system

Abstract

The system according to this embodiment aims to automatically carry out insect rearing and breeding under optimal conditions. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a simulation unit, and an adjustment unit. The data collection unit collects data on insect growth conditions and rearing environment. The analysis unit analyzes the data collected by the data collection unit. The simulation unit derives optimal rearing conditions based on the data obtained by the analysis unit. The adjustment unit monitors the progress of rearing and breeding in real time based on the rearing conditions derived by the simulation unit and makes adjustments as necessary.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including steps of 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that it was difficult to find and maintain the optimal conditions for breeding and reproduction of insects.

[0005] The system according to the embodiment aims to automatically perform the breeding and reproduction of insects under optimal conditions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a simulation unit, and an adjustment unit. The data collection unit collects data on insect growth conditions and rearing environment. The analysis unit analyzes the data collected by the data collection unit. The simulation unit derives optimal rearing conditions based on the data obtained by the analysis unit. The adjustment unit monitors the progress of rearing and breeding in real time based on the rearing conditions derived by the simulation unit and makes adjustments as necessary. [Effects of the Invention]

[0007] The system according to this embodiment can automatically rear and breed insects under optimal conditions. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] 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.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The Insect Breeder AI Agent System according to an embodiment of the present invention is a system that fully automates insect rearing and breeding. Unlike conventional mechanical automation, this Insect Breeder AI Agent System uses an AI that aggregates advanced knowledge to assess the situation and perform optimal rearing and breeding. For example, the Insect Breeder AI Agent System collects and analyzes data on insect growth conditions and living environment. Next, the Insect Breeder AI Agent System derives optimal rearing conditions based on the collected data. This improves production efficiency and leads to cost reduction. The Insect Breeder AI Agent System also simulates the effects of different rearing methods and environmental conditions on insect growth and formulates an optimal production strategy. Furthermore, the Insect Breeder AI Agent System monitors the progress of insect rearing and breeding in real time and makes adjustments as needed. For example, it automatically adjusts temperature and humidity control and feed supply to maintain the optimal health of the insects. This system not only streamlines insect rearing and breeding and reduces costs, but also improves quality. In particular, in the markets for edible and beneficial insects, it becomes possible to establish a stable supply system and improve price competitiveness. Furthermore, the Insect Breeder AI Agent System can further improve the accuracy of rearing and breeding by accumulating insect growth data and continuously learning. This contributes to sustainable agriculture and food production and also contributes to reducing environmental impact. As a result, the Insect Breeder AI Agent System can efficiently automate insect rearing and breeding and provide optimal rearing conditions.

[0029] The insect breeder AI agent system according to this embodiment comprises a data collection unit, an analysis unit, a simulation unit, and an adjustment unit. The data collection unit collects data related to the growth conditions and environment of insects. For example, the data collection unit collects data such as temperature, humidity, and light intensity. The data collection unit can also collect data such as the type of breeding container and the type of feed. For example, the data collection unit measures the temperature of the breeding environment using a temperature sensor and collects the data. It measures the humidity of the breeding environment using a humidity sensor and collects the data. It measures the light intensity of the breeding environment using a light sensor and collects the data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit evaluates the growth conditions of insects based on the collected data. The analysis unit can also evaluate the appropriateness of the breeding environment based on the collected data. For example, the analysis unit evaluates the temperature range suitable for insect growth based on the temperature data. It evaluates the humidity range suitable for insect growth based on the humidity data. It evaluates the light intensity range suitable for insect growth based on the light intensity data. The simulation unit derives the optimal breeding conditions based on the data obtained by the analysis unit. The simulation unit simulates different rearing conditions and derives the optimal conditions. The simulation unit can also simulate different environmental conditions and derive the optimal environmental conditions. For example, the simulation unit performs simulations with varying temperatures to derive the optimal temperature conditions. It performs simulations with varying humidity to derive the optimal humidity conditions. It performs simulations with varying light intensity to derive the optimal light intensity conditions. The adjustment unit monitors the progress of rearing and breeding in real time based on the rearing conditions derived by the simulation unit and makes adjustments as needed. The adjustment unit manages temperature and humidity, for example. The adjustment unit can also supply feed. For example, the adjustment unit monitors the temperature of the rearing environment using a temperature sensor and adjusts the temperature as needed. It monitors the humidity of the rearing environment using a humidity sensor and adjusts the humidity as needed. It supplies feed to insects using a feed supply device. As a result, the insect breeder AI agent system according to this embodiment can efficiently automate insect rearing and breeding and provide optimal rearing conditions.

[0030] The data collection unit collects data on insect growth conditions and their environment. Specifically, it uses various sensors to collect environmental data such as temperature, humidity, and light intensity. For example, a temperature sensor accurately measures the temperature of the rearing environment and collects data in real time. A humidity sensor measures the humidity of the rearing environment and provides data to understand the humidity range suitable for insect growth. A light sensor measures the light intensity of the rearing environment and collects data to ensure the amount of light necessary for insect growth. Furthermore, the data collection unit can also collect data such as the type of rearing container and the type of feed. For example, it collects information on the material, shape, and size of the rearing container and provides data to select the optimal rearing container for insect growth. It also collects data on the type, amount, and frequency of feed and provides information to maintain the optimal nutritional status of the insects. In this way, the data collection unit can comprehensively understand the diverse factors that affect insect growth and provide the data necessary to optimize the rearing environment. Furthermore, the data collection unit can transmit the collected data to a cloud server and collaborate with other systems and departments. For example, the collected data can be made accessible to the analysis unit and simulation unit, and the data can be shared in real time. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes data collected by the data collection department to evaluate the suitability of insect growth conditions and rearing environments. Specifically, it evaluates the temperature range suitable for insect growth based on collected temperature data, the humidity range suitable for insect growth based on humidity data, and the light intensity range suitable for insect growth based on light intensity data. The analysis department processes this data in real time using AI to identify the optimal environmental conditions for insect growth. For example, the AI ​​uses historical data and statistical information to analyze temperature data and identify the optimal temperature range for insect growth. It uses anomaly detection algorithms to analyze humidity data and identify the optimal humidity range for insect growth. It uses image recognition technology to analyze light intensity data and identify the optimal light intensity range for insect growth. This allows the analysis department to quickly and accurately analyze collected data and identify the optimal environmental conditions for insect growth. Furthermore, the analysis department can also use historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on historical growth data, it can predict fluctuations in growth under specific environmental conditions and time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The simulation unit derives optimal rearing conditions based on data obtained by the analysis unit. Specifically, it uses AI to simulate different rearing conditions and derive the optimal conditions. For example, it performs simulations with varying temperatures to derive the optimal temperature conditions, simulations with varying humidity to derive the optimal humidity conditions, and simulations with varying light intensity to derive the optimal light intensity conditions. Through these simulations, the simulation unit identifies the optimal environmental conditions for insect growth. The simulation unit can also simulate different environmental conditions and derive the optimal conditions. For example, it performs simulations with varying types of rearing containers and types of feed to derive the optimal rearing containers and feed types. In this way, the simulation unit can identify the optimal rearing conditions for insect growth and support the optimization of the rearing environment. Furthermore, the simulation unit can continuously modify the simulation results based on data updated in real time and respond to the latest situations. For example, if the temperature or humidity changes rapidly, the simulation unit immediately takes in new data and updates the simulation results. The simulation unit can also perform more accurate simulations by considering regional characteristics and past growth history. This allows the simulation unit to provide highly accurate simulations based on the latest information at all times, enabling it to determine the optimal rearing conditions for insect growth.

[0033] The adjustment unit monitors the progress of rearing and breeding in real time based on the rearing conditions derived by the simulation unit and makes adjustments as needed. Specifically, it manages temperature and humidity to maintain an optimal environment for insect growth. For example, it monitors the temperature of the rearing environment using a temperature sensor and adjusts the temperature as needed. It monitors the humidity of the rearing environment using a humidity sensor and adjusts the humidity as needed. It monitors the light intensity of the rearing environment using a light sensor and adjusts the light intensity as needed. Furthermore, the adjustment unit can also supply food. For example, it supplies food to the insects using a food supply device to maintain their nutritional status optimally. The adjustment unit also cleans and maintains the rearing environment to create an environment that maintains the health of the insects. In this way, the adjustment unit can maintain an optimal environment for insect growth and improve the efficiency of rearing and breeding. In addition, the adjustment unit can continuously modify the adjustment settings based on data that is updated in real time and respond to the latest situation. For example, if the temperature or humidity changes rapidly, the adjustment unit immediately takes in the new data and updates the adjustment settings. Furthermore, the adjustment unit can make more precise adjustments by taking into account the characteristics of each region and past growth history. This allows the adjustment unit to provide highly accurate adjustments based on the latest information at all times, maintaining an optimal breeding environment for insect growth.

