation of agriculture. In addition to functions that collect and analyze environmental information and control automated equipment to perform agricultural work, it is equipped with an emotion engine that recognizes the user's emotions. This emotion engine has the ability to understand the user's emotional state and adjust the system's response, thereby improving the user experience.

[0568] Specifically, the terminal first collects temperature, humidity, moisture content, and other data from sensors placed to measure the agricultural environment. This data is sent to a server and stored in a database. The server analyzes the received environmental data and calculates the optimal cultivation conditions. Machine learning and data analysis algorithms are used for this analysis.

[0569] Next, the server generates optimal control information based on the analysis results and sends instructions to the automated equipment, which acts as the terminal. The automated equipment, upon receiving the instructions, automatically performs agricultural tasks such as watering, fertilizing, and harvesting at the specified locations.

[0570] The emotion engine recognizes the user's emotional state from their voice and facial expressions when they interact with the interactive interface. Users can make adjustment requests to the system through the interface as needed, and their emotional state is analyzed at that time. For example, if a user is feeling stressed, the system adjusts the interface's response to a more supportive tone to reduce the user's burden.

[0571] Furthermore, the identified emotional state is reflected in the system's feedback process, allowing the analysis means to flexibly adjust the conditions and plans for farm work. For example, if the user's emotional state is anxious, the server can simplify the farm work plan and reduce the tasks performed by the terminal. In this way, the present invention realizes flexible control in response to the user's emotions, providing a comfortable and effective farming experience.

[0572] The following describes the processing flow.

[0573] Step 1:

[0574] The terminal periodically collects environmental information such as temperature, humidity, and moisture content from environmental monitoring devices placed in the field. Accurate measurement of this data enhances the reliability of subsequent processing.

[0575] Step 2:

[0576] The terminal converts the collected environmental data into data packets and sends them to the server. Transmission occurs in real time, ensuring a smooth data flow.

[0577] Step 3:

[0578] The server stores the received data packets in a database for analysis. This storage process enables the management of time-series data, allowing for advanced analysis that integrates past and present data.

[0579] Step 4:

[0580] The server inputs environmental information stored in the database into the analysis device and begins data analysis. The analysis device uses machine learning algorithms to calculate the optimal cultivation conditions. These analysis results include appropriate watering amounts and fertilizer application timings.

[0581] Step 5:

[0582] The server generates specific instructions for automated equipment based on the optimal control information generated by the analysis system. These instructions are sent to the terminal and reflected in the actual farm work plan.

[0583] Step 6:

[0584] The terminal operates automated equipment according to instructions from the server, automatically performing tasks such as watering, fertilizing, and harvesting on-site. This significantly reduces the workload on human labor.

[0585] Step 7:

[0586] Users interact with the system through an interactive interface and check its operating status. When users give adjustment instructions to the interface, the emotion engine recognizes the user's emotions through voice and video.

[0587] Step 8:

[0588] The server incorporates the user's emotional state, identified by the emotion engine, into its analysis and adjusts the interactive interface's responses as needed. For example, if the user's emotions are unstable, the interface's responses are changed to be calmer and more reassuring.

[0589] Step 9:

[0590] The server incorporates the emotional state obtained from the analysis into the farm work plan, flexibly changing the conditions and content of the farm work as needed. For example, if the user's emotional state is high stress, the server reduces the user's burden by simplifying tasks or adjusting priorities.

[0591] Step 10:

[0592] Based on new instructions from the server, the terminal readjusts automated equipment under an optimized plan and continuously performs farm work. This process allows the system to adapt to real-time environmental changes as well as the user's emotional state, always providing optimal agricultural management.

[0593] (Example 2)

[0594] 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."

[0595] While conventional agricultural systems could control automated equipment based on environmental information, they lacked the ability to adjust responses to consider the user's emotional state, limiting the potential for improving the user experience. Furthermore, there was a need for flexible adjustments to agricultural operations in response to changes in environmental conditions.

[0596] 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.

[0597] In this invention, the server includes means for acquiring environmental information, means for analyzing the environmental information and calculating optimal conditions, means for controlling the behavioral device to perform tasks based on the optimal conditions, and means for recognizing the user's emotional state and adjusting the response. This enables flexible work control and response adjustment that takes the user's emotional state into consideration, improving the user experience and enabling efficient agricultural work.

[0598] "Environmental information" refers to information that indicates environmental conditions in agriculture, and includes data such as temperature, humidity, and moisture content.

[0599] "Optimal conditions" refer to the environment and work schedule that are most suitable for plant growth and agricultural work, and are calculated based on the analysis of environmental information.

[0600] An "action device" is a machine or device that automatically performs agricultural tasks, such as watering, fertilizing, and harvesting, according to instructions.

[0601] "User emotional state" refers to information that indicates the user's psychological and emotional state, and is recognized through interactions via the interface.

[0602] "Means of adjusting responses" refer to functions that modify the content and tone of system responses based on the user's emotional state to improve the user experience.

[0603] "Analysis methods" refer to computational methods and algorithms used to analyze collected environmental information and derive optimal conditions.

[0604] This invention is a system aimed at improving efficiency and automation in agriculture. It collects and analyzes environmental information to transmit optimal control instructions to equipment and execute tasks. It also has a function to recognize user emotions and adjust responses accordingly.

[0605] The terminal first collects environmental information using various sensors. Specifically, sensors that measure temperature, humidity, and moisture content are used. This makes it possible to understand the current environmental conditions in the field in real time.

[0606] Once environmental information is acquired, the server receives it and stores it in a database. This provides the basis for comparison and analysis with past data while maintaining data integrity.

