System

Abstract

An object of the system according to the embodiment is to efficiently observe and record the growth status and disease signs of tropical fish in a water tank.SOLUTION: A system according to an embodiment includes a fixed-point observation unit, an individual identification unit, a recording unit, and a reporting unit. The fixed-point observation unit observes tropical fish in the water tank. The individual identification unit identifies the tropical fish observed by the fixed-point observation unit. The recording part records information on the tropical fish identified by the individual identification part. The report unit reports a growth state or a sign of a disease based on the information recorded by the recording unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to efficiently observe and record the growth status and signs of illness of tropical fish in an aquarium.

[0005] The system according to the embodiment aims to efficiently observe and record the growth status and signs of illness of tropical fish in an aquarium. [Means for solving the problem]

[0006] The system according to the embodiment includes a fixed-point observation unit, an individual identification unit, a recording unit, and a reporting unit. The fixed-point observation unit observes tropical fish in an aquarium. The individual identification unit identifies the tropical fish observed by the fixed-point observation unit. The recording unit records information about the tropical fish identified by the individual identification unit. The reporting unit reports the growth status or signs of illness based on the information recorded by the recording unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently observe and record the growth status and signs of illness of tropical fish in an aquarium. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The aquarium management system according to an embodiment of the present invention is a system that performs fixed-point observations of tropical fish in an aquarium, records the body color and other characteristics of the tropical fish while identifying each individual fish, and reports on their growth status and signs of illness. This allows the aquarium management system to efficiently monitor the growth status and signs of illness of the tropical fish and prompt appropriate measures to be taken.

[0029] An aquarium management system according to an embodiment includes a fixed-point observation unit, an individual identification unit, a recording unit, and a reporting unit. The fixed-point observation unit observes tropical fish in an aquarium. For example, the fixed-point observation unit observes the tropical fish using a camera installed in the aquarium. The fixed-point observation unit can set an observation frequency and periodically acquire images of the tropical fish. The fixed-point observation unit can fix an observation position and observe the tropical fish from the same position. The individual identification unit identifies the tropical fish observed by the fixed-point observation unit. For example, the individual identification unit analyzes characteristics of the tropical fish, such as body color, pattern, and size, and identifies each individual fish. The individual identification unit can identify the observed tropical fish with high accuracy using an identification algorithm. The individual identification unit can store information about the identified tropical fish in a database. The recording unit records information about the tropical fish identified by the individual identification unit. For example, the recording unit records changes in the tropical fish's body color, weight gain / loss, and changes in behavioral patterns. The recording unit can also set the type of information to be recorded and select and record necessary information. The recording unit can also set the recording frequency and periodically record information about the tropical fish. The reporting unit reports the growth status and signs of illness based on the information recorded by the recording unit. For example, the reporting unit analyzes the recorded information and reports the growth status and signs of illness. The reporting unit can also issue an alert if an abnormality is detected. The reporting unit can also set the report format and select and report necessary information. As a result, the aquarium management system according to the embodiment can efficiently monitor the growth status and signs of illness of the tropical fish and prompt appropriate action.

[0030] The fixed-point observation unit can use the generation AI to analyze the movements and behavior patterns of tropical fish and estimate their stress levels and health status. For example, the fixed-point observation unit uses the generation AI to analyze the movements and behavior patterns of tropical fish and estimate their stress levels. For example, it analyzes the frequency and speed of specific movements and issues an alert if stress levels are high. The fixed-point observation unit also monitors the behavior patterns of tropical fish over long periods of time and analyzes them with the generation AI to estimate their health status. For example, if an abnormal behavior pattern is observed, it suggests the possibility of a health abnormality. The fixed-point observation unit also uses the generation AI to analyze the movements and behavior patterns of tropical fish in real time and estimate their stress levels and health status. For example, if a specific behavior is observed frequently, it is determined that stress levels are high. This allows the stress levels and health status of tropical fish to be estimated in real time.

[0031] The fixed-point observation unit uses a high-resolution camera to capture subtle changes in the body color and pattern of tropical fish, which are then analyzed using generative AI, allowing for early detection of health abnormalities. For example, the fixed-point observation unit uses a high-resolution camera to capture subtle changes in the body color and pattern of tropical fish, which are then analyzed using generative AI. For example, if a change in body color is observed, it will suggest the possibility of a health abnormality. The fixed-point observation unit also uses generative AI to analyze changes in the body color and pattern of tropical fish in real time, allowing for early detection of health abnormalities. For example, if the body color becomes paler, it will suggest the possibility of nutritional deficiency. The fixed-point observation unit also analyzes images taken with the high-resolution camera using generative AI to detect subtle changes in the body color and pattern of tropical fish. For example, if a change in pattern is observed, it will suggest signs of illness. This allows for early detection of health abnormalities in tropical fish.

[0032] The fixed-point observation unit simultaneously observes other organisms in the aquarium (e.g., shrimp and shellfish) and analyzes their interactions, thereby assessing the health of the entire ecosystem. The fixed-point observation unit, for example, uses a generation AI to simultaneously observe other organisms in the aquarium, such as shrimp and shellfish, and analyzes their interactions. For example, if the shrimp are moving actively, it determines that the overall health of the aquarium is good. The fixed-point observation unit also analyzes the behavioral patterns of other organisms in the aquarium to assess the health of the entire ecosystem. For example, if the shellfish are behaving abnormally, it suggests that there may be a problem with the environment in the aquarium. The fixed-point observation unit also uses a generation AI to analyze the movements and behavioral patterns of other organisms in the aquarium to assess the health of the entire ecosystem. For example, if the shrimp and shellfish are behaving normally, it determines that the environment of the entire aquarium is appropriate. This makes it possible to assess the health of the entire ecosystem in the aquarium.

[0033] The fixed-point observation unit simultaneously monitors the external environment of the aquarium (for example, room temperature and humidity) and analyzes it with the generating AI, thereby enabling more accurate optimization of the breeding environment. The fixed-point observation unit, for example, monitors the external environment of the aquarium (for example, room temperature and humidity) and analyzes it with the generating AI. For example, if the room temperature is high, it adjusts the water temperature in the aquarium. The fixed-point observation unit also uses the generating AI to analyze the external environmental data of the aquarium and optimize the breeding environment. For example, if the humidity is low, it suggests measures to prevent water evaporation from the aquarium. The fixed-point observation unit also monitors the room temperature and humidity in real time and analyzes it with the generating AI to optimize the breeding environment. For example, if the room temperature is low, it adjusts the aquarium heater. This makes it possible to accurately optimize the breeding environment while also taking the external environment of the aquarium into consideration.