[0034] The adjustment unit includes a temperature control unit and a humidity control unit that manage temperature and humidity. The temperature control unit manages the temperature of the rearing environment. For example, the temperature control unit measures the temperature of the rearing environment using a temperature sensor and adjusts the temperature as needed. The temperature control unit can set a temperature range and maintain the temperature within that range. For example, if the temperature of the rearing environment exceeds the set range, the temperature control unit activates a cooling device to lower the temperature. The temperature control unit can also activate a heating device to raise the temperature if the temperature of the rearing environment falls below the set range. The humidity control unit manages the humidity of the rearing environment. For example, the humidity control unit measures the humidity of the rearing environment using a humidity sensor and adjusts the humidity as needed. The humidity control unit can set a humidity range and maintain the humidity within that range. For example, if the humidity of the rearing environment exceeds the set range, the humidity control unit activates a dehumidifier to lower the humidity. The humidity control unit can also activate a humidifier to raise the humidity if the humidity of the rearing environment falls below the set range. This automates the management of temperature and humidity, and allows for the optimal maintenance of the insect rearing environment. Some or all of the above-described processes in the temperature control unit and humidity control unit may be performed using AI, for example, or without AI. For example, the temperature control unit can input data from a temperature sensor into a generating AI and cause the generating AI to execute instructions for temperature adjustment. The humidity control unit can input data from a humidity sensor into a generating AI and cause the generating AI to execute instructions for humidity adjustment.

[0035] The adjustment unit includes a feed supply unit that supplies feed. The feed supply unit supplies feed to insects. The feed supply unit supplies feed to insects, for example, using a feed supply device. The feed supply unit can set the frequency and amount of feed to be supplied and supply feed based on those settings. For example, the feed supply unit adjusts the frequency and amount of feed supplied according to the growth stage of the insect. The feed supply unit can also adjust the frequency and amount of feed supplied according to the health condition of the insect. For example, the feed supply unit increases the frequency of feed supply according to the growth rate during the larval stage. During the adult stage, it adjusts the amount of feed supplied according to reproductive behavior. This automates the supply of feed and maintains the health condition of the insects optimally. Some or all of the above-described processes in the feed supply unit may be performed using AI, for example, or without AI. For example, the feed supply unit can input insect growth data into a generating AI and cause the generating AI to execute instructions for feed supply.

[0036] The data collection unit includes a learning unit that stores insect growth data and continuously learns from it. The learning unit stores insect growth data and continuously learns from it. For example, the learning unit collects insect growth data and learns from that data. The learning unit can improve the accuracy of rearing and breeding based on the collected data. For example, the learning unit collects data on the growth rate and health status of insects and derives optimal rearing conditions based on that data. The learning unit can also identify areas for improvement in the rearing environment based on the insect growth data. For example, the learning unit optimizes methods for adjusting temperature and humidity based on the insect growth data. It also optimizes methods for supplying food. In this way, the accuracy of rearing and breeding can be improved by accumulating growth data and continuously learning from it. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input insect growth data into a generating AI and have the generating AI perform optimization of the learning algorithm.

[0037] The data collection unit dynamically changes the type of data it collects according to the insect's growth stage. For example, during the larval stage, the data collection unit focuses on collecting data on growth rate and body weight. During the pupal stage, it can also collect data on the hardening state and color changes of the pupa. During the adult stage, it can also collect data on reproductive behavior and lifespan. This allows for the collection of more accurate data by collecting data according to the growth stage. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input insect growth stage data into a generating AI and have the generating AI dynamically change the type of data to collect.

[0038] The data collection unit analyzes insect behavior patterns during data collection and detects abnormal behavior. For example, if an insect deviates from its normal behavior pattern, the data collection unit detects it as abnormal behavior. When abnormal behavior is detected, the data collection unit can also issue an alert and notify the user. The data collection unit can also analyze the cause of the abnormal behavior and propose countermeasures. This allows for early countermeasures to be taken by detecting abnormal behavior. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input insect behavior data into a generating AI and have the generating AI perform abnormal behavior detection.

[0039] The data collection unit collects data while considering the geographical conditions of the insect's habitat. For example, in hot and humid regions, the data collection unit will focus on collecting temperature and humidity data. In dry regions, the data collection unit may also focus on collecting water supply data. In high-altitude areas, the data collection unit may also collect atmospheric pressure and oxygen concentration data. This allows for the provision of a more appropriate rearing environment by collecting data that takes geographical conditions into account. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical condition data into a generating AI and have the generating AI execute instructions for data collection.

[0040] The data collection unit analyzes the genetic information of insects during data collection to identify factors that affect growth. For example, the data collection unit analyzes genetic information to identify genes that affect growth rate. Based on the genetic information, the data collection unit can also identify factors that affect reproductive success rate. The data collection unit can also analyze genetic information to identify factors that affect health status. In this way, by analyzing genetic information, factors that affect growth can be identified and optimal rearing conditions can be provided. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the genetic information of insects into a generating AI and have the generating AI identify factors that affect growth.

[0041] The analysis unit integrates insect growth data and environmental data during analysis to analyze their correlations. For example, the analysis unit can integrate growth data and temperature data to analyze the correlation between growth rate and temperature. The analysis unit can also integrate growth data and humidity data to analyze the correlation between growth rate and humidity. The analysis unit can also integrate growth data and feed supply data to analyze the correlation between growth rate and the type and amount of feed. By analyzing the correlation between growth data and environmental data, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input growth data and environmental data into a generating AI and have the generating AI perform the correlation analysis.

[0042] The analysis unit detects anomalies by comparing them with past data during analysis and issues alerts. For example, the analysis unit can detect abnormal growth rates by comparing them with past growth data. The analysis unit can also detect abnormal temperatures and humidity by comparing them with past environmental data. The analysis unit can also detect abnormal feed consumption by comparing it with past feed supply data. This allows for early countermeasures to be taken by detecting anomalies and issuing alerts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the detection of anomalies.

[0043] The analysis unit analyzes data while considering fluctuations in the insect's habitat. For example, the analysis unit analyzes growth data while considering seasonal temperature fluctuations. The analysis unit can also analyze growth data while considering humidity fluctuations. The analysis unit can also analyze growth data while considering fluctuations in the amount of food supplied. This allows for more accurate analysis results by analyzing data while considering fluctuations in the habitat. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input growth environment fluctuation data into a generating AI and have the generating AI perform the data analysis.

[0044] The analysis unit evaluates the health status of insects during analysis and proposes preventive measures. For example, the analysis unit can detect signs of disease based on health status data. The analysis unit can also propose appropriate preventive measures based on health status data. The analysis unit can also propose improvements to the rearing environment based on health status data. In this way, the health of insects can be maintained by evaluating their health status and proposing preventive measures. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input health status data into a generating AI and have the generating AI execute the proposal of preventive measures.

[0045] The simulation unit compares different rearing conditions during the simulation and derives the optimal conditions. For example, the simulation unit can perform a simulation with varying temperatures to derive the optimal temperature conditions. The simulation unit can also perform a simulation with varying humidity to derive the optimal humidity conditions. The simulation unit can also perform a simulation with varying types and amounts of feed to derive the optimal feed supply conditions. In this way, the optimal rearing conditions can be derived by comparing different rearing conditions. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input rearing condition data into a generating AI and have the generating AI perform the deriving of the optimal conditions.

[0046] The simulation unit predicts insect growth during simulations and creates future production plans. For example, the simulation unit predicts future growth based on growth data. The simulation unit can also create an optimal production plan based on the growth prediction. The simulation unit can also plan the necessary resources based on the growth prediction. In this way, future production plans can be made by performing growth predictions. Some or all of the above processes in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input growth data into a generating AI and have the generating AI perform growth predictions.

[0047] The simulation unit formulates an optimal breeding strategy during the simulation, taking into account the genetic diversity of insects. For example, the simulation unit selects the optimal mating pair, taking genetic diversity into consideration. The simulation unit can also formulate a strategy to increase the breeding success rate based on genetic diversity. The simulation unit can also select healthy individuals, taking genetic diversity into consideration. In this way, by considering genetic diversity, it is possible to achieve the breeding of healthy and diverse insects. Some or all of the above processes in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input genetic diversity data into a generating AI and have the generating AI formulate an optimal breeding strategy.

[0048] The simulation unit simulates fluctuations in the insect's growth environment during the simulation and performs risk management. For example, the simulation unit can simulate temperature fluctuations and perform risk management. The simulation unit can also simulate humidity fluctuations and perform risk management. The simulation unit can also simulate fluctuations in food supply and perform risk management. In this way, by simulating fluctuations in the growth environment, risk management can be performed and a stable rearing environment can be provided. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input growth environment fluctuation data into a generating AI and have the generating AI execute a risk management simulation.

