system

Abstract

Provide a system. 【Solution means】 Sensor means for collecting environmental information, Artificial intelligence means for analyzing the data received from the sensor means and generating a work flow, Device means for executing the generated work flow, Communication means for controlling the device means, Reporting means for providing analysis content and work status, Interface means for the user to provide feedback, Means for managing an urban environment farm, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern agriculture, the shortage of labor force due to aging and the concentration of population in cities has become a serious problem. Traditional farming operations are labor-intensive, which has a negative impact on production efficiency and stable food supply. In addition, agricultural production in an uncertain environment due to climate change is extremely difficult. Under such circumstances, there is a demand for a system that can efficiently and autonomously manage farms and reduce the labor burden.

Means for Solving the Problems

[0005] The present invention solves the above problems by providing an agricultural management system equipped with sensor means for collecting environmental information, artificial intelligence means for generating a work flow based on the received data, robot means for executing the generated work flow, communication means for controlling the robot means, and reporting means for reporting the work status to an administrator. This makes it possible to autonomously solve the labor shortage problem in agriculture and to ensure a stable food supply and improve production efficiency.

[0006] "Sensor means for collecting environmental information" refers to equipment that collects data on the surrounding environment, such as soil moisture, temperature, and sunlight on a farm.

[0007] "Artificial intelligence means" refers to a combination of software and hardware that analyzes data received from sensor means and dynamically generates the optimal workflow.

[0008] A "robot means" is an autonomous mechanical device that executes a work flow generated by artificial intelligence means, and is a device for automating agricultural work.

[0009] "Communication means" refers to network technologies and protocols for sending and receiving data between various devices within a system, including robotic means.

[0010] A "reporting mechanism" is an interface that provides users with information for managing the analysis results of the system and the progress of the work. [Brief explanation of the drawing]

[0011] [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, when an emotion engine is combined. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] This invention is a system for efficiently and autonomously managing tasks in agricultural settings. This system primarily consists of a server, terminals, and users, each playing a specific role. Specific embodiments are described below.

[0033] Collection of environmental information

[0034] The terminal acts as a group of sensors placed on the farm. These sensors collect environmental information such as soil moisture, temperature, and sunlight in real time. This makes it possible to quickly grasp changes in the environment.

[0035] Data analysis and workflow generation

[0036] The server receives environmental information sent from the terminal and analyzes the data using an artificial intelligence model. Based on the results of this analysis, it automatically generates the optimal workflow. For example, if rain is expected, it will change the instruction to stop watering in advance.

[0037] Robot control and task execution

[0038] The server issues instructions to the robot according to the generated workflow. Based on these instructions, the robot moves autonomously and performs the specified farm task. For example, the server might issue a command to start weeding at 9:00 AM.

[0039] Reporting results and providing instructions for corrections.

[0040] The server compiles a report detailing the progress of the tasks performed and any changes in the environment, and provides it to the user. Based on this report, the user can provide feedback to the server with additional correction instructions if necessary. For example, in response to a user instruction such as "watering is needed in the afternoon," the server updates the workflow.

[0041] Specific example

[0042] As a concrete example, suppose a farm is forecast to experience a rapid rise in temperature in the afternoon. In this case, a sensor terminal collects temperature data, and a server analyzes it to generate a workflow that "deploys shade to create shade." The generated workflow is then executed by a robot that operates the shade based on instructions from the server, maintaining the necessary agricultural environment.

[0043] This invention provides a concrete solution for efficiently managing farm work and improving productivity while addressing the problem of labor shortages.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The terminal acquires environmental information from sensors placed on the farm. Specifically, it measures data such as soil moisture, temperature, and sunlight. The measured data is transmitted to a server in real time using 5G communication.

[0047] Step 2:

[0048] The server receives sensor data transmitted from the terminal and stores it in a database. Next, it uses an artificial intelligence model to analyze the received data. This analysis identifies the current environmental conditions and the optimal workflow. For example, if the soil moisture is low, it detects the need for watering.

[0049] Step 3:

[0050] Based on the analysis results, the server generates specific work instructions for the robot. These instructions include the start time, work location, and specific tasks. By sending these instructions to the robot, it autonomously performs the designated tasks.

[0051] Step 4:

[0052] The robot begins moving around the farm according to work instructions received from the server. At designated locations, it performs tasks such as watering or weeding. In this process, the robot can also automatically adjust its work actions according to the surrounding environment.

[0053] Step 5:

[0054] The server receives feedback from the robot and monitors the progress of the task. Once it confirms that the task is complete, it compiles the results and makes any necessary adjustments for the next task. This information is compiled into a report in natural language and provided to the user.

[0055] Step 6:

[0056] Users receive reports from the server to verify that the work is progressing according to plan. If necessary, they input additional instructions or corrections and provide feedback to the server. This allows for further optimization of the workflow in the future.

[0057] (Example 1)

[0058] 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."

[0059] In the agricultural sector, autonomous and efficient work management is challenging. In particular, there is a need to reduce labor and improve productivity while responding quickly to environmental changes. Furthermore, multiple work devices must operate in harmony to achieve optimal performance.

[0060] 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.

[0061] In this invention, the server includes a device for collecting environmental information, intelligent processing means for analyzing the information and generating novel work procedures, and an autonomous mobile device for executing the generated work procedures. This enables cooperative and efficient agricultural work while responding immediately to changes in the environment.

[0062] "Environmental information" refers to data on physical conditions related to agricultural activities, such as temperature, humidity, sunlight, and soil conditions.

[0063] "Device" refers to equipment that uses sensors to collect physical environmental information.

[0064] "Intelligent processing means" refers to an artificial intelligence-based processing device that has the ability to analyze collected environmental information and generate new work procedures.

[0065] An "autonomous mobile device" refers to a machine or robot that autonomously performs a designated task.

[0066] "Information transmission means" refers to communication means used to send and receive instructions and information between a device and an autonomous mobile device, etc.

[0067] "Information provision means" refers to the means of reporting analysis results and work progress to users.

[0068] This invention is a system for efficiently and autonomously managing agricultural work, and is mainly composed of three elements: a server, terminals, and users. In this system, the terminals function as a group of sensors placed on the farm, and are responsible for collecting environmental information such as soil moisture, temperature, and sunlight in real time. The group of sensors periodically transmits their respective data to the server.

[0069] When the server receives environmental information, it analyzes it using a generative AI model. This analysis automatically generates the optimal work procedures based on the current farm conditions. Examples of generative AI models used include neural networks and machine learning algorithms. Based on the analysis results, the server predicts future weather fluctuations and constructs work instructions accordingly.

[0070] The generated work procedures are transmitted from the server to each autonomous mobile device. Each autonomous mobile device then follows the instructions from the server and autonomously moves while performing the designated agricultural activity. This enables efficient work with minimal human intervention.

[0071] Users receive reports from the server regarding work progress and environmental changes. Based on these reports, they can send additional instructions to the server as needed and modify work procedures. For example, if the weather changes more than expected, they can instruct the server to deploy additional shade over farm crops.

[0072] As a concrete example, in a situation where a temperature rise is predicted for the afternoon, a server analyzes the temperature data collected by sensor terminals and generates a workflow that "deploys shades to create shade." Based on this instruction, an autonomous mobile device that operates the shades carries out the instructions, maintaining the farm environment.

[0073] An example of a prompt used in this system is a specific instruction such as, "Optimize the automatic opening and closing schedule of the shades based on the temperature forecast data for the past few days." Through these prompts, the system can accurately reflect the user's intentions.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The terminal uses various sensors installed on the farm to collect environmental information such as soil moisture, temperature, and sunlight. The data (input) obtained from the sensors is immediately digitized and used in subsequent processing steps. Specifically, the sensors measure information in real time, and this data is collected at regular intervals.

[0077] Step 2:

[0078] The terminal collects environmental data and sends it to the server. The transmitted data (input) is aggregated on the server as information useful for understanding the overall situation of the farm. Specifically, the terminal performs the action of transmitting data to the server using wireless communication.

[0079] Step 3:

[0080] The server analyzes environmental data received from the terminal. The received data (input) is analyzed using a generative AI model to extract temperature and humidity fluctuation patterns. The output provides predictive information and anomaly detection results based on the analysis. Specifically, a machine learning algorithm runs within the server, and data processing is performed automatically.

[0081] Step 4:

[0082] The server generates optimized work procedures based on the analysis results. The generating AI model assists in this optimization process and constructs a specific work flow (output) based on the insights gained. For example, this includes actions such as "deploy the shade to create shade."

[0083] Step 5:

[0084] The server transmits the generated work procedures to the autonomous mobile devices. The work procedures (inputs) are sent wirelessly, and each autonomous mobile device is prepared to execute them. Specifically, instructions are sent from the server to each device, and the device that receives the instructions prepares to operate.

[0085] Step 6:

[0086] The autonomous mobile device performs specified tasks based on the instructions it receives. Following the instructions (input), it performs autonomous operations, such as deploying shades within a farm. Specific actions include the robot autonomously moving along a designated route and performing mechanical operations.

[0087] (Application Example 1)

[0088] 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."

[0089] Managing urban farms relies on traditional manual labor, making efficient operation difficult. Furthermore, it's challenging to respond quickly to changes in environmental information and coordinate multiple tasks, failing to adequately address the unique challenges of urban environments.

[0090] 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.

[0091] In this invention, the server includes sensor means for collecting environmental information, artificial intelligence means for analyzing the data received from the sensor means and generating a workflow, and device means for executing the generated workflow. This makes it possible to efficiently manage farms even in urban environments and to respond quickly to environmental changes and the coordination of multiple tasks.

[0092] "Environmental information" refers to information that includes data on the climate and soil of farms and cities.

[0093] A "sensing device" is a device positioned to collect environmental information.

[0094] "Data" refers to information obtained from sensor devices.

[0095] "Analysis" is the process of processing data and extracting useful knowledge.

[0096] A "workflow" is a series of processes or procedures performed to achieve a specific objective.

[0097] "Artificial intelligence tools" refer to technologies for analyzing data and generating optimal workflows.

[0098] "Device means" refers to machines or devices used to execute the generated workflow.

[0099] "Communication means" refers to methods or techniques for exchanging information between a device and a control device.

[0100] A "reporting method" refers to a means of providing a summary of analysis results and work status.

[0101] An "interface means" is a technology that provides a point of contact for users to give feedback.

[0102] The term "urban environment" refers to the unique conditions found in urban areas, and includes elements that should be considered when conducting agriculture.

[0103] "Coordination" refers to the efficient operation of multiple devices working together.

[0104] "Feedback" is the process of incorporating opinions and information provided by users.

[0105] To implement this invention, a system is needed to support the management of farms in urban environments. This system consists of sensor devices, a server, and a user interface. The server runs a Python program on the Django framework and uses TENSORFLOW® for data analysis. The user interface is provided through a smartphone application built with React Native.

[0106] The sensors collect environmental information such as soil moisture, temperature, and light intensity in real time and transmit this data to a server. The server analyzes the received environmental data and generates an optimal workflow using an AI model. This AI model predicts environmental changes based on past data and adjusts the necessary tasks.

[0107] The generated workflow is transmitted to the device, enabling autonomous farm work. Users can monitor the work status via a smartphone application and provide feedback as needed. This feedback serves as crucial input for the system to perform automatic updates.

[0108] As a concrete example, consider a situation where you are growing vegetables on a farm and you need to decide whether you need to water them in the afternoon. In this case, a sensor collects temperature data in real time, and a server uses AI analysis to determine whether watering is necessary. An example of a prompt would be, "If the temperature data exceeds 30 degrees, ask the AI ​​whether you should water the vegetables."

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The terminal collects environmental information in real time through sensors installed within the farm. It acquires data such as soil moisture, temperature, and light intensity as input, and converts this data into digital signals. The output is raw data sent to the server.

[0112] Step 2:

[0113] The server receives data sent from the terminal. The raw input data is recorded in the database, and at the same time, data formatting and preprocessing are performed. This results in the output of a dataset in a format suitable for analysis.

[0114] Step 3:

[0115] The server inputs the formatted data into a generating AI model. The AI ​​model compares it with past data, recognizes patterns, and predicts environmental changes. Once the data analysis is complete, the optimal workflow is generated as output.

[0116] Step 4:

[0117] The server sends the generated workflow as instructions to the device. This workflow includes specific tasks and timings, serving as fundamental data for autonomous device operation. The output is a clear work instruction.

[0118] Step 5:

[0119] Users can check the progress of their work and environmental information in real time through a smartphone app. Inputs include analysis results from the server and the device's execution status, which are displayed in the app. Outputs include users providing feedback to the system through an intuitive interface.