[0034] The recording unit can use the generation AI to perform a detailed analysis of the growth rate and weight changes of the tropical fish and identify the optimal growth conditions. For example, the recording unit can use the generation AI to perform a detailed analysis of the growth rate of the tropical fish and identify the optimal growth conditions. For example, if the growth rate is slow, the type and amount of food can be adjusted. The recording unit can also use the generation AI to analyze changes in the weight of the tropical fish and identify the optimal growth conditions. For example, if the weight is decreasing, it can suggest the possibility of nutritional deficiency and increase the amount of food. The recording unit can also use the generation AI to analyze the growth rate and weight changes of the tropical fish in real time and identify the optimal growth conditions. For example, if the growth rate is fast, it can determine that the current breeding environment is appropriate. This allows for a detailed analysis of the growth rate and weight changes of the tropical fish and identify the optimal growth conditions.

[0035] The recording unit monitors the behavioral patterns of tropical fish over a long period of time and analyzes them with a generating AI, making it possible to detect early signs of illness. The recording unit, for example, uses a generating AI to monitor the behavioral patterns of tropical fish over a long period of time and detects early signs of illness. For example, if abnormal behavior is observed, it suggests the possibility of illness. The recording unit also analyzes the behavioral patterns of tropical fish in detail and detects early signs of illness. For example, if a specific behavior is observed frequently, it suggests signs of illness. The recording unit also uses a generating AI to analyze the behavioral patterns of tropical fish in real time and detect early signs of illness. For example, if there is a sudden change in behavior, it suggests the possibility of illness. This makes it possible to detect early signs of illness in tropical fish.

[0036] The recording unit can identify general growth patterns and signs of illness by comparing it with data from other keepers and analyzing it with the generating AI. For example, the recording unit collects data from other keepers and analyzes it with the generating AI to identify general growth patterns. For example, it determines that a particular growth rate is common. The recording unit can also compare it with data from other keepers and analyze it with the generating AI to identify general signs of illness. For example, it can determine that a particular behavioral pattern is a sign of illness. The recording unit can also use the generating AI to analyze data from other keepers and identify general growth patterns and signs of illness. For example, it can determine that a particular change in body color is a common growth pattern. This makes it possible to identify general growth patterns and signs of illness by comparing it with data from other keepers.

[0037] The recording unit can convert the growth status and signs of disease of the tropical fish into visual notes or mind maps to make them easier to understand visually. The recording unit, for example, uses a generation AI to convert the growth status and signs of disease of the tropical fish into visual notes. For example, it displays growth rate and changes in body color in graphs or diagrams. The recording unit also converts the growth status and signs of disease of the tropical fish into mind maps to make them easier to understand visually. For example, it displays growth patterns and signs of disease in association with each other. The recording unit also uses a generation AI to convert the growth status and signs of disease of the tropical fish into visual notes or mind maps to display them visually. For example, it displays growth status in chronological order and highlights signs of disease. This makes it easier to understand the growth status and signs of disease of the tropical fish visually.

[0038] The report unit can use the generation AI to suggest the optimal type and amount of food based on the growth status of the tropical fish. For example, the report unit uses the generation AI to analyze the growth status of the tropical fish and suggest the optimal type of food. For example, it selects food with a good nutritional balance based on growth rate and changes in weight. The report unit also analyzes the growth status of the tropical fish in detail and suggests the optimal amount of food using the generation AI. For example, if the weight is increasing, it suggests reducing the amount of food. The report unit also uses the generation AI to analyze the growth status of the tropical fish in real time and suggest the optimal type and amount of food. For example, if the growth rate is slow, it suggests food with high nutritional value. This makes it possible to suggest the optimal type and amount of food based on the growth status of the tropical fish.

[0039] The report unit can analyze water quality data in detail and use generation AI to propose optimal water quality management methods. The report unit, for example, uses generation AI to analyze water quality data in detail and propose optimal water quality management methods. For example, it proposes methods for adjusting water quality based on water temperature, pH, and oxygen concentration. The report unit also monitors water quality data in real time and analyzes it with generation AI to propose optimal water quality management methods. For example, if water quality deteriorates, it will suggest the timing for water changes. The report unit also uses generation AI to analyze water quality data and propose optimal water quality management methods. For example, if a specific water quality parameter shows an abnormal value, it will suggest appropriate measures. This makes it possible to analyze water quality data in detail and propose optimal water quality management methods.

[0040] The report unit can propose the optimal breeding method by collecting success stories of other breeders and analyzing them with the generation AI. The report unit, for example, collects success stories of other breeders and analyzes them with the generation AI to propose the optimal breeding method. For example, it can propose the type and amount of food based on the success stories. The report unit also uses the generation AI to analyze the success stories of other breeders and propose the optimal breeding method. For example, it can propose methods of water quality management based on the success stories. The report unit also collects success stories of other breeders and analyzes them with the generation AI to propose the optimal breeding method. For example, it can propose methods of aquarium layout and lighting adjustment based on the success stories. In this way, it can propose the optimal breeding method based on the success stories of other breeders.

[0041] The report unit compares the growth status of tropical fish with different seasons and climatic conditions and analyzes it using the generation AI, thereby being able to propose the optimal breeding method for each season. For example, the report unit uses the generation AI to compare the growth status of tropical fish with different seasons and climatic conditions and propose the optimal breeding method. For example, it may propose setting the water temperature higher in winter. The report unit also analyzes the growth status of tropical fish in detail and uses the generation AI to propose the optimal breeding method for each season. For example, it may propose setting the water temperature lower in summer. The report unit also uses the generation AI to compare the growth status of tropical fish with different seasons and climatic conditions and propose the optimal breeding method. For example, it may propose strengthening water quality management in spring. This makes it possible to propose the optimal breeding method for each season.

[0042] The report unit uses generative AI to analyze images of tropical fish in detail and detect signs of illness early. The report unit, for example, uses generative AI to analyze images of tropical fish in detail and detect signs of illness early. For example, it detects changes in body color and damage to fins. The report unit also analyzes images of tropical fish in real time and uses generative AI to detect signs of illness early. For example, it detects abnormal behavior. The report unit also uses generative AI to analyze images of tropical fish in detail and detect signs of illness early. For example, it detects changes in body color and damage to fins. This makes it possible to detect signs of illness in tropical fish early.

[0043] The reporting unit can use the generating AI to suggest the optimal prescription when signs of illness are found and monitor the progress. The reporting unit, for example, uses the generating AI to suggest the optimal prescription when signs of illness are found. For example, it can suggest the use of a specific medication. The reporting unit can also use the generating AI to suggest the optimal prescription when signs of illness are found and monitor the progress. For example, it can suggest the frequency of medication use. The reporting unit can also use the generating AI to suggest the optimal prescription when signs of illness are found and monitor the progress. For example, it can suggest the amount of medication to use. This makes it possible to suggest the optimal prescription when signs of illness are found and monitor the progress.