[0049] The adjustment unit dynamically changes the adjustment settings according to the insect's growth stage during adjustment. For example, during the larval stage, the adjustment unit focuses on adjusting temperature and humidity. During the pupal stage, the adjustment unit can also adjust temperature and humidity according to the hardening state of the pupa. During the adult stage, the adjustment unit can also adjust the amount of food supplied according to reproductive behavior. By making adjustments according to the growth stage, a more appropriate rearing environment can be provided. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input insect growth stage data into a generating AI and cause the generating AI to dynamically change the adjustment settings.

[0050] The adjustment unit monitors the health status of the insects during adjustment and intervenes as necessary. For example, the adjustment unit intervenes if an abnormality is detected based on the health status data. The adjustment unit can also propose and implement preventive measures based on the health status data. The adjustment unit can also propose and implement improvements to the rearing environment based on the health status data. In this way, the health of the insects can be maintained by monitoring their health status and intervening as necessary. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input health status data into a generating AI and have the generating AI execute instructions for intervention.

[0051] The adjustment unit makes adjustments while considering the geographical conditions of the insect's habitat. For example, in hot and humid regions, the adjustment unit focuses on adjusting temperature and humidity. In dry regions, the adjustment unit may also focus on adjusting water supply. In high altitudes, the adjustment unit may also adjust atmospheric pressure and oxygen concentration. This allows for the provision of a more appropriate rearing environment by making adjustments that take geographical conditions into account. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input geographical condition data into a generating AI and have the generating AI execute adjustment instructions.

[0052] The adjustment unit analyzes the insect's behavioral patterns during adjustment, detects abnormal behavior, and makes adjustments. For example, if an insect deviates from its normal behavioral pattern, the adjustment unit detects it as abnormal behavior and makes adjustments. When abnormal behavior is detected, the adjustment unit can also issue an alert, notify the user, and make adjustments. The adjustment unit can also analyze the cause of the abnormal behavior, propose countermeasures, and make adjustments. This allows for early countermeasures to be taken by detecting and adjusting for abnormal behavior. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input insect behavioral data into a generating AI and have the generating AI perform abnormal behavior detection and adjustments.

[0053] The temperature control unit dynamically changes the temperature setting according to the growth stage of the insect during temperature control. For example, during the larval stage, the temperature control unit changes the temperature setting according to the growth rate. During the pupal stage, the temperature control unit may also change the temperature setting according to the hardening state of the pupa. During the adult stage, the temperature control unit may also change the temperature setting according to the reproductive behavior. This allows for the provision of a more appropriate rearing environment by setting the temperature according to the growth stage. Some or all of the above processes in the temperature control unit may be performed using AI, for example, or without AI. For example, the temperature control unit can input insect growth stage data into a generating AI and have the generating AI perform dynamic changes to the temperature setting.

[0054] The temperature control unit sets the temperature considering the geographical conditions of the insect's habitat. For example, in hot and humid regions, the temperature control unit sets the temperature lower. In dry regions, the temperature control unit may also set the temperature higher. In high-altitude areas, the temperature control unit may also set the temperature considering atmospheric pressure and oxygen concentration. This allows for the provision of a more appropriate rearing environment by setting the temperature considering geographical conditions. Some or all of the above processes in the temperature control unit may be performed using AI, for example, or without AI. For example, the temperature control unit can input geographical condition data into a generating AI and have the generating AI execute instructions for temperature setting.

[0055] The humidity control unit dynamically changes the humidity settings according to the growth stage of the insect during humidity control. For example, during the larval stage, the humidity control unit changes the humidity settings according to the growth rate. During the pupal stage, the humidity control unit can also change the humidity settings according to the hardening state of the pupa. During the adult stage, the humidity control unit can also change the humidity settings according to the reproductive behavior. This allows for the provision of a more appropriate rearing environment by setting the humidity according to the growth stage. Some or all of the above processing in the humidity control unit may be performed using AI, for example, or without AI. For example, the humidity control unit can input insect growth stage data into a generating AI and have the generating AI perform the dynamic changes to the humidity settings.

[0056] The humidity control unit sets the humidity level considering the geographical conditions of the insect's habitat. For example, in hot and humid regions, the humidity control unit sets the humidity level lower. In dry regions, the humidity control unit can also set the humidity level higher. In high-altitude areas, the humidity control unit can also set the humidity level considering atmospheric pressure and oxygen concentration. This allows for the provision of a more appropriate rearing environment by setting the humidity level considering geographical conditions. Some or all of the above processes in the humidity control unit may be performed using AI, for example, or without AI. For example, the humidity control unit can input geographical condition data into a generating AI and have the generating AI execute instructions for setting the humidity level.

[0057] The feed supply unit dynamically changes the type and amount of feed according to the growth stage of the insect when supplying feed. For example, during the larval stage, the feed supply unit changes the type and amount of feed according to the growth rate. During the pupal stage, the feed supply unit can also change the type and amount of feed according to the hardening state of the pupa. During the adult stage, the feed supply unit can also change the type and amount of feed according to the reproductive behavior. In this way, a more appropriate rearing environment can be provided by changing the type and amount of feed according to the growth stage. Some or all of the above processing in the feed supply unit may be performed using AI, for example, or without using AI. For example, the feed supply unit can input insect growth stage data into a generating AI and cause the generating AI to perform dynamic changes in the type and amount of feed.

[0058] The feed supply unit sets the type and amount of feed considering the geographical conditions of the insect's habitat when supplying feed. For example, the feed supply unit adjusts the type and amount of feed in hot and humid regions. The feed supply unit can also adjust the type and amount of feed in dry regions. In high-altitude areas, the feed supply unit can also set the type and amount of feed considering atmospheric pressure and oxygen concentration. This allows for the provision of a more appropriate rearing environment by setting the type and amount of feed considering geographical conditions. Some or all of the above processing in the feed supply unit may be performed using AI, for example, or without AI. For example, the feed supply unit can input geographical condition data into a generating AI and have the generating AI execute the setting of the type and amount of feed.

[0059] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects the optimal learning algorithm based on past learning data. The learning unit can also adjust the parameters of the learning algorithm based on past learning data. The learning unit can also improve the accuracy of the learning algorithm based on past learning data. In this way, the accuracy of the learning algorithm can be improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0060] The learning unit weights the learning data based on the timing of insect growth data submission during the learning process. For example, the learning unit weights important data based on the timing of growth data submission. The learning unit can also weight seasonal data based on the timing of growth data submission. The learning unit can also weight data for specific growth stages based on the timing of growth data submission. This allows for more accurate learning by weighting based on the timing of growth data submission. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input growth data submission timing data into a generating AI and have the generating AI perform the weighting of the learning data.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The data collection unit can analyze insect behavior patterns and detect abnormal behavior when collecting insect growth data. For example, if an insect deviates from its normal behavior pattern, it can be detected as abnormal behavior, an alert can be issued, and the user can be notified. It can also analyze the cause of the abnormal behavior and propose countermeasures. This allows for early detection of abnormal behavior and the implementation of countermeasures, thereby maintaining the insect's health at an optimal level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input insect behavior data into a generating AI and have the generating AI perform abnormal behavior detection.

[0063] The adjustment unit can dynamically change the adjustment settings according to the insect's growth stage. For example, during the larval stage, the adjustment of temperature and humidity is the primary focus. During the pupal stage, the temperature and humidity can be adjusted according to the hardening state of the pupa. During the adult stage, the amount of food supplied can be adjusted according to the reproductive behavior. By making adjustments according to the growth stage, a more appropriate rearing environment can be provided. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input insect growth stage data into a generating AI and cause the generating AI to dynamically change the adjustment settings.

[0064] The data collection unit may include a learning unit that stores insect growth data and continuously learns from it. The learning unit collects insect growth data and learns from that data. For example, it can collect data on the growth rate and health status of insects and derive optimal rearing conditions based on that data. The learning unit can also identify areas for improvement in the rearing environment based on the insect growth data. In this way, the accuracy of rearing and breeding can be improved by accumulating growth data and continuously learning from it. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input insect growth data into a generating AI and have the generating AI perform optimization of the learning algorithm.

[0065] The data collection unit can dynamically change the types of data it collects according to the insect's growth stage. For example, during the larval stage, it can focus on collecting data on growth rate and weight. During the pupal stage, it can also collect data on the hardening state and color changes of the pupa. During the adult stage, it can also collect data on reproductive behavior and lifespan. This allows for the collection of more accurate data by collecting data according to the growth stage. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input insect growth stage data into a generating AI and have the generating AI dynamically change the types of data to collect.

[0066] The data collection unit can analyze insect behavior patterns during data collection and detect abnormal behavior. For example, if an insect deviates from its normal behavior pattern, it can be detected as abnormal behavior, an alert can be issued, and the user can be notified. It can also analyze the cause of the abnormal behavior and propose countermeasures. This allows for early detection of abnormal behavior and the implementation of countermeasures, thereby maintaining the insects' health at an optimal level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input insect behavior data into a generating AI and have the generating AI perform abnormal behavior detection.