[0120] Step 6:

[0121] The server receives user feedback, readjusts the workflow as needed, and adapts flexibly to environmental changes. User feedback is the input, and a new workflow is output based on it.

[0122] 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.

[0123] This invention is a system that combines an emotion engine with an agricultural management system, enabling feedback and workflow adjustments based on user emotions. This system consists of three components: a server, a terminal, and a user, each working in coordination.

[0124] Collection of environmental information and emotional data

[0125] The terminals act as sensors placed on the farm, collecting environmental data such as soil moisture, temperature, and sunlight. They can also acquire emotional data through user facial recognition and voice input. This allows for simultaneous understanding of both the physical environment and the user's emotional state.

[0126] Data analysis and feedback generation

[0127] The server analyzes environmental information and emotional data sent from the terminal. Using artificial intelligence, it formulates an optimal workflow based on the environmental data, while the emotional engine analyzes the user's emotions and generates appropriate feedback. For example, if the user is feeling stressed, it suggests a work schedule to alleviate that stress.

[0128] Executing and adjusting the workflow

[0129] The workflow generated by the server is sent to the robot as instructions. The robot follows these instructions and begins actual work on the farm. In response to dynamic environmental changes and user feedback, the emotional engine can adjust the workflow. This maximizes work efficiency while also addressing the user's emotional needs.

[0130] Reporting the results and proposing the next steps

[0131] After all tasks are completed, the server generates a report based on the analyzed data and provides it to the user. The user can review the report and decide on their next course of action based on the system's suggestions. Because it includes insights from an emotion engine, it presents approaches and adjustments that are likely to be accepted by the user.

[0132] Specific example

[0133] For example, suppose the temperature on a farm rises sharply, and the user is experiencing stress from dealing with the situation. In this case, the terminal sends temperature and user emotion data to the server. The server receives and analyzes this data, generates feedback such as "deploy the shades to lower the temperature and incorporate a new schedule," and has the robot execute it. This entire process enables proactive farm management that takes user emotions into account.

[0134] The embodiment of this invention aims to improve the efficiency of agricultural work and enhance the user experience.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The device collects environmental information from sensors placed on the farm. Specifically, it measures soil moisture, temperature, and sunlight, and transmits this data to a server in real time. It also uses a camera and microphone to acquire emotional data from the user's facial expressions and voice tone, and transmits this data as well.

[0138] Step 2:

[0139] The server receives environmental information and emotional data transmitted from the terminal and stores it in a database. Then, it uses an artificial intelligence model based on the environmental information to generate an optimal workflow. Furthermore, the emotional engine analyzes the user's emotional data to understand the user's current emotional state.

[0140] Step 3:

[0141] Based on the analysis results, the server generates work instructions for the robot that take the user's emotions into consideration. For example, if the user is feeling stressed, it will suggest a schedule to reduce the workload. These instructions are then sent to the robot.

[0142] Step 4:

[0143] The robots autonomously perform tasks on the farm according to instructions received from the server. For example, they may deploy shade when the temperature is high or take breaks when excessive work is anticipated.

[0144] Step 5:

[0145] The server monitors the robot's work progress in real time and immediately issues corrective commands if any abnormalities occur. In addition, it reports the work results and analysis findings to the user in natural language. This report is based on insights from an emotion engine and is presented in a user-friendly format.

[0146] Step 6:

[0147] The user reviews the report provided by the server and decides on the next action based on the system's suggestions. If the user enters new instructions or comments, the server incorporates them as feedback and uses them to revise the workflow for the next time.

[0148] (Example 2)

[0149] 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 will be referred to as the "terminal."

[0150] In modern agriculture, a crucial challenge is to respond quickly and efficiently to changes in environmental conditions while reducing the mental burden on workers. Furthermore, there is a need to improve work efficiency while flexibly adjusting work flows based on user emotions. However, conventional systems have limitations in collecting and analyzing environmental data, and therefore cannot adequately meet the emotional needs of users.

[0151] 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.

[0152] In this invention, the server includes a detection device for collecting environmental data, a function for acquiring user emotion data and generating feedback based on that emotion, and a function for dynamically adjusting work procedures and feedback based on predicted environmental changes and the user's emotional state. This enables rapid response to environmental changes and adjustment of the work flow to be sensitive to the user's emotions.

[0153] "Environmental data" refers to information about physical conditions in agricultural settings, such as soil moisture, temperature, and sunlight.

[0154] "Detection device" refers to sensors and devices used to acquire environmental data and user emotion data.

[0155] An "information processing device" refers to a computer system that analyzes acquired data and generates work procedures based on that analysis.

[0156] "Automated equipment" refers to machines and robots that perform actual agricultural work based on generated work procedures.

[0157] "Communication function" refers to a function that enables the transmission and reception of data between different devices, allowing for the coordination of work procedures.

[0158] The "reporting function" refers to a function that provides users with analysis results and work progress status.

[0159] "User emotional data" refers to information that indicates the user's emotional state, and includes data obtained through facial recognition and voice input.

[0160] This invention relates to an agricultural management system consisting of a server, a terminal, and a user. This system achieves efficient farming and addresses the emotional needs of users through the collection of environmental data, analysis of emotional data, and execution and adjustment of work procedures.

[0161] The terminal operates as a detection device placed in farmland. Specifically, it includes various sensors that measure soil moisture, temperature, and sunlight. Furthermore, the terminal is equipped with facial recognition and voice input functions to collect user emotion data. This allows the terminal to simultaneously understand the physical environmental conditions of the site and the user's emotional state.

[0162] The server analyzes environmental and emotional data sent from the terminal. The generative AI model used here develops the optimal work procedure based on the data. The emotional engine also analyzes the user's emotions and generates appropriate feedback. For example, if the user is feeling stressed, the system adjusts the work schedule and offers suggestions to reduce stress. The server then sends the work procedure to the automated equipment, initiating the actual farm work.

[0163] The user receives a report from the system and decides on their next course of action based on its contents. The report includes suggestions that reflect the user's emotional state and provides information that contributes to the user's decision.

[0164] As a concrete example, consider a scenario where the temperature rises rapidly on a farm. In this case, the terminal sends temperature data and user emotion data to the server. The server analyzes this data and formulates appropriate work procedures, such as deploying shades to lower the temperature, and proposes a new work schedule. Subsequently, automated equipment actually deploys the shades and performs the work based on the feedback, resulting in agricultural management that takes the user's emotions into consideration.

[0165] An example of a prompt for the generating AI model is, "If a user is experiencing stress due to a rapid rise in temperature, please suggest possible countermeasures." This system is built to enable agricultural management that comprehensively considers user emotions and environmental conditions.

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] The terminal collects environmental data using sensors placed on the farm. Specifically, various sensors such as soil moisture sensors, temperature sensors, and sunlight sensors measure data. The input is real-time environmental data from each sensor, which is collected and temporarily stored within the terminal. The output is a set of aggregated environmental data.

[0169] Step 2:

[0170] The device acquires emotional data using the user's facial recognition technology and voice input function. Inputs include image data from the camera and audio data from the microphone, which are then analyzed to convert them into the user's emotional state (stress, joy, etc.). The output is the detected emotional data.

[0171] Step 3:

[0172] The terminal transmits the collected environmental and emotional data to the server. The input is the integrated data obtained in steps 1 and 2, which is transmitted over the internet using a secure communication protocol. The output is the completion of the data transfer to the server.

[0173] Step 4:

[0174] The server analyzes the received data. First, it uses a generative AI model to determine what kind of work is needed in which part of the farm based on environmental data. The input is environmental data, and work procedures are formulated. The output is a work procedure that includes specific work instructions.

[0175] Step 5:

[0176] The server uses an emotion engine to analyze user emotion data and generate feedback based on the user's emotions. Emotion data is the input, and after analysis, suggestions for the user and proposed adjustments to the work schedule are output.

[0177] Step 6:

[0178] The server sends the generated work procedure to the automated device. The input is the work procedure generated in step 4, which is sent to the automated device as an instruction. The output is the instruction necessary to start the automated device, which is sent and received.

[0179] Step 7:

[0180] The automated device performs specific agricultural tasks according to instructions from the server. As input, mechanical actions are performed based on the server's instructions, and the work is executed. As output, the physical work is realized, and changes are observed.

[0181] Step 8:

[0182] The server compiles the results of completed tasks, generates a report, and provides it to the user. Input data includes work results and user emotional changes, and the output report reflects technical analysis results. Based on this, the user decides on their next course of action.

[0183] (Application Example 2)

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

[0185] In conventional nursing care facilities, work schedules are not adjusted to take into account the emotional state of care staff. As a result, staff fatigue and stress accumulate, leading to a decline in the quality of care and staff turnover. In this situation, there is a need for a system that can easily collect environmental information and emotional data and generate optimal work schedules.

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

[0187] In this invention, the server includes a detection means for collecting biometric information, an intelligent means for analyzing the data received from the detection means and generating a work schedule, and an operating means for executing the generated work schedule. This makes it possible to reduce the workload in nursing care facilities and improve the working environment for nursing care staff.

[0188] "Detection means for collecting biometric information" refers to sensors and devices used in nursing care facilities to understand the environment and the emotional state of care staff.

[0189] "Intelligent means" refers to an artificial intelligence system that analyzes collected biometric information and generates an optimal work schedule.

[0190] "Operating means" refers to execution systems that carry out work schedules generated by intelligent means, and includes smart devices and robots.

[0191] "Information and communication means" refers to the communication infrastructure and communication protocols used to control operating means and to send and receive necessary data.

[0192] "Information provision means" refers to an interface for notifying care staff of analyzed data and proposed work schedules, and providing them with information.

[0193] The system for realizing this invention is designed to provide efficient and emotionally sensitive management within care facilities. The system consists of three components: a server, terminals, and users (care staff and residents), all of which work together in coordination.

[0194] The terminals are sensors and smart devices placed in designated locations within the care facility, collecting environmental data including biometric information, as well as the emotional states of care staff and residents. "OpenCV" is used for facial recognition technology, and sensors such as "DHT22" are utilized for environmental data collection.

[0195] The server analyzes data received from the terminal using an AI model based on TensorFlow and generates an optimal work schedule based on the emotional state. During this process, feedback is obtained to reduce the stress level of the care staff, and these instructions are communicated through a user interface built with React Native.

[0196] By receiving feedback from these systems, care staff can implement suggestions that are easier to understand. For example, when care staff are feeling fatigued, they may be offered suggestions for collaborative measures to reduce their workload.

[0197] As a concrete example, consider a situation where care staff are busy but feeling exhausted. In this case, the server analyzes biometric information from the terminals and suggests shift adjustments among staff, thereby enabling a more even distribution of the workload.

[0198] Examples of prompts for generative AI models include the following:

[0199] "Our care staff are experiencing fatigue. What kind of feedback and suggested work flows would be appropriate?"

[0200] "If a user is experiencing anxiety, please suggest appropriate responses based on emotional data."

[0201] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0202] Step 1:

[0203] The terminal collects biometric and environmental data using sensors installed within the care facility. This includes facial recognition technology to capture the expressions of care staff and residents, as well as sensor inputs such as temperature, humidity, and sound volume data within the facility. This data is transmitted to a server in real time.

[0204] Input: Face recognition data, environmental sensor data

[0205] Output: Sending data to the server

[0206] Step 2:

[0207] The server analyzes biometric and environmental data received from the terminal using an AI model. The TensorFlow algorithm used in the generating AI model determines the user's emotional state and stress level, and performs calculations to generate an optimal work schedule. Based on this analysis, dynamically corresponding feedback is calculated.

[0208] Input: Face recognition data, environmental sensor data

[0209] Output: Optimal work schedule, biological status feedback

[0210] Step 3:

[0211] Users receive analysis results and feedback sent from the server through a user interface built with React Native. The displayed feedback may include suggestions for specific stress reduction methods or collaboration strategies. The generated work schedule is presented to the user as concrete execution instructions.

[0212] Input: Optimal work schedule, biological status feedback

[0213] Output: Feedback and instructions displayed to the user

[0214] Step 4:

[0215] Based on feedback received from their devices, users share information with other care staff and collaborate to carry out efficient care activities. Newly adjusted work schedules are applied to the field in real time, promoting effective teamwork.

[0216] Input: Feedback and instructions displayed to the user.

[0217] Output: Execution of the adjusted work schedule

[0218] Step 5:

[0219] The server collects the results based on the completed work schedule as data again and uses it to generate the next work schedule. This loop is designed to continuously provide appropriate feedback and schedules that are tailored to the current situation in the caregiving field.

[0220] Input: Adjusted work schedule result

[0221] Output: Data for generating the next schedule

[0222] 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.

[0223] 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.

[0224] 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.