[0044] The reporting unit collects illness data from other keepers and analyzes it with the generating AI to identify common signs of illness and prescriptions. For example, the reporting unit collects illness data from other keepers and analyzes it with the generating AI to identify common signs of illness. For example, it determines that a specific change in body color is a sign of illness. The reporting unit also compares this with the illness data from other keepers and analyzes it with the generating AI to identify common prescriptions. For example, it determines that the use of a specific medicine is common. The reporting unit also uses the generating AI to analyze the illness data from other keepers and identify common signs of illness and prescriptions. For example, it determines that a specific behavioral pattern is a sign of illness. This makes it possible to identify common signs of illness and prescriptions based on the illness data from other keepers.

[0045] The reporting unit can convert signs of disease into visual notes or mind maps to make them easier to understand visually. For example, the reporting unit uses generative AI to convert signs of disease into visual notes. For example, changes in body color or damage to fins can be displayed in graphs or diagrams. The reporting unit can also convert signs of disease into mind maps to make them easier to understand visually. For example, signs of disease can be displayed in association with each other. The reporting unit can also use generative AI to convert signs of disease into visual notes or mind maps to display them visually. For example, signs of disease can be displayed in chronological order to highlight prescriptions. This makes signs of disease easier to understand visually.

[0046] The fixed-point observation unit uses the generating AI to collect environmental data within the aquarium in real time, allowing it to maintain optimal environmental conditions. For example, the fixed-point observation unit uses the generating AI to collect environmental data such as water temperature, pH, and oxygen concentration in the aquarium in real time, maintaining optimal environmental conditions. For example, if the water temperature falls outside the appropriate range, it adjusts the heater or cooler. The fixed-point observation unit also analyzes the environmental data within the aquarium using the generating AI and suggests specific actions to maintain optimal environmental conditions. For example, if the pH is too high, it adds an acidity regulator. The fixed-point observation unit also uses the generating AI to monitor environmental data within the aquarium in real time and builds an automatic adjustment system to maintain optimal environmental conditions. For example, if the oxygen concentration drops, it increases aeration. This allows the fixed-point observation unit to collect environmental data within the aquarium in real time and maintain optimal environmental conditions.

[0047] The fixed-point observation unit can use the generation AI to suggest methods for adjusting water temperature and pH according to the local season and climate. For example, the fixed-point observation unit uses the generation AI to analyze local seasonal and climate data and suggest methods for adjusting water temperature and pH. For example, it suggests setting the water temperature higher in winter. The fixed-point observation unit also collects local climate data in real time and analyzes it with the generation AI to suggest optimal methods for adjusting water temperature and pH. For example, it suggests setting the water temperature lower in summer. The fixed-point observation unit also uses the generation AI to suggest methods for adjusting water temperature and pH according to the local season and climate. For example, it suggests strengthening water quality management in spring. This makes it possible to suggest methods for adjusting water temperature and pH according to the local season and climate.

[0048] The fixed-point observation unit can use the generation AI to analyze environmental data within the aquarium and suggest the optimal type and amount of food. The fixed-point observation unit, for example, uses the generation AI to analyze environmental data within the aquarium and suggest the optimal type of food. For example, it selects food with a good nutritional balance depending on the water quality and water temperature. The fixed-point observation unit also analyzes the environmental data within the aquarium in detail and uses the generation AI to suggest the optimal amount of food. For example, if the water temperature is high, it will suggest reducing the amount of food. The fixed-point observation unit also uses the generation AI to analyze environmental data within the aquarium in real time and suggest the optimal type and amount of food. For example, if the water quality is deteriorating, it will suggest changing the type of food. This makes it possible to suggest the optimal type and amount of food based on the environmental data within the aquarium.

[0049] The fixed point observation unit can use the generating AI to analyze environmental data within the aquarium and suggest the optimal timing for cleaning. The fixed point observation unit, for example, uses the generating AI to analyze environmental data within the aquarium and suggest the optimal timing for cleaning. For example, if the water quality is deteriorating, it will suggest increasing the frequency of cleaning. The fixed point observation unit also monitors the environmental data within the aquarium in real time and analyzes it with the generating AI to suggest the optimal timing for cleaning. For example, if the oxygen concentration is decreasing, it will suggest cleaning. The fixed point observation unit also uses the generating AI to analyze environmental data within the aquarium and suggest the optimal timing for cleaning. For example, if the pH is too high, it will suggest cleaning. In this way, it is possible to suggest the optimal timing for cleaning based on the environmental data within the aquarium.

[0050] The fixed-point observation unit can use the generating AI to analyze environmental data within the aquarium and suggest the optimal timing for water changes. The fixed-point observation unit, for example, uses the generating AI to analyze environmental data within the aquarium and suggest the optimal timing for water changes. For example, if the water quality is deteriorating, it will suggest increasing the frequency of water changes. The fixed-point observation unit also monitors environmental data within the aquarium in real time and analyzes it with the generating AI to suggest the optimal timing for water changes. For example, if the oxygen concentration is decreasing, it will suggest performing a water change. The fixed-point observation unit also uses the generating AI to analyze environmental data within the aquarium and suggest the optimal timing for water changes. For example, if the pH is too high, it will suggest performing a water change. This makes it possible to suggest the optimal timing for water changes based on the environmental data within the aquarium.

[0051] The fixed-point observation unit can use the generating AI to analyze environmental data within the aquarium and suggest the optimal method for using water conditioners. The fixed-point observation unit, for example, uses the generating AI to analyze environmental data within the aquarium and suggest the optimal method for using water conditioners. For example, if the water quality is deteriorating, it will suggest using a specific conditioner. The fixed-point observation unit also monitors environmental data within the aquarium in real time and analyzes it with the generating AI to suggest the optimal method for using water conditioners. For example, if the oxygen concentration is decreasing, it will suggest using a specific conditioner. The fixed-point observation unit also uses the generating AI to analyze environmental data within the aquarium and suggest the optimal method for using water conditioners. For example, if the pH is too high, it will suggest using a specific conditioner. This makes it possible to suggest the optimal method for using water conditioners based on the environmental data within the aquarium.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The aquarium management system can further include a voice recognition unit. The voice recognition unit recognizes voice commands from the keeper and allows the keeper to operate the system by voice. For example, if the keeper says, "Check the water temperature," the voice recognition unit recognizes the command and displays the water temperature data. Also, if the keeper says, "Feed me," the voice recognition unit recognizes the command and can activate an automatic feeder. This allows the keeper to operate the system without using their hands, improving convenience.

[0054] The aquarium management system can further include a virtual assistant section, which provides the keeper with advice and information regarding aquarium management. For example, if the keeper asks, "How is the health of my tropical fish?", the virtual assistant section will report on their health status based on the latest data. Also, if the keeper asks, "When is the next water change?", the virtual assistant section can suggest the optimal timing for the water change. This allows the keeper to easily obtain information regarding aquarium management.