[0067] The data collection unit can collect data while considering the geographical conditions of the insect's habitat. For example, in hot and humid regions, it can focus on collecting temperature and humidity data. In dry regions, it can focus on collecting water supply data. In high altitudes, it can also collect atmospheric pressure and oxygen concentration data. This allows for the provision of a more appropriate rearing environment by collecting data that takes geographical conditions into account. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical condition data into a generating AI and have the generating AI execute instructions for data collection.

[0068] The data collection unit can analyze the genetic information of insects during data collection and identify factors that affect their growth. For example, it can analyze genetic information to identify genes that affect growth rate. Based on genetic information, it can also identify factors that affect reproductive success rate. It can also analyze genetic information to identify factors that affect health status. In this way, by analyzing genetic information, it is possible to identify factors that affect growth and provide optimal rearing conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the genetic information of insects into a generating AI and have the generating AI identify factors that affect growth.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The data collection unit collects data on insect growth conditions and living environment. For example, it collects data on temperature, humidity, light intensity, type of rearing container, and type of feed. It measures and collects various data on the rearing environment using temperature sensors, humidity sensors, and light sensors. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it evaluates the suitability of the insect's growth conditions and rearing environment based on the collected data. It evaluates the range suitable for insect growth based on temperature data, humidity data, and light intensity data. Step 3: The simulation unit derives the optimal rearing conditions based on the data obtained by the analysis unit. For example, it simulates different rearing and environmental conditions to determine the optimal temperature, humidity, and light conditions. Step 4: The adjustment unit monitors the progress of rearing and breeding in real time based on the rearing conditions derived by the simulation unit, and makes adjustments as needed. For example, it manages temperature and humidity and supplies feed. It monitors the rearing environment using temperature and humidity sensors and makes adjustments as needed.

[0071] (Example of form 2) The Insect Breeder AI Agent System according to an embodiment of the present invention is a system that fully automates insect rearing and breeding. Unlike conventional mechanical automation, this Insect Breeder AI Agent System uses an AI that aggregates advanced knowledge to assess the situation and perform optimal rearing and breeding. For example, the Insect Breeder AI Agent System collects and analyzes data on insect growth conditions and living environment. Next, the Insect Breeder AI Agent System derives optimal rearing conditions based on the collected data. This improves production efficiency and leads to cost reduction. The Insect Breeder AI Agent System also simulates the effects of different rearing methods and environmental conditions on insect growth and formulates an optimal production strategy. Furthermore, the Insect Breeder AI Agent System monitors the progress of insect rearing and breeding in real time and makes adjustments as needed. For example, it automatically adjusts temperature and humidity control and feed supply to maintain the optimal health of the insects. This system not only streamlines insect rearing and breeding and reduces costs, but also improves quality. In particular, in the markets for edible and beneficial insects, it becomes possible to establish a stable supply system and improve price competitiveness. Furthermore, the Insect Breeder AI Agent System can further improve the accuracy of rearing and breeding by accumulating insect growth data and continuously learning. This contributes to sustainable agriculture and food production and also contributes to reducing environmental impact. As a result, the Insect Breeder AI Agent System can efficiently automate insect rearing and breeding and provide optimal rearing conditions.

[0072] The insect breeder AI agent system according to this embodiment comprises a data collection unit, an analysis unit, a simulation unit, and an adjustment unit. The data collection unit collects data related to the growth conditions and environment of insects. For example, the data collection unit collects data such as temperature, humidity, and light intensity. The data collection unit can also collect data such as the type of breeding container and the type of feed. For example, the data collection unit measures the temperature of the breeding environment using a temperature sensor and collects the data. It measures the humidity of the breeding environment using a humidity sensor and collects the data. It measures the light intensity of the breeding environment using a light sensor and collects the data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit evaluates the growth conditions of insects based on the collected data. The analysis unit can also evaluate the appropriateness of the breeding environment based on the collected data. For example, the analysis unit evaluates the temperature range suitable for insect growth based on the temperature data. It evaluates the humidity range suitable for insect growth based on the humidity data. It evaluates the light intensity range suitable for insect growth based on the light intensity data. The simulation unit derives the optimal breeding conditions based on the data obtained by the analysis unit. The simulation unit simulates different rearing conditions and derives the optimal conditions. The simulation unit can also simulate different environmental conditions and derive the optimal environmental conditions. For example, the simulation unit performs simulations with varying temperatures to derive the optimal temperature conditions. It performs simulations with varying humidity to derive the optimal humidity conditions. It performs simulations with varying light intensity to derive the optimal light intensity conditions. The adjustment unit monitors the progress of rearing and breeding in real time based on the rearing conditions derived by the simulation unit and makes adjustments as needed. The adjustment unit manages temperature and humidity, for example. The adjustment unit can also supply feed. For example, the adjustment unit monitors the temperature of the rearing environment using a temperature sensor and adjusts the temperature as needed. It monitors the humidity of the rearing environment using a humidity sensor and adjusts the humidity as needed. It supplies feed to insects using a feed supply device. As a result, the insect breeder AI agent system according to this embodiment can efficiently automate insect rearing and breeding and provide optimal rearing conditions.

[0073] The data collection unit collects data on insect growth conditions and their environment. Specifically, it uses various sensors to collect environmental data such as temperature, humidity, and light intensity. For example, a temperature sensor accurately measures the temperature of the rearing environment and collects data in real time. A humidity sensor measures the humidity of the rearing environment and provides data to understand the humidity range suitable for insect growth. A light sensor measures the light intensity of the rearing environment and collects data to ensure the amount of light necessary for insect growth. Furthermore, the data collection unit can also collect data such as the type of rearing container and the type of feed. For example, it collects information on the material, shape, and size of the rearing container and provides data to select the optimal rearing container for insect growth. It also collects data on the type, amount, and frequency of feed and provides information to maintain the optimal nutritional status of the insects. In this way, the data collection unit can comprehensively understand the diverse factors that affect insect growth and provide the data necessary to optimize the rearing environment. Furthermore, the data collection unit can transmit the collected data to a cloud server and collaborate with other systems and departments. For example, the collected data can be made accessible to the analysis unit and simulation unit, and the data can be shared in real time. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0074] The analysis department analyzes data collected by the data collection department to evaluate the suitability of insect growth conditions and rearing environments. Specifically, it evaluates the temperature range suitable for insect growth based on collected temperature data, the humidity range suitable for insect growth based on humidity data, and the light intensity range suitable for insect growth based on light intensity data. The analysis department processes this data in real time using AI to identify the optimal environmental conditions for insect growth. For example, the AI ​​uses historical data and statistical information to analyze temperature data and identify the optimal temperature range for insect growth. It uses anomaly detection algorithms to analyze humidity data and identify the optimal humidity range for insect growth. It uses image recognition technology to analyze light intensity data and identify the optimal light intensity range for insect growth. This allows the analysis department to quickly and accurately analyze collected data and identify the optimal environmental conditions for insect growth. Furthermore, the analysis department can also use historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on historical growth data, it can predict fluctuations in growth under specific environmental conditions and time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0075] The simulation unit derives optimal rearing conditions based on data obtained by the analysis unit. Specifically, it uses AI to simulate different rearing conditions and derive the optimal conditions. For example, it performs simulations with varying temperatures to derive the optimal temperature conditions, simulations with varying humidity to derive the optimal humidity conditions, and simulations with varying light intensity to derive the optimal light intensity conditions. Through these simulations, the simulation unit identifies the optimal environmental conditions for insect growth. The simulation unit can also simulate different environmental conditions and derive the optimal conditions. For example, it performs simulations with varying types of rearing containers and types of feed to derive the optimal rearing containers and feed types. In this way, the simulation unit can identify the optimal rearing conditions for insect growth and support the optimization of the rearing environment. Furthermore, the simulation unit can continuously modify the simulation results based on data updated in real time and respond to the latest situations. For example, if the temperature or humidity changes rapidly, the simulation unit immediately takes in new data and updates the simulation results. The simulation unit can also perform more accurate simulations by considering regional characteristics and past growth history. This allows the simulation unit to provide highly accurate simulations based on the latest information at all times, enabling it to determine the optimal rearing conditions for insect growth.