[0225] [Second Embodiment]

[0226] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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".

[0238] This invention is a system for efficiently and autonomously managing tasks in agricultural settings. This system primarily consists of a server, terminals, and users, each playing a specific role. Specific embodiments are described below.

[0239] Collection of environmental information

[0240] The terminal acts as a group of sensors placed on the farm. These sensors collect environmental information such as soil moisture, temperature, and sunlight in real time. This makes it possible to quickly grasp changes in the environment.

[0241] Data analysis and workflow generation

[0242] The server receives environmental information sent from the terminal and analyzes the data using an artificial intelligence model. Based on the results of this analysis, it automatically generates the optimal workflow. For example, if rain is expected, it will change the instruction to stop watering in advance.

[0243] Robot control and task execution

[0244] The server issues instructions to the robot according to the generated workflow. Based on these instructions, the robot moves autonomously and performs the specified farm task. For example, the server might issue a command to start weeding at 9:00 AM.

[0245] Reporting results and providing instructions for corrections.

[0246] The server compiles a report detailing the progress of the tasks performed and any changes in the environment, and provides it to the user. Based on this report, the user can provide feedback to the server with additional correction instructions if necessary. For example, in response to a user instruction such as "watering is needed in the afternoon," the server updates the workflow.

[0247] Specific example

[0248] As a concrete example, suppose a farm is forecast to experience a rapid rise in temperature in the afternoon. In this case, a sensor terminal collects temperature data, and a server analyzes it to generate a workflow that "deploys shade to create shade." The generated workflow is then executed by a robot that operates the shade based on instructions from the server, maintaining the necessary agricultural environment.

[0249] This invention provides a concrete solution for efficiently managing farm work and improving productivity while addressing the problem of labor shortages.

[0250] The following describes the processing flow.

[0251] Step 1:

[0252] The terminal acquires environmental information from sensors placed on the farm. Specifically, it measures data such as soil moisture, temperature, and sunlight. The measured data is transmitted to a server in real time using 5G communication.

[0253] Step 2:

[0254] The server receives sensor data transmitted from the terminal and stores it in a database. Next, it uses an artificial intelligence model to analyze the received data. This analysis identifies the current environmental conditions and the optimal workflow. For example, if the soil moisture is low, it detects the need for watering.

[0255] Step 3:

[0256] Based on the analysis results, the server generates specific work instructions for the robot. These instructions include the start time, work location, and specific tasks. By sending these instructions to the robot, it autonomously performs the designated tasks.

[0257] Step 4:

[0258] The robot begins moving around the farm according to work instructions received from the server. At designated locations, it performs tasks such as watering or weeding. In this process, the robot can also automatically adjust its work actions according to the surrounding environment.

[0259] Step 5:

[0260] The server receives feedback from the robot and monitors the progress of the task. Once it confirms that the task is complete, it compiles the results and makes any necessary adjustments for the next task. This information is compiled into a report in natural language and provided to the user.

[0261] Step 6:

[0262] Users receive reports from the server to verify that the work is progressing according to plan. If necessary, they input additional instructions or corrections and provide feedback to the server. This allows for further optimization of the workflow in the future.

[0263] (Example 1)

[0264] 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."

[0265] In the agricultural sector, autonomous and efficient work management is challenging. In particular, there is a need to reduce labor and improve productivity while responding quickly to environmental changes. Furthermore, multiple work devices must operate in harmony to achieve optimal performance.

[0266] 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.

[0267] In this invention, the server includes a device for collecting environmental information, intelligent processing means for analyzing the information and generating novel work procedures, and an autonomous mobile device for executing the generated work procedures. This enables cooperative and efficient agricultural work while responding immediately to changes in the environment.

[0268] "Environmental information" refers to data on physical conditions related to agricultural activities, such as temperature, humidity, sunlight, and soil conditions.

[0269] "Device" refers to equipment that uses sensors to collect physical environmental information.

[0270] "Intelligent processing means" refers to an artificial intelligence-based processing device that has the ability to analyze collected environmental information and generate new work procedures.

[0271] An "autonomous mobile device" refers to a machine or robot that autonomously performs a designated task.

[0272] "Information transmission means" refers to communication means used to send and receive instructions and information between a device and an autonomous mobile device, etc.

[0273] "Information provision means" refers to the means of reporting analysis results and work progress to users.

[0274] This invention is a system for efficiently and autonomously managing agricultural work, and is mainly composed of three elements: a server, terminals, and users. In this system, the terminals function as a group of sensors placed on the farm, and are responsible for collecting environmental information such as soil moisture, temperature, and sunlight in real time. The group of sensors periodically transmits their respective data to the server.

[0275] When the server receives environmental information, it analyzes it using a generative AI model. This analysis automatically generates the optimal work procedures based on the current farm conditions. Examples of generative AI models used include neural networks and machine learning algorithms. Based on the analysis results, the server predicts future weather fluctuations and constructs work instructions accordingly.

[0276] The generated work procedures are transmitted from the server to each autonomous mobile device. Each autonomous mobile device then follows the instructions from the server and autonomously moves while performing the designated agricultural activity. This enables efficient work with minimal human intervention.

[0277] Users receive reports from the server regarding work progress and environmental changes. Based on these reports, they can send additional instructions to the server as needed and modify work procedures. For example, if the weather changes more than expected, they can instruct the server to deploy additional shade over farm crops.

[0278] As a specific example, in a situation where a temperature rise is predicted in the afternoon, the server analyzes the temperature data collected by the sensor terminal and generates an operation flow of "deploying a shade to create a shaded area". Based on this instruction, the autonomous mobile device that operates the shade executes the instruction to maintain the farm environment.

[0279] As an example of the prompt text used in this system, there is a specific instruction such as "Optimize the automatic opening and closing schedule of the shade based on the temperature prediction data for the past few days". Through this prompt text, the intention of the user can be accurately reflected in the system.

[0280] The flow of the specific process in Example 1 will be described using FIG. 11.

[0281] Step 1:

[0282] The terminal uses various sensors installed on the farm to collect environmental information such as soil humidity, temperature, and sunlight intensity. The data (input) obtained from the sensors is immediately digitized and used in subsequent processing steps. As a specific operation, the sensors measure information in real time and collect this data at regular intervals.

[0283] Step 2:

[0284] The terminal transmits the environmental data it has collected to the server. The data (input) to be transmitted is aggregated by the server as information useful for grasping the overall situation of the farm. Specifically, the terminal performs an operation of transmitting the data to the server using wireless communication means.

[0285] Step 3:

[0286] The server analyzes the environmental data received from the terminal. The received data (input) is analyzed using a generative AI model to extract the fluctuation patterns of temperature and humidity. As output, prediction information and anomaly detection results based on the analysis results are obtained. As a specific operation, a machine learning algorithm operates within the server, and data processing is automatically performed.

[0287] Step 4:

[0288] The server generates an optimized work procedure based on the analysis results. The generative AI model supports this optimization process and constructs a specific work flow (output) based on the findings obtained. For example, it includes the operation of generating work instructions such as "deploy the shade to create a shaded area".

[0289] Step 5:

[0290] The server transmits the generated work procedure to the autonomous mobile device. The work procedure (input) is delivered via wireless communication, and preparations are made for each autonomous mobile device to execute it. As a specific operation, an instruction from the server is sent to each device, and the device that receives the instruction content makes operational preparations.

[0291] Step 6:

[0292] The autonomous mobile device executes the specified work based on the received instruction. According to the instruction content (input), autonomous operations are performed, such as shade deployment work within the farm. Specific operations include the robot autonomously moving along the specified route and performing mechanical operations.

[0293] (Application Example 1)

[0294] 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".

[0295] Managing urban farms relies on traditional manual labor, making efficient operation difficult. Furthermore, it's challenging to respond quickly to changes in environmental information and coordinate multiple tasks, failing to adequately address the unique challenges of urban environments.

[0296] 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.

[0297] In this invention, the server includes sensor means for collecting environmental information, artificial intelligence means for analyzing the data received from the sensor means and generating a workflow, and device means for executing the generated workflow. This makes it possible to efficiently manage farms even in urban environments and to respond quickly to environmental changes and the coordination of multiple tasks.

[0298] "Environmental information" refers to information that includes data on the climate and soil of farms and cities.

[0299] A "sensing device" is a device positioned to collect environmental information.

[0300] "Data" refers to information obtained from sensor devices.

[0301] "Analysis" is the process of processing data and extracting useful knowledge.

[0302] A "workflow" is a series of processes or procedures performed to achieve a specific objective.

[0303] "Artificial intelligence tools" refer to technologies for analyzing data and generating optimal workflows.

[0304] "Device means" refers to machines or devices used to execute the generated workflow.

[0305] "Communication means" refers to methods or techniques for exchanging information between a device and a control device.

[0306] The "reporting means" is a means for summarizing and providing analysis results and work status.

[0307] The "interface means" is a technology that provides a contact point for the user to give feedback.

[0308] The "urban environment" refers to the specific conditions existing in urban areas and includes elements to be considered when conducting agriculture.

[0309] "Coordination" refers to multiple devices working together efficiently.

[0310] "Feedback" is a process of reflecting opinions and information conveyed from the user.

[0311] To implement this invention, a system for supporting the management of urban environment farms is required. This system is composed of a sensor device, a server, and a user interface. The server operates a Python program on the Django framework and uses TensorFlow for data analysis. The user interface is provided through a smartphone application built with React Native.

[0312] The sensor collects environmental information such as soil humidity, temperature, and light intensity in real time and transmits the data to the server. The server analyzes the received environmental data and uses an AI model to generate an optimal work flow. This AI model predicts environmental changes based on past data and adjusts the necessary work.

[0313] The generated work flow is sent to the device, enabling autonomous agricultural work. The user can monitor the work status through a smartphone application and provide feedback as needed. This feedback becomes an important input for the system to perform automatic updates.

[0314] As a concrete example, consider a situation where you are growing vegetables on a farm and you need to decide whether you need to water them in the afternoon. In this case, a sensor collects temperature data in real time, and a server uses AI analysis to determine whether watering is necessary. An example of a prompt would be, "If the temperature data exceeds 30 degrees, ask the AI ​​whether you should water the vegetables."

[0315] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0316] Step 1:

[0317] The terminal collects environmental information in real time through sensors installed within the farm. It acquires data such as soil moisture, temperature, and light intensity as input, and converts this data into digital signals. The output is raw data sent to the server.

[0318] Step 2:

[0319] The server receives data sent from the terminal. The raw input data is recorded in the database, and at the same time, data formatting and preprocessing are performed. This results in the output of a dataset in a format suitable for analysis.

[0320] Step 3:

[0321] The server inputs the formatted data into a generating AI model. The AI ​​model compares it with past data, recognizes patterns, and predicts environmental changes. Once the data analysis is complete, the optimal workflow is generated as output.

[0322] Step 4:

[0323] The server sends the generated workflow as instructions to the device. This workflow includes specific tasks and timings, serving as fundamental data for autonomous device operation. The output is a clear work instruction.

[0324] Step 5:

[0325] Users can check the progress of their work and environmental information in real time through a smartphone app. Inputs include analysis results from the server and the device's execution status, which are displayed in the app. Outputs include users providing feedback to the system through an intuitive interface.

[0326] Step 6:

[0327] The server receives feedback from users, readjusts the workflow as needed, and adapts flexibly to environmental changes. User feedback is the input, and a new workflow is output based on it.

[0328] 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.

[0329] This invention is a system that combines an emotion engine with an agricultural management system, enabling feedback and workflow adjustments based on user emotions. This system consists of three components: a server, a terminal, and a user, each working in coordination.

[0330] Collection of environmental information and emotional data

[0331] The terminals act as sensors placed on the farm, collecting environmental data such as soil moisture, temperature, and sunlight. They can also acquire emotional data through user facial recognition and voice input. This allows for simultaneous understanding of both the physical environment and the user's emotional state.

[0332] Data analysis and feedback generation

[0333] The server analyzes environmental information and emotional data sent from the terminal. Using artificial intelligence, it formulates an optimal workflow based on the environmental data, while the emotional engine analyzes the user's emotions and generates appropriate feedback. For example, if the user is feeling stressed, it suggests a work schedule to alleviate that stress.

[0334] Executing and adjusting the workflow

[0335] The workflow generated by the server is sent to the robot as instructions. The robot follows these instructions and begins actual work on the farm. In response to dynamic environmental changes and user feedback, the emotional engine can adjust the workflow. This maximizes work efficiency while also addressing the user's emotional needs.

[0336] Reporting the results and proposing the next steps

[0337] After all tasks are completed, the server generates a report based on the analyzed data and provides it to the user. The user can review the report and decide on their next course of action based on the system's suggestions. Because it includes insights from an emotion engine, it presents approaches and adjustments that are likely to be accepted by the user.