[0055] The aquarium management system can further include an entertainment section. The entertainment section provides content that breeders can enjoy. For example, it can stream videos of tropical fish observations in real time. It can also edit videos of tropical fish growth records and provide them to breeders. It can also provide games and quizzes based on the behavior of tropical fish, allowing breeders to learn while having fun. This makes aquarium management more enjoyable.

[0056] The aquarium management system can further include a community section. The community section provides a platform for sharing information and interacting with other aquarists. For example, aquarists can upload photos and videos of their aquariums and exchange opinions with other aquarists. Aquarists can also share advice and obtain information for solving problems. Furthermore, online events can be held regularly to promote interaction between aquarists. This allows aquarists to enjoy managing their aquariums without feeling isolated.

[0057] The aquarium management system can further include an eco-mode unit. The eco-mode unit provides functions to minimize the energy consumption of the aquarium. For example, it automatically adjusts the use of lights and heaters at night or when the keeper is away to reduce energy consumption. The eco-mode unit can also suggest optimal energy usage methods based on data on water quality and temperature. This allows the keeper to manage the aquarium while being considerate of the environment.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The fixed-point observation unit observes the tropical fish in the aquarium. For example, the fixed-point observation unit observes the tropical fish using a camera installed in the aquarium, sets the observation frequency, and periodically acquires images of the tropical fish. It is also possible to fix the observation position and observe the tropical fish from the same position. Step 2: The individual identification unit identifies the tropical fish observed by the fixed-point observation unit. For example, the individual identification unit analyzes characteristics such as the body color, pattern, and size of the tropical fish to identify each individual. In addition, the unit can use a classification algorithm to identify the observed tropical fish with high accuracy and store information about the identified tropical fish in a database. Step 3: The recording unit records information about the tropical fish identified by the individual identification unit. For example, the recording unit records changes in the tropical fish's body color, weight gain or loss, changes in behavioral patterns, etc. The type of information to be recorded can be set, necessary information can be selected and recorded, and the recording frequency can be set to record information about the tropical fish periodically. Step 4: The reporting unit reports on growth status and signs of illness based on the information recorded by the recording unit. For example, the reporting unit analyzes the recorded information and reports on growth status and signs of illness. It can also issue alerts if abnormalities are detected, set the report format, and select the necessary information to report.

[0060] (Example 2) The aquarium management system according to an embodiment of the present invention is a system that performs fixed-point observations of tropical fish in an aquarium, records the body color and other characteristics of the tropical fish while identifying each individual fish, and reports on their growth status and signs of illness. This allows the aquarium management system to efficiently monitor the growth status and signs of illness of the tropical fish and prompt appropriate measures to be taken.

[0061] An aquarium management system according to an embodiment includes a fixed-point observation unit, an individual identification unit, a recording unit, and a reporting unit. The fixed-point observation unit observes tropical fish in an aquarium. For example, the fixed-point observation unit observes the tropical fish using a camera installed in the aquarium. The fixed-point observation unit can set an observation frequency and periodically acquire images of the tropical fish. The fixed-point observation unit can fix an observation position and observe the tropical fish from the same position. The individual identification unit identifies the tropical fish observed by the fixed-point observation unit. For example, the individual identification unit analyzes characteristics of the tropical fish, such as body color, pattern, and size, and identifies each individual fish. The individual identification unit can identify the observed tropical fish with high accuracy using an identification algorithm. The individual identification unit can store information about the identified tropical fish in a database. The recording unit records information about the tropical fish identified by the individual identification unit. For example, the recording unit records changes in the tropical fish's body color, weight gain / loss, and changes in behavioral patterns. The recording unit can also set the type of information to be recorded and select and record necessary information. The recording unit can also set the recording frequency and periodically record information about the tropical fish. The reporting unit reports the growth status and signs of illness based on the information recorded by the recording unit. For example, the reporting unit analyzes the recorded information and reports the growth status and signs of illness. The reporting unit can also issue an alert if an abnormality is detected. The reporting unit can also set the report format and select and report necessary information. As a result, the aquarium management system according to the embodiment can efficiently monitor the growth status and signs of illness of the tropical fish and prompt appropriate action.

[0062] The fixed-point observation unit can use the generation AI to analyze the movements and behavior patterns of tropical fish and estimate their stress levels and health status. For example, the fixed-point observation unit uses the generation AI to analyze the movements and behavior patterns of tropical fish and estimate their stress levels. For example, it analyzes the frequency and speed of specific movements and issues an alert if stress levels are high. The fixed-point observation unit also monitors the behavior patterns of tropical fish over long periods of time and analyzes them with the generation AI to estimate their health status. For example, if an abnormal behavior pattern is observed, it suggests the possibility of a health abnormality. The fixed-point observation unit also uses the generation AI to analyze the movements and behavior patterns of tropical fish in real time and estimate their stress levels and health status. For example, if a specific behavior is observed frequently, it is determined that stress levels are high. This allows the stress levels and health status of tropical fish to be estimated in real time.

[0063] The fixed-point observation unit uses a high-resolution camera to capture subtle changes in the body color and pattern of tropical fish, which are then analyzed using generative AI, allowing for early detection of health abnormalities. For example, the fixed-point observation unit uses a high-resolution camera to capture subtle changes in the body color and pattern of tropical fish, which are then analyzed using generative AI. For example, if a change in body color is observed, it will suggest the possibility of a health abnormality. The fixed-point observation unit also uses generative AI to analyze changes in the body color and pattern of tropical fish in real time, allowing for early detection of health abnormalities. For example, if the body color becomes paler, it will suggest the possibility of nutritional deficiency. The fixed-point observation unit also analyzes images taken with the high-resolution camera using generative AI to detect subtle changes in the body color and pattern of tropical fish. For example, if a change in pattern is observed, it will suggest signs of illness. This allows for early detection of health abnormalities in tropical fish.

[0064] The fixed-point observation unit can use the emotion estimation function to estimate the keeper's emotions from the behavior of the tropical fish and provide advice to reduce the keeper's stress. The fixed-point observation unit, for example, uses the emotion estimation function to estimate the keeper's emotions from the behavior of the tropical fish. For example, if the tropical fish are moving actively, it determines that the keeper is relaxed. The fixed-point observation unit also uses a generative AI to analyze the behavior patterns of the tropical fish and estimate the keeper's emotions. For example, if the tropical fish are feeling stressed, it determines that the keeper may also be feeling stressed. The fixed-point observation unit also uses the emotion estimation function to analyze the keeper's emotions in real time from the behavior of the tropical fish and provide advice to reduce stress. For example, if the keeper is feeling stressed, it suggests ways to relax. This makes it possible to provide advice to reduce the keeper's stress.