[0076] The adjustment unit monitors the progress of rearing and breeding in real time based on the rearing conditions derived by the simulation unit and makes adjustments as needed. Specifically, it manages temperature and humidity to maintain an optimal environment for insect growth. For example, it monitors the temperature of the rearing environment using a temperature sensor and adjusts the temperature as needed. It monitors the humidity of the rearing environment using a humidity sensor and adjusts the humidity as needed. It monitors the light intensity of the rearing environment using a light sensor and adjusts the light intensity as needed. Furthermore, the adjustment unit can also supply food. For example, it supplies food to the insects using a food supply device to maintain their nutritional status optimally. The adjustment unit also cleans and maintains the rearing environment to create an environment that maintains the health of the insects. In this way, the adjustment unit can maintain an optimal environment for insect growth and improve the efficiency of rearing and breeding. In addition, the adjustment unit can continuously modify the adjustment settings based on data that is updated in real time and respond to the latest situation. For example, if the temperature or humidity changes rapidly, the adjustment unit immediately takes in the new data and updates the adjustment settings. Furthermore, the adjustment unit can make more precise adjustments by taking into account the characteristics of each region and past growth history. This allows the adjustment unit to provide highly accurate adjustments based on the latest information at all times, maintaining an optimal breeding environment for insect growth.

[0077] The adjustment unit includes a temperature control unit and a humidity control unit that manage temperature and humidity. The temperature control unit manages the temperature of the rearing environment. For example, the temperature control unit measures the temperature of the rearing environment using a temperature sensor and adjusts the temperature as needed. The temperature control unit can set a temperature range and maintain the temperature within that range. For example, if the temperature of the rearing environment exceeds the set range, the temperature control unit activates a cooling device to lower the temperature. The temperature control unit can also activate a heating device to raise the temperature if the temperature of the rearing environment falls below the set range. The humidity control unit manages the humidity of the rearing environment. For example, the humidity control unit measures the humidity of the rearing environment using a humidity sensor and adjusts the humidity as needed. The humidity control unit can set a humidity range and maintain the humidity within that range. For example, if the humidity of the rearing environment exceeds the set range, the humidity control unit activates a dehumidifier to lower the humidity. The humidity control unit can also activate a humidifier to raise the humidity if the humidity of the rearing environment falls below the set range. This automates the management of temperature and humidity, and allows for the optimal maintenance of the insect rearing environment. Some or all of the above-described processes in the temperature control unit and humidity control unit may be performed using AI, for example, or without AI. For example, the temperature control unit can input data from a temperature sensor into a generating AI and cause the generating AI to execute instructions for temperature adjustment. The humidity control unit can input data from a humidity sensor into a generating AI and cause the generating AI to execute instructions for humidity adjustment.

[0078] The adjustment unit includes a feed supply unit that supplies feed. The feed supply unit supplies feed to insects. The feed supply unit supplies feed to insects, for example, using a feed supply device. The feed supply unit can set the frequency and amount of feed to be supplied and supply feed based on those settings. For example, the feed supply unit adjusts the frequency and amount of feed supplied according to the growth stage of the insect. The feed supply unit can also adjust the frequency and amount of feed supplied according to the health condition of the insect. For example, the feed supply unit increases the frequency of feed supply according to the growth rate during the larval stage. During the adult stage, it adjusts the amount of feed supplied according to reproductive behavior. This automates the supply of feed and maintains the health condition of the insects optimally. Some or all of the above-described processes in the feed supply unit may be performed using AI, for example, or without AI. For example, the feed supply unit can input insect growth data into a generating AI and cause the generating AI to execute instructions for feed supply.

[0079] The data collection unit includes a learning unit that stores insect growth data and continuously learns from it. The learning unit stores insect growth data and continuously learns from it. For example, the learning unit collects insect growth data and learns from that data. The learning unit can improve the accuracy of rearing and breeding based on the collected data. For example, the learning unit collects data on the growth rate and health status of insects and derives optimal rearing conditions based on that data. The learning unit can also identify areas for improvement in the rearing environment based on the insect growth data. For example, the learning unit optimizes methods for adjusting temperature and humidity based on the insect growth data. It also optimizes methods for supplying food. In this way, the accuracy of rearing and breeding can be improved by accumulating growth data and continuously learning from it. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input insect growth data into a generating AI and have the generating AI perform optimization of the learning algorithm.

[0080] The data collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit reduces the frequency of data collection to alleviate the user's burden. If the user is relaxed, the data collection unit can also increase the frequency of data collection to collect more detailed data. If the user is in a hurry, the data collection unit can shorten the timing of data collection to collect data quickly. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0081] The data collection unit dynamically changes the type of data it collects according to the insect's growth stage. For example, during the larval stage, the data collection unit focuses on collecting data on growth rate and body weight. During the pupal stage, it can also collect data on the hardening state and color changes of the pupa. During the adult stage, it can also collect data on reproductive behavior and lifespan. This allows for the collection of more accurate data by collecting data according to the growth stage. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input insect growth stage data into a generating AI and have the generating AI dynamically change the type of data to collect.

[0082] The data collection unit analyzes insect behavior patterns during data collection and detects abnormal behavior. For example, if an insect deviates from its normal behavior pattern, the data collection unit detects it as abnormal behavior. When abnormal behavior is detected, the data collection unit can also issue an alert and notify the user. The data collection unit can also analyze the cause of the abnormal behavior and propose countermeasures. This allows for early countermeasures to be taken by detecting abnormal behavior. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input insect behavior data into a generating AI and have the generating AI perform abnormal behavior detection.

[0083] The data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit prioritizes collecting only important data. If the user is relaxed, the data collection unit may also prioritize collecting detailed data. If the user is in a hurry, the data collection unit may also prioritize collecting data that can be collected quickly. This enables efficient data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data to collect.

[0084] The data collection unit collects data while considering the geographical conditions of the insect's habitat. For example, in hot and humid regions, the data collection unit will focus on collecting temperature and humidity data. In dry regions, the data collection unit may also focus on collecting water supply data. In high-altitude areas, the data collection unit may also collect atmospheric pressure and oxygen concentration data. This allows for the provision of a more appropriate rearing environment by collecting data that takes geographical conditions into account. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical condition data into a generating AI and have the generating AI execute instructions for data collection.

[0085] The data collection unit analyzes the genetic information of insects during data collection to identify factors that affect growth. For example, the data collection unit analyzes genetic information to identify genes that affect growth rate. Based on the genetic information, the data collection unit can also identify factors that affect reproductive success rate. The data collection unit can also analyze genetic information to identify factors that affect health status. In this way, by analyzing genetic information, factors that affect growth can be identified and optimal rearing conditions can be provided. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the genetic information of insects into a generating AI and have the generating AI identify factors that affect growth.

[0086] The analysis unit estimates the user's emotions and adjusts the presentation of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a simple and easy-to-understand graph. If the user is relaxed, the analysis unit can also provide a report with detailed data. If the user is in a hurry, the analysis unit can also provide a concise summary. This facilitates user understanding by providing analysis results in a presentation style that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis results.

[0087] The analysis unit integrates insect growth data and environmental data during analysis to analyze their correlations. For example, the analysis unit can integrate growth data and temperature data to analyze the correlation between growth rate and temperature. The analysis unit can also integrate growth data and humidity data to analyze the correlation between growth rate and humidity. The analysis unit can also integrate growth data and feed supply data to analyze the correlation between growth rate and the type and amount of feed. By analyzing the correlation between growth data and environmental data, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input growth data and environmental data into a generating AI and have the generating AI perform the correlation analysis.

[0088] The analysis unit detects anomalies by comparing them with past data during analysis and issues alerts. For example, the analysis unit can detect abnormal growth rates by comparing them with past growth data. The analysis unit can also detect abnormal temperatures and humidity by comparing them with past environmental data. The analysis unit can also detect abnormal feed consumption by comparing it with past feed supply data. This allows for early countermeasures to be taken by detecting anomalies and issuing alerts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the detection of anomalies.

[0089] The analysis unit estimates the user's emotions and prioritizes the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit may prioritize displaying only the most important analysis results. If the user is relaxed, the analysis unit may also prioritize displaying detailed analysis results. If the user is in a hurry, the analysis unit may also prioritize displaying analysis results that can be quickly reviewed. This enables efficient data analysis by prioritizing analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of the analysis results.

[0090] The analysis unit analyzes data while considering fluctuations in the insect's habitat. For example, the analysis unit analyzes growth data while considering seasonal temperature fluctuations. The analysis unit can also analyze growth data while considering humidity fluctuations. The analysis unit can also analyze growth data while considering fluctuations in the amount of food supplied. This allows for more accurate analysis results by analyzing data while considering fluctuations in the habitat. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input growth environment fluctuation data into a generating AI and have the generating AI perform the data analysis.

[0091] The analysis unit evaluates the health status of insects during analysis and proposes preventive measures. For example, the analysis unit can detect signs of disease based on health status data. The analysis unit can also propose appropriate preventive measures based on health status data. The analysis unit can also propose improvements to the rearing environment based on health status data. In this way, the health of insects can be maintained by evaluating their health status and proposing preventive measures. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input health status data into a generating AI and have the generating AI execute the proposal of preventive measures.