[0607] Next, the server processes the data using analytical tools. This analysis utilizes machine learning algorithms and data analysis software to derive optimal conditions. These optimal conditions include specific farming plans, such as the amount of water to irrigate and the timing of fertilizer application, tailored to the current environment.

[0608] Subsequently, the server generates control information based on optimal conditions and transmits control signals to the operational device acting as a terminal. The operational device autonomously performs agricultural tasks according to the received information. For example, if the soil moisture content is low, it automatically sprinkles a specified amount of water.

[0609] Furthermore, as users interact with the system through the interactive interface, the system recognizes the user's emotional state. When the user's psychological state (e.g., stress, anxiety) is observed, the server adjusts its response based on that analysis to provide a more empathetic interface experience.

[0610] For example, when a user is worried about insufficient sunlight, the system can automatically suggest a farming plan to alleviate that anxiety. An example of a prompt message for a generative AI model is as follows:

[0611] Prompt example: "If the user is feeling anxious about the current farming situation, suggest how to adjust the work plan. Also, generate a more supportive interface response."

[0612] In this way, agricultural systems aim to improve the user experience and enable effective farming operations through efficient data processing and a highly responsive user interface.

[0613] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0614] Step 1:

[0615] The terminal collects environmental information through various sensors placed in the agricultural environment. Inputs include temperature, humidity, and moisture content. By aggregating this sensor data, the terminal obtains the current environmental conditions of the field. Specifically, the sensors detect data in real time and transmit it to the server using a secure communication protocol.

[0616] Step 2:

[0617] The server stores environmental information received from the terminals in a database. The input for this step is raw data transmitted from each sensor. By being stored in the database, it serves as fundamental information necessary for comparison and analysis with past data. Specifically, the server verifies data integrity and saves it appropriately to avoid data loss.

[0618] Step 3:

[0619] The server processes the stored environmental information for analysis. In this step, the input is historical and current environmental data stored in the database. Machine learning algorithms and statistical methods are used to process the data and calculate the optimal environmental conditions. The output is the optimal cultivation conditions at that time, and this information is used to guide the next action. Specifically, pattern recognition and predictive analysis are performed using data analysis software.

[0620] Step 4:

[0621] The server generates specific action instructions based on the analysis results and sends them to the terminal. These instructions include optimal environmental conditions as input and generate control signals as output. A specific action might be to instruct the action device to perform additional watering if the specified amount of moisture is insufficient.

[0622] Step 5:

[0623] The terminal receives instructions from the server, and the action device automatically performs agricultural tasks. The input for this step is the control signal provided by the server, and the output is the actual execution of agricultural work. Specific actions include a watering device operating to spray a specified amount of water, or a fertilizer spreader accurately distributing fertilizer.

[0624] Step 6:

[0625] The user interacts with the system through an interactive interface, and the emotion engine analyzes the user's voice and facial expressions. The input for this step is the user's interaction data (voice, facial expressions), and the output is the estimated emotional state. Specifically, information acquired by the camera and microphone is processed using voice and image analysis technology to understand the user's emotions.

[0626] Step 7:

[0627] The server adjusts the system's response content and tone according to the recognized user's emotions and reflects this in the user interface. The input for this step is the analyzed emotional state, and the output is the optimized user response. Specific actions include adjusting the display of more supportive messages or providing simpler operation guides.

[0628] (Application Example 2)

[0629] 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."

[0630] This invention aims to solve the problem of providing a more comfortable and effective work environment by improving the efficiency of automated work based on environmental information, as well as enabling system adjustments to match the user's emotional state. In particular, it focuses on reducing user stress and fatigue by making full use of emotion recognition technology.

[0631] 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.

[0632] In this invention, the server includes means for collecting ambient information acquired from an environmental detection device, means for analyzing the ambient information and generating optimal operation information, means for controlling an automated device to perform tasks based on the optimal operation information, and means for recognizing the user's emotional state using an emotion recognition engine and adjusting the system's response. This enables flexible adjustment of the workload according to the user's emotional state.

[0633] An "environmental detection device" is a device used to measure the physical conditions of the surroundings. Specifically, it includes sensors that measure temperature, humidity, moisture content, etc.

[0634] "Surrounding information" refers to information about the surrounding physical conditions acquired by environmental detection devices. This includes temperature, humidity, moisture content, etc.

[0635] "Optimal operation information" refers to instruction information generated based on analyzed peripheral information to optimize the operation of automated equipment.

[0636] An "automation device" is a machine or device that performs tasks through artificial control. It is used to automate agricultural work and factory operations.

[0637] An "emotion recognition engine" is a technology that detects and determines the user's emotional state. It analyzes emotions based on facial expressions and changes in voice.

[0638] A "user" is an individual or group that operates or adjusts the system through its interface.

[0639] An "interactive interface" is a means of communication that allows a user to interact directly with a system. It can utilize voice and screen displays.

[0640] "Workload" refers to the mental or physical burden that an employer bears when performing a task.

[0641] The system for carrying out this invention mainly consists of an environmental detection device, a server, an automation device, an emotion recognition engine, and an interactive interface.

[0642] The server first acquires ambient information from an environmental detection device. This device includes a temperature sensor, a humidity sensor, and a moisture content sensor, which accurately measure the state of the surrounding environment. The acquired ambient information is transmitted to the server and stored in a database. The server then analyzes the data using machine learning algorithms to generate optimal operational information.

[0643] The server controls automated equipment based on analyzed optimal operation information. For example, it can adjust the movement of robots in a factory to improve work efficiency. Automated equipment performs tasks by executing specified actions.