[0065] The fixed-point observation unit simultaneously observes other organisms in the aquarium (e.g., shrimp and shellfish) and analyzes their interactions, thereby assessing the health of the entire ecosystem. The fixed-point observation unit, for example, uses a generation AI to simultaneously observe other organisms in the aquarium, such as shrimp and shellfish, and analyzes their interactions. For example, if the shrimp are moving actively, it determines that the overall health of the aquarium is good. The fixed-point observation unit also analyzes the behavioral patterns of other organisms in the aquarium to assess the health of the entire ecosystem. For example, if the shellfish are behaving abnormally, it suggests that there may be a problem with the environment in the aquarium. The fixed-point observation unit also uses a generation AI to analyze the movements and behavioral patterns of other organisms in the aquarium to assess the health of the entire ecosystem. For example, if the shrimp and shellfish are behaving normally, it determines that the environment of the entire aquarium is appropriate. This makes it possible to assess the health of the entire ecosystem in the aquarium.

[0066] The fixed-point observation unit simultaneously monitors the external environment of the aquarium (for example, room temperature and humidity) and analyzes it with the generating AI, thereby enabling more accurate optimization of the breeding environment. The fixed-point observation unit, for example, monitors the external environment of the aquarium (for example, room temperature and humidity) and analyzes it with the generating AI. For example, if the room temperature is high, it adjusts the water temperature in the aquarium. The fixed-point observation unit also uses the generating AI to analyze the external environmental data of the aquarium and optimize the breeding environment. For example, if the humidity is low, it suggests measures to prevent water evaporation from the aquarium. The fixed-point observation unit also monitors the room temperature and humidity in real time and analyzes it with the generating AI to optimize the breeding environment. For example, if the room temperature is low, it adjusts the aquarium heater. This makes it possible to accurately optimize the breeding environment while also taking the external environment of the aquarium into consideration.

[0067] The fixed-point observation unit uses the emotion estimation function to analyze the emotions of the keeper when observing the aquarium in real time, and can suggest adjustments to the aquarium layout and lighting to bring out positive emotions. The fixed-point observation unit, for example, uses the emotion estimation function to analyze the emotions of the keeper when observing the aquarium in real time. For example, if the keeper is relaxed, it suggests lighting adjustments to maintain that state. The fixed-point observation unit also uses generative AI to analyze the keeper's emotions and suggests an aquarium layout to bring out positive emotions. For example, if the keeper is feeling stressed, it suggests a layout that allows the keeper to relax. The fixed-point observation unit also uses the emotion estimation function to analyze the keeper's emotions in real time, and suggests lighting adjustments to bring out positive emotions. For example, it adjusts the color and brightness of the lighting so that the keeper can relax. This makes it possible to suggest adjustments to the aquarium layout and lighting to bring out positive emotions in the keeper.

[0068] The recording unit can use the generation AI to perform a detailed analysis of the growth rate and weight changes of the tropical fish and identify the optimal growth conditions. For example, the recording unit can use the generation AI to perform a detailed analysis of the growth rate of the tropical fish and identify the optimal growth conditions. For example, if the growth rate is slow, the type and amount of food can be adjusted. The recording unit can also use the generation AI to analyze changes in the weight of the tropical fish and identify the optimal growth conditions. For example, if the weight is decreasing, it can suggest the possibility of nutritional deficiency and increase the amount of food. The recording unit can also use the generation AI to analyze the growth rate and weight changes of the tropical fish in real time and identify the optimal growth conditions. For example, if the growth rate is fast, it can determine that the current breeding environment is appropriate. This allows for a detailed analysis of the growth rate and weight changes of the tropical fish and identify the optimal growth conditions.

[0069] The recording unit monitors the behavioral patterns of tropical fish over a long period of time and analyzes them with a generating AI, making it possible to detect early signs of illness. The recording unit, for example, uses a generating AI to monitor the behavioral patterns of tropical fish over a long period of time and detects early signs of illness. For example, if abnormal behavior is observed, it suggests the possibility of illness. The recording unit also analyzes the behavioral patterns of tropical fish in detail and detects early signs of illness. For example, if a specific behavior is observed frequently, it suggests signs of illness. The recording unit also uses a generating AI to analyze the behavioral patterns of tropical fish in real time and detect early signs of illness. For example, if there is a sudden change in behavior, it suggests the possibility of illness. This makes it possible to detect early signs of illness in tropical fish.

[0070] The recording unit uses the emotion estimation function to analyze the relationship between the keeper's emotions and the health of the tropical fish, and can evaluate the impact of the keeper's emotions on the health of the tropical fish. The recording unit, for example, uses the emotion estimation function to analyze the relationship between the keeper's emotions and the health of the tropical fish. For example, if the keeper is feeling stressed, it determines that the health of the tropical fish is likely to deteriorate. The recording unit also uses a generative AI to analyze data on the keeper's emotions and the health of the tropical fish and evaluate the association. For example, if the keeper is relaxed, it determines that the health of the tropical fish is also good. The recording unit also uses the emotion estimation function to analyze data on the keeper's emotions and the health of the tropical fish in real time and evaluate the impact. For example, it determines that if the keeper's emotions change, the health of the tropical fish is likely to change as well. This makes it possible to evaluate the impact of the keeper's emotions on the health of the tropical fish.

[0071] The recording unit can identify general growth patterns and signs of illness by comparing it with data from other keepers and analyzing it with the generating AI. For example, the recording unit collects data from other keepers and analyzes it with the generating AI to identify general growth patterns. For example, it determines that a particular growth rate is common. The recording unit can also compare it with data from other keepers and analyze it with the generating AI to identify general signs of illness. For example, it can determine that a particular behavioral pattern is a sign of illness. The recording unit can also use the generating AI to analyze data from other keepers and identify general growth patterns and signs of illness. For example, it can determine that a particular change in body color is a common growth pattern. This makes it possible to identify general growth patterns and signs of illness by comparing it with data from other keepers.

[0072] The recording unit can convert the growth status and signs of disease of the tropical fish into visual notes or mind maps to make them easier to understand visually. The recording unit, for example, uses a generation AI to convert the growth status and signs of disease of the tropical fish into visual notes. For example, it displays growth rate and changes in body color in graphs or diagrams. The recording unit also converts the growth status and signs of disease of the tropical fish into mind maps to make them easier to understand visually. For example, it displays growth patterns and signs of disease in association with each other. The recording unit also uses a generation AI to convert the growth status and signs of disease of the tropical fish into visual notes or mind maps to display them visually. For example, it displays growth status in chronological order and highlights signs of disease. This makes it easier to understand the growth status and signs of disease of the tropical fish visually.