[0092] The simulation unit estimates the user's emotions and adjusts the simulation parameters based on the estimated emotions. For example, if the user is stressed, the simulation unit sets simple simulation parameters. If the user is relaxed, the simulation unit can also set detailed simulation parameters. If the user is in a hurry, the simulation unit can also set parameters to perform the simulation quickly. This reduces the user's burden by adjusting the simulation parameters according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, for example, or not using AI. For example, the simulation unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the simulation parameters.

[0093] The simulation unit compares different rearing conditions during the simulation and derives the optimal conditions. For example, the simulation unit can perform a simulation with varying temperatures to derive the optimal temperature conditions. The simulation unit can also perform a simulation with varying humidity to derive the optimal humidity conditions. The simulation unit can also perform a simulation with varying types and amounts of feed to derive the optimal feed supply conditions. In this way, the optimal rearing conditions can be derived by comparing different rearing conditions. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input rearing condition data into a generating AI and have the generating AI perform the deriving of the optimal conditions.

[0094] The simulation unit predicts insect growth during simulations and creates future production plans. For example, the simulation unit predicts future growth based on growth data. The simulation unit can also create an optimal production plan based on the growth prediction. The simulation unit can also plan the necessary resources based on the growth prediction. In this way, future production plans can be made by performing growth predictions. Some or all of the above processes in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input growth data into a generating AI and have the generating AI perform growth predictions.

[0095] The simulation unit estimates the user's emotions and adjusts the display method of the simulation results based on the estimated user emotions. For example, if the user is stressed, the simulation unit provides a simple and highly visible display method. If the user is relaxed, the simulation unit can also provide a display method that includes detailed information. If the user is in a hurry, the simulation unit can also provide a display method that gets straight to the point. This facilitates user understanding by providing simulation results in a display method that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, for example, or not using AI. For example, the simulation unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the simulation results.

[0096] The simulation unit formulates an optimal breeding strategy during the simulation, taking into account the genetic diversity of insects. For example, the simulation unit selects the optimal mating pair, taking genetic diversity into consideration. The simulation unit can also formulate a strategy to increase the breeding success rate based on genetic diversity. The simulation unit can also select healthy individuals, taking genetic diversity into consideration. In this way, by considering genetic diversity, it is possible to achieve the breeding of healthy and diverse insects. Some or all of the above processes in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input genetic diversity data into a generating AI and have the generating AI formulate an optimal breeding strategy.

[0097] The simulation unit simulates fluctuations in the insect's growth environment during the simulation and performs risk management. For example, the simulation unit can simulate temperature fluctuations and perform risk management. The simulation unit can also simulate humidity fluctuations and perform risk management. The simulation unit can also simulate fluctuations in food supply and perform risk management. In this way, by simulating fluctuations in the growth environment, risk management can be performed and a stable rearing environment can be provided. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input growth environment fluctuation data into a generating AI and have the generating AI execute a risk management simulation.

[0098] The adjustment unit estimates the user's emotions and determines the frequency of adjustments based on the estimated emotions. For example, if the user is stressed, the adjustment unit reduces the frequency of adjustments to alleviate the user's burden. If the user is relaxed, the adjustment unit can increase the frequency of adjustments and perform more detailed adjustments. If the user is in a hurry, the adjustment unit can also set a frequency for quick adjustments. This reduces the user's burden by determining the frequency of adjustments according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI determine the frequency of adjustments.

[0099] The adjustment unit dynamically changes the adjustment settings according to the insect's growth stage during adjustment. For example, during the larval stage, the adjustment unit focuses on adjusting temperature and humidity. During the pupal stage, the adjustment unit can also adjust temperature and humidity according to the hardening state of the pupa. During the adult stage, the adjustment unit can also adjust the amount of food supplied according to reproductive behavior. By making adjustments according to the growth stage, a more appropriate rearing environment can be provided. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input insect growth stage data into a generating AI and cause the generating AI to dynamically change the adjustment settings.

[0100] The adjustment unit monitors the health status of the insects during adjustment and intervenes as necessary. For example, the adjustment unit intervenes if an abnormality is detected based on the health status data. The adjustment unit can also propose and implement preventive measures based on the health status data. The adjustment unit can also propose and implement improvements to the rearing environment based on the health status data. In this way, the health of the insects can be maintained by monitoring their health status and intervening as necessary. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input health status data into a generating AI and have the generating AI execute instructions for intervention.

[0101] The adjustment unit estimates the user's emotions and determines the priority of adjustments based on the estimated emotions. For example, if the user is stressed, the adjustment unit may prioritize only important adjustments. If the user is relaxed, the adjustment unit may also prioritize detailed adjustments. If the user is in a hurry, the adjustment unit may also prioritize items that can be adjusted quickly. This enables efficient adjustments by determining the priority of adjustments according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI determine the priority of adjustments.

[0102] The adjustment unit makes adjustments while considering the geographical conditions of the insect's habitat. For example, in hot and humid regions, the adjustment unit focuses on adjusting temperature and humidity. In dry regions, the adjustment unit may also focus on adjusting water supply. In high altitudes, the adjustment unit may also adjust atmospheric pressure and oxygen concentration. This allows for the provision of a more appropriate rearing environment by making adjustments that take geographical conditions into account. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input geographical condition data into a generating AI and have the generating AI execute adjustment instructions.

[0103] The adjustment unit analyzes the insect's behavioral patterns during adjustment, detects abnormal behavior, and makes adjustments. For example, if an insect deviates from its normal behavioral pattern, the adjustment unit detects it as abnormal behavior and makes adjustments. When abnormal behavior is detected, the adjustment unit can also issue an alert, notify the user, and make adjustments. The adjustment unit can also analyze the cause of the abnormal behavior, propose countermeasures, and make adjustments. This allows for early countermeasures to be taken by detecting and adjusting for abnormal behavior. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input insect behavioral data into a generating AI and have the generating AI perform abnormal behavior detection and adjustments.

[0104] The temperature control unit estimates the user's emotions and adjusts the temperature control settings based on the estimated emotions. For example, if the user is stressed, the temperature control unit provides a simple temperature setting. If the user is relaxed, the temperature control unit can also provide a more detailed temperature setting. If the user is in a hurry, the temperature control unit can also provide a quickly adjustable temperature setting. This reduces the user's burden by adjusting the temperature control settings according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the temperature control unit may be performed using AI, for example, or not using AI. For example, the temperature control unit can input user emotion data into a generative AI and have the generative AI adjust the temperature control settings.

[0105] The temperature control unit dynamically changes the temperature setting according to the growth stage of the insect during temperature control. For example, during the larval stage, the temperature control unit changes the temperature setting according to the growth rate. During the pupal stage, the temperature control unit may also change the temperature setting according to the hardening state of the pupa. During the adult stage, the temperature control unit may also change the temperature setting according to the reproductive behavior. This allows for the provision of a more appropriate rearing environment by setting the temperature according to the growth stage. Some or all of the above processes in the temperature control unit may be performed using AI, for example, or without AI. For example, the temperature control unit can input insect growth stage data into a generating AI and have the generating AI perform dynamic changes to the temperature setting.

[0106] The temperature control unit estimates the user's emotions and determines the priority of temperature control based on the estimated emotions. For example, if the user is stressed, the temperature control unit will prioritize only the most important temperature settings. If the user is relaxed, the temperature control unit may also prioritize detailed temperature settings. If the user is in a hurry, the temperature control unit may also prioritize temperature settings that can be set quickly. This enables efficient temperature control by determining the priority of temperature control according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the temperature control unit may be performed using AI, or not using AI. For example, the temperature control unit can input user emotion data into a generative AI and have the generative AI determine the priority of temperature control.

[0107] The temperature control unit sets the temperature considering the geographical conditions of the insect's habitat. For example, in hot and humid regions, the temperature control unit sets the temperature lower. In dry regions, the temperature control unit may also set the temperature higher. In high-altitude areas, the temperature control unit may also set the temperature considering atmospheric pressure and oxygen concentration. This allows for the provision of a more appropriate rearing environment by setting the temperature considering geographical conditions. Some or all of the above processes in the temperature control unit may be performed using AI, for example, or without AI. For example, the temperature control unit can input geographical condition data into a generating AI and have the generating AI execute instructions for temperature setting.

[0108] The humidity control unit estimates the user's emotions and adjusts the humidity control settings based on the estimated emotions. For example, if the user is stressed, the humidity control unit provides a simple humidity setting. If the user is relaxed, the humidity control unit can also provide a detailed humidity setting. If the user is in a hurry, the humidity control unit can also provide a humidity setting that can be set quickly. This reduces the burden on the user by adjusting the humidity control settings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the humidity control unit may be performed using AI, for example, or not using AI. For example, the humidity control unit can input user emotion data into a generative AI and have the generative AI adjust the humidity control settings.

