system

Abstract

Provide a system. 【Solution means】 Means for collecting environmental data in real time, Means for analyzing the environmental data to detect special situations, Means for adjusting the fluid environment based on the special situation, Means for optimizing nutrient supply according to the growth stage, Means for detecting health status and signs of abnormalities and taking automatic countermeasures, Means for adjusting the shipping schedule based on growth prediction and market data, Means for monitoring quality indicators in the manufacturing line and automatically adjusting the manufacturing process as needed, A system including the above.

Description

Technical Field

[0004] , , , ,

[0005] , , , , , ,

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] [[ID=?]] In eel farming, depletion of natural resources has become a problem from the viewpoints of decreasing the catch of glass eels, which are juvenile eels, and protecting the ecosystem. In addition, although it is urgent to establish a sustainable complete eel farming technology, the current farming technology has problems such as a shortage of labor and an increase in feed costs. Furthermore, it is necessary to improve the current situation where a great deal of labor and cost are required for water quality and environment management, optimization of feeding, prediction and countermeasures against diseases, and improvement of shipping efficiency.

Means for Solving the Problems

[0005] It should be noted that there seems to be a missing ID in the original text where it says " " in the English translation part. Please check the original text for accuracy.The present invention provides a system that includes means for collecting environmental information in real time, means for analyzing environmental information to detect anomalies, means for adjusting the water environment based on anomalies, means for optimizing feeding according to the growth stage, means for detecting signs of disease or anomalies and automatically taking countermeasures, and means for adjusting the shipping schedule based on growth predictions and market information. This system enables stable and sustainable eel farming, and allows for efficient operation with reduced labor and lower costs.

[0006] "Real-time" means processing data and information with immediacy, and obtaining results immediately without any time delay.

[0007] "Environmental information" refers to data on physical and chemical conditions in the aquaculture environment, such as water quality, temperature, and oxygen concentration.

[0008] "Analysis" is the process of evaluating collected data using statistical or algorithmic methods to identify specific patterns or anomalies.

[0009] An "abnormality" refers to a state or event that deviates from defined normal standards or expected performance, and usually requires improvement or action.

[0010] "Adjusting the aquatic environment" means performing operations to maintain or restore water quality parameters to appropriate levels.

[0011] "Growth stage" refers to a specific growth phase in the life cycle of an eel, and each stage has different nutritional and management requirements.

[0012] "Optimizing feeding" means providing eels with food at the timing and in the amount that is most appropriate for their growth and health.

[0013] "Signs" refer to data that serves as a precursor or indicator of the possibility of illness or abnormal movement.

[0014] "Taking countermeasures" means taking preventive or corrective actions against the detected problems or abnormalities.

[0015] "Growth prediction" means inferring and estimating future growth patterns based on past and current growth data.

[0016] "Market information" refers to data on the demand, supply, and price of products and services and their predicted fluctuations.

[0017] "Adjusting the shipping schedule" means setting a plan to carry out the harvest and shipping of eels to the market at the optimal timing and executing it based on that plan.

Brief Explanation of Drawings

[0018] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10]Shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0022] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0025] 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."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] The complete eel farming system of this invention includes a software program for automating various processes. This program divides functions among the server, terminals, and users, processing and managing data according to each role. Its specific operation is described below.

[0040] The server, as the core of the system, performs data analysis and control. It receives real-time environmental information transmitted from terminals, analyzes it, and detects anomalies in water quality management. The server also uses a generative AI model to optimize feeding according to the eel's growth stage. Upon detecting an anomaly, the server immediately sends instructions to the terminals for corrective action and, if necessary, implements individual health management.

[0041] The terminal periodically collects environmental information and eel growth data through sensors. The terminal is responsible for transmitting this data to the server in real time. It also controls the aeration system and automatic feeding device based on instructions from the server to maintain environmental conditions.

[0042] Users can check harvest and shipping schedules based on growth forecast data and market information generated by the server. By following the server's suggestions and shipping at the optimal time, users can maximize their profits.

[0043] For example, if the oxygen concentration drops, the terminal detects this and reports it to the server. The server analyzes the data and, if it predicts that the low-oxygen condition will persist, immediately sends a signal to control the aeration device. This minimizes health risks to the eels and provides a stable farming environment. In addition, based on growth data, the server calculates the optimal amount of feed, preventing excessive feed waste while promoting uniform growth.

[0044] By enabling these operations, the present invention realizes sustainable and efficient complete eel farming.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The device uses multiple sensors to collect environmental information such as pH, oxygen concentration, and temperature in the water in real time.

[0048] Step 2:

[0049] The terminal sends the collected environmental information to the server as data packets at regular intervals.

[0050] Step 3:

[0051] The server stores the received environmental data in a database and simultaneously performs analysis using a generated AI model.

[0052] Step 4:

[0053] The server compares the analysis results with pre-set baseline values ​​to check for any abnormalities.

[0054] Step 5:

[0055] If an anomaly is detected, the server automatically creates a control command and sends it to the terminal.

[0056] Step 6:

[0057] The terminal receives control commands from the server and takes appropriate action as needed (e.g., initiating aeration, instructing water changes).

[0058] Step 7:

[0059] The device measures the eel's weight and length and sends the growth data to the server.

[0060] Step 8:

[0061] The server uses growth data and a generative AI model to calculate the optimal timing and amount of feeding.

[0062] Step 9:

[0063] Based on the calculation results, the server sends a feeding command to the terminal.

[0064] Step 10:

[0065] The terminal executes feeding commands and supplies the appropriate amount of food to the eels.

[0066] Step 11:

[0067] Users review growth forecast data and market information generated by the server to determine the optimal shipping schedule.

[0068] Step 12:

[0069] Based on the determined shipping schedule, users supply harvested eels to the market at the appropriate time.

[0070] (Example 1)

[0071] 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."

[0072] Current aquaculture systems face challenges in responding quickly to changes in environmental conditions and in providing appropriate management according to the growth stage of organisms. Furthermore, determining the appropriate timing for shipment to match market supply and demand is difficult. Under these circumstances, there is a need for systems that can improve the efficiency and productivity of aquaculture.

[0073] 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.

[0074] In this invention, the server includes a device for collecting environmental data, an information processing device for analyzing the data and detecting anomalies, and a control device for adjusting the aquatic environment based on the anomalies. This enables rapid response to environmental changes and management that optimizes the health and growth of organisms.

[0075] "Environmental data" refers to information about the aquaculture environment, such as water temperature, oxygen concentration, and pH values, which are obtained using physical sensors.

[0076] An "information processing device" is a computer device used to analyze collected data and detect anomalies.

[0077] A "control device" is a device that operates external devices such as aeration systems and feeding devices based on instructions from a server, in order to maintain environmental conditions.

[0078] A "mathematical model" is an algorithm or mathematical structure used to calculate the optimal amount of food to feed an organism, taking into account its growth stage.

[0079] A "display device" is an interface used to present market information and growth forecasts to users and to assist in adjusting shipping schedules.

[0080] "Functionality" refers to the ability to analyze information about the behavior and growth of organisms, detect abnormalities in real time, and propose rapid countermeasures.

[0081] Modes for carrying out the invention

[0082] In this invention, the server, terminal, and user each play a specific role in order to realize a system that efficiently optimizes aquaculture environment management and organism growth.

[0083] The server executes programs as a central processing unit. First, the server collects environmental data sent from terminals using Python and performs analysis using a data analysis library. This enables rapid response when anomalies are detected. Furthermore, it uses TENSORFLOW® as a generative AI model to optimize feeding based on the growth stage of organisms. This enables appropriate nutritional management.

[0084] The terminal acquires environmental data in real time using a group of sensors. A small computer such as a Raspberry Pi is used for this purpose, utilizing Python libraries to acquire data from the sensors. The data is then transmitted to a server via Wi-Fi using the MQTT protocol. The terminal also receives instructions from the server and controls automatic feeding devices and aeration systems.

[0085] Users manage their shipping schedules based on growth forecast data and market information generated by the server. They can access this information via a dedicated web interface and make decisions based on that information to adjust the optimal harvest time.

[0086] As a concrete example, the server calculates the optimal amount of feed based on eel growth data. An example of a prompt message would be, "Generate a program that calculates the optimal amount of feed based on eel growth data." This system is expected to significantly improve the productivity and efficiency of eel farming.

[0087] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0088] Step 1:

[0089] The device acquires environmental data such as water temperature, oxygen concentration, and pH value from environmental sensors. This is done using a Raspberry Pi and Python libraries. The input is raw data from the sensors, and the output is formatted numerical data. Specifically, the device periodically polls the sensors to collect data.

[0090] Step 2:

[0091] The terminal sends the acquired environmental data to the server via Wi-Fi. The MQTT protocol is used here, with formatted data from the terminal as input and data transmission to the server as output. Specifically, the terminal acts as an MQTT client, publishing data to a specific topic on the server.

[0092] Step 3:

[0093] The server receives the environmental data and performs data analysis. The input is the environmental data sent from the terminal, and the output is a list of anomalies or flags as a result of the analysis. Here, the server uses Python's Pandas library to clean the data and identify anomalies that exceed a threshold.

[0094] Step 4:

[0095] When the server detects an anomaly, it generates a control signal and sends it to the terminal. The input is the analysis result, and the output is control instruction data for the terminal. Specifically, if a low-oxygen condition is predicted to persist, it instructs the terminal to activate the aeration device.

[0096] Step 5:

[0097] The server uses a generative AI model to calculate the optimal feeding amount based on growth data. The input is the growth data of the organism, and the output is the feeding schedule and amount. The server uses TensorFlow to infer this from the model.

[0098] Step 6:

[0099] The terminal operates automatic feeding devices and aeration systems based on control instructions from the server. Inputs are control signals from the server, and outputs are the physical operation of the devices. Specifically, GPIO is used to control the devices.

[0100] Step 7:

[0101] Users review growth forecast data and market information provided by the server and adjust their shipping plans accordingly. Inputs are forecast data and market information from the server, while outputs are adjustments based on the user's decision-making. Users access the web interface via a browser and make decisions based on the information.

[0102] (Application Example 1)

[0103] 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."

[0104] There is a need to improve the efficiency and quality control of production lines in food manufacturing. In current systems, monitoring of environmental conditions and quality indicators is often done manually, which can lead to overlooking anomalies. Furthermore, a lack of process optimization and automatic adjustment capabilities can reduce the overall efficiency of the production line. This can result in inconsistent product quality.

[0105] 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.

[0106] In this invention, the server includes means for collecting environmental data in real time, means for analyzing the environmental data to detect unusual situations, and means for monitoring quality indicators on the manufacturing line and automatically adjusting the manufacturing process as needed. This automates quality control on the manufacturing line and enables rapid and efficient response to anomalies.

[0107] "Environmental data" refers to information that shows conditions such as temperature, humidity, and oxygen concentration in the production line and aquaculture environment.

[0108] An "unusual situation" refers to a condition that deviates from normal operating conditions or exceeds standard values.

[0109] "Fluid environment" refers to the state of water, including environmental conditions such as water quality and temperature necessary for the growth of aquatic organisms being farmed.

[0110] "Nutritional supply" refers to the process of providing the food and nutrients necessary for the growth of an organism.

[0111] "Health status" refers to an indicator that shows the growth of products or target organisms on the production line, and indicates signs of disease or abnormality.

[0112] "Growth forecast data" refers to data used to predict the future growth and production volume of a subject.

[0113] "Market data" refers to market information, such as product demand and pricing, that is used as a reference when planning shipments.

[0114] "Quality indicators" are standards and numerical values ​​used to evaluate the quality of a product, and are necessary to maintain a certain level of quality.

[0115] This invention provides a system that monitors the manufacturing environment in real time and automatically optimizes quality and efficiency. The server collects and analyzes environmental data using a single-board computer or cloud-based platform. Specific hardware such as a Raspberry Pi is used, and the data is processed using Python. This allows for the detection of unusual situations and, if necessary, adjustments to the manufacturing process. The AI ​​model is designed to perform data analysis and production optimization using generative AI technology.

[0116] The terminal uses various sensors to measure quality indicators on the manufacturing line and provides the collected data to the server. This enables real-time environmental monitoring, supporting early detection and rapid response to unusual situations. Users determine shipping timing based on the generated growth forecast data and market data, and adjust production activities considering the system's suggestions. This ensures that the manufacturing line operates efficiently and sustainably.