[0338] Specific example

[0339] For example, suppose the temperature on a farm rises sharply, and the user is experiencing stress from dealing with the situation. In this case, the terminal sends temperature and user emotion data to the server. The server receives and analyzes this data, generates feedback such as "deploy the shades to lower the temperature and incorporate a new schedule," and has the robot execute it. This entire process enables proactive farm management that takes user emotions into account.

[0340] The embodiment of this invention aims to improve the efficiency of agricultural work and enhance the user experience.

[0341] The following describes the processing flow.

[0342] Step 1:

[0343] The device collects environmental information from sensors placed on the farm. Specifically, it measures soil moisture, temperature, and sunlight, and transmits this data to a server in real time. It also uses a camera and microphone to acquire emotional data from the user's facial expressions and voice tone, and transmits this data as well.

[0344] Step 2:

[0345] The server receives environmental information and emotional data transmitted from the terminal and stores it in a database. Then, it uses an artificial intelligence model based on the environmental information to generate an optimal workflow. Furthermore, the emotional engine analyzes the user's emotional data to understand the user's current emotional state.

[0346] Step 3:

[0347] Based on the analysis results, the server generates work instructions for the robot that take the user's emotions into consideration. For example, if the user is feeling stressed, it will suggest a schedule to reduce the workload. These instructions are then sent to the robot.

[0348] Step 4:

[0349] The robots autonomously perform tasks on the farm according to instructions received from the server. For example, they may deploy shade when the temperature is high or take breaks when excessive work is anticipated.

[0350] Step 5:

[0351] The server monitors the robot's work progress in real time and immediately issues corrective commands if any abnormalities occur. In addition, it reports the work results and analysis findings to the user in natural language. This report is based on insights from an emotion engine and is presented in a user-friendly format.

[0352] Step 6:

[0353] The user reviews the report provided by the server and decides on the next action based on the system's suggestions. If the user enters new instructions or comments, the server incorporates them as feedback and uses them to revise the workflow for the next time.

[0354] (Example 2)

[0355] 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".

[0356] In modern agriculture, a crucial challenge is to respond quickly and efficiently to changes in environmental conditions while reducing the mental burden on workers. Furthermore, there is a need to improve work efficiency while flexibly adjusting work flows based on user emotions. However, conventional systems have limitations in collecting and analyzing environmental data, and therefore cannot adequately meet the emotional needs of users.

[0357] 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.

[0358] In this invention, the server includes a detection device for collecting environmental data, a function for acquiring user emotion data and generating feedback based on that emotion, and a function for dynamically adjusting work procedures and feedback based on predicted environmental changes and the user's emotional state. This enables rapid response to environmental changes and adjustment of the work flow to be sensitive to the user's emotions.

[0359] "Environmental data" refers to information about physical conditions in agricultural settings, such as soil moisture, temperature, and sunlight.

[0360] "Detection device" refers to sensors and devices used to acquire environmental data and user emotion data.

[0361] An "information processing device" refers to a computer system that analyzes acquired data and generates work procedures based on that analysis.

[0362] "Automated equipment" refers to machines and robots that perform actual agricultural work based on generated work procedures.

[0363] "Communication function" refers to a function that enables the transmission and reception of data between different devices, allowing for the coordination of work procedures.

[0364] The "reporting function" refers to a function that provides users with analysis results and work progress status.

[0365] "User emotional data" refers to information that indicates the user's emotional state, and includes data obtained through facial recognition and voice input.

[0366] This invention relates to an agricultural management system consisting of a server, a terminal, and a user. This system achieves efficient farming and addresses the emotional needs of users through the collection of environmental data, analysis of emotional data, and execution and adjustment of work procedures.

[0367] The terminal operates as a detection device placed in farmland. Specifically, it includes various sensors that measure soil moisture, temperature, and sunlight. Furthermore, the terminal is equipped with facial recognition and voice input functions to collect user emotion data. This allows the terminal to simultaneously understand the physical environmental conditions of the site and the user's emotional state.

[0368] The server analyzes environmental and emotional data sent from the terminal. The generative AI model used here develops the optimal work procedure based on the data. The emotional engine also analyzes the user's emotions and generates appropriate feedback. For example, if the user is feeling stressed, the system adjusts the work schedule and offers suggestions to reduce stress. The server then sends the work procedure to the automated equipment, initiating the actual farm work.

[0369] The user receives a report from the system and decides on their next course of action based on its contents. The report includes suggestions that reflect the user's emotional state and provides information that contributes to the user's decision.

[0370] As a concrete example, consider a scenario where the temperature rises rapidly on a farm. In this case, the terminal sends temperature data and user emotion data to the server. The server analyzes this data and formulates appropriate work procedures, such as deploying shades to lower the temperature, and proposes a new work schedule. Subsequently, automated equipment actually deploys the shades and performs the work based on the feedback, resulting in agricultural management that takes the user's emotions into consideration.

[0371] An example of a prompt for the generating AI model is, "If a user is experiencing stress due to a rapid rise in temperature, please suggest possible countermeasures." This system is built to enable agricultural management that comprehensively considers user emotions and environmental conditions.

[0372] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0373] Step 1:

[0374] The terminal collects environmental data using sensors placed on the farm. Specifically, various sensors such as soil moisture sensors, temperature sensors, and sunlight sensors measure data. The input is real-time environmental data from each sensor, which is collected and temporarily stored within the terminal. The output is a set of aggregated environmental data.

[0375] Step 2:

[0376] The device acquires emotional data using the user's facial recognition technology and voice input function. Inputs include image data from the camera and audio data from the microphone, which are then analyzed to convert them into the user's emotional state (stress, joy, etc.). The output is the detected emotional data.

[0377] Step 3:

[0378] The terminal transmits the collected environmental and emotional data to the server. The input is the integrated data obtained in steps 1 and 2, which is transmitted over the internet using a secure communication protocol. The output is the completion of the data transfer to the server.

[0379] Step 4:

[0380] The server analyzes the received data. First, it uses a generative AI model to determine what kind of work is needed in which part of the farm based on environmental data. The input is environmental data, and work procedures are formulated. The output is a work procedure that includes specific work instructions.

[0381] Step 5:

[0382] The server uses an emotion engine to analyze user emotion data and generate feedback based on the user's emotions. Emotion data is the input, and after analysis, suggestions for the user and proposed adjustments to the work schedule are output.

[0383] Step 6:

[0384] The server sends the generated work procedure to the automated device. The input is the work procedure generated in step 4, which is sent to the automated device as an instruction. The output is the instruction necessary to start the automated device, which is sent and received.

[0385] Step 7:

[0386] The automated device performs specific agricultural tasks according to instructions from the server. As input, mechanical actions are performed based on the server's instructions, and the work is executed. As output, the physical work is realized, and changes are observed.

[0387] Step 8:

[0388] The server compiles the results of completed tasks, generates a report, and provides it to the user. Input data includes work results and changes in the user's emotions, and the output report reflects the technical analysis results. Based on this, the user decides on their next course of action.

[0389] (Application Example 2)

[0390] 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."

[0391] In conventional nursing care facilities, work schedules are not adjusted to take into account the emotional state of care staff. As a result, staff fatigue and stress accumulate, leading to a decline in the quality of care and staff turnover. In this situation, there is a need for a system that can easily collect environmental information and emotional data and generate optimal work schedules.

[0392] 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.

[0393] In this invention, the server includes a detection means for collecting biometric information, an intelligent means for analyzing the data received from the detection means and generating a work schedule, and an operating means for executing the generated work schedule. This makes it possible to reduce the workload in nursing care facilities and improve the working environment for nursing care staff.

[0394] "Detection means for collecting biometric information" refers to sensors and devices used in nursing care facilities to understand the environment and the emotional state of care staff.

[0395] "Intelligent means" refers to an artificial intelligence system that analyzes collected biometric information and generates an optimal work schedule.

[0396] "Operating means" refers to execution systems that carry out work schedules generated by intelligent means, and includes smart devices and robots.

[0397] "Information and communication means" refers to the communication infrastructure and communication protocols used to control operating means and to send and receive necessary data.

[0398] "Information provision means" refers to an interface for notifying care staff of analyzed data and proposed work schedules, and providing them with information.

[0399] The system for realizing this invention is designed to provide efficient and emotionally sensitive management within care facilities. The system consists of three components: a server, terminals, and users (care staff and residents), all of which work together in coordination.

[0400] The terminals are sensors and smart devices placed in designated locations within the care facility, collecting environmental data including biometric information, as well as the emotional states of care staff and residents. "OpenCV" is used for facial recognition technology, and sensors such as "DHT22" are utilized for environmental data collection.

[0401] The server analyzes data received from the terminal using an AI model based on TensorFlow and generates an optimal work schedule based on the emotional state. During this process, feedback is obtained to reduce the stress level of the care staff, and these instructions are communicated through a user interface built with React Native.

[0402] By receiving feedback from these systems, care staff can implement suggestions that are easier to understand. For example, when care staff are feeling fatigued, they may be offered suggestions for collaborative measures to reduce their workload.

[0403] As a concrete example, consider a situation where care staff are busy but feeling exhausted. In this case, the server analyzes biometric information from the terminals and suggests shift adjustments among staff, thereby enabling a more even distribution of the workload.

[0404] Examples of prompts for generative AI models include the following:

[0405] "Our care staff are experiencing fatigue. What kind of feedback and suggested work flow improvements would be appropriate?"

[0406] "If a user is experiencing anxiety, please suggest appropriate responses based on emotional data."

[0407] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0408] Step 1:

[0409] The terminal collects biometric and environmental data using sensors installed within the care facility. This includes facial recognition technology to capture the expressions of care staff and residents, as well as sensor inputs such as temperature, humidity, and sound volume data within the facility. This data is transmitted to a server in real time.

[0410] Input: Face recognition data, environmental sensor data

[0411] Output: Sending data to the server

[0412] Step 2:

[0413] The server analyzes biometric and environmental data received from the terminal using an AI model. The TensorFlow algorithm used in the generating AI model determines the user's emotional state and stress level, and performs calculations to generate an optimal work schedule. Based on this analysis, dynamically corresponding feedback is calculated.

[0414] Input: Face recognition data, environmental sensor data

[0415] Output: Optimal work schedule, biological status feedback

[0416] Step 3:

[0417] Users receive analysis results and feedback sent from the server through a user interface built with React Native. The displayed feedback may include suggestions for specific stress reduction methods or collaboration strategies. The generated work schedule is presented to the user as concrete execution instructions.

[0418] Input: Optimal work schedule, biological status feedback

[0419] Output: Feedback and instructions displayed to the user

[0420] Step 4:

[0421] Based on feedback received from their devices, users share information with other care staff and collaborate to carry out efficient care activities. Newly adjusted work schedules are applied to the field in real time, promoting effective teamwork.

[0422] Input: Feedback and instructions displayed to the user.

[0423] Output: Execution of the adjusted work schedule

[0424] Step 5:

[0425] The server collects the results based on the completed work schedule as data again and uses it to generate the next work schedule. This loop is designed to continuously provide appropriate feedback and schedules that are tailored to the current situation in the caregiving field.

[0426] Input: Adjusted work schedule result

[0427] Output: Data for generating the next schedule

[0428] 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.

[0429] 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.

[0430] 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.

[0431] [Third Embodiment]

[0432] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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).

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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".

[0444] This invention is a system for efficiently and autonomously managing tasks in agricultural settings. This system primarily consists of a server, terminals, and users, each playing a specific role. Specific embodiments are described below.

[0445] Collection of environmental information

[0446] The terminal acts as a group of sensors placed on the farm. These sensors collect environmental information such as soil moisture, temperature, and sunlight in real time. This makes it possible to quickly grasp changes in the environment.

[0447] Data analysis and workflow generation

[0448] The server receives environmental information sent from the terminal and analyzes the data using an artificial intelligence model. Based on the results of this analysis, it automatically generates the optimal workflow. For example, if rain is expected, it will change the instruction to stop watering in advance.

[0449] Robot control and task execution

[0450] The server issues instructions to the robot according to the generated workflow. Based on these instructions, the robot moves autonomously and performs the specified farm task. For example, the server might issue a command to start weeding at 9:00 AM.

[0451] Reporting results and providing instructions for corrections.

[0452] The server compiles a report detailing the progress of the tasks performed and any changes in the environment, and provides it to the user. Based on this report, the user can provide feedback to the server with additional correction instructions if necessary. For example, in response to a user instruction such as "watering is needed in the afternoon," the server updates the workflow.