[0109] The humidity control unit dynamically changes the humidity settings according to the growth stage of the insect during humidity control. For example, during the larval stage, the humidity control unit changes the humidity settings according to the growth rate. During the pupal stage, the humidity control unit can also change the humidity settings according to the hardening state of the pupa. During the adult stage, the humidity control unit can also change the humidity settings according to the reproductive behavior. This allows for the provision of a more appropriate rearing environment by setting the humidity according to the growth stage. Some or all of the above processing in the humidity control unit may be performed using AI, for example, or without AI. For example, the humidity control unit can input insect growth stage data into a generating AI and have the generating AI perform the dynamic changes to the humidity settings.

[0110] The humidity control unit estimates the user's emotions and determines the priority of humidity control based on the estimated emotions. For example, if the user is stressed, the humidity control unit will prioritize only the most important humidity settings. If the user is relaxed, the humidity control unit may also prioritize detailed humidity settings. If the user is in a hurry, the humidity control unit may also prioritize humidity settings that can be set quickly. This enables efficient humidity control by determining the priority of humidity control according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the humidity control unit may be performed using AI or not using AI. For example, the humidity control unit can input user emotion data into a generative AI and have the generative AI determine the priority of humidity control.

[0111] The humidity control unit sets the humidity level considering the geographical conditions of the insect's habitat. For example, in hot and humid regions, the humidity control unit sets the humidity level lower. In dry regions, the humidity control unit can also set the humidity level higher. In high-altitude areas, the humidity control unit can also set the humidity level considering atmospheric pressure and oxygen concentration. This allows for the provision of a more appropriate rearing environment by setting the humidity level considering geographical conditions. Some or all of the above processes in the humidity control unit may be performed using AI, for example, or without AI. For example, the humidity control unit can input geographical condition data into a generating AI and have the generating AI execute instructions for setting the humidity level.

[0112] The feeding unit estimates the user's emotions and adjusts the timing of feeding based on the estimated emotions. For example, if the user is stressed, the feeding unit provides a simple feeding timing. If the user is relaxed, the feeding unit can also provide a detailed feeding timing. If the user is in a hurry, the feeding unit can also provide a feeding timing that allows for rapid feeding. This reduces the user's burden by adjusting the feeding timing according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feeding unit may be performed using AI or not. For example, the feeding unit can input user emotion data into the generative AI and have the generative AI adjust the feeding timing.

[0113] The feed supply unit dynamically changes the type and amount of feed according to the growth stage of the insect when supplying feed. For example, during the larval stage, the feed supply unit changes the type and amount of feed according to the growth rate. During the pupal stage, the feed supply unit can also change the type and amount of feed according to the hardening state of the pupa. During the adult stage, the feed supply unit can also change the type and amount of feed according to the reproductive behavior. In this way, a more appropriate rearing environment can be provided by changing the type and amount of feed according to the growth stage. Some or all of the above processing in the feed supply unit may be performed using AI, for example, or without using AI. For example, the feed supply unit can input insect growth stage data into a generating AI and cause the generating AI to perform dynamic changes in the type and amount of feed.

[0114] The feeding unit estimates the user's emotions and determines the priority of feeding based on the estimated emotions. For example, if the user is stressed, the feeding unit will prioritize feeding only important items. If the user is relaxed, the feeding unit may also prioritize feeding detailed items. If the user is in a hurry, the feeding unit may also prioritize feeding items that can be supplied quickly. This allows for efficient feeding by prioritizing feeding according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feeding unit may be performed using AI or not. For example, the feeding unit can input user emotion data into a generative AI and have the generative AI determine the priority of feeding.

[0115] The feed supply unit sets the type and amount of feed considering the geographical conditions of the insect's habitat when supplying feed. For example, the feed supply unit adjusts the type and amount of feed in hot and humid regions. The feed supply unit can also adjust the type and amount of feed in dry regions. In high-altitude areas, the feed supply unit can also set the type and amount of feed considering atmospheric pressure and oxygen concentration. This allows for the provision of a more appropriate rearing environment by setting the type and amount of feed considering geographical conditions. Some or all of the above processing in the feed supply unit may be performed using AI, for example, or without AI. For example, the feed supply unit can input geographical condition data into a generating AI and have the generating AI execute the setting of the type and amount of feed.

[0116] The learning unit estimates the user's emotions and selects training data based on the estimated emotions. For example, if the user is stressed, the learning unit selects simple training data. If the user is relaxed, the learning unit can also select detailed training data. If the user is in a hurry, the learning unit can also select data that allows for rapid learning. This enables efficient learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform the selection of training data.

[0117] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects the optimal learning algorithm based on past learning data. The learning unit can also adjust the parameters of the learning algorithm based on past learning data. The learning unit can also improve the accuracy of the learning algorithm based on past learning data. In this way, the accuracy of the learning algorithm can be improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0118] The learning unit estimates the user's emotions and adjusts the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit reduces the learning frequency to alleviate the user's burden. If the user is relaxed, the learning unit can also increase the learning frequency to perform more detailed learning. If the user is in a hurry, the learning unit can set a frequency that allows for rapid learning. This reduces the user's burden by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI adjust the learning frequency.

[0119] The learning unit weights the learning data based on the timing of insect growth data submission during the learning process. For example, the learning unit weights important data based on the timing of growth data submission. The learning unit can also weight seasonal data based on the timing of growth data submission. The learning unit can also weight data for specific growth stages based on the timing of growth data submission. This allows for more accurate learning by weighting based on the timing of growth data submission. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input growth data submission timing data into a generating AI and have the generating AI perform the weighting of the learning data.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The data collection unit can analyze insect behavior patterns and detect abnormal behavior when collecting insect growth data. For example, if an insect deviates from its normal behavior pattern, it can be detected as abnormal behavior, an alert can be issued, and the user can be notified. It can also analyze the cause of the abnormal behavior and propose countermeasures. This allows for early detection of abnormal behavior and the implementation of countermeasures, thereby maintaining the insect's health at an optimal level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input insect behavior data into a generating AI and have the generating AI perform abnormal behavior detection.

[0122] The adjustment unit can dynamically change the adjustment settings according to the insect's growth stage. For example, during the larval stage, the adjustment of temperature and humidity is the primary focus. During the pupal stage, the temperature and humidity can be adjusted according to the hardening state of the pupa. During the adult stage, the amount of food supplied can be adjusted according to the reproductive behavior. By making adjustments according to the growth stage, a more appropriate rearing environment can be provided. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input insect growth stage data into a generating AI and cause the generating AI to dynamically change the adjustment settings.

[0123] The data collection unit may include a learning unit that stores insect growth data and continuously learns from it. The learning unit collects insect growth data and learns from that data. For example, it can collect data on the growth rate and health status of insects and derive optimal rearing conditions based on that data. The learning unit can also identify areas for improvement in the rearing environment based on the insect growth data. In this way, the accuracy of rearing and breeding can be improved by accumulating growth data and continuously learning from it. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input insect growth data into a generating AI and have the generating AI perform optimization of the learning algorithm.

[0124] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. If the user is relaxed, the frequency of data collection can be increased to collect more detailed data. If the user is in a hurry, the timing of data collection can be shortened to collect data quickly. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0125] The data collection unit can dynamically change the types of data it collects according to the insect's growth stage. For example, during the larval stage, it can focus on collecting data on growth rate and weight. During the pupal stage, it can also collect data on the hardening state and color changes of the pupa. During the adult stage, it can also collect data on reproductive behavior and lifespan. This allows for the collection of more accurate data by collecting data according to the growth stage. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input insect growth stage data into a generating AI and have the generating AI dynamically change the types of data to collect.

[0126] The data collection unit can analyze insect behavior patterns during data collection and detect abnormal behavior. For example, if an insect deviates from its normal behavior pattern, it can be detected as abnormal behavior, an alert can be issued, and the user can be notified. It can also analyze the cause of the abnormal behavior and propose countermeasures. This allows for early detection of abnormal behavior and the implementation of countermeasures, thereby maintaining the insects' health at an optimal level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input insect behavior data into a generating AI and have the generating AI perform abnormal behavior detection.

[0127] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, only important data can be prioritized for collection. If the user is relaxed, detailed data can be prioritized for collection. If the user is in a hurry, data that can be collected quickly can be prioritized for collection. This enables efficient data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data to collect.

[0128] The data collection unit can collect data while considering the geographical conditions of the insect's habitat. For example, in hot and humid regions, it can focus on collecting temperature and humidity data. In dry regions, it can focus on collecting water supply data. In high altitudes, it can also collect atmospheric pressure and oxygen concentration data. This allows for the provision of a more appropriate rearing environment by collecting data that takes geographical conditions into account. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical condition data into a generating AI and have the generating AI execute instructions for data collection.

[0129] The data collection unit can analyze the genetic information of insects during data collection and identify factors that affect their growth. For example, it can analyze genetic information to identify genes that affect growth rate. Based on genetic information, it can also identify factors that affect reproductive success rate. It can also analyze genetic information to identify factors that affect health status. In this way, by analyzing genetic information, it is possible to identify factors that affect growth and provide optimal rearing conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the genetic information of insects into a generating AI and have the generating AI identify factors that affect growth.