[0644] Next, the user communicates with the system through an interactive interface. Through this interface, the user's voice and facial expressions are analyzed by an emotion recognition engine to recognize the user's emotional state. If the user is experiencing stress, the server detects this and adjusts the system's response and the operation of automated devices. This reduces the user's mental and physical burden, providing a comfortable working environment.

[0645] For example, if a factory worker is under excessive stress, this system can analyze their emotional state and automatically adjust the robot's workload. This balances the workload by reducing the workload of one staff member and having other staff members or machines take over some of it.

[0646] An example of a prompt to input into the generating AI model might be, "How can we optimize the robot's movements based on environmental data and staff emotional states within the factory?" This allows the system to acquire information for appropriate analysis and optimization, enabling effective control.

[0647] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0648] Step 1:

[0649] The system collects ambient information from environmental detection devices. The server acquires sensor data such as temperature, humidity, and moisture content from these devices. This data represents the physical conditions of the surroundings and serves as input to the server. The acquired data is temporarily stored within the server.

[0650] Step 2:

[0651] The system analyzes surrounding information and generates optimal operating information. The server uses machine learning algorithms to analyze the collected data. This process calculates the optimal operating conditions and parameters. Specifically, statistical analysis and pattern recognition are performed based on the data. The optimal operating information is generated as output and used to control the automated equipment.

[0652] Step 3:

[0653] The system controls automated equipment to perform tasks. Based on optimal operational information, the server sends commands to the automated equipment. This allows automated equipment, such as robots, to operate according to the environment. Specific examples of such operations include starting and stopping tasks at the appropriate time, and adjusting the movement of transport equipment.

[0654] Step 4:

[0655] The system recognizes the user's emotional state. As the user interacts with the system through an interactive interface, the server uses an emotion recognition engine to analyze voice and facial expression data. The user's voice and camera images are used as input, and the results of the emotional state analysis are output.

[0656] Step 5:

[0657] The system adjusts its response based on the user's emotional state. The server adjusts the system's response and operation based on analysis results from the emotion recognition engine. For example, if a user is experiencing stress, the automated device will receive instructions to reduce their workload. This reduces the user's burden and allows the work to proceed more smoothly.

[0658] Step 6:

[0659] The system performs analysis using prompts. The server uses a generated AI model as needed, taking prompt sentences as input to enhance the system's analysis and suggestions. Examples of input prompt sentences include, "How can we optimize robot movements based on environmental data and staff emotional states within the factory?" This generates specific and effective analysis results, leading to the optimization of the entire system.

[0660] 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.

[0661] 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.

[0662] 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.

[0663] [Fourth Embodiment]

[0664] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0665] 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.

[0666] 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).

[0667] 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.

[0668] 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.

[0669] 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).

[0670] 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.

[0671] 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.

[0672] 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.

[0673] 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.

[0674] 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.

[0675] 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.

[0676] 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".

[0677] This invention aims to digitize and automate agriculture by providing a system that utilizes an environmental measurement device, analysis means, automation equipment, and an interactive interface. This system continuously monitors the agricultural environment, calculates optimal cultivation conditions, and automates agricultural work based on those conditions.

[0678] First, the terminal collects environmental information such as field temperature, humidity, and moisture content through an environmental measurement device. This information is measured by sensors and transmitted to the server as data packets at regular intervals.

[0679] The server stores the received environmental information in a database and processes the data using an analysis tool. The analysis tool uses a machine learning algorithm to calculate optimal agricultural conditions from the collected data. These conditions include, for example, the amount of water needed, the timing of fertilization, and the adjustment of light.

[0680] Based on the calculated optimal conditions, the server generates optimal control information for controlling the automated equipment. This allows the automated equipment, acting as the terminal, to automatically perform tasks such as watering, fertilizing, and harvesting.

[0681] Users can communicate with the system through an interactive interface. This allows users to check the system's operating status and make adjustments as needed. For example, if a rapid response is required to sudden weather changes or anomaly detection, users can issue readjustment instructions through the interface.

[0682] As a concrete example, consider a situation where soil moisture is insufficient. The terminal detects the lack of moisture using a sensor and transmits this information to the server. The server calculates the optimal amount of water to be sprayed through an analysis mechanism and sends instructions to the automated equipment. The terminal (automated equipment) follows these instructions and sprays the appropriate amount of water, supporting the healthy growth of crops. In this way, the present invention provides a comprehensive system for achieving labor efficiency and sustainable agriculture.

[0683] The following describes the processing flow.

[0684] Step 1:

[0685] The terminal periodically collects data such as temperature, humidity, and moisture content from multiple environmental monitoring devices placed in the field. This data is obtained as precise numerical values ​​through sensors.

[0686] Step 2:

[0687] The terminal organizes the collected data into data packets and sends them to the server. This transmission occurs in real time, ensuring that data is received without delay.

[0688] Step 3:

[0689] The server stores the received data packets in a database for analysis. This allows for the storage of past and present environmental conditions in chronological order, making them available for analysis.

[0690] Step 4:

[0691] The server inputs the stored environmental data into an analysis device and performs the analysis. This analysis device processes the data using machine learning and data analysis algorithms to find the optimal cultivation conditions for the crops.

[0692] Step 5:

[0693] The server generates optimal control information based on the cultivation conditions derived from the analysis. This information includes specific instructions such as the amount of water to be sprayed and the timing of fertilizer application.