[0117] For example, if the temperature on the production line deviates from the standard value, the system immediately detects the anomaly and issues instructions for process adjustment, thereby ensuring quality control. Examples of prompts include "Suggest countermeasures for when the quality index on the production line deteriorates" and "Adjust the production environment to the optimal level based on temperature sensor data." This allows the system to autonomously analyze problems and propose countermeasures.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The device collects environmental data from sensors. This data includes temperature, humidity, and quality indicators. The input is the environmental information obtained from the sensors, and the output is providing this information to the server. The device transmits this data to the server in real time.

[0121] Step 2:

[0122] The server analyzes environmental data received from the terminal. The input is environmental data from the terminal, and the output is the analysis results for detecting unusual situations. The server uses a generative AI model to analyze the data and identify unusual situations and abnormal patterns.

[0123] Step 3:

[0124] The server generates instructions to adjust the manufacturing process based on the analysis results. The input is the analysis results, and the output is the adjustment instructions for the manufacturing line. The AI ​​model determines the optimal process adjustment to respond to specific situations.

[0125] Step 4:

[0126] The user reviews growth forecast data and market data from the server to determine the optimal shipping timing. The input is forecast data from the server, and the output is the optimal shipping schedule. The user then uses the system's suggestions to optimize their manufacturing activities.

[0127] Step 5:

[0128] The server verifies that the manufacturing process adjustments are complete and monitors the overall system performance. Inputs are the adjustment results and subsequent production data, while outputs represent the system's stable operational status. This process ensures efficient management of the manufacturing line.

[0129] 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.

[0130] This invention combines an eel farming system with an emotion engine, aiming not only to collect, analyze, and respond to environmental data, but also to recognize user emotions and optimize the system's behavior. The system comprises a server, terminals, and an emotion engine, all of which function in coordination.

[0131] The server acts as the central hub of the system, managing and directing each process. Environmental information is sent from terminals and analyzed by the server. The server analyzes the data using a generative AI model and issues instructions as needed. An emotion engine is also integrated into the server, which analyzes emotional information through user interaction and uses this to optimize the user interface.

[0132] The device collects environmental information in real time via sensors and measures growth and behavioral data. This data is sent to a server, and the device takes action according to commands from the server. The device also receives feedback from an emotion engine and dynamically adjusts the user interface and displayed content.

[0133] Users can receive suggestions based on server-side analysis results and emotion engine processing results, and incorporate them into managing the aquaculture environment and determining shipping schedules. Suggestions that reflect the user's intentions and emotions improve the user experience and support intuitive decision-making.

[0134] For example, if the oxygen concentration drops, the terminal reports this to the server, which immediately sends a command to the terminal to start aeration. At the same time, if the user is concerned about the environmental changes, the emotion engine senses this concern and provides reassuring feedback to the user. This feedback is provided through the terminal as intuitive information displays and situational explanations.

[0135] This invention aims to realize an efficient and human-centered eel farming system through these interactions.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The device uses multiple sensors to collect environmental information such as pH, oxygen concentration, and temperature in the water in real time.

[0139] Step 2:

[0140] The device sends the collected environmental information to the server.

[0141] Step 3:

[0142] The server stores the received environmental information in a database and performs analysis using a generated AI model to detect anomalies.

[0143] Step 4:

[0144] Based on the detected anomaly, the server determines the appropriate countermeasures (e.g., performing aeration or adjusting the amount of feed) and sends a command to the terminal.

[0145] Step 5:

[0146] The terminal receives commands from the server and takes appropriate action (such as performing aeration or adjusting automatic feeding).

[0147] Step 6:

[0148] The device acquires emotional data through interaction with the user and sends it to the server.

[0149] Step 7:

[0150] The server uses an emotion engine to analyze the user's emotions and generate situation-appropriate feedback.

[0151] Step 8:

[0152] Based on the analysis results, the server sends appropriate feedback information regarding the user's emotions to the terminal and displays an interface accordingly.

[0153] Step 9:

[0154] Users receive feedback and suggestions from the server to adjust aquaculture management and shipping schedules.

[0155] Step 10:

[0156] The results of user actions and decisions are fed back from the terminal to the server, contributing to system learning and improvement.

[0157] (Example 2)

[0158] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0159] In the aquaculture of organisms such as eels, there is a need to quickly detect environmental changes and abnormal conditions in real time and take appropriate action. However, conventional systems have been insufficient in analyzing environmental information and automating responses, which has sometimes affected the health and growth of organisms. Furthermore, there has been a lack of appropriate feedback to support user emotions and decision-making, so there is a need to build a more comprehensive and efficient system.

[0160] 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.

[0161] In this invention, the server includes a device for collecting ambient environmental indicator information, a device for analyzing the indicator information to identify abnormal patterns, and a device for performing data analysis using a generative AI model. This enables accurate, real-time understanding of the environmental situation and prompt, appropriate responses. Furthermore, by analyzing the user's emotional state and optimizing the interface, the user experience is improved, and more intuitive management is achieved.

[0162] "Surrounding environmental indicator information" refers to data consisting of indicators that show environmental conditions in the aquaculture environment, such as water temperature, oxygen concentration, and pH value.

[0163] An "abnormal pattern" is an indicator that refers to a situation that deviates from the normal environment or growth state, and defines a condition that the system should respond to quickly.

[0164] A "generative AI model" is a model that applies artificial intelligence technology used for data analysis and prediction, enabling real-time data processing and anomaly detection.

[0165] "Interface optimization" is the process of improving the way information is displayed and the interaction with users based on their emotional state, thereby enhancing the user experience.

[0166] A "shipping plan" refers to a schedule or plan for bringing products to market, which is constructed based on growth forecast data and market information.

[0167] "Rapid response suggestions" refers to a function that provides appropriate and immediate countermeasures and actions in response to anomalies detected in real time.

[0168] This invention is a system for efficiently cultivating organisms such as eels, in which a server, terminal, and emotion engine work together to collect, analyze, and respond to environmental information, and to optimize the user experience.

[0169] The server plays a central role in the aquaculture system, managing various data. The generative AI model integrated into the server identifies abnormal patterns in collected environmental data in real time, enabling rapid response. Specifically, the server analyzes ambient environmental indicators such as water temperature and oxygen concentration received from terminals, and if an anomaly is detected, it determines countermeasures and issues commands to the terminals. Furthermore, it uses an emotion engine to analyze emotional information acquired during user interaction and optimize the interface.

[0170] The terminal uses sensor technology to collect various data about the aquaculture environment in real time. The terminal transmits this data to a server, and based on the resulting commands, it performs necessary environmental adjustments, such as adjusting aeration and feeding. The terminal also displays feedback to provide reassurance to the user and provides information intuitively using situation-appropriate infographics.

[0171] Users make decisions related to managing the aquaculture environment based on analysis results and suggestions provided by the server. Users can adjust shipping plans based on growth prediction data. For example, they can make decisions using predictive data, such as optimizing the timing of shipments to improve profitability.

[0172] For example, if the oxygen concentration drops, the terminal detects this and reports it to the server. The server uses a generative AI model to analyze the situation and sends a command to the terminal to start aeration to ensure sufficient oxygen is supplied. Based on this command, the terminal quickly begins aeration. Meanwhile, the emotion engine detects if the user is feeling anxious about the environmental changes and provides the user with a reassuring message such as, "Aeration was successful. The environmental conditions are normal."

[0173] An example of a prompt message could be, "If the current oxygen concentration falls below the target value, what impact will this have on the eels' health, and what countermeasures should be taken?" The AI ​​model can then be used to derive appropriate countermeasures.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] The terminal collects environmental indicator information such as water temperature, oxygen concentration, and pH value in real time via sensors. The input is environmental data, and the output is a package of the collected data. This data is temporarily stored inside the terminal and prepared for transmission to the server. The operation involves periodic signal checks of the sensors and formatting of the data.

[0177] Step 2:

[0178] The terminal sends the collected environmental data to the server. The input in this process is the environmental data collected in the previous step, and the output is the data sent to the server. The data is encrypted and transmitted to the server over the network, ensuring the security and accuracy of the transmission. Specific operations include data encryption and the use of network protocols.

[0179] Step 3:

[0180] The server analyzes environmental data received from the terminal. In this case, the input is the environmental data sent from the terminal, and the output is the analysis result indicating whether or not there are anomalies. The server uses a generative AI model to analyze the data and identify anomaly patterns and problems. The operation involves running the AI ​​model and applying the analysis algorithm.

[0181] Step 4:

[0182] The server determines the necessary countermeasures based on the analysis results and sends commands to the terminal. The input here is the analysis results identifying the anomaly, and the output is the command information to the terminal. Specifically, the server automatically generates countermeasures and, for example, instructs the terminal to start aeration in response to a decrease in oxygen concentration.

[0183] Step 5:

[0184] The terminal receives commands from the server and performs the actual environmental adjustments. The input for this step is the server's commands, and the output is the result of the environmental adjustments. These operations include starting the aeration system and operating other adjustment devices.

[0185] Step 6:

[0186] The server uses an emotion engine to analyze the user's emotional information. The input is interaction data with the user, and the output is the user's emotional state. The results of the emotion analysis are used to optimize the user experience.

[0187] Step 7:

[0188] The device adjusts the feedback provided to the user based on the analysis results of the emotion engine. The input is the analysis results of the emotional state, and the output is the feedback information presented to the user. Specifically, this includes displaying messages that provide a sense of security according to the situation, and providing intuitive and easy-to-understand information.

[0189] (Application Example 2)

[0190] 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 device 14 will be referred to as the "terminal."

[0191] In modern aquaculture systems, real-time analysis of environmental information and growth data is crucial, but there is also a need to optimize the system while considering the user's emotional state. Furthermore, the challenge lies in using emotional information to support decision-making and create a more comfortable and intuitive user interface.

[0192] 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. In this invention, the server includes means for collecting environmental information in real time, means for analyzing emotional states and generating individually optimized feedback, and means for supporting decision-making using the user's emotional information. This enables not only environmental adjustment but also efficient and human-centered system operation that responds to the user's emotional state.

[0193] "Environmental information" refers to physical data such as temperature, oxygen concentration, and humidity in the target environment.

[0194] An "abnormality" refers to patterns or values ​​in environmental information or vital data that are different from the norm.

[0195] "Emotional state" refers to the user's psychological state inferred from information obtained through facial recognition and sensors.

[0196] "Feedback" refers to information and suggestions provided by a system to a user, tailored to the user's emotional state and needs.

[0197] "Decision support" is a process that provides information and suggestions to help users make decisions.

[0198] "Real-time" means that data collection, analysis, and response are performed with virtually no delay.

[0199] "Optimization" refers to improving systems and processes to suit specific purposes and to operate them efficiently.

[0200] This invention is a system that monitors the health status and emotions of residents in nursing care facilities in real time and provides appropriate care suggestions to staff. The system consists of a server, terminals, and an emotion engine that analyzes emotional states.

[0201] The server plays a central role in managing the entire system and performing data analysis. The software used employs machine learning libraries such as TensorFlow to perform sentiment analysis and provides real-time feedback of the data analysis results from generative AI models to staff. Furthermore, it provides information to support staff decision-making based on the emotional states obtained through the sentiment engine.

[0202] The terminal consists of a smartphone and external sensors, and collects residents' health data (heart rate, oxygen saturation, etc.) in real time. This data is sent to a server, and commands based on the analysis results are sent back to the terminal. The terminal receives feedback from an emotion engine and displays information to staff, providing support in an intuitive and easy-to-understand format.

[0203] Based on the information provided through these systems, users can develop individualized care plans for residents. For example, if facial recognition data of a resident detects that they are feeling tired, a notification will be sent to staff stating, "Resident B appears to be feeling tired. Please consider a break time." An example of a prompt in this case would be, "Resident B's current emotional state indicates they need a break. Please suggest appropriate care."

[0204] This invention makes it possible to comprehensively manage the health and emotional state of residents and improve the quality of care.

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The server receives health data (heart rate, oxygen saturation, etc.) from residents transmitted from terminals. This input data is first stored in a database, and then basic statistics are calculated to check for any abnormal values. During this process, an initial analysis is performed to check for deviations from standard values.