[0453] Specific example

[0454] As a concrete example, suppose a farm is forecast to experience a rapid rise in temperature in the afternoon. In this case, a sensor terminal collects temperature data, and a server analyzes it to generate a workflow that "deploys shade to create shade." The generated workflow is then executed by a robot that operates the shade based on instructions from the server, maintaining the necessary agricultural environment.

[0455] This invention provides a concrete solution for efficiently managing farm work and improving productivity while addressing the problem of labor shortages.

[0456] The following describes the processing flow.

[0457] Step 1:

[0458] The terminal acquires environmental information from sensors placed on the farm. Specifically, it measures data such as soil moisture, temperature, and sunlight. The measured data is transmitted to a server in real time using 5G communication.

[0459] Step 2:

[0460] The server receives sensor data transmitted from the terminal and stores it in a database. Next, it uses an artificial intelligence model to analyze the received data. This analysis identifies the current environmental conditions and the optimal workflow. For example, if the soil moisture is low, it detects the need for watering.

[0461] Step 3:

[0462] Based on the analysis results, the server generates specific work instructions for the robot. These instructions include the start time, work location, and specific tasks. By sending these instructions to the robot, it autonomously performs the designated tasks.

[0463] Step 4:

[0464] The robot begins moving around the farm according to work instructions received from the server. At designated locations, it performs tasks such as watering or weeding. In this process, the robot can also automatically adjust its work actions according to the surrounding environment.

[0465] Step 5:

[0466] The server receives feedback from the robot and monitors the progress of the task. Once it confirms that the task is complete, it compiles the results and makes any necessary adjustments for the next task. This information is compiled into a report in natural language and provided to the user.

[0467] Step 6:

[0468] Users receive reports from the server to verify that the work is progressing according to plan. If necessary, they input additional instructions or corrections and provide feedback to the server. This allows for further optimization of the workflow in the future.

[0469] (Example 1)

[0470] 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."

[0471] In the agricultural sector, autonomous and efficient work management is challenging. In particular, there is a need to reduce labor and improve productivity while responding quickly to environmental changes. Furthermore, multiple work devices must operate in harmony to achieve optimal performance.

[0472] 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.

[0473] In this invention, the server includes a device for collecting environmental information, intelligent processing means for analyzing the information and generating novel work procedures, and an autonomous mobile device for executing the generated work procedures. This enables cooperative and efficient agricultural work while responding immediately to changes in the environment.

[0474] "Environmental information" refers to data on physical conditions related to agricultural activities, such as temperature, humidity, sunlight, and soil conditions.

[0475] "Device" refers to equipment that uses sensors to collect physical environmental information.

[0476] "Intelligent processing means" refers to an artificial intelligence-based processing device that has the ability to analyze collected environmental information and generate new work procedures.

[0477] An "autonomous mobile device" refers to a machine or robot that autonomously performs a designated task.

[0478] "Information transmission means" refers to communication means used to send and receive instructions and information between a device and an autonomous mobile device, etc.

[0479] "Information provision means" refers to the means of reporting analysis results and work progress to users.

[0480] This invention is a system for efficiently and autonomously managing agricultural work, and is mainly composed of three elements: a server, terminals, and users. In this system, the terminals function as a group of sensors placed on the farm, and are responsible for collecting environmental information such as soil moisture, temperature, and sunlight in real time. The group of sensors periodically transmits their respective data to the server.

[0481] When the server receives environmental information, it analyzes it using a generative AI model. This analysis automatically generates the optimal work procedures based on the current farm conditions. Examples of generative AI models used include neural networks and machine learning algorithms. Based on the analysis results, the server predicts future weather fluctuations and constructs work instructions accordingly.

[0482] The generated work procedures are transmitted from the server to each autonomous mobile device. Each autonomous mobile device then follows the instructions from the server and autonomously moves while performing the designated agricultural activity. This enables efficient work with minimal human intervention.

[0483] Users receive reports from the server regarding work progress and environmental changes. Based on these reports, they can send additional instructions to the server as needed and modify work procedures. For example, if the weather changes more than expected, they can instruct the server to deploy additional shade over farm crops.

[0484] As a concrete example, in a situation where a temperature rise is predicted for the afternoon, a server analyzes the temperature data collected by sensor terminals and generates a workflow that "deploys shades to create shade." Based on this instruction, an autonomous mobile device that operates the shades carries out the instructions, maintaining the farm environment.

[0485] An example of a prompt used in this system is a specific instruction such as, "Optimize the automatic opening and closing schedule of the shades based on the temperature forecast data for the past few days." Through these prompts, the system can accurately reflect the user's intentions.

[0486] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0487] Step 1:

[0488] The terminal uses various sensors installed on the farm to collect environmental information such as soil moisture, temperature, and sunlight. The data (input) obtained from the sensors is immediately digitized and used in subsequent processing steps. Specifically, the sensors measure information in real time, and this data is collected at regular intervals.

[0489] Step 2:

[0490] The terminal collects environmental data and sends it to the server. The transmitted data (input) is aggregated on the server as information useful for understanding the overall situation of the farm. Specifically, the terminal performs the action of transmitting data to the server using wireless communication.

[0491] Step 3:

[0492] The server analyzes environmental data received from the terminal. The received data (input) is analyzed using a generative AI model to extract temperature and humidity fluctuation patterns. The output provides predictive information and anomaly detection results based on the analysis. Specifically, a machine learning algorithm runs within the server, and data processing is performed automatically.

[0493] Step 4:

[0494] The server generates optimized work procedures based on the analysis results. The generating AI model assists in this optimization process and constructs a specific work flow (output) based on the insights gained. For example, this includes actions such as "deploy the shade to create shade."

[0495] Step 5:

[0496] The server transmits the generated work procedures to the autonomous mobile devices. The work procedures (inputs) are sent wirelessly, and each autonomous mobile device is prepared to execute them. Specifically, instructions are sent from the server to each device, and the device that receives the instructions prepares to operate.

[0497] Step 6:

[0498] The autonomous mobile device performs specified tasks based on the instructions it receives. Following the instructions (input), it performs autonomous operations, such as deploying shades within a farm. Specific actions include the robot autonomously moving along a designated route and performing mechanical operations.

[0499] (Application Example 1)

[0500] 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."

[0501] Managing urban farms relies on traditional manual labor, making efficient operation difficult. Furthermore, it's challenging to respond quickly to changes in environmental information and coordinate multiple tasks, failing to adequately address the unique challenges of urban environments.

[0502] 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.

[0503] In this invention, the server includes sensor means for collecting environmental information, artificial intelligence means for analyzing the data received from the sensor means and generating a workflow, and device means for executing the generated workflow. This makes it possible to efficiently manage farms even in urban environments and to respond quickly to environmental changes and the coordination of multiple tasks.

[0504] "Environmental information" refers to information that includes data on the climate and soil of farms and cities.

[0505] A "sensing device" is a device positioned to collect environmental information.

[0506] "Data" refers to information obtained from sensor devices.

[0507] "Analysis" is the process of processing data and extracting useful knowledge.

[0508] A "workflow" is a series of processes or procedures performed to achieve a specific objective.

[0509] "Artificial intelligence tools" refer to technologies for analyzing data and generating optimal workflows.

[0510] "Device means" refers to machines or devices used to execute the generated workflow.

[0511] "Communication means" refers to methods or techniques for exchanging information between a device and a control device.

[0512] A "reporting method" refers to a means of providing a summary of analysis results and work status.

[0513] An "interface means" is a technology that provides a point of contact for users to give feedback.

[0514] The term "urban environment" refers to the unique conditions found in urban areas, and includes elements that should be considered when conducting agriculture.

[0515] "Coordination" refers to the efficient operation of multiple devices working together.

[0516] "Feedback" is the process of incorporating opinions and information provided by users.

[0517] To implement this invention, a system is needed to support the management of farms in urban environments. This system consists of sensor devices, a server, and a user interface. The server runs Python programs on the Django framework and uses TensorFlow for data analysis. The user interface is provided through a smartphone application built with React Native.

[0518] The sensors collect environmental information such as soil moisture, temperature, and light intensity in real time and transmit this data to a server. The server analyzes the received environmental data and generates an optimal workflow using an AI model. This AI model predicts environmental changes based on past data and adjusts the necessary tasks.

[0519] The generated workflow is transmitted to the device, enabling autonomous farm work. Users can monitor the work status via a smartphone application and provide feedback as needed. This feedback serves as crucial input for the system to perform automatic updates.

[0520] As a concrete example, consider a situation where you are growing vegetables on a farm and you need to decide whether you need to water them in the afternoon. In this case, a sensor collects temperature data in real time, and a server uses AI analysis to determine whether watering is necessary. An example of a prompt would be, "If the temperature data exceeds 30 degrees, ask the AI ​​whether you should water the vegetables."

[0521] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0522] Step 1:

[0523] The terminal collects environmental information in real time through sensors installed within the farm. It acquires data such as soil moisture, temperature, and light intensity as input, and converts this data into digital signals. The output is raw data sent to the server.

[0524] Step 2:

[0525] The server receives data sent from the terminal. The raw input data is recorded in the database, and at the same time, data formatting and preprocessing are performed. This results in the output of a dataset in a format suitable for analysis.

[0526] Step 3:

[0527] The server inputs the formatted data into a generating AI model. The AI ​​model compares it with past data, recognizes patterns, and predicts environmental changes. Once the data analysis is complete, the optimal workflow is generated as output.

[0528] Step 4:

[0529] The server sends the generated workflow as instructions to the device. This workflow includes specific tasks and timings, serving as fundamental data for autonomous device operation. The output is a clear work instruction.

[0530] Step 5:

[0531] Users can check the progress of their work and environmental information in real time through a smartphone app. Inputs include analysis results from the server and the device's execution status, which are displayed in the app. Outputs include users providing feedback to the system through an intuitive interface.

[0532] Step 6:

[0533] The server receives user feedback, readjusts the workflow as needed, and adapts flexibly to environmental changes. User feedback is the input, and a new workflow is output based on it.

[0534] 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.

[0535] This invention is a system that combines an emotion engine with an agricultural management system, enabling feedback and workflow adjustments based on user emotions. This system consists of three components: a server, a terminal, and a user, each working in coordination.

[0536] Collection of environmental information and emotional data

[0537] The terminals act as sensors placed on the farm, collecting environmental data such as soil moisture, temperature, and sunlight. They can also acquire emotional data through user facial recognition and voice input. This allows for simultaneous understanding of both the physical environment and the user's emotional state.

[0538] Data analysis and feedback generation

[0539] The server analyzes environmental information and emotional data sent from the terminal. Using artificial intelligence, it formulates an optimal workflow based on the environmental data, while the emotional engine analyzes the user's emotions and generates appropriate feedback. For example, if the user is feeling stressed, it suggests a work schedule to alleviate that stress.

[0540] Executing and adjusting the workflow

[0541] The workflow generated by the server is sent to the robot as instructions. The robot follows these instructions and begins actual work on the farm. In response to dynamic environmental changes and user feedback, the emotional engine can adjust the workflow. This maximizes work efficiency while also addressing the user's emotional needs.

[0542] Reporting the results and proposing the next steps

[0543] After all tasks are completed, the server generates a report based on the analyzed data and provides it to the user. The user can review the report and decide on their next course of action based on the system's suggestions. Because it includes insights from an emotion engine, it presents approaches and adjustments that are likely to be accepted by the user.

[0544] Specific example

[0545] For example, suppose the temperature on a farm rises sharply, and the user is experiencing stress from dealing with the situation. In this case, the terminal sends temperature and user emotion data to the server. The server receives and analyzes this data, generates feedback such as "deploy the shades to lower the temperature and incorporate a new schedule," and has the robot execute it. This entire process enables proactive farm management that takes user emotions into account.

[0546] The embodiment of this invention aims to improve the efficiency of agricultural work and enhance the user experience.

[0547] The following describes the processing flow.

[0548] Step 1:

[0549] The device collects environmental information from sensors placed on the farm. Specifically, it measures soil moisture, temperature, and sunlight, and transmits this data to a server in real time. It also uses a camera and microphone to acquire emotional data from the user's facial expressions and voice tone, and transmits this data as well.

[0550] Step 2:

[0551] The server receives environmental information and emotional data transmitted from the terminal and stores it in a database. Then, it uses an artificial intelligence model based on the environmental information to generate an optimal workflow. Furthermore, the emotional engine analyzes the user's emotional data to understand the user's current emotional state.

[0552] Step 3:

[0553] Based on the analysis results, the server generates work instructions for the robot that take the user's emotions into consideration. For example, if the user is feeling stressed, it will suggest a schedule to reduce the workload. These instructions are then sent to the robot.