[0694] Step 6:

[0695] The server sends the generated optimal control information to the terminal. Based on this information, the terminal controls the automated agricultural equipment and prepares to perform actual farm work.

[0696] Step 7:

[0697] The terminals, following instructions from the server, operate agricultural equipment to perform tasks such as watering with the required amount of water, spreading fertilizer, and automatically removing weeds. This allows agricultural work to proceed more efficiently.

[0698] Step 8:

[0699] Users can check the system's operating status through an interactive interface. If necessary, users can communicate additional instructions or adjustments to the system via the interactive interface.

[0700] Step 9:

[0701] The server receives feedback from user input and incorporates it into its analysis tools, preparing future farming operations for more effective results. This feedback function improves the system's flexibility and accuracy.

[0702] (Example 1)

[0703] 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".

[0704] Modern agriculture requires the rapid collection of environmental information and the efficient control of automated equipment based on that information. Furthermore, to cope with sudden environmental changes, a system with predictive capabilities that reflects user input is necessary. However, current systems have challenges in real-time data analysis and user interface enhancement, and overcoming these problems is essential.

[0705] 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.

[0706] In this invention, the server includes means for aggregating data acquired from a device that records environmental information, means for computationally processing the data and generating optimal management information, and means for managing equipment and executing tasks based on the management information. This enables automated agricultural management and efficient task execution.

[0707] "Environmental information" refers to data such as temperature, humidity, and moisture conditions in agriculture and the natural environment, and includes all information necessary for crop cultivation and management.

[0708] "Means of aggregating data" refers to technologies for efficiently collecting and summarizing environmental information, and includes processes and devices for centrally managing data from sensors.

[0709] "Computational processing" refers to the act of analyzing acquired data and, as necessary, using mathematical and statistical methods to derive meaningful inferences or results from that data.

[0710] "Management information" refers to instructions and policies generated based on the results of data analysis, and includes information for effectively controlling the operation of automated equipment.

[0711] "Interactive means" refers to an interface that allows users to communicate with the system, and includes technologies and devices for effectively receiving user instructions and feedback.

[0712] A "detector" refers to a sensor that measures temperature, humidity, and moisture levels, and is a device installed for the purpose of accurately acquiring environmental information.

[0713] This invention provides a system for efficiently collecting environmental information and automating optimal agricultural operations based on that information. Specific embodiments of the system are shown below.

[0714] The terminal acquires environmental information using multiple sensors placed in the farmland. Specifically, various types of sensors are used to measure temperature, humidity, and moisture levels. This information is aggregated as digital signals and transmitted as data packets using a communication module. Specific hardware examples include the use of Arduino sensors and Raspberry Pi modules.

[0715] The server runs on a cloud platform to analyze the received data packets. Specifically, platforms such as AWS and Microsoft Azure are used. The analysis methods on the server include data processing modules written in Python and machine learning algorithms utilizing TensorFlow. This generates optimal management information from the collected data. This management information includes the required amount of watering and the timing of fertilization.

[0716] Based on the generated management information, the automated equipment, acting as a terminal, performs its tasks. Using a Raspberry Pi or other control modules, it controls motors and valves to perform specified tasks. Examples include automated irrigation systems and automated fertilizer application devices.

[0717] Users communicate with the system using an interactive interface via smartphones or PCs. This interface allows for real-time data visualization and control adjustments, and includes a function that allows users to give instructions to the system using prompts, enabling flexible responses to unexpected environmental changes. For example, a prompt such as, "Please tell me how this agricultural automation system can help grow healthy crops that are lacking water during dry seasons," can be used.

[0718] Thus, this invention solves various problems in agriculture and contributes to the realization of efficient and sustainable agriculture.

[0719] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0720] Step 1:

[0721] The terminal collects environmental information using sensors installed in the field. The input consists of raw data measuring temperature, humidity, and moisture content. These sensors acquire information in real time and convert it into digital signals. The converted digital data is then prepared to be passed to the communication module as data packets. The output is the data packets converted into a communication-ready format.

[0722] Step 2:

[0723] The terminal sends prepared data packets to the server at regular intervals. The input here is the converted data packets. The data is uploaded to the cloud using Wi-Fi or LPWAN technology. The output is environment information stored in a database on the cloud.

[0724] Step 3:

[0725] The server analyzes the received data. The input is environmental information stored in a database. A data processing module using Python cleanses the data, and a machine learning algorithm (using TensorFlow) calculates the optimal farming conditions. The output is a set of instructions documented as optimal management information.

[0726] Step 4:

[0727] The server sends control signals to the automated devices (terminals) based on the generated management information. The input is the optimal management information. The control signals are sent to the automated devices equipped with Raspberry Pi and interpreted as actual work commands. The output is the specific signals necessary for starting and adjusting the operation of the devices.

[0728] Step 5:

[0729] The automated equipment, acting as the terminal, receives control signals and performs agricultural tasks. The input is the specific control signal sent from the server. Motors and valves are activated, for example, to spray a specified amount of water or supply the necessary fertilizer. The output is the actual physical agricultural work that is performed.

[0730] Step 6:

[0731] The user monitors the system status through the interface and adjusts the system's operation using prompt messages as needed. Inputs are the system's current operating status and user instructions. When the user changes settings via a smartphone app and sends a prompt message, the changes are reflected in the system. Outputs are the adjusted new operating schedule and commands.

[0732] (Application Example 1)

[0733] 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".

[0734] Modern agriculture demands proper management of environmental information and automation of operations. However, the challenge lies in the lack of concrete means to achieve efficient and sustainable agriculture, as it is not easy to collect data in real time that is tailored to the specific conditions of individual farms and community gardens, and to implement optimal control and provide feedback to users based on that data.