[0208] Step 2:

[0209] The terminal performs facial recognition through a camera that captures images of residents and sends the resulting video data to a server. The server inputs this video data into an emotion engine and analyzes the emotional state using a facial recognition algorithm. As a result of the analysis, it outputs emotional scores such as happiness level and fatigue level.

[0210] Step 3:

[0211] The server uses a generative AI model to assess the resident's overall living condition based on initial analysis results of health data and emotional scores. This process generates potential care suggestions through model inference based on the analysis. These outputs constitute specific care recommendations.

[0212] Step 4:

[0213] The server formats the generated care suggestion as a prompt and sends it to the terminal. An example of a prompt might be, "Person C may be feeling tired. Please consider offering additional rest time or relaxation."

[0214] Step 5:

[0215] Users can take immediate action based on care suggestions received through their devices. For example, a user can propose a suggested rest period to a resident, observe the results, and provide feedback to the system. This allows users to make more comprehensive and intuitive decisions.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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".

[0232] The complete eel farming system of this invention includes a software program for automating various processes. This program divides functions among the server, terminals, and users, processing and managing data according to each role. Its specific operation is described below.

[0233] The server, as the core of the system, performs data analysis and control. It receives real-time environmental information transmitted from terminals, analyzes it, and detects anomalies in water quality management. The server also uses a generative AI model to optimize feeding according to the eel's growth stage. Upon detecting an anomaly, the server immediately sends instructions to the terminals for corrective action and, if necessary, implements individual health management.

[0234] The terminal periodically collects environmental information and eel growth data through sensors. The terminal is responsible for transmitting this data to the server in real time. It also controls the aeration system and automatic feeding device based on instructions from the server to maintain environmental conditions.

[0235] Users can check harvest and shipping schedules based on growth forecast data and market information generated by the server. By following the server's suggestions and shipping at the optimal time, users can maximize their profits.

[0236] For example, if the oxygen concentration drops, the terminal detects this and reports it to the server. The server analyzes the data and, if it predicts that the low-oxygen condition will persist, immediately sends a signal to control the aeration device. This minimizes health risks to the eels and provides a stable farming environment. In addition, based on growth data, the server calculates the optimal amount of feed, preventing excessive feed waste while promoting uniform growth.

[0237] By enabling these operations, the present invention realizes sustainable and efficient complete eel farming.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] The device uses multiple sensors to collect environmental information such as pH, oxygen concentration, and temperature in the water in real time.

[0241] Step 2:

[0242] The terminal sends the collected environmental information to the server as data packets at regular intervals.

[0243] Step 3:

[0244] The server stores the received environmental data in a database and simultaneously performs analysis using a generated AI model.

[0245] Step 4:

[0246] The server compares the analysis results with pre-set baseline values ​​to check for any abnormalities.

[0247] Step 5:

[0248] If an anomaly is detected, the server automatically creates a control command and sends it to the terminal.

[0249] Step 6:

[0250] The terminal receives control commands from the server and takes appropriate action as needed (e.g., initiating aeration, instructing water changes).

[0251] Step 7:

[0252] The device measures the eel's weight and length and sends the growth data to the server.

[0253] Step 8:

[0254] The server uses growth data and a generative AI model to calculate the optimal timing and amount of feeding.

[0255] Step 9:

[0256] Based on the calculation results, the server sends a feeding command to the terminal.

[0257] Step 10:

[0258] The terminal executes feeding commands and supplies the appropriate amount of food to the eels.

[0259] Step 11:

[0260] Users review growth forecast data and market information generated by the server to determine the optimal shipping schedule.

[0261] Step 12:

[0262] Based on the determined shipping schedule, users supply harvested eels to the market at the appropriate time.

[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] Current aquaculture systems face challenges in responding quickly to changes in environmental conditions and in providing appropriate management according to the growth stage of organisms. Furthermore, determining the appropriate timing for shipment to match market supply and demand is difficult. Under these circumstances, there is a need for systems that can improve the efficiency and productivity of aquaculture.

[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 data, an information processing device for analyzing the data and detecting anomalies, and a control device for adjusting the aquatic environment based on the anomalies. This enables rapid response to environmental changes and management that optimizes the health and growth of organisms.

[0268] "Environmental data" refers to information about the aquaculture environment, such as water temperature, oxygen concentration, and pH values, which are obtained using physical sensors.

[0269] An "information processing device" is a computer device used to analyze collected data and detect anomalies.

[0270] A "control device" is a device that operates external devices such as aeration systems and feeding devices based on instructions from a server, in order to maintain environmental conditions.

[0271] A "mathematical model" is an algorithm or mathematical structure used to calculate the optimal amount of food to feed an organism, taking into account its growth stage.

[0272] A "display device" is an interface used to present market information and growth forecasts to users and to assist in adjusting shipping schedules.

[0273] "Functionality" refers to the ability to analyze information about the behavior and growth of organisms, detect abnormalities in real time, and propose rapid countermeasures.

[0274] Modes for carrying out the invention

[0275] In this invention, the server, terminal, and user each play a specific role in order to realize a system that efficiently optimizes aquaculture environment management and organism growth.

[0276] The server acts as a central processing unit, executing programs. First, the server uses Python to collect environmental data sent from terminals and analyzes it using data analysis libraries. This enables rapid response when anomalies are detected. Furthermore, TensorFlow is used as a generative AI model to optimize feeding based on the growth stage of organisms. This ensures appropriate nutritional management.

[0277] The terminal acquires environmental data in real time using a group of sensors. A small computer such as a Raspberry Pi is used for this purpose, utilizing Python libraries to acquire data from the sensors. The data is then transmitted to a server via Wi-Fi using the MQTT protocol. The terminal also receives instructions from the server and controls automatic feeding devices and aeration systems.

[0278] Users manage their shipping schedules based on growth forecast data and market information generated by the server. They can access this information via a dedicated web interface and make decisions based on that information to adjust the optimal harvest time.

[0279] As a concrete example, the server calculates the optimal amount of feed based on eel growth data. An example of a prompt message would be, "Generate a program that calculates the optimal amount of feed based on eel growth data." This system is expected to significantly improve the productivity and efficiency of eel farming.

[0280] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0281] Step 1:

[0282] The device acquires environmental data such as water temperature, oxygen concentration, and pH value from environmental sensors. This is done using a Raspberry Pi and Python libraries. The input is raw data from the sensors, and the output is formatted numerical data. Specifically, the device periodically polls the sensors to collect data.

[0283] Step 2:

[0284] The terminal sends the acquired environmental data to the server via Wi-Fi. Here, the MQTT protocol is used. The input is the formatted data from the terminal, and the output is the data transmission to the server. Specifically, the terminal, acting as an MQTT client, publishes the data to a specific topic on the server.

[0285] Step 3:

[0286] The server receives the acquired environmental data and performs data analysis. The input is the environmental data sent from the terminal, and the output is a list of abnormal values or flags as the analysis result. Here, the server uses Python's Pandas to clean the data and identify abnormalities exceeding the threshold.

[0287] Step 4:

[0288] When an abnormality is detected, the server creates a control signal and sends it to the terminal. The input is the analysis result, and the output is the control instruction data to the terminal. Specifically, when it is predicted that the low-oxygen state will continue, it instructs to start the aeration device.

[0289] Step 5:

[0290] The server uses the generated AI model to calculate the optimal feeding amount based on the growth data. The input is the growth data of the organism, and the output is the feeding schedule and amount. The server uses TensorFlow to infer this from the model.

[0291] Step 6:

[0292] Based on the control instructions from the server, the terminal operates the automatic feeding device and the aeration system. The input is the control signal from the server, and the output is the operation of the physical device. Specifically, the GPIO is used to control the device.

[0293] Step 7:

[0294] Users review growth forecast data and market information provided by the server and adjust their shipping plans accordingly. Inputs are forecast data and market information from the server, while outputs are adjustments based on the user's decision-making. Users access the web interface via a browser and make decisions based on the information.

[0295] (Application Example 1)

[0296] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0297] There is a need to improve the efficiency and quality control of production lines in food manufacturing. In current systems, monitoring of environmental conditions and quality indicators is often done manually, which can lead to overlooking anomalies. Furthermore, a lack of process optimization and automatic adjustment capabilities can reduce the overall efficiency of the production line. This can result in inconsistent product quality.

[0298] 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.

[0299] In this invention, the server includes means for collecting environmental data in real time, means for analyzing the environmental data to detect unusual situations, and means for monitoring quality indicators on the manufacturing line and automatically adjusting the manufacturing process as needed. This automates quality control on the manufacturing line and enables rapid and efficient response to anomalies.

[0300] "Environmental data" refers to information that shows conditions such as temperature, humidity, and oxygen concentration in the production line and aquaculture environment.

[0301] An "unusual situation" refers to a condition that deviates from normal operating conditions or exceeds standard values.

[0302] "Fluid environment" refers to the state of water including environmental conditions such as water quality and water temperature necessary for the growth of cultured aquatic organisms.

[0303] "Nutrient supply" refers to the process of providing food and nutrients necessary for the growth of the target.

[0304] "Health status" is an indicator showing the growth of products or target organisms on the production line, and indicates signs of illness or abnormalities.

[0305] "Growth prediction data" is data for predicting the future growth and production volume of the target.

[0306] "Market data" is information on the market, such as product demand and price, which is used as a reference when planning shipments.

[0307] "Quality indicators" are criteria and numerical values for evaluating the quality of products, and are necessary to maintain a certain quality.

[0308] This invention provides a system that monitors the status of the manufacturing environment in real time and automatically optimizes quality and efficiency. The server collects environmental data using a single-board computer or a cloud-based platform and analyzes it. As specific hardware, Raspberry Pi etc. are used, and the data is processed using Python. Thereby, specific situations are detected, and the manufacturing process is adjusted if necessary. The AI model is designed to utilize generative AI technology for data analysis and production optimization.

[0309] The terminal measures the quality indicators of the production line using various sensors and provides the collected data to the server. This enables real-time environmental monitoring and supports early detection and prompt response to specific situations. The user determines the shipping timing based on the generated growth prediction data and market data, and adjusts production activities considering the proposals from the system. Thereby, the production line is operated efficiently and sustainably.

[0310] For example, if the temperature on the production line deviates from the standard value, the system immediately detects the anomaly and issues instructions for process adjustment, thereby ensuring quality control. Examples of prompts include "Suggest countermeasures for when the quality index on the production line deteriorates" and "Adjust the production environment to the optimal level based on temperature sensor data." This allows the system to autonomously analyze problems and propose countermeasures.

[0311] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0312] Step 1:

[0313] The device collects environmental data from sensors. This data includes temperature, humidity, and quality indicators. The input is the environmental information obtained from the sensors, and the output is providing this information to the server. The device transmits this data to the server in real time.

[0314] Step 2:

[0315] The server analyzes environmental data received from the terminal. The input is environmental data from the terminal, and the output is the analysis results for detecting unusual situations. The server uses a generative AI model to analyze the data and identify unusual situations and abnormal patterns.

[0316] Step 3:

[0317] The server generates instructions to adjust the manufacturing process based on the analysis results. The input is the analysis results, and the output is the adjustment instructions for the manufacturing line. The AI ​​model determines the optimal process adjustment to respond to specific situations.

[0318] Step 4:

[0319] The user reviews growth forecast data and market data from the server to determine the optimal shipping timing. The input is forecast data from the server, and the output is the optimal shipping schedule. The user then uses the system's suggestions to optimize their manufacturing activities.

[0320] Step 5:

[0321] The server verifies that the manufacturing process adjustments are complete and monitors the overall system performance. Inputs are the adjustment results and subsequent production data, while outputs represent the system's stable operational status. This process ensures efficient management of the manufacturing line.

[0322] 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.

[0323] This invention combines an eel farming system with an emotion engine, aiming not only to collect, analyze, and respond to environmental data, but also to recognize user emotions and optimize the system's behavior. The system comprises a server, terminals, and an emotion engine, all of which function in coordination.

[0324] The server acts as the central hub of the system, managing and directing each process. Environmental information is sent from terminals and analyzed by the server. The server analyzes the data using a generative AI model and issues instructions as needed. An emotion engine is also integrated into the server, which analyzes emotional information through user interaction and uses this to optimize the user interface.

[0325] The device collects environmental information in real time via sensors and measures growth and behavioral data. This data is sent to a server, and the device takes action according to commands from the server. The device also receives feedback from an emotion engine and dynamically adjusts the user interface and displayed content.