[0073] The recording unit can use the emotion estimation function to analyze the emotions felt by the keeper when receiving the report and suggest a report format that will elicit positive emotions. For example, the recording unit uses the emotion estimation function to analyze the emotions felt by the keeper when receiving the report. For example, it identifies a report format that elicits strong positive emotions. The recording unit also uses a generation AI to analyze the emotions felt by the keeper and suggest a report format that will elicit positive emotions. For example, it prioritizes the adoption of report formats with high emotion scores. The recording unit also uses the emotion estimation function to analyze the emotions felt by the keeper when receiving the report in real time and suggest a report format that will elicit positive emotions. For example, it suggests color usage and layout that will elicit positive emotions. This makes it possible to suggest a report format that will elicit positive emotions when the keeper receives the report.

[0074] The report unit can use the generation AI to suggest the optimal type and amount of food based on the growth status of the tropical fish. For example, the report unit uses the generation AI to analyze the growth status of the tropical fish and suggest the optimal type of food. For example, it selects food with a good nutritional balance based on growth rate and changes in weight. The report unit also analyzes the growth status of the tropical fish in detail and suggests the optimal amount of food using the generation AI. For example, if the weight is increasing, it suggests reducing the amount of food. The report unit also uses the generation AI to analyze the growth status of the tropical fish in real time and suggest the optimal type and amount of food. For example, if the growth rate is slow, it suggests food with high nutritional value. This makes it possible to suggest the optimal type and amount of food based on the growth status of the tropical fish.

[0075] The report unit can analyze water quality data in detail and use generation AI to propose optimal water quality management methods. The report unit, for example, uses generation AI to analyze water quality data in detail and propose optimal water quality management methods. For example, it proposes methods for adjusting water quality based on water temperature, pH, and oxygen concentration. The report unit also monitors water quality data in real time and analyzes it with generation AI to propose optimal water quality management methods. For example, if water quality deteriorates, it will suggest the timing for water changes. The report unit also uses generation AI to analyze water quality data and propose optimal water quality management methods. For example, if a specific water quality parameter shows an abnormal value, it will suggest appropriate measures. This makes it possible to analyze water quality data in detail and propose optimal water quality management methods.

[0076] The report unit can use the emotion estimation function to provide care advice to reduce stress based on the keeper's emotions. The report unit, for example, uses the emotion estimation function to analyze the keeper's emotions and provide care advice to reduce stress. For example, if the keeper is feeling stressed, the report unit suggests a care method that will help the keeper relax. The report unit also uses the generation AI to analyze the keeper's emotional data and provide specific care advice to reduce stress. For example, the report unit suggests changing the layout of the aquarium so that the keeper can relax. The report unit also uses the emotion estimation function to analyze the keeper's emotions in real time and provide care advice to reduce stress. For example, the report unit suggests adjusting the color or brightness of the lighting so that the keeper can relax. This makes it possible to provide care advice to reduce stress based on the keeper's emotions.

[0077] The report unit can propose the optimal breeding method by collecting success stories of other breeders and analyzing them with the generation AI. The report unit, for example, collects success stories of other breeders and analyzes them with the generation AI to propose the optimal breeding method. For example, it can propose the type and amount of food based on the success stories. The report unit also uses the generation AI to analyze the success stories of other breeders and propose the optimal breeding method. For example, it can propose methods of water quality management based on the success stories. The report unit also collects success stories of other breeders and analyzes them with the generation AI to propose the optimal breeding method. For example, it can propose methods of aquarium layout and lighting adjustment based on the success stories. In this way, it can propose the optimal breeding method based on the success stories of other breeders.

[0078] The report unit compares the growth status of tropical fish with different seasons and climatic conditions and analyzes it using the generation AI, thereby being able to propose the optimal breeding method for each season. For example, the report unit uses the generation AI to compare the growth status of tropical fish with different seasons and climatic conditions and propose the optimal breeding method. For example, it may propose setting the water temperature higher in winter. The report unit also analyzes the growth status of tropical fish in detail and uses the generation AI to propose the optimal breeding method for each season. For example, it may propose setting the water temperature lower in summer. The report unit also uses the generation AI to compare the growth status of tropical fish with different seasons and climatic conditions and propose the optimal breeding method. For example, it may propose strengthening water quality management in spring. This makes it possible to propose the optimal breeding method for each season.

[0079] The report unit can use the emotion estimation function to analyze the emotions of the keeper when receiving advice and suggest an advice format that will elicit positive emotions. The report unit, for example, uses the emotion estimation function to analyze the emotions of the keeper when receiving advice. For example, it identifies advice formats that evoke strong positive emotions. The report unit also uses a generation AI to analyze the emotions of the keeper and suggest an advice format that will elicit positive emotions. For example, it prioritizes the adoption of advice formats with a high emotion score. The report unit also uses the emotion estimation function to analyze the emotions of the keeper when receiving advice in real time and suggest an advice format that will elicit positive emotions. For example, it suggests wording and expressions that will elicit positive emotions. This makes it possible to suggest an advice format that will elicit positive emotions when the keeper receives advice.

[0080] The report unit uses generative AI to analyze images of tropical fish in detail and detect signs of illness early. The report unit, for example, uses generative AI to analyze images of tropical fish in detail and detect signs of illness early. For example, it detects changes in body color and damage to fins. The report unit also analyzes images of tropical fish in real time and uses generative AI to detect signs of illness early. For example, it detects abnormal behavior. The report unit also uses generative AI to analyze images of tropical fish in detail and detect signs of illness early. For example, it detects changes in body color and damage to fins. This makes it possible to detect signs of illness in tropical fish early.

[0081] The reporting unit can use the generating AI to suggest the optimal prescription when signs of illness are found and monitor the progress. The reporting unit, for example, uses the generating AI to suggest the optimal prescription when signs of illness are found. For example, it can suggest the use of a specific medication. The reporting unit can also use the generating AI to suggest the optimal prescription when signs of illness are found and monitor the progress. For example, it can suggest the frequency of medication use. The reporting unit can also use the generating AI to suggest the optimal prescription when signs of illness are found and monitor the progress. For example, it can suggest the amount of medication to use. This makes it possible to suggest the optimal prescription when signs of illness are found and monitor the progress.

[0082] The report unit can use the emotion estimation function to provide prescription suggestions for reducing stress based on the emotions of the keeper. The report unit, for example, uses the emotion estimation function to analyze the emotions of the keeper and provide prescription suggestions for reducing stress. For example, if the keeper is feeling stressed, it proposes a prescription that will help the keeper relax. The report unit also uses a generation AI to analyze the emotion data of the keeper and provide specific prescription suggestions for reducing stress. For example, it proposes how to use medicine so that the keeper can relax. The report unit also uses the emotion estimation function to analyze the emotions of the keeper in real time and provide prescription suggestions for reducing stress. For example, it proposes adjusting the frequency of medicine use so that the keeper can relax. In this way, it is possible to provide prescription suggestions for reducing stress based on the emotions of the keeper.