[0554] Step 4:

[0555] The robots autonomously perform tasks on the farm according to instructions received from the server. For example, they may deploy shade when the temperature is high or take breaks when excessive work is anticipated.

[0556] Step 5:

[0557] The server monitors the robot's work progress in real time and immediately issues corrective commands if any abnormalities occur. In addition, it reports the work results and analysis findings to the user in natural language. This report is based on insights from an emotion engine and is presented in a user-friendly format.

[0558] Step 6:

[0559] The user reviews the report provided by the server and decides on the next action based on the system's suggestions. If the user enters new instructions or comments, the server incorporates them as feedback and uses them to revise the workflow for the next time.

[0560] (Example 2)

[0561] 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."

[0562] In modern agriculture, a crucial challenge is to respond quickly and efficiently to changes in environmental conditions while reducing the mental burden on workers. Furthermore, there is a need to improve work efficiency while flexibly adjusting work flows based on user emotions. However, conventional systems have limitations in collecting and analyzing environmental data, and therefore cannot adequately meet the emotional needs of users.

[0563] 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.

[0564] In this invention, the server includes a detection device for collecting environmental data, a function for acquiring user emotion data and generating feedback based on that emotion, and a function for dynamically adjusting work procedures and feedback based on predicted environmental changes and the user's emotional state. This enables rapid response to environmental changes and adjustment of the work flow to be sensitive to the user's emotions.

[0565] "Environmental data" refers to information about physical conditions in agricultural settings, such as soil moisture, temperature, and sunlight.

[0566] "Detection device" refers to sensors and devices used to acquire environmental data and user emotion data.

[0567] An "information processing device" refers to a computer system that analyzes acquired data and generates work procedures based on that analysis.

[0568] "Automated equipment" refers to machines and robots that perform actual agricultural work based on generated work procedures.

[0569] "Communication function" refers to a function that enables the transmission and reception of data between different devices, allowing for the coordination of work procedures.

[0570] The "reporting function" refers to a function that provides users with analysis results and work progress status.

[0571] "User emotional data" refers to information that indicates the user's emotional state, and includes data obtained through facial recognition and voice input.

[0572] This invention relates to an agricultural management system consisting of a server, a terminal, and a user. This system achieves efficient farming and addresses the emotional needs of users through the collection of environmental data, analysis of emotional data, and execution and adjustment of work procedures.

[0573] The terminal operates as a detection device placed in farmland. Specifically, it includes various sensors that measure soil moisture, temperature, and sunlight. Furthermore, the terminal is equipped with facial recognition and voice input functions to collect user emotion data. This allows the terminal to simultaneously understand the physical environmental conditions of the site and the user's emotional state.

[0574] The server analyzes environmental and emotional data sent from the terminal. The generative AI model used here develops the optimal work procedure based on the data. The emotional engine also analyzes the user's emotions and generates appropriate feedback. For example, if the user is feeling stressed, the system adjusts the work schedule and offers suggestions to reduce stress. The server then sends the work procedure to the automated equipment, initiating the actual farm work.

[0575] The user receives a report from the system and decides on their next course of action based on its contents. The report includes suggestions that reflect the user's emotional state and provides information that contributes to the user's decision.

[0576] As a concrete example, consider a scenario where the temperature rises rapidly on a farm. In this case, the terminal sends temperature data and user emotion data to the server. The server analyzes this data and formulates appropriate work procedures, such as deploying shades to lower the temperature, and proposes a new work schedule. Subsequently, automated equipment actually deploys the shades and performs the work based on the feedback, resulting in agricultural management that takes the user's emotions into consideration.

[0577] An example of a prompt for the generating AI model is, "If a user is experiencing stress due to a rapid rise in temperature, please suggest possible countermeasures." This system is built to enable agricultural management that comprehensively considers user emotions and environmental conditions.

[0578] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0579] Step 1:

[0580] The terminal collects environmental data using sensors placed on the farm. Specifically, various sensors such as soil moisture sensors, temperature sensors, and sunlight sensors measure data. The input is real-time environmental data from each sensor, which is collected and temporarily stored within the terminal. The output is a set of aggregated environmental data.

[0581] Step 2:

[0582] The device acquires emotional data using the user's facial recognition technology and voice input function. Inputs include image data from the camera and audio data from the microphone, which are then analyzed to convert them into the user's emotional state (stress, joy, etc.). The output is the detected emotional data.

[0583] Step 3:

[0584] The terminal transmits the collected environmental and emotional data to the server. The input is the integrated data obtained in steps 1 and 2, which is transmitted over the internet using a secure communication protocol. The output is the completion of the data transfer to the server.

[0585] Step 4:

[0586] The server analyzes the received data. First, it uses a generative AI model to determine what kind of work is needed in which part of the farm based on environmental data. The input is environmental data, and work procedures are formulated. The output is a work procedure that includes specific work instructions.

[0587] Step 5:

[0588] The server uses an emotion engine to analyze user emotion data and generate feedback based on the user's emotions. Emotion data is the input, and after analysis, suggestions for the user and proposed adjustments to the work schedule are output.

[0589] Step 6:

[0590] The server sends the generated work procedure to the automated device. The input is the work procedure generated in step 4, which is sent to the automated device as an instruction. The output is the instruction necessary to start the automated device, which is sent and received.

[0591] Step 7:

[0592] The automated device performs specific agricultural tasks according to instructions from the server. As input, mechanical actions are performed based on the server's instructions, and the work is executed. As output, the physical work is realized, and changes are observed.

[0593] Step 8:

[0594] The server compiles the results of completed tasks, generates a report, and provides it to the user. Input data includes work results and user emotional changes, and the output report reflects technical analysis results. Based on this, the user decides on their next course of action.

[0595] (Application Example 2)

[0596] 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."

[0597] In conventional nursing care facilities, work schedules are not adjusted to take into account the emotional state of care staff. As a result, staff fatigue and stress accumulate, leading to a decline in the quality of care and staff turnover. In this situation, there is a need for a system that can easily collect environmental information and emotional data and generate optimal work schedules.

[0598] 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.

[0599] In this invention, the server includes a detection means for collecting biometric information, an intelligent means for analyzing the data received from the detection means and generating a work schedule, and an operating means for executing the generated work schedule. This makes it possible to reduce the workload in nursing care facilities and improve the working environment for nursing care staff.

[0600] "Detection means for collecting biometric information" refers to sensors and devices used in nursing care facilities to understand the environment and the emotional state of care staff.

[0601] "Intelligent means" refers to an artificial intelligence system that analyzes collected biometric information and generates an optimal work schedule.

[0602] "Operating means" refers to execution systems that carry out work schedules generated by intelligent means, and includes smart devices and robots.

[0603] "Information and communication means" refers to the communication infrastructure and communication protocols used to control operating means and to send and receive necessary data.

[0604] "Information provision means" refers to an interface for notifying care staff of analyzed data and proposed work schedules, and providing them with information.

[0605] The system for realizing this invention is designed to provide efficient and emotionally sensitive management within care facilities. The system consists of three components: a server, terminals, and users (care staff and residents), all of which work together in coordination.

[0606] The terminals are sensors and smart devices placed in designated locations within the care facility, collecting environmental data including biometric information, as well as the emotional states of care staff and residents. "OpenCV" is used for facial recognition technology, and sensors such as "DHT22" are utilized for environmental data collection.

[0607] The server analyzes data received from the terminal using an AI model based on TensorFlow and generates an optimal work schedule based on the emotional state. During this process, feedback is obtained to reduce the stress level of the care staff, and these instructions are communicated through a user interface built with React Native.

[0608] By receiving feedback from these systems, care staff can implement suggestions that are easier to understand. For example, when care staff are feeling fatigued, they may be offered suggestions for collaborative measures to reduce their workload.

[0609] As a concrete example, consider a situation where care staff are busy but feeling exhausted. In this case, the server analyzes biometric information from the terminals and suggests shift adjustments among staff, thereby enabling a more even distribution of the workload.

[0610] Examples of prompts for generative AI models include the following:

[0611] "Our care staff are experiencing fatigue. What kind of feedback and suggested work flows would be appropriate?"

[0612] "If a user is experiencing anxiety, please suggest appropriate responses based on emotional data."

[0613] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0614] Step 1:

[0615] The terminal collects biometric and environmental data using sensors installed within the care facility. This includes facial recognition technology to capture the expressions of care staff and residents, as well as sensor inputs such as temperature, humidity, and sound volume data within the facility. This data is transmitted to a server in real time.

[0616] Input: Face recognition data, environmental sensor data

[0617] Output: Sending data to the server

[0618] Step 2:

[0619] The server analyzes biometric and environmental data received from the terminal using an AI model. The TensorFlow algorithm used in the generating AI model determines the user's emotional state and stress level, and performs calculations to generate an optimal work schedule. Based on this analysis, dynamically corresponding feedback is calculated.

[0620] Input: Face recognition data, environmental sensor data

[0621] Output: Optimal work schedule, biological status feedback

[0622] Step 3:

[0623] Users receive analysis results and feedback sent from the server through a user interface built with React Native. The displayed feedback may include suggestions for specific stress reduction methods or collaboration strategies. The generated work schedule is presented to the user as concrete execution instructions.

[0624] Input: Optimal work schedule, biological status feedback

[0625] Output: Feedback and instructions displayed to the user

[0626] Step 4:

[0627] Based on feedback received from their devices, users share information with other care staff and collaborate to carry out efficient care activities. Newly adjusted work schedules are applied to the field in real time, promoting effective teamwork.

[0628] Input: Feedback and instructions displayed to the user.

[0629] Output: Execution of the adjusted work schedule

[0630] Step 5:

[0631] The server collects the results based on the completed work schedule as data again and uses it to generate the next work schedule. This loop is designed to continuously provide appropriate feedback and schedules that are tailored to the current situation in the caregiving field.

[0632] Input: Adjusted work schedule result

[0633] Output: Data for generating the next schedule

[0634] 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.

[0635] 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.

[0636] 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.

[0637] [Fourth Embodiment]

[0638] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0639] 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.

[0640] 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).

[0641] 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.

[0642] 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.

[0643] 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).

[0644] 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.

[0645] 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.

[0646] 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.

[0647] 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.

[0648] 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.

[0649] 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.

[0650] 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".

[0651] This invention is a system for efficiently and autonomously managing tasks in agricultural settings. This system primarily consists of a server, terminals, and users, each playing a specific role. Specific embodiments are described below.

[0652] Collection of environmental information

[0653] The terminal acts as a group of sensors placed on the farm. These sensors collect environmental information such as soil moisture, temperature, and sunlight in real time. This makes it possible to quickly grasp changes in the environment.

[0654] Data analysis and workflow generation

[0655] The server receives environmental information sent from the terminal and analyzes the data using an artificial intelligence model. Based on the results of this analysis, it automatically generates the optimal workflow. For example, if rain is expected, it will change the instruction to stop watering in advance.

[0656] Robot control and task execution

[0657] The server issues instructions to the robot according to the generated workflow. Based on these instructions, the robot moves autonomously and performs the specified farm task. For example, the server might issue a command to start weeding at 9:00 AM.

[0658] Reporting results and providing instructions for corrections.

[0659] The server compiles a report detailing the progress of the tasks performed and any changes in the environment, and provides it to the user. Based on this report, the user can provide feedback to the server with additional correction instructions if necessary. For example, in response to a user instruction such as "watering is needed in the afternoon," the server updates the workflow.

[0660] Specific example

[0661] As a concrete example, suppose a farm is forecast to experience a rapid rise in temperature in the afternoon. In this case, a sensor terminal collects temperature data, and a server analyzes it to generate a workflow that "deploys shade to create shade." The generated workflow is then executed by a robot that operates the shade based on instructions from the server, maintaining the necessary agricultural environment.

[0662] This invention provides a concrete solution for efficiently managing farm work and improving productivity while addressing the problem of labor shortages.

[0663] The following describes the processing flow.

[0664] Step 1:

[0665] The terminal acquires environmental information from sensors placed on the farm. Specifically, it measures data such as soil moisture, temperature, and sunlight. The measured data is transmitted to a server in real time using 5G communication.

[0666] Step 2:

[0667] The server receives sensor data transmitted from the terminal and stores it in a database. Next, it uses an artificial intelligence model to analyze the received data. This analysis identifies the current environmental conditions and the optimal workflow. For example, if the soil moisture is low, it detects the need for watering.

[0668] Step 3:

[0669] Based on the analysis results, the server generates specific work instructions for the robot. These instructions include the start time, work location, and specific tasks. By sending these instructions to the robot, it autonomously performs the designated tasks.