[0326] Users can receive suggestions based on server-side analysis results and emotion engine processing results, and incorporate them into managing the aquaculture environment and determining shipping schedules. Suggestions that reflect the user's intentions and emotions improve the user experience and support intuitive decision-making.

[0327] For example, if the oxygen concentration drops, the terminal reports this to the server, which immediately sends a command to the terminal to start aeration. At the same time, if the user is concerned about the environmental changes, the emotion engine senses this concern and provides reassuring feedback to the user. This feedback is provided through the terminal as intuitive information displays and situational explanations.

[0328] This invention aims to realize an efficient and human-centered eel farming system through these interactions.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] The device uses multiple sensors to collect environmental information such as pH, oxygen concentration, and temperature in the water in real time.

[0332] Step 2:

[0333] The device sends the collected environmental information to the server.

[0334] Step 3:

[0335] The server stores the received environmental information in a database and performs analysis using a generated AI model to detect anomalies.

[0336] Step 4:

[0337] Based on the detected anomaly, the server determines the appropriate countermeasures (e.g., performing aeration or adjusting the amount of feed) and sends a command to the terminal.

[0338] Step 5:

[0339] The terminal receives commands from the server and takes appropriate action (such as performing aeration or adjusting automatic feeding).

[0340] Step 6:

[0341] The device acquires emotional data through interaction with the user and sends it to the server.

[0342] Step 7:

[0343] The server uses an emotion engine to analyze the user's emotions and generate situation-appropriate feedback.

[0344] Step 8:

[0345] Based on the analysis results, the server sends appropriate feedback information regarding the user's emotions to the terminal and displays an interface accordingly.

[0346] Step 9:

[0347] Users receive feedback and suggestions from the server to adjust aquaculture management and shipping schedules.

[0348] Step 10:

[0349] The results of user actions and decisions are fed back from the terminal to the server, contributing to system learning and improvement.

[0350] (Example 2)

[0351] 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".

[0352] In the aquaculture of organisms such as eels, there is a need to quickly detect environmental changes and abnormal conditions in real time and take appropriate action. However, conventional systems have been insufficient in analyzing environmental information and automating responses, which has sometimes affected the health and growth of organisms. Furthermore, there has been a lack of appropriate feedback to support user emotions and decision-making, so there is a need to build a more comprehensive and efficient system.

[0353] 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.

[0354] In this invention, the server includes a device for collecting ambient environmental indicator information, a device for analyzing the indicator information to identify abnormal patterns, and a device for performing data analysis using a generative AI model. This enables accurate, real-time understanding of the environmental situation and prompt, appropriate responses. Furthermore, by analyzing the user's emotional state and optimizing the interface, the user experience is improved, and more intuitive management is achieved.

[0355] "Surrounding environmental indicator information" refers to data consisting of indicators that show environmental conditions in the aquaculture environment, such as water temperature, oxygen concentration, and pH value.

[0356] An "abnormal pattern" is an indicator that refers to a situation that deviates from the normal environment or growth state, and defines a condition that the system should respond to quickly.

[0357] A "generative AI model" is a model that applies artificial intelligence technology used for data analysis and prediction, enabling real-time data processing and anomaly detection.

[0358] "Interface optimization" is the process of improving the way information is displayed and the interaction with users based on their emotional state, thereby enhancing the user experience.

[0359] A "shipping plan" refers to a schedule or plan for bringing products to market, which is constructed based on growth forecast data and market information.

[0360] "Rapid response suggestions" refers to a function that provides appropriate and immediate countermeasures and actions in response to anomalies detected in real time.

[0361] This invention is a system for efficiently cultivating organisms such as eels, in which a server, terminal, and emotion engine work together to collect, analyze, and respond to environmental information, and to optimize the user experience.

[0362] The server plays a central role in the aquaculture system, managing various data. The generative AI model integrated into the server identifies abnormal patterns in collected environmental data in real time, enabling rapid response. Specifically, the server analyzes ambient environmental indicators such as water temperature and oxygen concentration received from terminals, and if an anomaly is detected, it determines countermeasures and issues commands to the terminals. Furthermore, it uses an emotion engine to analyze emotional information acquired during user interaction and optimize the interface.

[0363] The terminal uses sensor technology to collect various data about the aquaculture environment in real time. The terminal transmits this data to a server, and based on the resulting commands, it performs necessary environmental adjustments, such as adjusting aeration and feeding. The terminal also displays feedback to provide reassurance to the user and provides information intuitively using situation-appropriate infographics.

[0364] Users make decisions related to managing the aquaculture environment based on analysis results and suggestions provided by the server. Users can adjust shipping plans based on growth prediction data. For example, they can make decisions using predictive data, such as optimizing the timing of shipments to improve profitability.

[0365] For example, if the oxygen concentration drops, the terminal detects this and reports it to the server. The server uses a generative AI model to analyze the situation and sends a command to the terminal to start aeration to ensure sufficient oxygen is supplied. Based on this command, the terminal quickly begins aeration. Meanwhile, the emotion engine detects if the user is feeling anxious about the environmental changes and provides the user with a reassuring message such as, "Aeration was successful. The environmental conditions are normal."

[0366] An example of a prompt message could be, "If the current oxygen concentration falls below the target value, what impact will this have on the eels' health, and what countermeasures should be taken?" The AI ​​model can then be used to derive appropriate countermeasures.

[0367] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0368] Step 1:

[0369] The terminal collects environmental indicator information such as water temperature, oxygen concentration, and pH value in real time via sensors. The input is environmental data, and the output is a package of the collected data. This data is temporarily stored inside the terminal and prepared for transmission to the server. The operation involves periodic signal checks of the sensors and formatting of the data.

[0370] Step 2:

[0371] The terminal sends the collected environmental data to the server. The input in this process is the environmental data collected in the previous step, and the output is the data sent to the server. The data is encrypted and transmitted to the server over the network, ensuring the security and accuracy of the transmission. Specific operations include data encryption and the use of network protocols.

[0372] Step 3:

[0373] The server analyzes environmental data received from the terminal. In this case, the input is the environmental data sent from the terminal, and the output is the analysis result indicating whether or not there are anomalies. The server uses a generative AI model to analyze the data and identify anomaly patterns and problems. The operation involves running the AI ​​model and applying the analysis algorithm.

[0374] Step 4:

[0375] The server determines the necessary countermeasures based on the analysis results and sends commands to the terminal. The input here is the analysis results identifying the anomaly, and the output is the command information to the terminal. Specifically, the server automatically generates countermeasures and, for example, instructs the terminal to start aeration in response to a decrease in oxygen concentration.

[0376] Step 5:

[0377] The terminal receives commands from the server and performs the actual environmental adjustments. The input for this step is the server's commands, and the output is the result of the environmental adjustments. These operations include starting the aeration system and operating other adjustment devices.

[0378] Step 6:

[0379] The server uses an emotion engine to analyze the user's emotional information. The input is interaction data with the user, and the output is the user's emotional state. The results of the emotion analysis are used to optimize the user experience.

[0380] Step 7:

[0381] The device adjusts the feedback provided to the user based on the analysis results of the emotion engine. The input is the analysis results of the emotional state, and the output is the feedback information presented to the user. Specifically, this includes displaying messages that provide a sense of security according to the situation, and providing intuitive and easy-to-understand information.

[0382] (Application Example 2)

[0383] 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."

[0384] In modern aquaculture systems, real-time analysis of environmental information and growth data is crucial, but there is also a need to optimize the system while considering the user's emotional state. Furthermore, the challenge lies in using emotional information to support decision-making and create a more comfortable and intuitive user interface.

[0385] 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. In this invention, the server includes means for collecting environmental information in real time, means for analyzing emotional states and generating individually optimized feedback, and means for supporting decision-making using the user's emotional information. This enables not only environmental adjustment but also efficient and human-centered system operation that responds to the user's emotional state.

[0386] "Environmental information" refers to physical data such as temperature, oxygen concentration, and humidity in the target environment.

[0387] An "abnormality" refers to patterns or values ​​in environmental information or vital data that are different from the norm.

[0388] "Emotional state" refers to the user's psychological state inferred from information obtained through facial recognition and sensors.

[0389] "Feedback" refers to information and suggestions provided by a system to a user, tailored to the user's emotional state and needs.

[0390] "Decision support" is a process that provides information and suggestions to help users make decisions.

[0391] "Real-time" means that data collection, analysis, and response are performed with virtually no delay.

[0392] "Optimization" refers to improving systems and processes to suit specific purposes and to operate them efficiently.

[0393] This invention is a system that monitors the health status and emotions of residents in nursing care facilities in real time and provides appropriate care suggestions to staff. The system consists of a server, terminals, and an emotion engine that analyzes emotional states.

[0394] The server plays a central role in managing the entire system and performing data analysis. The software used employs machine learning libraries such as TensorFlow to perform sentiment analysis and provides real-time feedback of the data analysis results from generative AI models to staff. Furthermore, it provides information to support staff decision-making based on the emotional states obtained through the sentiment engine.

[0395] The terminal consists of a smartphone and external sensors, and collects residents' health data (heart rate, oxygen saturation, etc.) in real time. This data is sent to a server, and commands based on the analysis results are sent back to the terminal. The terminal receives feedback from an emotion engine and displays information to staff, providing support in an intuitive and easy-to-understand format.

[0396] Based on the information provided through these systems, users can develop individualized care plans for residents. For example, if facial recognition data of a resident detects that they are feeling tired, a notification will be sent to staff stating, "Resident B appears to be feeling tired. Please consider a break time." An example of a prompt in this case would be, "Resident B's current emotional state indicates they need a break. Please suggest appropriate care."

[0397] This invention makes it possible to comprehensively manage the health and emotional state of residents and improve the quality of care.

[0398] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0399] Step 1:

[0400] The server receives health data (heart rate, oxygen saturation, etc.) from residents transmitted from terminals. This input data is first stored in a database, and then basic statistics are calculated to check for any abnormal values. During this process, an initial analysis is performed to check for deviations from standard values.

[0401] Step 2:

[0402] The terminal performs facial recognition through a camera that captures images of residents and sends the resulting video data to a server. The server inputs this video data into an emotion engine and analyzes the emotional state using a facial recognition algorithm. As a result of the analysis, it outputs emotional scores such as happiness level and fatigue level.

[0403] Step 3:

[0404] The server uses a generative AI model to assess the resident's overall living condition based on initial analysis results of health data and emotional scores. This process generates potential care suggestions through model inference based on the analysis. These outputs constitute specific care recommendations.

[0405] Step 4:

[0406] The server formats the generated care suggestion as a prompt and sends it to the terminal. An example of a prompt might be, "Person C may be feeling tired. Please consider offering additional rest time or relaxation."

[0407] Step 5:

[0408] Users can take immediate action based on care suggestions received through their devices. For example, a user can propose a suggested rest period to a resident, observe the results, and provide feedback to the system. This allows users to make more comprehensive and intuitive decisions.

[0409] 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.

[0410] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0411] 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.

[0412] [Third Embodiment]

[0413] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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).

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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".

[0425] The complete eel farming system of this invention includes a software program for automating various processes. This program divides functions among the server, terminals, and users, processing and managing data according to each role. Its specific operation is described below.

[0426] The server, as the core of the system, performs data analysis and control. It receives real-time environmental information transmitted from terminals, analyzes it, and detects anomalies in water quality management. The server also uses a generative AI model to optimize feeding according to the eel's growth stage. Upon detecting an anomaly, the server immediately sends instructions to the terminals for corrective action and, if necessary, implements individual health management.

[0427] The terminal periodically collects environmental information and eel growth data through sensors. The terminal is responsible for transmitting this data to the server in real time. It also controls the aeration system and automatic feeding device based on instructions from the server to maintain environmental conditions.

[0428] Users can check harvest and shipping schedules based on growth forecast data and market information generated by the server. By following the server's suggestions and shipping at the optimal time, users can maximize their profits.

[0429] For example, if the oxygen concentration drops, the terminal detects this and reports it to the server. The server analyzes the data and, if it predicts that the low-oxygen condition will persist, immediately sends a signal to control the aeration device. This minimizes health risks to the eels and provides a stable farming environment. In addition, based on growth data, the server calculates the optimal amount of feed, preventing excessive feed waste while promoting uniform growth.