[0130] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is stressed, a simple and easy-to-understand graph can be provided. If the user is relaxed, a report with detailed data can be provided. If the user is in a hurry, a concise summary can be provided. This facilitates user understanding by providing analysis results in a presentation style that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis results.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The data collection unit collects data on insect growth conditions and living environment. For example, it collects data on temperature, humidity, light intensity, type of rearing container, and type of feed. It measures and collects various data on the rearing environment using temperature sensors, humidity sensors, and light sensors. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it evaluates the suitability of the insect's growth conditions and rearing environment based on the collected data. It evaluates the range suitable for insect growth based on temperature data, humidity data, and light intensity data. Step 3: The simulation unit derives the optimal rearing conditions based on the data obtained by the analysis unit. For example, it simulates different rearing and environmental conditions to determine the optimal temperature, humidity, and light conditions. Step 4: The adjustment unit monitors the progress of rearing and breeding in real time based on the rearing conditions derived by the simulation unit, and makes adjustments as needed. For example, it manages temperature and humidity and supplies feed. It monitors the rearing environment using temperature and humidity sensors and makes adjustments as needed.

[0133] 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.

[0134] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0135] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the data acquisition unit, analysis unit, simulation unit, and adjustment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data acquisition unit collects data on insect growth conditions and rearing environments using the temperature sensor, humidity sensor, and light sensor of the smart device 14. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to evaluate the insect growth conditions. The simulation unit simulates different rearing conditions using the specific processing unit 290 of the data processing unit 12 to derive the optimal conditions. The adjustment unit manages the temperature and humidity of the rearing environment and supplies feed using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] 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.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0140] 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.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0142] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] 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.

[0144] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the data acquisition unit, analysis unit, simulation unit, and adjustment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data acquisition unit collects data on insect growth conditions and rearing environments using the temperature sensor, humidity sensor, and light sensor of the smart glasses 214. The analysis unit analyzes the collected data, for example, by the specific processing unit 290 of the data processing unit 12, and evaluates the insect growth conditions. The simulation unit simulates different rearing conditions, for example, by the specific processing unit 290 of the data processing unit 12, and derives the optimal conditions. The adjustment unit manages the temperature and humidity of the rearing environment and supplies feed, for example, by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] 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.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0156] 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.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0158] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] 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.

[0160] 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.

[0161] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the data acquisition unit, analysis unit, simulation unit, and adjustment unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the data acquisition unit collects data on insect growth conditions and rearing environments using the temperature sensor, humidity sensor, and light sensor of the headset terminal 314. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 to evaluate the insect growth conditions. The simulation unit simulates different rearing conditions by the specific processing unit 290 of the data processing unit 12 to derive the optimal conditions. The adjustment unit manages the temperature and humidity of the rearing environment and supplies feed by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0172] 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.

[0173] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0174] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0175] 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.

[0176] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0177] 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.

[0178] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0179] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0180] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0181] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0182] 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.

[0183] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0185] Each of the multiple elements described above, including the data acquisition unit, analysis unit, simulation unit, and adjustment unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data acquisition unit collects data on insect growth conditions and rearing environments using the temperature sensor, humidity sensor, and light sensor of the robot 414. The analysis unit analyzes the collected data by, for example, the specific processing unit 290 of the data processing unit 12 to evaluate the insect growth conditions. The simulation unit simulates different rearing conditions by, for example, the specific processing unit 290 of the data processing unit 12 to derive the optimal conditions. The adjustment unit manages the temperature and humidity of the rearing environment and supplies feed by, for example, the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0186] 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.

[0187] Figure 9 shows the 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.

[0188] 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.

[0189] 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.

[0190] 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, and motorcycles, 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 based, for example, 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.

[0191] 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."

[0192] 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.

[0193] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0202] 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 other things 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.

[0203] 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.

[0204] (Note 1) A data collection unit that collects data on insect growth conditions and habitats, An analysis unit analyzes the data collected by the aforementioned data collection unit, A simulation unit that derives optimal breeding conditions based on the data obtained by the analysis unit, The system includes an adjustment unit that monitors the progress of rearing and breeding in real time based on the rearing conditions derived by the simulation unit and makes adjustments as necessary. A system characterized by the following features. (Note 2) The adjustment unit is, It is equipped with a temperature control unit and a humidity control unit for managing temperature and humidity. The system described in Appendix 1, characterized by the features described herein. (Note 3) The adjustment unit is, It is equipped with a feed supply unit for supplying feed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned data acquisition unit is It has a learning unit that accumulates insect growth data and continuously learns from it. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned data acquisition unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned data acquisition unit is The type of data collected is dynamically changed according to the insect's growth stage. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned data acquisition unit is During data collection, the insect's behavioral patterns are analyzed to detect abnormal behavior. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned data acquisition unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned data acquisition unit is When collecting data, the geographical conditions of the insect's habitat should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned data acquisition unit is During data collection, we analyze the genetic information of insects to identify factors that influence their growth. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During the analysis, insect growth data and environmental data are integrated and their correlations are analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, the system detects anomalies by comparing them with historical data and issues alerts. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During the analysis, the data will be analyzed while taking into account fluctuations in the insect's habitat. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During the analysis, we assess the health status of the insects and propose preventive measures. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation parameters based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned simulation unit, During the simulation, different breeding conditions are compared to determine the optimal conditions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned simulation unit, During the simulation, insect growth is predicted, and future production plans are made. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned simulation unit, It estimates the user's emotions and adjusts how the simulation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned simulation unit, During the simulation, the optimal breeding strategy is formulated by considering the genetic diversity of insects. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned simulation unit, During the simulation, fluctuations in the insect's habitat are simulated, and risk management is performed. The system described in Appendix 1, characterized by the features described herein. (Note 23) The adjustment unit is, The system estimates the user's emotions and determines the frequency of adjustments based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The adjustment unit is, During adjustment, the adjustment settings are dynamically changed according to the insect's growth stage. The system described in Appendix 1, characterized by the features described herein. (Note 25) The adjustment unit is, During adjustments, monitor the health of the insects and intervene as needed. The system described in Appendix 1, characterized by the features described herein. (Note 26) The adjustment unit is, It estimates the user's emotions and determines the priority of adjustments based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, During the adjustment process, the geographical conditions of the insect's habitat are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The adjustment unit is, During adjustments, the insect's behavioral patterns are analyzed, and abnormal behavior is detected and corrected. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned temperature control unit is It estimates the user's emotions and adjusts the temperature control settings based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned temperature control unit is During temperature control, the temperature setting is dynamically changed according to the growth stage of the insect. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned temperature control unit is It estimates the user's emotions and determines the priority of temperature control based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned temperature control unit is When managing temperature, the temperature setting should take into account the geographical conditions of the insect's habitat. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned humidity control unit is It estimates the user's emotions and adjusts the humidity control settings based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned humidity control unit is During humidity control, the humidity setting is dynamically changed according to the growth stage of the insect. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned humidity control unit is It estimates the user's emotions and determines humidity control priorities based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned humidity control unit is When managing humidity, set the humidity level considering the geographical conditions of the insect's habitat. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned feed supply unit is The system estimates the user's emotions and adjusts the timing of feeding based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned feed supply unit is During feeding, the type and amount of food are dynamically changed according to the growth stage of the insects. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned feed supply unit is The system estimates the user's emotions and determines the priority of feeding based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned feed supply unit is When supplying food, the type and amount of food should be determined considering the geographical conditions of the insects' habitat. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned learning unit, During training, the training data is weighted based on when the insect growth data was submitted. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data on insect growth conditions and habitats, An analysis unit analyzes the data collected by the aforementioned data collection unit, A simulation unit that derives optimal breeding conditions based on the data obtained by the analysis unit, The system includes an adjustment unit that monitors the progress of rearing and breeding in real time based on the rearing conditions derived by the simulation unit and makes adjustments as necessary. A system characterized by the following features.

2. The adjustment unit is, It is equipped with a temperature control unit and a humidity control unit for managing temperature and humidity. The system according to feature 1.

3. The adjustment unit is, It is equipped with a feed supply unit for supplying feed. The system according to feature 1.

4. The aforementioned data acquisition unit is It has a learning unit that accumulates insect growth data and continuously learns from it. The system according to feature 1.

5. The aforementioned data acquisition unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

6. The aforementioned data acquisition unit is The type of data collected is dynamically changed according to the insect's growth stage. The system according to feature 1.

7. The aforementioned data acquisition unit is During data collection, the insect's behavioral patterns are analyzed to detect abnormal behavior. The system according to feature 1.

8. The aforementioned data acquisition unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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