[0735] 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.

[0736] In this invention, the server includes a function for accumulating environmental data obtained from an environmental measurement device, a function for analyzing the environmental data and generating appropriate control data, and a function for controlling machinery and performing work based on the appropriate control data. This enables real-time collection and analysis of environmental data, as well as easy-to-understand feedback to the user.

[0737] An "environmental measurement device" is a device used to measure physical environmental information in farms and community gardens, and includes sensors for acquiring data such as temperature, humidity, and moisture content.

[0738] "Environmental data" refers to information that indicates the environmental conditions of a specific location, such as temperature, humidity, and moisture content, collected by environmental measurement devices.

[0739] The "data aggregation function" refers to the ability to centrally collect and store multiple environmental data acquired from environmental measurement devices.

[0740] "Analysis means" refers to the ability to process collected environmental data and derive control data that is optimal for a specific purpose.

[0741] "Optimal control data" refers to control information generated to perform optimal operations and management based on analyzed environmental data.

[0742] The "function of controlling machinery to perform tasks" refers to the ability to operate automated equipment according to generated appropriate control data and perform necessary tasks (e.g., watering or fertilizing).

[0743] "User feedback" refers to the transmission of information that returns analysis results, machine control status, and environmental changes to the user, enabling them to operate the machine and check its status.

[0744] To implement this invention, a system consisting of an environmental measurement device, a server for data analysis, and a terminal capable of interacting with the user is required. Specifically, the invention can be implemented using the following components and procedures.

[0745] Environmental measurement device

[0746] This device is used to measure the physical conditions of agricultural environments. Equipped with sensors, it collects environmental data such as temperature, humidity, and moisture content. This device is installed in managed areas such as farms and community gardens.

[0747] server

[0748] The server collects environmental data transmitted from environmental measurement devices and analyzes it using machine learning algorithms. It generates appropriate control data from the analysis results, which is then used to control machinery and perform agricultural tasks. Furthermore, the server provides feedback to the user by transmitting the analysis results to a terminal.

[0749] terminal

[0750] This terminal provides an interface with the user. It receives feedback from the server and displays it to the user. The user can check the progress of their work through the interface and send feedback to the server as needed.

[0751] Specific example

[0752] For example, if you are growing tomatoes in a community garden, and an environmental monitoring device detects a decrease in moisture levels, this data is sent to a server where the optimal watering amount is calculated. This information is then sent to your device, and you can check it through an application. This allows you to adjust the operation as needed.

[0753] Example of a prompt

[0754] "As the manager of the tomato garden, given the high temperatures and lack of moisture, what watering rate should I set?"

[0755] In this way, we provide a system that enables efficient and automated agriculture tailored to the environment.

[0756] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0757] Step 1:

[0758] Environmental measurement devices use sensors to measure environmental data such as temperature, humidity, and moisture content. This measured data is generated as data packets in a digital format.

[0759] Input: Environmental data measured by sensors

[0760] Output: Digital data packets

[0761] Step 2:

[0762] The terminal receives data packets transmitted from the environmental measurement device. The received data is then transferred to the server using a secure communication protocol.

[0763] Input: Data packets from environmental measuring device

[0764] Output: Transfer of data packets to the server

[0765] Step 3:

[0766] The server analyzes the received data packets. Machine learning algorithms running on the server process environmental data and calculate optimal agricultural conditions. The results of this calculation are generated as appropriate control data.

[0767] Input: Environmental data transferred from the terminal

[0768] Output: Optimal control data indicating optimal agricultural conditions

[0769] Step 4:

[0770] The server sends the generated appropriate control data to the machine. This allows the machine to automatically perform tasks based on the specified agricultural conditions and initiate necessary actions (e.g., watering or fertilizing).

[0771] Input: Optimal control data

[0772] Output: Machine control instructions and their execution results

[0773] Step 5:

[0774] The server sends appropriate control data and its execution results as feedback to the terminal.

[0775] Input: Appropriate control data and its execution results

[0776] Output: Feedback data to the terminal

[0777] Step 6:

[0778] The terminal displays feedback data received from the server to the user. The user reviews the displayed information and, if necessary, sends feedback to the server through the interface. Further adjustments are made based on this interaction.

[0779] Input: Feedback data from the server

[0780] Output: Data displayed to the user and feedback data from the user.

[0781] 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.

[0782] This invention is a system aimed at improving the efficiency and automation of agriculture. In addition to functions that collect and analyze environmental information and control automated equipment to perform agricultural work, it is equipped with an emotion engine that recognizes the user's emotions. This emotion engine has the ability to understand the user's emotional state and adjust the system's response, thereby improving the user experience.

[0783] Specifically, the terminal first collects temperature, humidity, moisture content, and other data from sensors placed to measure the agricultural environment. This data is sent to a server and stored in a database. The server analyzes the received environmental data and calculates the optimal cultivation conditions. Machine learning and data analysis algorithms are used for this analysis.

[0784] Next, the server generates optimal control information based on the analysis results and sends instructions to the automated equipment, which acts as the terminal. The automated equipment, upon receiving the instructions, automatically performs agricultural tasks such as watering, fertilizing, and harvesting at the specified locations.

[0785] The emotion engine recognizes the user's emotional state from their voice and facial expressions when they interact with the interactive interface. Users can make adjustment requests to the system through the interface as needed, and their emotional state is analyzed at that time. For example, if a user is feeling stressed, the system adjusts the interface's response to a more supportive tone to reduce the user's burden.