[0430] By enabling these operations, the present invention realizes sustainable and efficient complete eel farming.

[0431] The following describes the processing flow.

[0432] Step 1:

[0433] The device uses multiple sensors to collect environmental information such as pH, oxygen concentration, and temperature in the water in real time.

[0434] Step 2:

[0435] The terminal sends the collected environmental information to the server as data packets at regular intervals.

[0436] Step 3:

[0437] The server stores the received environmental data in a database and simultaneously performs analysis using a generated AI model.

[0438] Step 4:

[0439] The server compares the analysis results with pre-set baseline values ​​to check for any abnormalities.

[0440] Step 5:

[0441] If an anomaly is detected, the server automatically creates a control command and sends it to the terminal.

[0442] Step 6:

[0443] The terminal receives control commands from the server and takes appropriate action as needed (e.g., initiating aeration, instructing water changes).

[0444] Step 7:

[0445] The device measures the eel's weight and length and sends the growth data to the server.

[0446] Step 8:

[0447] The server uses growth data and a generative AI model to calculate the optimal timing and amount of feeding.

[0448] Step 9:

[0449] Based on the calculation results, the server sends a feeding command to the terminal.

[0450] Step 10:

[0451] The terminal executes feeding commands and supplies the appropriate amount of food to the eels.

[0452] Step 11:

[0453] Users review growth forecast data and market information generated by the server to determine the optimal shipping schedule.

[0454] Step 12:

[0455] Based on the determined shipping schedule, users supply harvested eels to the market at the appropriate time.

[0456] (Example 1)

[0457] 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."

[0458] Current aquaculture systems face challenges in responding quickly to changes in environmental conditions and in providing appropriate management according to the growth stage of organisms. Furthermore, determining the appropriate timing for shipment to match market supply and demand is difficult. Under these circumstances, there is a need for systems that can improve the efficiency and productivity of aquaculture.

[0459] 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.

[0460] In this invention, the server includes a device for collecting environmental data, an information processing device for analyzing the data and detecting anomalies, and a control device for adjusting the aquatic environment based on the anomalies. This enables rapid response to environmental changes and management that optimizes the health and growth of organisms.

[0461] "Environmental data" refers to information about the aquaculture environment, such as water temperature, oxygen concentration, and pH values, which are obtained using physical sensors.

[0462] An "information processing device" is a computer device used to analyze collected data and detect anomalies.

[0463] A "control device" is a device that operates external devices such as aeration systems and feeding devices based on instructions from a server, in order to maintain environmental conditions.

[0464] A "mathematical model" is an algorithm or mathematical structure used to calculate the optimal amount of food to feed an organism, taking into account its growth stage.

[0465] A "display device" is an interface used to present market information and growth forecasts to users and to assist in adjusting shipping schedules.

[0466] "Functionality" refers to the ability to analyze information about the behavior and growth of organisms, detect abnormalities in real time, and propose rapid countermeasures.

[0467] Modes for carrying out the invention

[0468] In this invention, the server, terminal, and user each play a specific role in order to realize a system that efficiently optimizes aquaculture environment management and organism growth.

[0469] The server acts as a central processing unit, executing programs. First, the server uses Python to collect environmental data sent from terminals and analyzes it using data analysis libraries. This enables rapid response when anomalies are detected. Furthermore, TensorFlow is used as a generative AI model to optimize feeding based on the growth stage of organisms. This ensures appropriate nutritional management.

[0470] The terminal acquires environmental data in real time using a group of sensors. A small computer such as a Raspberry Pi is used for this purpose, utilizing Python libraries to acquire data from the sensors. The data is then transmitted to a server via Wi-Fi using the MQTT protocol. The terminal also receives instructions from the server and controls automatic feeding devices and aeration systems.

[0471] Users manage their shipping schedules based on growth forecast data and market information generated by the server. They can access this information via a dedicated web interface and make decisions based on that information to adjust the optimal harvest time.

[0472] As a concrete example, the server calculates the optimal amount of feed based on eel growth data. An example of a prompt message would be, "Generate a program that calculates the optimal amount of feed based on eel growth data." This system is expected to significantly improve the productivity and efficiency of eel farming.

[0473] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0474] Step 1:

[0475] The device acquires environmental data such as water temperature, oxygen concentration, and pH value from environmental sensors. This is done using a Raspberry Pi and Python libraries. The input is raw data from the sensors, and the output is formatted numerical data. Specifically, the device periodically polls the sensors to collect data.

[0476] Step 2:

[0477] The terminal sends the acquired environmental data to the server via Wi-Fi. The MQTT protocol is used here, with formatted data from the terminal as input and data transmission to the server as output. Specifically, the terminal acts as an MQTT client, publishing data to a specific topic on the server.

[0478] Step 3:

[0479] The server receives the environmental data and performs data analysis. The input is the environmental data sent from the terminal, and the output is a list of anomalies or flags as a result of the analysis. Here, the server uses Python's Pandas library to clean the data and identify anomalies that exceed a threshold.

[0480] Step 4:

[0481] When the server detects an anomaly, it generates a control signal and sends it to the terminal. The input is the analysis result, and the output is control instruction data for the terminal. Specifically, if a low-oxygen condition is predicted to persist, it instructs the terminal to activate the aeration device.

[0482] Step 5:

[0483] The server uses a generative AI model to calculate the optimal feeding amount based on growth data. The input is the growth data of the organism, and the output is the feeding schedule and amount. The server uses TensorFlow to infer this from the model.

[0484] Step 6:

[0485] The terminal operates automatic feeding devices and aeration systems based on control instructions from the server. Inputs are control signals from the server, and outputs are the physical operation of the devices. Specifically, GPIO is used to control the devices.

[0486] Step 7:

[0487] Users review growth forecast data and market information provided by the server and adjust their shipping plans accordingly. Inputs are forecast data and market information from the server, while outputs are adjustments based on the user's decision-making. Users access the web interface via a browser and make decisions based on the information.

[0488] (Application Example 1)

[0489] 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."

[0490] There is a need to improve the efficiency and quality control of production lines in food manufacturing. In current systems, monitoring of environmental conditions and quality indicators is often done manually, which can lead to overlooking anomalies. Furthermore, a lack of process optimization and automatic adjustment capabilities can reduce the overall efficiency of the production line. This can result in inconsistent product quality.

[0491] 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.

[0492] In this invention, the server includes means for collecting environmental data in real time, means for analyzing the environmental data to detect unusual situations, and means for monitoring quality indicators on the manufacturing line and automatically adjusting the manufacturing process as needed. This automates quality control on the manufacturing line and enables rapid and efficient response to anomalies.

[0493] "Environmental data" refers to information that shows conditions such as temperature, humidity, and oxygen concentration in the production line and aquaculture environment.

[0494] An "unusual situation" refers to a condition that deviates from normal operating conditions or exceeds standard values.

[0495] "Fluid environment" refers to the state of water, including environmental conditions such as water quality and temperature necessary for the growth of aquatic organisms being farmed.

[0496] "Nutritional supply" refers to the process of providing the food and nutrients necessary for the growth of an organism.

[0497] "Health status" refers to an indicator that shows the growth of products or target organisms on the production line, and indicates signs of disease or abnormality.

[0498] "Growth forecast data" refers to data used to predict the future growth and production volume of a subject.

[0499] "Market data" refers to market information, such as product demand and pricing, that is used as a reference when planning shipments.

[0500] "Quality indicators" are standards and numerical values ​​used to evaluate the quality of a product, and are necessary to maintain a certain level of quality.

[0501] This invention provides a system that monitors the manufacturing environment in real time and automatically optimizes quality and efficiency. The server collects and analyzes environmental data using a single-board computer or cloud-based platform. Specific hardware such as a Raspberry Pi is used, and the data is processed using Python. This allows for the detection of unusual situations and, if necessary, adjustments to the manufacturing process. The AI ​​model is designed to perform data analysis and production optimization using generative AI technology.

[0502] The terminal uses various sensors to measure quality indicators on the manufacturing line and provides the collected data to the server. This enables real-time environmental monitoring, supporting early detection and rapid response to unusual situations. Users determine shipping timing based on the generated growth forecast data and market data, and adjust production activities considering the system's suggestions. This ensures that the manufacturing line operates efficiently and sustainably.

[0503] For example, if the temperature on the production line deviates from the standard value, the system immediately detects the anomaly and issues instructions for process adjustment, thereby ensuring quality control. Examples of prompts include "Suggest countermeasures for when the quality index on the production line deteriorates" and "Adjust the production environment to the optimal level based on temperature sensor data." This allows the system to autonomously analyze problems and propose countermeasures.

[0504] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0505] Step 1:

[0506] The device collects environmental data from sensors. This data includes temperature, humidity, and quality indicators. The input is the environmental information obtained from the sensors, and the output is providing this information to the server. The device transmits this data to the server in real time.

[0507] Step 2:

[0508] The server analyzes environmental data received from the terminal. The input is environmental data from the terminal, and the output is the analysis results for detecting unusual situations. The server uses a generative AI model to analyze the data and identify unusual situations and abnormal patterns.

[0509] Step 3:

[0510] The server generates instructions to adjust the manufacturing process based on the analysis results. The input is the analysis results, and the output is the adjustment instructions for the manufacturing line. The AI ​​model determines the optimal process adjustment to respond to specific situations.

[0511] Step 4:

[0512] The user reviews growth forecast data and market data from the server to determine the optimal shipping timing. The input is forecast data from the server, and the output is the optimal shipping schedule. The user then uses the system's suggestions to optimize their manufacturing activities.

[0513] Step 5:

[0514] The server verifies that the manufacturing process adjustments are complete and monitors the overall system performance. Inputs are the adjustment results and subsequent production data, while outputs represent the system's stable operational status. This process ensures efficient management of the manufacturing line.

[0515] 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.

[0516] This invention combines an eel farming system with an emotion engine, aiming not only to collect, analyze, and respond to environmental data, but also to recognize user emotions and optimize the system's behavior. The system comprises a server, terminals, and an emotion engine, all of which function in coordination.

[0517] The server acts as the central hub of the system, managing and directing each process. Environmental information is sent from terminals and analyzed by the server. The server analyzes the data using a generative AI model and issues instructions as needed. An emotion engine is also integrated into the server, which analyzes emotional information through user interaction and uses this to optimize the user interface.

[0518] The device collects environmental information in real time via sensors and measures growth and behavioral data. This data is sent to a server, and the device takes action according to commands from the server. The device also receives feedback from an emotion engine and dynamically adjusts the user interface and displayed content.

[0519] Users can receive suggestions based on server-side analysis results and emotion engine processing results, and incorporate them into managing the aquaculture environment and determining shipping schedules. Suggestions that reflect the user's intentions and emotions improve the user experience and support intuitive decision-making.

[0520] For example, if the oxygen concentration drops, the terminal reports this to the server, which immediately sends a command to the terminal to start aeration. At the same time, if the user is concerned about the environmental changes, the emotion engine senses this concern and provides reassuring feedback to the user. This feedback is provided through the terminal as intuitive information displays and situational explanations.

[0521] This invention aims to realize an efficient and human-centered eel farming system through these interactions.

[0522] The following describes the processing flow.

[0523] Step 1:

[0524] The device uses multiple sensors to collect environmental information such as pH, oxygen concentration, and temperature in the water in real time.

[0525] Step 2:

[0526] The device sends the collected environmental information to the server.

[0527] Step 3:

[0528] The server stores the received environmental information in a database and performs analysis using a generated AI model to detect anomalies.

[0529] Step 4:

[0530] Based on the detected anomaly, the server determines the appropriate countermeasures (e.g., performing aeration or adjusting the amount of feed) and sends a command to the terminal.

[0531] Step 5:

[0532] The terminal receives commands from the server and takes appropriate action (such as performing aeration or adjusting automatic feeding).

[0533] Step 6:

[0534] The device acquires emotional data through interaction with the user and sends it to the server.

[0535] Step 7:

[0536] The server uses an emotion engine to analyze the user's emotions and generate situation-appropriate feedback.

[0537] Step 8:

[0538] Based on the analysis results, the server sends appropriate feedback information regarding the user's emotions to the terminal and displays an interface accordingly.

[0539] Step 9:

[0540] Users receive feedback and suggestions from the server to adjust aquaculture management and shipping schedules.

[0541] Step 10:

[0542] The results of user actions and decisions are fed back from the terminal to the server, contributing to system learning and improvement.