[0083] The reporting unit collects illness data from other keepers and analyzes it with the generating AI to identify common signs of illness and prescriptions. For example, the reporting unit collects illness data from other keepers and analyzes it with the generating AI to identify common signs of illness. For example, it determines that a specific change in body color is a sign of illness. The reporting unit also compares this with the illness data from other keepers and analyzes it with the generating AI to identify common prescriptions. For example, it determines that the use of a specific medicine is common. The reporting unit also uses the generating AI to analyze the illness data from other keepers and identify common signs of illness and prescriptions. For example, it determines that a specific behavioral pattern is a sign of illness. This makes it possible to identify common signs of illness and prescriptions based on the illness data from other keepers.

[0084] The reporting unit can convert signs of disease into visual notes or mind maps to make them easier to understand visually. For example, the reporting unit uses generative AI to convert signs of disease into visual notes. For example, changes in body color or damage to fins can be displayed in graphs or diagrams. The reporting unit can also convert signs of disease into mind maps to make them easier to understand visually. For example, signs of disease can be displayed in association with each other. The reporting unit can also use generative AI to convert signs of disease into visual notes or mind maps to display them visually. For example, signs of disease can be displayed in chronological order to highlight prescriptions. This makes signs of disease easier to understand visually.

[0085] The report unit can use the emotion estimation function to analyze the emotion of the keeper when receiving the prescription proposal and propose a prescription proposal format that will elicit positive emotions. The report unit, for example, uses the emotion estimation function to analyze the emotion of the keeper when receiving the prescription proposal. For example, it identifies a prescription proposal format that elicits strong positive emotions. The report unit also uses a generation AI to analyze the emotion of the keeper and propose a prescription proposal format that will elicit positive emotions. For example, it preferentially adopts prescription proposal formats with a high emotion score. The report unit also uses the emotion estimation function to analyze the emotion of the keeper when receiving the prescription proposal in real time and proposes a prescription proposal format that will elicit positive emotions. For example, it suggests wording and expressions that will elicit positive emotions. This makes it possible to propose a prescription proposal format that will elicit positive emotions when the keeper receives the prescription proposal.

[0086] The fixed-point observation unit uses the generating AI to collect environmental data within the aquarium in real time, allowing it to maintain optimal environmental conditions. For example, the fixed-point observation unit uses the generating AI to collect environmental data such as water temperature, pH, and oxygen concentration in the aquarium in real time, maintaining optimal environmental conditions. For example, if the water temperature falls outside the appropriate range, it adjusts the heater or cooler. The fixed-point observation unit also analyzes the environmental data within the aquarium using the generating AI and suggests specific actions to maintain optimal environmental conditions. For example, if the pH is too high, it adds an acidity regulator. The fixed-point observation unit also uses the generating AI to monitor environmental data within the aquarium in real time and builds an automatic adjustment system to maintain optimal environmental conditions. For example, if the oxygen concentration drops, it increases aeration. This allows the fixed-point observation unit to collect environmental data within the aquarium in real time and maintain optimal environmental conditions.

[0087] The fixed-point observation unit can use the generation AI to suggest methods for adjusting water temperature and pH according to the local season and climate. For example, the fixed-point observation unit uses the generation AI to analyze local seasonal and climate data and suggest methods for adjusting water temperature and pH. For example, it suggests setting the water temperature higher in winter. The fixed-point observation unit also collects local climate data in real time and analyzes it with the generation AI to suggest optimal methods for adjusting water temperature and pH. For example, it suggests setting the water temperature lower in summer. The fixed-point observation unit also uses the generation AI to suggest methods for adjusting water temperature and pH according to the local season and climate. For example, it suggests strengthening water quality management in spring. This makes it possible to suggest methods for adjusting water temperature and pH according to the local season and climate.

[0088] The fixed-point observation unit can use the generation AI to analyze environmental data within the aquarium and suggest the optimal type and amount of food. The fixed-point observation unit, for example, uses the generation AI to analyze environmental data within the aquarium and suggest the optimal type of food. For example, it selects food with a good nutritional balance depending on the water quality and water temperature. The fixed-point observation unit also analyzes the environmental data within the aquarium in detail and uses the generation AI to suggest the optimal amount of food. For example, if the water temperature is high, it will suggest reducing the amount of food. The fixed-point observation unit also uses the generation AI to analyze environmental data within the aquarium in real time and suggest the optimal type and amount of food. For example, if the water quality is deteriorating, it will suggest changing the type of food. This makes it possible to suggest the optimal type and amount of food based on the environmental data within the aquarium.

[0089] The fixed point observation unit can use the generating AI to analyze environmental data within the aquarium and suggest the optimal timing for cleaning. The fixed point observation unit, for example, uses the generating AI to analyze environmental data within the aquarium and suggest the optimal timing for cleaning. For example, if the water quality is deteriorating, it will suggest increasing the frequency of cleaning. The fixed point observation unit also monitors the environmental data within the aquarium in real time and analyzes it with the generating AI to suggest the optimal timing for cleaning. For example, if the oxygen concentration is decreasing, it will suggest cleaning. The fixed point observation unit also uses the generating AI to analyze environmental data within the aquarium and suggest the optimal timing for cleaning. For example, if the pH is too high, it will suggest cleaning. In this way, it is possible to suggest the optimal timing for cleaning based on the environmental data within the aquarium.

[0090] The fixed-point observation unit can use the generating AI to analyze environmental data within the aquarium and suggest the optimal timing for water changes. The fixed-point observation unit, for example, uses the generating AI to analyze environmental data within the aquarium and suggest the optimal timing for water changes. For example, if the water quality is deteriorating, it will suggest increasing the frequency of water changes. The fixed-point observation unit also monitors environmental data within the aquarium in real time and analyzes it with the generating AI to suggest the optimal timing for water changes. For example, if the oxygen concentration is decreasing, it will suggest performing a water change. The fixed-point observation unit also uses the generating AI to analyze environmental data within the aquarium and suggest the optimal timing for water changes. For example, if the pH is too high, it will suggest performing a water change. This makes it possible to suggest the optimal timing for water changes based on the environmental data within the aquarium.

[0091] The fixed-point observation unit can use the generating AI to analyze environmental data within the aquarium and suggest the optimal method for using water conditioners. The fixed-point observation unit, for example, uses the generating AI to analyze environmental data within the aquarium and suggest the optimal method for using water conditioners. For example, if the water quality is deteriorating, it will suggest using a specific conditioner. The fixed-point observation unit also monitors environmental data within the aquarium in real time and analyzes it with the generating AI to suggest the optimal method for using water conditioners. For example, if the oxygen concentration is decreasing, it will suggest using a specific conditioner. The fixed-point observation unit also uses the generating AI to analyze environmental data within the aquarium and suggest the optimal method for using water conditioners. For example, if the pH is too high, it will suggest using a specific conditioner. This makes it possible to suggest the optimal method for using water conditioners based on the environmental data within the aquarium.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The aquarium management system can further include a voice recognition unit. The voice recognition unit recognizes voice commands from the keeper and allows the keeper to operate the system by voice. For example, if the keeper says, "Check the water temperature," the voice recognition unit recognizes the command and displays the water temperature data. Also, if the keeper says, "Feed me," the voice recognition unit recognizes the command and can activate an automatic feeder. This allows the keeper to operate the system without using their hands, improving convenience.