[0670] Step 4:

[0671] The robot begins moving around the farm according to work instructions received from the server. At designated locations, it performs tasks such as watering or weeding. In this process, the robot can also automatically adjust its work actions according to the surrounding environment.

[0672] Step 5:

[0673] The server receives feedback from the robot and monitors the progress of the task. Once it confirms that the task is complete, it compiles the results and makes any necessary adjustments for the next task. This information is compiled into a report in natural language and provided to the user.

[0674] Step 6:

[0675] Users receive reports from the server to verify that the work is progressing according to plan. If necessary, they input additional instructions or corrections and provide feedback to the server. This allows for further optimization of the workflow in the future.

[0676] (Example 1)

[0677] 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".

[0678] In the agricultural sector, autonomous and efficient work management is challenging. In particular, there is a need to reduce labor and improve productivity while responding quickly to environmental changes. Furthermore, multiple work devices must operate in harmony to achieve optimal performance.

[0679] 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.

[0680] In this invention, the server includes a device for collecting environmental information, intelligent processing means for analyzing the information and generating novel work procedures, and an autonomous mobile device for executing the generated work procedures. This enables cooperative and efficient agricultural work while responding immediately to changes in the environment.

[0681] "Environmental information" refers to data on physical conditions related to agricultural activities, such as temperature, humidity, sunlight, and soil conditions.

[0682] "Device" refers to equipment that uses sensors to collect physical environmental information.

[0683] "Intelligent processing means" refers to an artificial intelligence-based processing device that has the ability to analyze collected environmental information and generate new work procedures.

[0684] An "autonomous mobile device" refers to a machine or robot that autonomously performs a designated task.

[0685] "Information transmission means" refers to communication means used to send and receive instructions and information between a device and an autonomous mobile device, etc.

[0686] "Information provision means" refers to the means of reporting analysis results and work progress to users.

[0687] This invention is a system for efficiently and autonomously managing agricultural work, and is mainly composed of three elements: a server, terminals, and users. In this system, the terminals function as a group of sensors placed on the farm, and are responsible for collecting environmental information such as soil moisture, temperature, and sunlight in real time. The group of sensors periodically transmits their respective data to the server.

[0688] When the server receives environmental information, it analyzes it using a generative AI model. This analysis automatically generates the optimal work procedures based on the current farm conditions. Examples of generative AI models used include neural networks and machine learning algorithms. Based on the analysis results, the server predicts future weather fluctuations and constructs work instructions accordingly.

[0689] The generated work procedures are transmitted from the server to each autonomous mobile device. Each autonomous mobile device then follows the instructions from the server and autonomously moves while performing the designated agricultural activity. This enables efficient work with minimal human intervention.

[0690] Users receive reports from the server regarding work progress and environmental changes. Based on these reports, they can send additional instructions to the server as needed and modify work procedures. For example, if the weather changes more than expected, they can instruct the server to deploy additional shade over farm crops.

[0691] As a concrete example, in a situation where a temperature rise is predicted for the afternoon, a server analyzes the temperature data collected by sensor terminals and generates a workflow that "deploys shades to create shade." Based on this instruction, an autonomous mobile device that operates the shades carries out the instructions, maintaining the farm environment.

[0692] An example of a prompt used in this system is a specific instruction such as, "Optimize the automatic opening and closing schedule of the shades based on the temperature forecast data for the past few days." Through these prompts, the system can accurately reflect the user's intentions.

[0693] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0694] Step 1:

[0695] The terminal uses various sensors installed on the farm to collect environmental information such as soil moisture, temperature, and sunlight. The data (input) obtained from the sensors is immediately digitized and used in subsequent processing steps. Specifically, the sensors measure information in real time, and this data is collected at regular intervals.

[0696] Step 2:

[0697] The terminal collects environmental data and sends it to the server. The transmitted data (input) is aggregated on the server as information useful for understanding the overall situation of the farm. Specifically, the terminal performs the action of transmitting data to the server using wireless communication.

[0698] Step 3:

[0699] The server analyzes environmental data received from the terminal. The received data (input) is analyzed using a generative AI model to extract temperature and humidity fluctuation patterns. The output provides predictive information and anomaly detection results based on the analysis. Specifically, a machine learning algorithm runs within the server, and data processing is performed automatically.

[0700] Step 4:

[0701] The server generates optimized work procedures based on the analysis results. The generating AI model assists in this optimization process and constructs a specific work flow (output) based on the insights gained. For example, this includes actions such as "deploy the shade to create shade."

[0702] Step 5:

[0703] The server transmits the generated work procedures to the autonomous mobile devices. The work procedures (inputs) are sent wirelessly, and each autonomous mobile device is prepared to execute them. Specifically, instructions are sent from the server to each device, and the device that receives the instructions prepares to operate.

[0704] Step 6:

[0705] The autonomous mobile device performs specified tasks based on the instructions it receives. Following the instructions (input), it performs autonomous operations, such as deploying shades within a farm. Specific actions include the robot autonomously moving along a designated route and performing mechanical operations.

[0706] (Application Example 1)

[0707] 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".

[0708] Managing urban farms relies on traditional manual labor, making efficient operation difficult. Furthermore, it's challenging to respond quickly to changes in environmental information and coordinate multiple tasks, failing to adequately address the unique challenges of urban environments.

[0709] 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.

[0710] In this invention, the server includes sensor means for collecting environmental information, artificial intelligence means for analyzing the data received from the sensor means and generating a workflow, and device means for executing the generated workflow. This makes it possible to efficiently manage farms even in urban environments and to respond quickly to environmental changes and the coordination of multiple tasks.

[0711] "Environmental information" refers to information that includes data on the climate and soil of farms and cities.

[0712] A "sensing device" is a device positioned to collect environmental information.

[0713] "Data" refers to information obtained from sensor devices.

[0714] "Analysis" is the process of processing data and extracting useful knowledge.

[0715] A "workflow" is a series of processes or procedures performed to achieve a specific objective.

[0716] "Artificial intelligence tools" refer to technologies for analyzing data and generating optimal workflows.

[0717] "Device means" refers to machines or devices used to execute the generated workflow.

[0718] "Communication means" refers to methods or techniques for exchanging information between a device and a control device.

[0719] A "reporting method" refers to a means of providing a summary of analysis results and work status.

[0720] An "interface means" is a technology that provides a point of contact for users to give feedback.

[0721] The term "urban environment" refers to the unique conditions found in urban areas, and includes elements that should be considered when conducting agriculture.

[0722] "Coordination" refers to the efficient operation of multiple devices working together.

[0723] "Feedback" is the process of incorporating opinions and information provided by users.

[0724] To implement this invention, a system is needed to support the management of farms in urban environments. This system consists of sensor devices, a server, and a user interface. The server runs Python programs on the Django framework and uses TensorFlow for data analysis. The user interface is provided through a smartphone application built with React Native.

[0725] The sensors collect environmental information such as soil moisture, temperature, and light intensity in real time and transmit this data to a server. The server analyzes the received environmental data and generates an optimal workflow using an AI model. This AI model predicts environmental changes based on past data and adjusts the necessary tasks.

[0726] The generated workflow is transmitted to the device, enabling autonomous farm work. Users can monitor the work status via a smartphone application and provide feedback as needed. This feedback serves as crucial input for the system to perform automatic updates.

[0727] As a concrete example, consider a situation where you are growing vegetables on a farm and you need to decide whether you need to water them in the afternoon. In this case, a sensor collects temperature data in real time, and a server uses AI analysis to determine whether watering is necessary. An example of a prompt would be, "If the temperature data exceeds 30 degrees, ask the AI ​​whether you should water the vegetables."

[0728] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0729] Step 1:

[0730] The terminal collects environmental information in real time through sensors installed within the farm. It acquires data such as soil moisture, temperature, and light intensity as input, and converts this data into digital signals. The output is raw data sent to the server.

[0731] Step 2:

[0732] The server receives data sent from the terminal. The raw input data is recorded in the database, and at the same time, data formatting and preprocessing are performed. This results in the output of a dataset in a format suitable for analysis.

[0733] Step 3:

[0734] The server inputs the formatted data into a generating AI model. The AI ​​model compares it with past data, recognizes patterns, and predicts environmental changes. Once the data analysis is complete, the optimal workflow is generated as output.

[0735] Step 4:

[0736] The server sends the generated workflow as instructions to the device. This workflow includes specific tasks and timings, serving as fundamental data for autonomous device operation. The output is a clear work instruction.

[0737] Step 5:

[0738] Users can check the progress of their work and environmental information in real time through a smartphone app. Inputs include analysis results from the server and the device's execution status, which are displayed in the app. Outputs include users providing feedback to the system through an intuitive interface.

[0739] Step 6:

[0740] The server receives user feedback, readjusts the workflow as needed, and adapts flexibly to environmental changes. User feedback is the input, and a new workflow is output based on it.

[0741] 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.

[0742] This invention is a system that combines an emotion engine with an agricultural management system, enabling feedback and workflow adjustments based on user emotions. This system consists of three components: a server, a terminal, and a user, each working in coordination.

[0743] Collection of environmental information and emotional data

[0744] The terminals act as sensors placed on the farm, collecting environmental data such as soil moisture, temperature, and sunlight. They can also acquire emotional data through user facial recognition and voice input. This allows for simultaneous understanding of both the physical environment and the user's emotional state.

[0745] Data analysis and feedback generation

[0746] The server analyzes environmental information and emotional data sent from the terminal. Using artificial intelligence, it formulates an optimal workflow based on the environmental data, while the emotional engine analyzes the user's emotions and generates appropriate feedback. For example, if the user is feeling stressed, it suggests a work schedule to alleviate that stress.

[0747] Executing and adjusting the workflow

[0748] The workflow generated by the server is sent to the robot as instructions. The robot follows these instructions and begins actual work on the farm. In response to dynamic environmental changes and user feedback, the emotional engine can adjust the workflow. This maximizes work efficiency while also addressing the user's emotional needs.

[0749] Reporting the results and proposing the next steps

[0750] After all tasks are completed, the server generates a report based on the analyzed data and provides it to the user. The user can review the report and decide on their next course of action based on the system's suggestions. Because it includes insights from an emotion engine, it presents approaches and adjustments that are likely to be accepted by the user.

[0751] Specific example

[0752] For example, suppose the temperature on a farm rises sharply, and the user is experiencing stress from dealing with the situation. In this case, the terminal sends temperature and user emotion data to the server. The server receives and analyzes this data, generates feedback such as "deploy the shades to lower the temperature and incorporate a new schedule," and has the robot execute it. This entire process enables proactive farm management that takes user emotions into account.

[0753] The embodiment of this invention aims to improve the efficiency of agricultural work and enhance the user experience.

[0754] The following describes the processing flow.

[0755] Step 1:

[0756] The device collects environmental information from sensors placed on the farm. Specifically, it measures soil moisture, temperature, and sunlight, and transmits this data to a server in real time. It also uses a camera and microphone to acquire emotional data from the user's facial expressions and voice tone, and transmits this data as well.

[0757] Step 2:

[0758] The server receives environmental information and emotional data transmitted from the terminal and stores it in a database. Then, it uses an artificial intelligence model based on the environmental information to generate an optimal workflow. Furthermore, the emotional engine analyzes the user's emotional data to understand the user's current emotional state.

[0759] Step 3:

[0760] Based on the analysis results, the server generates work instructions for the robot that take the user's emotions into consideration. For example, if the user is feeling stressed, it will suggest a schedule to reduce the workload. These instructions are then sent to the robot.

[0761] Step 4:

[0762] The robots autonomously perform tasks on the farm according to instructions received from the server. For example, they may deploy shade when the temperature is high or take breaks when excessive work is anticipated.

[0763] Step 5:

[0764] The server monitors the robot's work progress in real time and immediately issues corrective commands if any abnormalities occur. In addition, it reports the work results and analysis findings to the user in natural language. This report is based on insights from an emotion engine and is presented in a user-friendly format.

[0765] Step 6:

[0766] The user reviews the report provided by the server and decides on the next action based on the system's suggestions. If the user enters new instructions or comments, the server incorporates them as feedback and uses them to revise the workflow for the next time.

[0767] (Example 2)

[0768] 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".

[0769] In modern agriculture, a crucial challenge is to respond quickly and efficiently to changes in environmental conditions while reducing the mental burden on workers. Furthermore, there is a need to improve work efficiency while flexibly adjusting work flows based on user emotions. However, conventional systems have limitations in collecting and analyzing environmental data, and therefore cannot adequately meet the emotional needs of users.

[0770] 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.

[0771] In this invention, the server includes a detection device for collecting environmental data, a function for acquiring user emotion data and generating feedback based on that emotion, and a function for dynamically adjusting work procedures and feedback based on predicted environmental changes and the user's emotional state. This enables rapid response to environmental changes and adjustment of the work flow to be sensitive to the user's emotions.