[0543] (Example 2)

[0544] 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."

[0545] In the aquaculture of organisms such as eels, there is a need to quickly detect environmental changes and abnormal conditions in real time and take appropriate action. However, conventional systems have been insufficient in analyzing environmental information and automating responses, which has sometimes affected the health and growth of organisms. Furthermore, there has been a lack of appropriate feedback to support user emotions and decision-making, so there is a need to build a more comprehensive and efficient system.

[0546] 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.

[0547] In this invention, the server includes a device for collecting ambient environmental indicator information, a device for analyzing the indicator information to identify abnormal patterns, and a device for performing data analysis using a generative AI model. This enables accurate, real-time understanding of the environmental situation and prompt, appropriate responses. Furthermore, by analyzing the user's emotional state and optimizing the interface, the user experience is improved, and more intuitive management is achieved.

[0548] "Surrounding environmental indicator information" refers to data consisting of indicators that show environmental conditions in the aquaculture environment, such as water temperature, oxygen concentration, and pH value.

[0549] An "abnormal pattern" is an indicator that refers to a situation that deviates from the normal environment or growth state, and defines a condition that the system should respond to quickly.

[0550] A "generative AI model" is a model that applies artificial intelligence technology used for data analysis and prediction, enabling real-time data processing and anomaly detection.

[0551] "Interface optimization" is the process of improving the way information is displayed and the interaction with users based on their emotional state, thereby enhancing the user experience.

[0552] A "shipping plan" refers to a schedule or plan for bringing products to market, which is constructed based on growth forecast data and market information.

[0553] "Rapid response suggestions" refers to a function that provides appropriate and immediate countermeasures and actions in response to anomalies detected in real time.

[0554] This invention is a system for efficiently cultivating organisms such as eels, in which a server, terminal, and emotion engine work together to collect, analyze, and respond to environmental information, and to optimize the user experience.

[0555] The server plays a central role in the aquaculture system, managing various data. The generative AI model integrated into the server identifies abnormal patterns in collected environmental data in real time, enabling rapid response. Specifically, the server analyzes ambient environmental indicators such as water temperature and oxygen concentration received from terminals, and if an anomaly is detected, it determines countermeasures and issues commands to the terminals. Furthermore, it uses an emotion engine to analyze emotional information acquired during user interaction and optimize the interface.

[0556] The terminal uses sensor technology to collect various data about the aquaculture environment in real time. The terminal transmits this data to a server, and based on the resulting commands, it performs necessary environmental adjustments, such as adjusting aeration and feeding. The terminal also displays feedback to provide reassurance to the user and provides information intuitively using situation-appropriate infographics.

[0557] Users make decisions related to managing the aquaculture environment based on analysis results and suggestions provided by the server. Users can adjust shipping plans based on growth prediction data. For example, they can make decisions using predictive data, such as optimizing the timing of shipments to improve profitability.

[0558] For example, if the oxygen concentration drops, the terminal detects this and reports it to the server. The server uses a generative AI model to analyze the situation and sends a command to the terminal to start aeration to ensure sufficient oxygen is supplied. Based on this command, the terminal quickly begins aeration. Meanwhile, the emotion engine detects if the user is feeling anxious about the environmental changes and provides the user with a reassuring message such as, "Aeration was successful. The environmental conditions are normal."

[0559] An example of a prompt message could be, "If the current oxygen concentration falls below the target value, what impact will this have on the eels' health, and what countermeasures should be taken?" The AI ​​model can then be used to derive appropriate countermeasures.

[0560] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0561] Step 1:

[0562] The terminal collects environmental indicator information such as water temperature, oxygen concentration, and pH value in real time via sensors. The input is environmental data, and the output is a package of the collected data. This data is temporarily stored inside the terminal and prepared for transmission to the server. The operation involves periodic signal checks of the sensors and formatting of the data.

[0563] Step 2:

[0564] The terminal sends the collected environmental data to the server. The input in this process is the environmental data collected in the previous step, and the output is the data sent to the server. The data is encrypted and transmitted to the server over the network, ensuring the security and accuracy of the transmission. Specific operations include data encryption and the use of network protocols.

[0565] Step 3:

[0566] The server analyzes environmental data received from the terminal. In this case, the input is the environmental data sent from the terminal, and the output is the analysis result indicating whether or not there are anomalies. The server uses a generative AI model to analyze the data and identify anomaly patterns and problems. The operation involves running the AI ​​model and applying the analysis algorithm.

[0567] Step 4:

[0568] The server determines the necessary countermeasures based on the analysis results and sends commands to the terminal. The input here is the analysis results identifying the anomaly, and the output is the command information to the terminal. Specifically, the server automatically generates countermeasures and, for example, instructs the terminal to start aeration in response to a decrease in oxygen concentration.

[0569] Step 5:

[0570] The terminal receives commands from the server and performs the actual environmental adjustments. The input for this step is the server's commands, and the output is the result of the environmental adjustments. These operations include starting the aeration system and operating other adjustment devices.

[0571] Step 6:

[0572] The server uses an emotion engine to analyze the user's emotional information. The input is interaction data with the user, and the output is the user's emotional state. The results of the emotion analysis are used to optimize the user experience.

[0573] Step 7:

[0574] The device adjusts the feedback provided to the user based on the analysis results of the emotion engine. The input is the analysis results of the emotional state, and the output is the feedback information presented to the user. Specifically, this includes displaying messages that provide a sense of security according to the situation, and providing intuitive and easy-to-understand information.

[0575] (Application Example 2)

[0576] 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."

[0577] In modern aquaculture systems, real-time analysis of environmental information and growth data is crucial, but there is also a need to optimize the system while considering the user's emotional state. Furthermore, the challenge lies in using emotional information to support decision-making and create a more comfortable and intuitive user interface.

[0578] 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. In this invention, the server includes means for collecting environmental information in real time, means for analyzing emotional states and generating individually optimized feedback, and means for supporting decision-making using the user's emotional information. This enables not only environmental adjustment but also efficient and human-centered system operation that responds to the user's emotional state.

[0579] "Environmental information" refers to physical data such as temperature, oxygen concentration, and humidity in the target environment.

[0580] An "abnormality" refers to patterns or values ​​in environmental information or vital data that are different from the norm.

[0581] "Emotional state" refers to the user's psychological state inferred from information obtained through facial recognition and sensors.

[0582] "Feedback" refers to information and suggestions provided by a system to a user, tailored to the user's emotional state and needs.

[0583] "Decision support" is a process that provides information and suggestions to help users make decisions.

[0584] "Real-time" means that data collection, analysis, and response are performed with virtually no delay.

[0585] "Optimization" refers to improving systems and processes to suit specific purposes and to operate them efficiently.

[0586] This invention is a system that monitors the health status and emotions of residents in nursing care facilities in real time and provides appropriate care suggestions to staff. The system consists of a server, terminals, and an emotion engine that analyzes emotional states.

[0587] The server plays a central role in managing the entire system and performing data analysis. The software used employs machine learning libraries such as TensorFlow to perform sentiment analysis and provides real-time feedback of the data analysis results from generative AI models to staff. Furthermore, it provides information to support staff decision-making based on the emotional states obtained through the sentiment engine.

[0588] The terminal consists of a smartphone and external sensors, and collects residents' health data (heart rate, oxygen saturation, etc.) in real time. This data is sent to a server, and commands based on the analysis results are sent back to the terminal. The terminal receives feedback from an emotion engine and displays information to staff, providing support in an intuitive and easy-to-understand format.

[0589] Based on the information provided through these systems, users can develop individualized care plans for residents. For example, if facial recognition data of a resident detects that they are feeling tired, a notification will be sent to staff stating, "Resident B appears to be feeling tired. Please consider a break time." An example of a prompt in this case would be, "Resident B's current emotional state indicates they need a break. Please suggest appropriate care."

[0590] This invention makes it possible to comprehensively manage the health and emotional state of residents and improve the quality of care.

[0591] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0592] Step 1:

[0593] The server receives health data (heart rate, oxygen saturation, etc.) from residents transmitted from terminals. This input data is first stored in a database, and then basic statistics are calculated to check for any abnormal values. During this process, an initial analysis is performed to check for deviations from standard values.

[0594] Step 2:

[0595] The terminal performs facial recognition through a camera that captures images of residents and sends the resulting video data to a server. The server inputs this video data into an emotion engine and analyzes the emotional state using a facial recognition algorithm. As a result of the analysis, it outputs emotional scores such as happiness level and fatigue level.

[0596] Step 3:

[0597] The server uses a generative AI model to assess the resident's overall living condition based on initial analysis results of health data and emotional scores. This process generates potential care suggestions through model inference based on the analysis. These outputs constitute specific care recommendations.

[0598] Step 4:

[0599] The server formats the generated care suggestion as a prompt and sends it to the terminal. An example of a prompt might be, "Person C may be feeling tired. Please consider offering additional rest time or relaxation."

[0600] Step 5:

[0601] Users can take immediate action based on care suggestions received through their devices. For example, a user can propose a suggested rest period to a resident, observe the results, and provide feedback to the system. This allows users to make more comprehensive and intuitive decisions.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] [Fourth Embodiment]

[0606] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0607] 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.

[0608] 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).

[0609] 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.

[0610] 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.

[0611] 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).

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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".

[0619] The complete eel farming system of this invention includes a software program for automating various processes. This program divides functions among the server, terminals, and users, processing and managing data according to each role. Its specific operation is described below.

[0620] The server, as the core of the system, performs data analysis and control. It receives real-time environmental information transmitted from terminals, analyzes it, and detects anomalies in water quality management. The server also uses a generative AI model to optimize feeding according to the eel's growth stage. Upon detecting an anomaly, the server immediately sends instructions to the terminals for corrective action and, if necessary, implements individual health management.

[0621] The terminal periodically collects environmental information and eel growth data through sensors. The terminal is responsible for transmitting this data to the server in real time. It also controls the aeration system and automatic feeding device based on instructions from the server to maintain environmental conditions.

[0622] Users can check harvest and shipping schedules based on growth forecast data and market information generated by the server. By following the server's suggestions and shipping at the optimal time, users can maximize their profits.

[0623] For example, if the oxygen concentration drops, the terminal detects this and reports it to the server. The server analyzes the data and, if it predicts that the low-oxygen condition will persist, immediately sends a signal to control the aeration device. This minimizes health risks to the eels and provides a stable farming environment. In addition, based on growth data, the server calculates the optimal amount of feed, preventing excessive feed waste while promoting uniform growth.

[0624] By enabling these operations, the present invention realizes sustainable and efficient complete eel farming.

[0625] The following describes the processing flow.

[0626] Step 1:

[0627] The device uses multiple sensors to collect environmental information such as pH, oxygen concentration, and temperature in the water in real time.

[0628] Step 2:

[0629] The terminal sends the collected environmental information to the server as data packets at regular intervals.

[0630] Step 3:

[0631] The server stores the received environmental data in a database and simultaneously performs analysis using a generated AI model.

[0632] Step 4:

[0633] The server compares the analysis results with pre-set baseline values ​​to check for any abnormalities.

[0634] Step 5:

[0635] If an anomaly is detected, the server automatically creates a control command and sends it to the terminal.

[0636] Step 6:

[0637] The terminal receives control commands from the server and takes appropriate action as needed (e.g., initiating aeration, instructing water changes).

[0638] Step 7:

[0639] The device measures the eel's weight and length and sends the growth data to the server.

[0640] Step 8:

[0641] The server uses growth data and a generative AI model to calculate the optimal timing and amount of feeding.

[0642] Step 9:

[0643] Based on the calculation results, the server sends a feeding command to the terminal.

[0644] Step 10:

[0645] The terminal executes feeding commands and supplies the appropriate amount of food to the eels.

[0646] Step 11:

[0647] Users review growth forecast data and market information generated by the server to determine the optimal shipping schedule.

[0648] Step 12:

[0649] Based on the determined shipping schedule, users supply harvested eels to the market at the appropriate time.

[0650] (Example 1)

[0651] 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".

[0652] Current aquaculture systems face challenges in responding quickly to changes in environmental conditions and in providing appropriate management according to the growth stage of organisms. Furthermore, determining the appropriate timing for shipment to match market supply and demand is difficult. Under these circumstances, there is a need for systems that can improve the efficiency and productivity of aquaculture.