[0786] Furthermore, the identified emotional state is reflected in the system's feedback process, allowing the analysis means to flexibly adjust the conditions and plans for farm work. For example, if the user's emotional state is anxious, the server can simplify the farm work plan and reduce the tasks performed by the terminal. In this way, the present invention realizes flexible control in response to the user's emotions, providing a comfortable and effective farming experience.

[0787] The following describes the processing flow.

[0788] Step 1:

[0789] The terminal periodically collects environmental information such as temperature, humidity, and moisture content from environmental monitoring devices placed in the field. Accurate measurement of this data enhances the reliability of subsequent processing.

[0790] Step 2:

[0791] The terminal converts the collected environmental data into data packets and sends them to the server. Transmission occurs in real time, ensuring a smooth data flow.

[0792] Step 3:

[0793] The server stores the received data packets in a database for analysis. This storage process enables the management of time-series data, allowing for advanced analysis that integrates past and present data.

[0794] Step 4:

[0795] The server inputs environmental information stored in the database into the analysis device and begins data analysis. The analysis device uses machine learning algorithms to calculate the optimal cultivation conditions. These analysis results include appropriate watering amounts and fertilizer application timings.

[0796] Step 5:

[0797] The server generates specific instructions for automated equipment based on the optimal control information generated by the analysis system. These instructions are sent to the terminal and reflected in the actual farm work plan.

[0798] Step 6:

[0799] The terminal operates automated equipment according to instructions from the server, automatically performing tasks such as watering, fertilizing, and harvesting on-site. This significantly reduces the workload on human labor.

[0800] Step 7:

[0801] Users interact with the system through an interactive interface and check its operating status. When users give adjustment instructions to the interface, the emotion engine recognizes the user's emotions through voice and video.

[0802] Step 8:

[0803] The server incorporates the user's emotional state, identified by the emotion engine, into its analysis and adjusts the interactive interface's responses as needed. For example, if the user's emotions are unstable, the interface's responses are changed to be calmer and more reassuring.

[0804] Step 9:

[0805] The server incorporates the emotional state obtained from the analysis into the farm work plan, flexibly changing the conditions and content of the farm work as needed. For example, if the user's emotional state is high stress, the server reduces the user's burden by simplifying tasks or adjusting priorities.

[0806] Step 10:

[0807] Based on new instructions from the server, the terminal readjusts automated equipment under an optimized plan and continuously performs farm work. This process allows the system to adapt to real-time environmental changes as well as the user's emotional state, always providing optimal agricultural management.

[0808] (Example 2)

[0809] 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".

[0810] While conventional agricultural systems could control automated equipment based on environmental information, they lacked the ability to adjust responses to consider the user's emotional state, limiting the potential for improving the user experience. Furthermore, there was a need for flexible adjustments to agricultural operations in response to changes in environmental conditions.

[0811] 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.

[0812] In this invention, the server includes means for acquiring environmental information, means for analyzing the environmental information and calculating optimal conditions, means for controlling the behavioral device to perform tasks based on the optimal conditions, and means for recognizing the user's emotional state and adjusting the response. This enables flexible work control and response adjustment that takes the user's emotional state into consideration, improving the user experience and enabling efficient agricultural work.

[0813] "Environmental information" refers to information that indicates environmental conditions in agriculture, and includes data such as temperature, humidity, and moisture content.

[0814] "Optimal conditions" refer to the environment and work schedule that are most suitable for plant growth and agricultural work, and are calculated based on the analysis of environmental information.

[0815] An "action device" is a machine or device that automatically performs agricultural tasks, such as watering, fertilizing, and harvesting, according to instructions.

[0816] "User emotional state" refers to information that indicates the user's psychological and emotional state, and is recognized through interactions via the interface.

[0817] "Means of adjusting responses" refer to functions that modify the content and tone of system responses based on the user's emotional state to improve the user experience.

[0818] "Analysis methods" refer to computational methods and algorithms used to analyze collected environmental information and derive optimal conditions.

[0819] This invention is a system aimed at improving efficiency and automation in agriculture. It collects and analyzes environmental information to transmit optimal control instructions to equipment and execute tasks. It also has a function to recognize user emotions and adjust responses accordingly.

[0820] The terminal first collects environmental information using various sensors. Specifically, sensors that measure temperature, humidity, and moisture content are used. This makes it possible to understand the current environmental conditions in the field in real time.

[0821] Once environmental information is acquired, the server receives it and stores it in a database. This provides the basis for comparison and analysis with past data while maintaining data integrity.

[0822] Next, the server processes the data using analytical tools. This analysis utilizes machine learning algorithms and data analysis software to derive optimal conditions. These optimal conditions include specific farming plans, such as the amount of water to irrigate and the timing of fertilizer application, tailored to the current environment.

[0823] Subsequently, the server generates control information based on optimal conditions and transmits control signals to the operational device acting as a terminal. The operational device autonomously performs agricultural tasks according to the received information. For example, if the soil moisture content is low, it automatically sprinkles a specified amount of water.

[0824] Furthermore, as users interact with the system through the interactive interface, the system recognizes the user's emotional state. When the user's psychological state (e.g., stress, anxiety) is observed, the server adjusts its response based on that analysis to provide a more empathetic interface experience.

[0825] For example, when a user is worried about insufficient sunlight, the system can automatically suggest a farming plan to alleviate that anxiety. An example of a prompt message for a generative AI model is as follows:

[0826] Prompt example: "If the user is feeling anxious about the current farming situation, suggest how to adjust the work plan. Also, generate a more supportive interface response."