[0094] The aquarium management system can further include a virtual assistant section, which provides the keeper with advice and information regarding aquarium management. For example, if the keeper asks, "How is the health of my tropical fish?", the virtual assistant section will report on their health status based on the latest data. Also, if the keeper asks, "When is the next water change?", the virtual assistant section can suggest the optimal timing for the water change. This allows the keeper to easily obtain information regarding aquarium management.

[0095] The aquarium management system can further include an entertainment section. The entertainment section provides content that breeders can enjoy. For example, it can stream videos of tropical fish observations in real time. It can also edit videos of tropical fish growth records and provide them to breeders. It can also provide games and quizzes based on the behavior of tropical fish, allowing breeders to learn while having fun. This makes aquarium management more enjoyable.

[0096] The aquarium management system can further include a community section. The community section provides a platform for sharing information and interacting with other aquarists. For example, aquarists can upload photos and videos of their aquariums and exchange opinions with other aquarists. Aquarists can also share advice and obtain information for solving problems. Furthermore, online events can be held regularly to promote interaction between aquarists. This allows aquarists to enjoy managing their aquariums without feeling isolated.

[0097] The aquarium management system can further include an eco-mode unit. The eco-mode unit provides functions to minimize the energy consumption of the aquarium. For example, it automatically adjusts the use of lights and heaters at night or when the keeper is away to reduce energy consumption. The eco-mode unit can also suggest optimal energy usage methods based on data on water quality and temperature. This allows the keeper to manage the aquarium while being considerate of the environment.

[0098] The fixed-point observation unit can use the emotion estimation function to estimate the keeper's emotions from the behavior of the tropical fish and provide advice to reduce the keeper's stress. For example, if the tropical fish are moving actively, it can determine that the keeper is relaxed and provide advice to maintain that state. Also, if the tropical fish are feeling stressed, it can determine that the keeper may also be feeling stressed and suggest ways to help the keeper relax. This makes it possible to provide advice to reduce the keeper's stress.

[0099] The recording unit uses the emotion estimation function to analyze the relationship between the keeper's emotions and the health of the tropical fish, and can evaluate the impact of the keeper's emotions on the health of the tropical fish. For example, if the keeper is feeling stressed, it can determine that the health of the tropical fish is likely to deteriorate. On the other hand, if the keeper is relaxed, it can determine that the health of the tropical fish is good. This makes it possible to evaluate the impact of the keeper's emotions on the health of the tropical fish.

[0100] The report unit can use the emotion estimation function to provide care advice to reduce stress based on the keeper's emotions. For example, if the keeper is feeling stressed, the report unit can suggest a care method that will help the keeper relax. It can also suggest changing the layout of the aquarium so that the keeper can relax. This makes it possible to provide care advice to reduce stress based on the keeper's emotions.

[0101] The report unit uses the emotion estimation function to analyze the emotions felt by the keeper when receiving advice, and can suggest advice formats that will elicit positive emotions. For example, it can identify advice formats that evoke strong positive emotions and prioritize those with high emotion scores. It can also suggest wording and expressions that will elicit positive emotions. This makes it possible to suggest advice formats that will elicit positive emotions when the keeper receives advice.

[0102] The report unit uses the emotion estimation function to analyze the emotions felt by the caregiver when receiving a prescription proposal, and can propose a prescription proposal format that will elicit positive emotions. For example, it can identify prescription proposal formats that evoke strong positive emotions and prioritize prescription proposal formats with high emotion scores. It also suggests wording and expressions that will elicit positive emotions. This makes it possible to propose a prescription proposal format that will elicit positive emotions when the caregiver receives a prescription proposal.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The fixed-point observation unit observes the tropical fish in the aquarium. For example, the fixed-point observation unit observes the tropical fish using a camera installed in the aquarium, sets the observation frequency, and periodically acquires images of the tropical fish. It is also possible to fix the observation position and observe the tropical fish from the same position. Step 2: The individual identification unit identifies the tropical fish observed by the fixed-point observation unit. For example, the individual identification unit analyzes characteristics such as the body color, pattern, and size of the tropical fish to identify each individual. In addition, the unit can use a classification algorithm to identify the observed tropical fish with high accuracy and store information about the identified tropical fish in a database. Step 3: The recording unit records information about the tropical fish identified by the individual identification unit. For example, the recording unit records changes in the tropical fish's body color, weight gain or loss, changes in behavioral patterns, etc. The type of information to be recorded can be set, necessary information can be selected and recorded, and the recording frequency can be set to record information about the tropical fish periodically. Step 4: The reporting unit reports on growth status and signs of illness based on the information recorded by the recording unit. For example, the reporting unit analyzes the recorded information and reports on growth status and signs of illness. It can also issue alerts if abnormalities are detected, set the report format, and select the necessary information to report.

[0105] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a 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.

[0140] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] 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.

[0164] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A fixed observation section to observe tropical fish in the aquarium, an individual identification unit that identifies the tropical fish observed by the fixed point observation unit; a recording unit for recording information on the tropical fish identified by the individual identification unit; a reporting unit that reports the growth status or signs of illness based on the information recorded by the recording unit. A system characterized by:

2. The fixed point observation unit Generative AI is used to analyze the movements and behavior patterns of the tropical fish to estimate their stress levels and health status.

2. The system of claim 1.

3. The fixed point observation unit A high-resolution camera is used to capture subtle changes in the color or pattern of the tropical fish, which are then analyzed using generative AI to detect early health abnormalities.

2. The system of claim 1.

4. The fixed point observation unit Inferring the emotions of the keeper from the behavior of the tropical fish and providing advice to reduce stress for the keeper.

2. The system of claim 1.

5. The fixed point observation unit The shrimp or shellfish living in the tank are also simultaneously observed and their interactions are analyzed to assess the health of the ecosystem.

2. The system of claim 1.

6. The fixed point observation unit By simultaneously monitoring the temperature and humidity of the room, which is the external environment of the aquarium, and analyzing this with the generative AI, we aim to optimize the breeding environment with greater precision.

2. The system of claim 1.

7. The fixed point observation unit Analyzing the emotions of the breeder when observing the aquarium in real time and suggesting adjustments to the aquarium layout or lighting to bring out the positive emotions.

2. The system of claim 1.

8. The recording unit Using generative AI, the growth rate or weight changes of the tropical fish are analyzed in detail to identify the optimal growth conditions.

2. The system of claim 1.

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