[0653] 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.

[0654] In this invention, the server includes a device for collecting environmental data, an information processing device for analyzing the data and detecting anomalies, and a control device for adjusting the aquatic environment based on the anomalies. This enables rapid response to environmental changes and management that optimizes the health and growth of organisms.

[0655] "Environmental data" refers to information about the aquaculture environment, such as water temperature, oxygen concentration, and pH values, which are obtained using physical sensors.

[0656] An "information processing device" is a computer device used to analyze collected data and detect anomalies.

[0657] A "control device" is a device that operates external devices such as aeration systems and feeding devices based on instructions from a server, in order to maintain environmental conditions.

[0658] A "mathematical model" is an algorithm or mathematical structure used to calculate the optimal amount of food to feed an organism, taking into account its growth stage.

[0659] A "display device" is an interface used to present market information and growth forecasts to users and to assist in adjusting shipping schedules.

[0660] "Functionality" refers to the ability to analyze information about the behavior and growth of organisms, detect abnormalities in real time, and propose rapid countermeasures.

[0661] Modes for carrying out the invention

[0662] In this invention, the server, terminal, and user each play a specific role in order to realize a system that efficiently optimizes aquaculture environment management and organism growth.

[0663] The server acts as a central processing unit, executing programs. First, the server uses Python to collect environmental data sent from terminals and analyzes it using data analysis libraries. This enables rapid response when anomalies are detected. Furthermore, TensorFlow is used as a generative AI model to optimize feeding based on the growth stage of organisms. This ensures appropriate nutritional management.

[0664] The terminal acquires environmental data in real time using a group of sensors. A small computer such as a Raspberry Pi is used for this purpose, utilizing Python libraries to acquire data from the sensors. The data is then transmitted to a server via Wi-Fi using the MQTT protocol. The terminal also receives instructions from the server and controls automatic feeding devices and aeration systems.

[0665] Users manage their shipping schedules based on growth forecast data and market information generated by the server. They can access this information via a dedicated web interface and make decisions based on that information to adjust the optimal harvest time.

[0666] As a concrete example, the server calculates the optimal amount of feed based on eel growth data. An example of a prompt message would be, "Generate a program that calculates the optimal amount of feed based on eel growth data." This system is expected to significantly improve the productivity and efficiency of eel farming.

[0667] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0668] Step 1:

[0669] The device acquires environmental data such as water temperature, oxygen concentration, and pH value from environmental sensors. This is done using a Raspberry Pi and Python libraries. The input is raw data from the sensors, and the output is formatted numerical data. Specifically, the device periodically polls the sensors to collect data.

[0670] Step 2:

[0671] The terminal sends the acquired environmental data to the server via Wi-Fi. The MQTT protocol is used here, with formatted data from the terminal as input and data transmission to the server as output. Specifically, the terminal acts as an MQTT client, publishing data to a specific topic on the server.

[0672] Step 3:

[0673] The server receives the environmental data and performs data analysis. The input is the environmental data sent from the terminal, and the output is a list of anomalies or flags as a result of the analysis. Here, the server uses Python's Pandas library to clean the data and identify anomalies that exceed a threshold.

[0674] Step 4:

[0675] When the server detects an anomaly, it generates a control signal and sends it to the terminal. The input is the analysis result, and the output is control instruction data for the terminal. Specifically, if a low-oxygen condition is predicted to persist, it instructs the terminal to activate the aeration device.

[0676] Step 5:

[0677] The server uses a generative AI model to calculate the optimal feeding amount based on growth data. The input is the growth data of the organism, and the output is the feeding schedule and amount. The server uses TensorFlow to infer this from the model.

[0678] Step 6:

[0679] The terminal operates automatic feeding devices and aeration systems based on control instructions from the server. Inputs are control signals from the server, and outputs are the physical operation of the devices. Specifically, GPIO is used to control the devices.

[0680] Step 7:

[0681] Users review growth forecast data and market information provided by the server and adjust their shipping plans accordingly. Inputs are forecast data and market information from the server, while outputs are adjustments based on the user's decision-making. Users access the web interface via a browser and make decisions based on the information.

[0682] (Application Example 1)

[0683] 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".

[0684] There is a need to improve the efficiency and quality control of production lines in food manufacturing. In current systems, monitoring of environmental conditions and quality indicators is often done manually, which can lead to overlooking anomalies. Furthermore, a lack of process optimization and automatic adjustment capabilities can reduce the overall efficiency of the production line. This can result in inconsistent product quality.

[0685] 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.

[0686] In this invention, the server includes means for collecting environmental data in real time, means for analyzing the environmental data to detect unusual situations, and means for monitoring quality indicators on the manufacturing line and automatically adjusting the manufacturing process as needed. This automates quality control on the manufacturing line and enables rapid and efficient response to anomalies.

[0687] "Environmental data" refers to information that shows conditions such as temperature, humidity, and oxygen concentration in the production line and aquaculture environment.

[0688] An "unusual situation" refers to a condition that deviates from normal operating conditions or exceeds standard values.

[0689] "Fluid environment" refers to the state of water, including environmental conditions such as water quality and temperature necessary for the growth of aquatic organisms being farmed.

[0690] "Nutritional supply" refers to the process of providing the food and nutrients necessary for the growth of an organism.

[0691] "Health status" refers to an indicator that shows the growth of products or target organisms on the production line, and indicates signs of disease or abnormality.

[0692] "Growth forecast data" refers to data used to predict the future growth and production volume of a subject.

[0693] "Market data" refers to market information, such as product demand and pricing, that is used as a reference when planning shipments.

[0694] "Quality indicators" are standards and numerical values ​​used to evaluate the quality of a product, and are necessary to maintain a certain level of quality.

[0695] This invention provides a system that monitors the manufacturing environment in real time and automatically optimizes quality and efficiency. The server collects and analyzes environmental data using a single-board computer or cloud-based platform. Specific hardware such as a Raspberry Pi is used, and the data is processed using Python. This allows for the detection of unusual situations and, if necessary, adjustments to the manufacturing process. The AI ​​model is designed to perform data analysis and production optimization using generative AI technology.

[0696] The terminal uses various sensors to measure quality indicators on the manufacturing line and provides the collected data to the server. This enables real-time environmental monitoring, supporting early detection and rapid response to unusual situations. Users determine shipping timing based on the generated growth forecast data and market data, and adjust production activities considering the system's suggestions. This ensures that the manufacturing line operates efficiently and sustainably.

[0697] For example, if the temperature on the production line deviates from the standard value, the system immediately detects the anomaly and issues instructions for process adjustment, thereby ensuring quality control. Examples of prompts include "Suggest countermeasures for when the quality index on the production line deteriorates" and "Adjust the production environment to the optimal level based on temperature sensor data." This allows the system to autonomously analyze problems and propose countermeasures.

[0698] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0699] Step 1:

[0700] The device collects environmental data from sensors. This data includes temperature, humidity, and quality indicators. The input is the environmental information obtained from the sensors, and the output is providing this information to the server. The device transmits this data to the server in real time.

[0701] Step 2:

[0702] The server analyzes environmental data received from the terminal. The input is environmental data from the terminal, and the output is the analysis results for detecting unusual situations. The server uses a generative AI model to analyze the data and identify unusual situations and abnormal patterns.

[0703] Step 3:

[0704] The server generates instructions to adjust the manufacturing process based on the analysis results. The input is the analysis results, and the output is the adjustment instructions for the manufacturing line. The AI ​​model determines the optimal process adjustment to respond to specific situations.

[0705] Step 4:

[0706] The user reviews growth forecast data and market data from the server to determine the optimal shipping timing. The input is forecast data from the server, and the output is the optimal shipping schedule. The user then uses the system's suggestions to optimize their manufacturing activities.

[0707] Step 5:

[0708] The server verifies that the manufacturing process adjustments are complete and monitors the overall system performance. Inputs are the adjustment results and subsequent production data, while outputs represent the system's stable operational status. This process ensures efficient management of the manufacturing line.

[0709] 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.

[0710] This invention combines an eel farming system with an emotion engine, aiming not only to collect, analyze, and respond to environmental data, but also to recognize user emotions and optimize the system's behavior. The system comprises a server, terminals, and an emotion engine, all of which function in coordination.

[0711] The server acts as the central hub of the system, managing and directing each process. Environmental information is sent from terminals and analyzed by the server. The server analyzes the data using a generative AI model and issues instructions as needed. An emotion engine is also integrated into the server, which analyzes emotional information through user interaction and uses this to optimize the user interface.

[0712] The device collects environmental information in real time via sensors and measures growth and behavioral data. This data is sent to a server, and the device takes action according to commands from the server. The device also receives feedback from an emotion engine and dynamically adjusts the user interface and displayed content.

[0713] Users can receive suggestions based on server-side analysis results and emotion engine processing results, and incorporate them into managing the aquaculture environment and determining shipping schedules. Suggestions that reflect the user's intentions and emotions improve the user experience and support intuitive decision-making.

[0714] For example, if the oxygen concentration drops, the terminal reports this to the server, which immediately sends a command to the terminal to start aeration. At the same time, if the user is concerned about the environmental changes, the emotion engine senses this concern and provides reassuring feedback to the user. This feedback is provided through the terminal as intuitive information displays and situational explanations.

[0715] This invention aims to realize an efficient and human-centered eel farming system through these interactions.

[0716] The following describes the processing flow.

[0717] Step 1:

[0718] The device uses multiple sensors to collect environmental information such as pH, oxygen concentration, and temperature in the water in real time.

[0719] Step 2:

[0720] The device sends the collected environmental information to the server.

[0721] Step 3:

[0722] The server stores the received environmental information in a database and performs analysis using a generated AI model to detect anomalies.

[0723] Step 4:

[0724] Based on the detected anomaly, the server determines the appropriate countermeasures (e.g., performing aeration or adjusting the amount of feed) and sends a command to the terminal.

[0725] Step 5:

[0726] The terminal receives commands from the server and takes appropriate action (such as performing aeration or adjusting automatic feeding).

[0727] Step 6:

[0728] The device acquires emotional data through interaction with the user and sends it to the server.

[0729] Step 7:

[0730] The server uses an emotion engine to analyze the user's emotions and generate situation-appropriate feedback.

[0731] Step 8:

[0732] Based on the analysis results, the server sends appropriate feedback information regarding the user's emotions to the terminal and displays an interface accordingly.

[0733] Step 9:

[0734] Users receive feedback and suggestions from the server to adjust aquaculture management and shipping schedules.

[0735] Step 10:

[0736] The results of user actions and decisions are fed back from the terminal to the server, contributing to system learning and improvement.

[0737] (Example 2)

[0738] 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".

[0739] In the aquaculture of organisms such as eels, there is a need to quickly detect environmental changes and abnormal conditions in real time and take appropriate action. However, conventional systems have been insufficient in analyzing environmental information and automating responses, which has sometimes affected the health and growth of organisms. Furthermore, there has been a lack of appropriate feedback to support user emotions and decision-making, so there is a need to build a more comprehensive and efficient system.

[0740] 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.

[0741] In this invention, the server includes a device for collecting ambient environmental indicator information, a device for analyzing the indicator information to identify abnormal patterns, and a device for performing data analysis using a generative AI model. This enables accurate, real-time understanding of the environmental situation and prompt, appropriate responses. Furthermore, by analyzing the user's emotional state and optimizing the interface, the user experience is improved, and more intuitive management is achieved.

[0742] "Surrounding environmental indicator information" refers to data consisting of indicators that show environmental conditions in the aquaculture environment, such as water temperature, oxygen concentration, and pH value.

[0743] An "abnormal pattern" is an indicator that refers to a situation that deviates from the normal environment or growth state, and defines a condition that the system should respond to quickly.

[0744] A "generative AI model" is a model that applies artificial intelligence technology used for data analysis and prediction, enabling real-time data processing and anomaly detection.

[0745] "Interface optimization" is the process of improving the way information is displayed and the interaction with users based on their emotional state, thereby enhancing the user experience.

[0746] A "shipping plan" refers to a schedule or plan for bringing products to market, which is constructed based on growth forecast data and market information.

[0747] "Rapid response suggestions" refers to a function that provides appropriate and immediate countermeasures and actions in response to anomalies detected in real time.

[0748] This invention is a system for efficiently cultivating organisms such as eels, in which a server, terminal, and emotion engine work together to collect, analyze, and respond to environmental information, and to optimize the user experience.