[0827] In this way, agricultural systems aim to improve the user experience and enable effective farming operations through efficient data processing and a highly responsive user interface.

[0828] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0829] Step 1:

[0830] The terminal collects environmental information through various sensors placed in the agricultural environment. Inputs include temperature, humidity, and moisture content. By aggregating this sensor data, the terminal obtains the current environmental conditions of the field. Specifically, the sensors detect data in real time and transmit it to the server using a secure communication protocol.

[0831] Step 2:

[0832] The server stores environmental information received from the terminals in a database. The input for this step is raw data transmitted from each sensor. By being stored in the database, it serves as fundamental information necessary for comparison and analysis with past data. Specifically, the server verifies data integrity and saves it appropriately to avoid data loss.

[0833] Step 3:

[0834] The server processes the stored environmental information for analysis. In this step, the input is historical and current environmental data stored in the database. Machine learning algorithms and statistical methods are used to process the data and calculate the optimal environmental conditions. The output is the optimal cultivation conditions at that time, and this information is used to guide the next action. Specifically, pattern recognition and predictive analysis are performed using data analysis software.

[0835] Step 4:

[0836] The server generates specific action instructions based on the analysis results and sends them to the terminal. These instructions include optimal environmental conditions as input and generate control signals as output. A specific action might be to instruct the action device to perform additional watering if the specified amount of moisture is insufficient.

[0837] Step 5:

[0838] The terminal receives instructions from the server, and the action device automatically performs agricultural tasks. The input for this step is the control signal provided by the server, and the output is the actual execution of agricultural work. Specific actions include a watering device operating to spray a specified amount of water, or a fertilizer spreader accurately distributing fertilizer.

[0839] Step 6:

[0840] The user interacts with the system through an interactive interface, and the emotion engine analyzes the user's voice and facial expressions. The input for this step is the user's interaction data (voice, facial expressions), and the output is the estimated emotional state. Specifically, information acquired by the camera and microphone is processed using voice and image analysis technology to understand the user's emotions.

[0841] Step 7:

[0842] The server adjusts the system's response content and tone according to the recognized user's emotions and reflects this in the user interface. The input for this step is the analyzed emotional state, and the output is the optimized user response. Specific actions include adjusting the display of more supportive messages or providing simpler operation guides.

[0843] (Application Example 2)

[0844] 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".

[0845] This invention aims to solve the problem of providing a more comfortable and effective work environment by improving the efficiency of automated work based on environmental information, as well as enabling system adjustments to match the user's emotional state. In particular, it focuses on reducing user stress and fatigue by making full use of emotion recognition technology.

[0846] 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.

[0847] In this invention, the server includes means for collecting ambient information acquired from an environmental detection device, means for analyzing the ambient information and generating optimal operation information, means for controlling an automated device to perform tasks based on the optimal operation information, and means for recognizing the user's emotional state using an emotion recognition engine and adjusting the system's response. This enables flexible adjustment of the workload according to the user's emotional state.

[0848] An "environmental detection device" is a device used to measure the physical conditions of the surroundings. Specifically, it includes sensors that measure temperature, humidity, moisture content, etc.

[0849] "Surrounding information" refers to information about the surrounding physical conditions acquired by environmental detection devices. This includes temperature, humidity, moisture content, etc.

[0850] "Optimal operation information" refers to instruction information generated based on analyzed peripheral information to optimize the operation of automated equipment.

[0851] An "automation device" is a machine or device that performs tasks through artificial control. It is used to automate agricultural work and factory operations.

[0852] An "emotion recognition engine" is a technology that detects and determines the user's emotional state. It analyzes emotions based on facial expressions and changes in voice.

[0853] A "user" is an individual or group that operates or adjusts the system through its interface.

[0854] An "interactive interface" is a means of communication that allows a user to interact directly with a system. It can utilize voice and screen displays.

[0855] "Workload" refers to the mental or physical burden that an employer bears when performing a task.

[0856] The system for carrying out this invention mainly consists of an environmental detection device, a server, an automation device, an emotion recognition engine, and an interactive interface.

[0857] The server first acquires ambient information from an environmental detection device. This device includes a temperature sensor, a humidity sensor, and a moisture content sensor, which accurately measure the state of the surrounding environment. The acquired ambient information is transmitted to the server and stored in a database. The server then analyzes the data using machine learning algorithms to generate optimal operational information.

[0858] The server controls automated equipment based on analyzed optimal operation information. For example, it can adjust the movement of robots in a factory to improve work efficiency. Automated equipment performs tasks by executing specified actions.

[0859] Next, the user communicates with the system through an interactive interface. Through this interface, the user's voice and facial expressions are analyzed by an emotion recognition engine to recognize the user's emotional state. If the user is experiencing stress, the server detects this and adjusts the system's response and the operation of automated devices. This reduces the user's mental and physical burden, providing a comfortable working environment.

[0860] For example, if a factory worker is under excessive stress, this system can analyze their emotional state and automatically adjust the robot's workload. This balances the workload by reducing the workload of one staff member and having other staff members or machines take over some of it.

[0861] An example of a prompt to input into the generating AI model might be, "How can we optimize the robot's movements based on environmental data and staff emotional states within the factory?" This allows the system to acquire information for appropriate analysis and optimization, enabling effective control.

[0862] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0863] Step 1:

[0864] The system collects ambient information from environmental detection devices. The server acquires sensor data such as temperature, humidity, and moisture content from these devices. This data represents the physical conditions of the surroundings and serves as input to the server. The acquired data is temporarily stored within the server.