[0772] "Environmental data" refers to information about physical conditions in agricultural settings, such as soil moisture, temperature, and sunlight.

[0773] "Detection device" refers to sensors and devices used to acquire environmental data and user emotion data.

[0774] An "information processing device" refers to a computer system that analyzes acquired data and generates work procedures based on that analysis.

[0775] "Automated equipment" refers to machines and robots that perform actual agricultural work based on generated work procedures.

[0776] "Communication function" refers to a function that enables the transmission and reception of data between different devices, allowing for the coordination of work procedures.

[0777] The "reporting function" refers to a function that provides users with analysis results and work progress status.

[0778] "User emotional data" refers to information that indicates the user's emotional state, and includes data obtained through facial recognition and voice input.

[0779] This invention relates to an agricultural management system consisting of a server, a terminal, and a user. This system achieves efficient farming and addresses the emotional needs of users through the collection of environmental data, analysis of emotional data, and execution and adjustment of work procedures.

[0780] The terminal operates as a detection device placed in farmland. Specifically, it includes various sensors that measure soil moisture, temperature, and sunlight. Furthermore, the terminal is equipped with facial recognition and voice input functions to collect user emotion data. This allows the terminal to simultaneously understand the physical environmental conditions of the site and the user's emotional state.

[0781] The server analyzes environmental and emotional data sent from the terminal. The generative AI model used here develops the optimal work procedure based on the data. The emotional engine also analyzes the user's emotions and generates appropriate feedback. For example, if the user is feeling stressed, the system adjusts the work schedule and offers suggestions to reduce stress. The server then sends the work procedure to the automated equipment, initiating the actual farm work.

[0782] The user receives a report from the system and decides on their next course of action based on its contents. The report includes suggestions that reflect the user's emotional state and provides information that contributes to the user's decision.

[0783] As a concrete example, consider a scenario where the temperature rises rapidly on a farm. In this case, the terminal sends temperature data and user emotion data to the server. The server analyzes this data and formulates appropriate work procedures, such as deploying shades to lower the temperature, and proposes a new work schedule. Subsequently, automated equipment actually deploys the shades and performs the work based on the feedback, resulting in agricultural management that takes the user's emotions into consideration.

[0784] An example of a prompt for the generating AI model is, "If a user is experiencing stress due to a rapid rise in temperature, please suggest possible countermeasures." This system is built to enable agricultural management that comprehensively considers user emotions and environmental conditions.

[0785] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0786] Step 1:

[0787] The terminal collects environmental data using sensors placed on the farm. Specifically, various sensors such as soil moisture sensors, temperature sensors, and sunlight sensors measure data. The input is real-time environmental data from each sensor, which is collected and temporarily stored within the terminal. The output is a set of aggregated environmental data.

[0788] Step 2:

[0789] The device acquires emotional data using the user's facial recognition technology and voice input function. Inputs include image data from the camera and audio data from the microphone, which are then analyzed to convert them into the user's emotional state (stress, joy, etc.). The output is the detected emotional data.

[0790] Step 3:

[0791] The terminal transmits the collected environmental and emotional data to the server. The input is the integrated data obtained in steps 1 and 2, which is transmitted over the internet using a secure communication protocol. The output is the completion of the data transfer to the server.

[0792] Step 4:

[0793] The server analyzes the received data. First, it uses a generative AI model to determine what kind of work is needed in which part of the farm based on environmental data. The input is environmental data, and work procedures are formulated. The output is a work procedure that includes specific work instructions.

[0794] Step 5:

[0795] The server uses an emotion engine to analyze user emotion data and generate feedback based on the user's emotions. Emotion data is the input, and after analysis, suggestions for the user and proposed adjustments to the work schedule are output.

[0796] Step 6:

[0797] The server sends the generated work procedure to the automated device. The input is the work procedure generated in step 4, which is sent to the automated device as an instruction. The output is the instruction necessary to start the automated device, which is sent and received.

[0798] Step 7:

[0799] The automated device performs specific agricultural tasks according to instructions from the server. As input, mechanical actions are performed based on the server's instructions, and the work is executed. As output, the physical work is realized, and changes are observed.

[0800] Step 8:

[0801] The server compiles the results of completed tasks, generates a report, and provides it to the user. Input data includes work results and user emotional changes, and the output report reflects technical analysis results. Based on this, the user decides on their next course of action.

[0802] (Application Example 2)

[0803] 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".

[0804] In conventional nursing care facilities, work schedules are not adjusted to take into account the emotional state of care staff. As a result, staff fatigue and stress accumulate, leading to a decline in the quality of care and staff turnover. In this situation, there is a need for a system that can easily collect environmental information and emotional data and generate optimal work schedules.

[0805] 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.

[0806] In this invention, the server includes a detection means for collecting biometric information, an intelligent means for analyzing the data received from the detection means and generating a work schedule, and an operating means for executing the generated work schedule. This makes it possible to reduce the workload in nursing care facilities and improve the working environment for nursing care staff.

[0807] "Detection means for collecting biometric information" refers to sensors and devices used in nursing care facilities to understand the environment and the emotional state of care staff.

[0808] "Intelligent means" refers to an artificial intelligence system that analyzes collected biometric information and generates an optimal work schedule.

[0809] "Operating means" refers to execution systems that carry out work schedules generated by intelligent means, and includes smart devices and robots.

[0810] "Information and communication means" refers to the communication infrastructure and communication protocols used to control operating means and to send and receive necessary data.

[0811] "Information provision means" refers to an interface for notifying care staff of analyzed data and proposed work schedules, and providing them with information.

[0812] The system for realizing this invention is designed to provide efficient and emotionally sensitive management within care facilities. The system consists of three components: a server, terminals, and users (care staff and residents), all of which work together in coordination.

[0813] The terminals are sensors and smart devices placed in designated locations within the care facility, collecting environmental data including biometric information, as well as the emotional states of care staff and residents. "OpenCV" is used for facial recognition technology, and sensors such as "DHT22" are utilized for environmental data collection.

[0814] The server analyzes data received from the terminal using an AI model based on TensorFlow and generates an optimal work schedule based on the emotional state. During this process, feedback is obtained to reduce the stress level of the care staff, and these instructions are communicated through a user interface built with React Native.

[0815] By receiving feedback from these systems, care staff can implement suggestions that are easier to understand. For example, when care staff are feeling fatigued, they may be offered suggestions for collaborative measures to reduce their workload.

[0816] As a concrete example, consider a situation where care staff are busy but feeling exhausted. In this case, the server analyzes biometric information from the terminals and suggests shift adjustments among staff, thereby enabling a more even distribution of the workload.

[0817] Examples of prompts for generative AI models include the following:

[0818] "Our care staff are experiencing fatigue. What kind of feedback and suggested work flows would be appropriate?"

[0819] "If a user is experiencing anxiety, please suggest appropriate responses based on emotional data."

[0820] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0821] Step 1:

[0822] The terminal collects biometric and environmental data using sensors installed within the care facility. This includes facial recognition technology to capture the expressions of care staff and residents, as well as sensor inputs such as temperature, humidity, and sound volume data within the facility. This data is transmitted to a server in real time.

[0823] Input: Face recognition data, environmental sensor data

[0824] Output: Sending data to the server

[0825] Step 2:

[0826] The server analyzes biometric and environmental data received from the terminal using an AI model. The TensorFlow algorithm used in the generating AI model determines the user's emotional state and stress level, and performs calculations to generate an optimal work schedule. Based on this analysis, dynamically corresponding feedback is calculated.

[0827] Input: Face recognition data, environmental sensor data

[0828] Output: Optimal work schedule, biological status feedback

[0829] Step 3:

[0830] Users receive analysis results and feedback sent from the server through a user interface built with React Native. The displayed feedback may include suggestions for specific stress reduction methods or collaboration strategies. The generated work schedule is presented to the user as concrete execution instructions.

[0831] Input: Optimal work schedule, biological status feedback

[0832] Output: Feedback and instructions displayed to the user

[0833] Step 4:

[0834] Based on feedback received from their devices, users share information with other care staff and collaborate to carry out efficient care activities. Newly adjusted work schedules are applied to the field in real time, promoting effective teamwork.

[0835] Input: Feedback and instructions displayed to the user.

[0836] Output: Execution of the adjusted work schedule

[0837] Step 5:

[0838] The server collects the results based on the completed work schedule as data again and uses it to generate the next work schedule. This loop is designed to continuously provide appropriate feedback and schedules that are tailored to the current situation in the caregiving field.

[0839] Input: Adjusted work schedule result

[0840] Output: Data for generating the next schedule

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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."

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] The following is further disclosed regarding the embodiments described above.

[0863] (Claim 1)

[0864] Sensor means for collecting environmental information,

[0865] An artificial intelligence means that analyzes the data received by the aforementioned sensor means and generates a workflow,

[0866] A robotic means to execute the generated workflow,

[0867] A communication means for controlling the robot means,

[0868] A reporting method that provides the analysis content and work status,

[0869] Agricultural management systems including

[0870] (Claim 2)

[0871] The agricultural management system according to claim 1, which is a means for multiple robots to cooperate in performing tasks.

[0872] (Claim 3)

[0873] The agricultural management system according to claim 1, wherein the artificial intelligence means comprises means for dynamically adjusting the work flow based on predicted environmental changes.

[0874] "Example 1"

[0875] (Claim 1)

[0876] A device for collecting environmental information,

[0877] An intelligent processing means that analyzes the information collected by the aforementioned device and generates a new work procedure,

[0878] An autonomous mobile device that executes the generated work procedure,

[0879] Information transmission means for controlling the autonomous mobile device,

[0880] Information provision means that provide analysis results and work progress,

[0881] A means for users to send instructions and update work procedures,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, which is a means for multiple autonomous mobile devices to work together to perform a task.

[0885] (Claim 3)

[0886] The system according to claim 1, wherein the intelligent processing means comprises means for dynamically modifying the work procedure based on predicted environmental fluctuations.

[0887] "Application Example 1"

[0888] (Claim 1)

[0889] Sensor means for collecting environmental information,

[0890] An artificial intelligence means that analyzes the data received by the aforementioned sensor means and generates a workflow,

[0891] Device means for executing the generated workflow,

[0892] A communication means for controlling the aforementioned device means,

[0893] A reporting method that provides the analysis content and work status,

[0894] An interface means for users to provide feedback,

[0895] Means for managing farms in urban environments,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, which is a means for performing work in cooperation with multiple devices.

[0899] (Claim 3)

[0900] The system according to claim 1, wherein the artificial intelligence means includes means for dynamically adjusting the workflow based on predicted environmental changes and for reflecting user feedback.

[0901] "Example 2 of combining an emotion engine"

[0902] (Claim 1)

[0903] A detection device for collecting environmental data,

[0904] An information processing device that analyzes information acquired from the aforementioned detection device and generates a work procedure,

[0905] A function that acquires user emotion data and generates feedback based on that emotion,

[0906] An automated device that executes the generated work procedure,

[0907] A communication function that controls the aforementioned automated device and dynamically adjusts the work procedure,

[0908] A reporting function that provides analysis results and work progress,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, having the function of performing work in cooperation with multiple automated devices.

[0912] (Claim 3)

[0913] The system according to claim 1, wherein the information processing device has a function to dynamically adjust work procedures and feedback based on predicted environmental changes and the user's emotional state.

[0914] "Application example 2 when combining with an emotional engine"

[0915] (Claim 1)

[0916] A detection means for collecting biological information,

[0917] An intelligent means analyzes the data received from the detection means and generates a work schedule,

[0918] An operating means for executing the generated work schedule,

[0919] Information communication means for controlling the operating means,

[0920] Information provision means that provide analysis details and work status,

[0921] A management system that includes this.

[0922] (Claim 2)

[0923] The system according to claim 1, which is a means for performing work in coordination among multiple operating means.

[0924] (Claim 3)

[0925] The system according to claim 1, wherein the intelligent means comprises means for dynamically adjusting the work schedule based on predicted environmental changes and emotional states. [Explanation of symbols]

[0926] 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. Sensor means for collecting environmental information, An artificial intelligence means that analyzes the data received by the aforementioned sensor means and generates a workflow, Device means for executing the generated workflow, A communication means for controlling the aforementioned device means, A reporting method that provides the analysis content and work status, An interface means for users to provide feedback, Means for managing farms in urban environments, A system that includes this.

2. The system according to claim 1, which is a means for performing work in cooperation with multiple devices.

3. The system according to claim 1, wherein the artificial intelligence means includes means for dynamically adjusting the workflow based on predicted environmental changes and for reflecting user feedback.

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