[0749] The server plays a central role in the aquaculture system, managing various data. The generative AI model integrated into the server identifies abnormal patterns in collected environmental data in real time, enabling rapid response. Specifically, the server analyzes ambient environmental indicators such as water temperature and oxygen concentration received from terminals, and if an anomaly is detected, it determines countermeasures and issues commands to the terminals. Furthermore, it uses an emotion engine to analyze emotional information acquired during user interaction and optimize the interface.

[0750] The terminal uses sensor technology to collect various data about the aquaculture environment in real time. The terminal transmits this data to a server, and based on the resulting commands, it performs necessary environmental adjustments, such as adjusting aeration and feeding. The terminal also displays feedback to provide reassurance to the user and provides information intuitively using situation-appropriate infographics.

[0751] Users make decisions related to managing the aquaculture environment based on analysis results and suggestions provided by the server. Users can adjust shipping plans based on growth prediction data. For example, they can make decisions using predictive data, such as optimizing the timing of shipments to improve profitability.

[0752] For example, if the oxygen concentration drops, the terminal detects this and reports it to the server. The server uses a generative AI model to analyze the situation and sends a command to the terminal to start aeration to ensure sufficient oxygen is supplied. Based on this command, the terminal quickly begins aeration. Meanwhile, the emotion engine detects if the user is feeling anxious about the environmental changes and provides the user with a reassuring message such as, "Aeration was successful. The environmental conditions are normal."

[0753] An example of a prompt message could be, "If the current oxygen concentration falls below the target value, what impact will this have on the eels' health, and what countermeasures should be taken?" The AI ​​model can then be used to derive appropriate countermeasures.

[0754] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0755] Step 1:

[0756] The terminal collects environmental indicator information such as water temperature, oxygen concentration, and pH value in real time via sensors. The input is environmental data, and the output is a package of the collected data. This data is temporarily stored inside the terminal and prepared for transmission to the server. The operation involves periodic signal checks of the sensors and formatting of the data.

[0757] Step 2:

[0758] The terminal sends the collected environmental data to the server. The input in this process is the environmental data collected in the previous step, and the output is the data sent to the server. The data is encrypted and transmitted to the server over the network, ensuring the security and accuracy of the transmission. Specific operations include data encryption and the use of network protocols.

[0759] Step 3:

[0760] The server analyzes environmental data received from the terminal. In this case, the input is the environmental data sent from the terminal, and the output is the analysis result indicating whether or not there are anomalies. The server uses a generative AI model to analyze the data and identify anomaly patterns and problems. The operation involves running the AI ​​model and applying the analysis algorithm.

[0761] Step 4:

[0762] The server determines the necessary countermeasures based on the analysis results and sends commands to the terminal. The input here is the analysis results identifying the anomaly, and the output is the command information to the terminal. Specifically, the server automatically generates countermeasures and, for example, instructs the terminal to start aeration in response to a decrease in oxygen concentration.

[0763] Step 5:

[0764] The terminal receives commands from the server and performs the actual environmental adjustments. The input for this step is the server's commands, and the output is the result of the environmental adjustments. These operations include starting the aeration system and operating other adjustment devices.

[0765] Step 6:

[0766] The server uses an emotion engine to analyze the user's emotional information. The input is interaction data with the user, and the output is the user's emotional state. The results of the emotion analysis are used to optimize the user experience.

[0767] Step 7:

[0768] The device adjusts the feedback provided to the user based on the analysis results of the emotion engine. The input is the analysis results of the emotional state, and the output is the feedback information presented to the user. Specifically, this includes displaying messages that provide a sense of security according to the situation, and providing intuitive and easy-to-understand information.

[0769] (Application Example 2)

[0770] 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".

[0771] In modern aquaculture systems, real-time analysis of environmental information and growth data is crucial, but there is also a need to optimize the system while considering the user's emotional state. Furthermore, the challenge lies in using emotional information to support decision-making and create a more comfortable and intuitive user interface.

[0772] 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. In this invention, the server includes means for collecting environmental information in real time, means for analyzing emotional states and generating individually optimized feedback, and means for supporting decision-making using the user's emotional information. This enables not only environmental adjustment but also efficient and human-centered system operation that responds to the user's emotional state.

[0773] "Environmental information" refers to physical data such as temperature, oxygen concentration, and humidity in the target environment.

[0774] An "abnormality" refers to patterns or values ​​in environmental information or vital data that are different from the norm.

[0775] "Emotional state" refers to the user's psychological state inferred from information obtained through facial recognition and sensors.

[0776] "Feedback" refers to information and suggestions provided by a system to a user, tailored to the user's emotional state and needs.

[0777] "Decision support" is a process that provides information and suggestions to help users make decisions.

[0778] "Real-time" means that data collection, analysis, and response are performed with virtually no delay.

[0779] "Optimization" refers to improving systems and processes to suit specific purposes and to operate them efficiently.

[0780] This invention is a system that monitors the health status and emotions of residents in nursing care facilities in real time and provides appropriate care suggestions to staff. The system consists of a server, terminals, and an emotion engine that analyzes emotional states.

[0781] The server plays a central role in managing the entire system and performing data analysis. The software used employs machine learning libraries such as TensorFlow to perform sentiment analysis and provides real-time feedback of the data analysis results from generative AI models to staff. Furthermore, it provides information to support staff decision-making based on the emotional states obtained through the sentiment engine.

[0782] The terminal consists of a smartphone and external sensors, and collects residents' health data (heart rate, oxygen saturation, etc.) in real time. This data is sent to a server, and commands based on the analysis results are sent back to the terminal. The terminal receives feedback from an emotion engine and displays information to staff, providing support in an intuitive and easy-to-understand format.

[0783] Based on the information provided through these systems, users can develop individualized care plans for residents. For example, if facial recognition data of a resident detects that they are feeling tired, a notification will be sent to staff stating, "Resident B appears to be feeling tired. Please consider a break time." An example of a prompt in this case would be, "Resident B's current emotional state indicates they need a break. Please suggest appropriate care."

[0784] This invention makes it possible to comprehensively manage the health and emotional state of residents and improve the quality of care.

[0785] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0786] Step 1:

[0787] The server receives health data (heart rate, oxygen saturation, etc.) from residents transmitted from terminals. This input data is first stored in a database, and then basic statistics are calculated to check for any abnormal values. During this process, an initial analysis is performed to check for deviations from standard values.

[0788] Step 2:

[0789] The terminal performs facial recognition through a camera that captures images of residents and sends the resulting video data to a server. The server inputs this video data into an emotion engine and analyzes the emotional state using a facial recognition algorithm. As a result of the analysis, it outputs emotional scores such as happiness level and fatigue level.

[0790] Step 3:

[0791] The server uses a generative AI model to assess the resident's overall living condition based on initial analysis results of health data and emotional scores. This process generates potential care suggestions through model inference based on the analysis. These outputs constitute specific care recommendations.

[0792] Step 4:

[0793] The server formats the generated care suggestion as a prompt and sends it to the terminal. An example of a prompt might be, "Person C may be feeling tired. Please consider offering additional rest time or relaxation."

[0794] Step 5:

[0795] Users can take immediate action based on care suggestions received through their devices. For example, a user can propose a suggested rest period to a resident, observe the results, and provide feedback to the system. This allows users to make more comprehensive and intuitive decisions.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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."

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] The following is further disclosed regarding the embodiments described above.

[0818] (Claim 1)

[0819] Means for collecting environmental information in real time,

[0820] A means for analyzing the aforementioned environmental information to detect anomalies,

[0821] Means for adjusting the aquatic environment based on the aforementioned abnormality,

[0822] A means of optimizing feeding according to the growth stage,

[0823] A means of automatically detecting signs of illness or abnormality and taking countermeasures,

[0824] Means for adjusting shipping schedules based on growth forecasts and market information,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, comprising means for generating growth forecast data and determining the optimal timing for shipment based on the growth forecast data.

[0828] (Claim 3)

[0829] The system according to claim 1, comprising means for analyzing eel behavior and growth data, detecting abnormalities in real time, and providing automated suggestions for rapid response.

[0830] "Example 1"

[0831] (Claim 1)

[0832] A device for collecting environmental data,

[0833] An information processing device for analyzing the aforementioned data and detecting anomalies,

[0834] A control device for adjusting the aquatic environment based on the aforementioned abnormality,

[0835] A mathematical model for optimizing feeding considering the growth stage,

[0836] A process that detects signs of disease or abnormality and automatically takes action,

[0837] A display device for adjusting shipping plans by referring to growth forecasts and market information,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The information processing system according to claim 1 for generating growth forecast data and determining the optimal timing for shipment based on that data.

[0841] (Claim 3)

[0842] The system according to claim 1, which includes a function for analyzing the behavior and growth information of organisms, detecting abnormalities in real time, and quickly proposing countermeasures.

[0843] "Application Example 1"

[0844] (Claim 1)

[0845] A means of collecting environmental data in real time,

[0846] A means for analyzing the aforementioned environmental data to detect unusual situations,

[0847] Means for adjusting the fluid environment based on the aforementioned unique circumstances,

[0848] Means for optimizing nutrient supply according to the growth stage,

[0849] A means of automatically detecting health conditions and signs of abnormalities and taking appropriate action,

[0850] Means for adjusting shipping schedules based on growth forecasts and market data,

[0851] A means for monitoring quality indicators on the manufacturing line and automatically adjusting the manufacturing process as needed,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, comprising means for generating growth forecast data and quality indicators, and determining the optimal timing for shipment based on the growth forecast data and quality indicators.

[0855] (Claim 3)

[0856] The system according to claim 1, comprising means for analyzing the behavior and growth data of a target, detecting unusual situations in real time, and making automated suggestions for rapid response.

[0857] "Example 2 of combining an emotion engine"

[0858] (Claim 1)

[0859] A device for collecting surrounding environmental indicator information,

[0860] A device that analyzes the aforementioned indicator information and identifies abnormal patterns,

[0861] A device for adjusting the water quality environment based on the identified abnormality,

[0862] A device that optimizes the feeding process according to the growth period,

[0863] A device that identifies diseases and abnormal patterns and automatically implements countermeasures,

[0864] A device that adjusts the shipping plan based on growth forecast data and market information,

[0865] A device that analyzes the user's emotional state and optimizes the interface,

[0866] A device that performs data analysis using a generative AI model,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, which generates growth prediction data and determines the optimal time for shipment based on the growth prediction data.

[0870] (Claim 3)

[0871] The system according to claim 1, comprising a device that analyzes the behavior and growth data of organisms, detects abnormal patterns in real time, and provides prompt response suggestions.

[0872] "Application example 2 when combining with an emotional engine"

[0873] (Claim 1)

[0874] Means for collecting environmental information in real time,

[0875] A means for analyzing the aforementioned environmental information to detect anomalies,

[0876] Means for adjusting the environment based on the aforementioned abnormality,

[0877] A means of optimizing feeding according to the growth stage,

[0878] A means to detect signs of abnormality and take countermeasures automatically,

[0879] Means for adjusting shipping schedules based on growth forecasts and market information,

[0880] A means for analyzing emotional states and generating individually optimized feedback,

[0881] A means of supporting decision-making using user sentiment information,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, comprising means for generating growth prediction data and determining the optimal timing for shipment based on the growth prediction data and sentiment data.

[0885] (Claim 3)

[0886] The system according to claim 1, comprising means for analyzing information, detecting anomalies in real time and providing automated suggestions and emotion-based care suggestions for prompt response. [Explanation of Symbols]

[0887] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting environmental data in real time, A means for analyzing the aforementioned environmental data to detect unusual situations, Means for adjusting the fluid environment based on the aforementioned unique circumstances, Means for optimizing nutrient supply according to the growth stage, A means of automatically detecting health conditions and signs of abnormalities and taking appropriate action, Means for adjusting shipping schedules based on growth forecasts and market data, A means for monitoring quality indicators on the manufacturing line and automatically adjusting the manufacturing process as needed, A system that includes this.

2. The system according to claim 1, comprising means for generating growth forecast data and quality indicators, and determining the optimal timing for shipment based on the growth forecast data and quality indicators.

3. The system according to claim 1, comprising means for analyzing the behavior and growth data of a target, detecting unusual situations in real time, and making automated suggestions for rapid response.

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