[0865] Step 2:

[0866] The system analyzes surrounding information and generates optimal operating information. The server uses machine learning algorithms to analyze the collected data. This process calculates the optimal operating conditions and parameters. Specifically, statistical analysis and pattern recognition are performed based on the data. The optimal operating information is generated as output and used to control the automated equipment.

[0867] Step 3:

[0868] The system controls automated equipment to perform tasks. Based on optimal operational information, the server sends commands to the automated equipment. This allows automated equipment, such as robots, to operate according to the environment. Specific examples of such operations include starting and stopping tasks at the appropriate time, and adjusting the movement of transport equipment.

[0869] Step 4:

[0870] The system recognizes the user's emotional state. As the user interacts with the system through an interactive interface, the server uses an emotion recognition engine to analyze voice and facial expression data. The user's voice and camera images are used as input, and the results of the emotional state analysis are output.

[0871] Step 5:

[0872] The system adjusts its response based on the user's emotional state. The server adjusts the system's response and operation based on analysis results from the emotion recognition engine. For example, if a user is experiencing stress, the automated device will receive instructions to reduce their workload. This reduces the user's burden and allows the work to proceed more smoothly.

[0873] Step 6:

[0874] The system performs analysis using prompts. The server uses a generated AI model as needed, taking prompt sentences as input to enhance the system's analysis and suggestions. Examples of input prompt sentences include, "How can we optimize robot movements based on environmental data and staff emotional states within the factory?" This generates specific and effective analysis results, leading to the optimization of the entire system.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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."

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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.

[0895] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0896] The following is further disclosed regarding the embodiments described above.

[0897] (Claim 1)

[0898] A means for collecting environmental information obtained from an environmental measuring device,

[0899] Means for analyzing the aforementioned environmental information and generating optimal control information,

[0900] A means for controlling automated equipment to perform agricultural work based on the aforementioned optimal control information,

[0901] A system that includes this.

[0902] (Claim 2)

[0903] The system according to claim 1, wherein the environmental measuring device is a sensor that measures temperature, humidity, and moisture content.

[0904] (Claim 3)

[0905] The system according to claim 1, which includes an interactive interface and receives adjustment instructions from the user and provides feedback to the analysis means.

[0906] "Example 1"

[0907] (Claim 1)

[0908] A means for aggregating data acquired from devices that record environmental information,

[0909] A means for calculating and processing the aforementioned data to generate optimal management information,

[0910] A means for managing equipment and performing tasks based on the aforementioned management information,

[0911] A means of predicting environmental changes using generative AI models,

[0912] An interactive means of reporting the situation to the user,

[0913] A system that includes this.

[0914] (Claim 2)

[0915] The system according to claim 1, wherein the device for recording the environmental information is a detector that measures temperature, humidity, and moisture status.

[0916] (Claim 3)

[0917] The system according to claim 1, comprising the interactive means, which accepts operation instructions from the user and reflects them in the calculation processing means.

[0918] "Application Example 1"

[0919] (Claim 1)

[0920] A function to collect environmental data obtained from environmental measurement devices,

[0921] The function analyzes the aforementioned environmental data and generates appropriate control data,

[0922] A function that controls the machine and performs work based on the aforementioned appropriate control data,

[0923] Features that provide environmental data to users,

[0924] A function to send user feedback to an analysis tool,

[0925] A system that includes this.

[0926] (Claim 2)

[0927] The system according to claim 1, wherein the environmental measurement device is a sensing device that measures physical quantities.

[0928] (Claim 3)

[0929] The system according to claim 1, which has an interface that allows for mutual dialogue, receives adjustment requests from users and sends them back to the analysis means.

[0930] "Example 2 of combining an emotion engine"

[0931] (Claim 1)

[0932] Means of acquiring environmental information,

[0933] A means for analyzing the aforementioned environmental information and calculating the optimal conditions,

[0934] A means for controlling the action device to perform the work based on the aforementioned optimal conditions,

[0935] A means of recognizing the user's emotional state and adjusting the response,

[0936] A system that includes this.

[0937] (Claim 2)

[0938] The system according to claim 1, wherein the environmental information includes temperature, humidity, and moisture content.

[0939] (Claim 3)

[0940] The system according to claim 1, which includes an interactive device that receives instructions from the user and provides feedback to the analysis means.

[0941] "Application example 2 when combining with an emotional engine"

[0942] (Claim 1)

[0943] A means for collecting surrounding information acquired from an environmental detection device,

[0944] A means for analyzing the aforementioned surrounding information and generating optimal operation information,

[0945] A means for controlling an automated device to perform work based on the aforementioned optimal operation information,

[0946] A means for recognizing the user's emotional state using an emotion recognition engine and adjusting the system's response,

[0947] A system that includes this.

[0948] (Claim 2)

[0949] The system according to claim 1, wherein the environmental detection device is a sensor that detects temperature, humidity, and moisture content.

[0950] (Claim 3)

[0951] The system according to claim 1, which includes an interactive interface, receives adjustment instructions from the user, provides feedback to the analysis means, and adjusts the workload based on the user's emotional state. [Explanation of symbols]

[0952] 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 means for collecting environmental information obtained from an environmental measuring device, Means for analyzing the aforementioned environmental information and generating optimal control information, A means for controlling automated equipment to perform agricultural work based on the aforementioned optimal control information, A system that includes this.

2. The system according to claim 1, wherein the environmental measuring device is a sensor that measures temperature, humidity, and moisture content.

3. The system according to claim 1, which includes an interactive interface and receives adjustment instructions from the user and provides feedback to the analysis means.

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