System

Abstract

An object of a system according to an embodiment is to grasp the behavior and health condition of livestock animals in real time and propose an appropriate care method and environment.SOLUTION: A system includes a collection unit, an analysis unit, a communication unit, and a proposal unit. The collection unit monitors behavior and a health condition of livestock. The analysis unit analyzes the data collected by the collection unit. The communication unit communicates with the livestock animal on the basis of an analysis result obtained by the analysis unit. The proposal unit proposes an appropriate care method and environment to the manager based on the information obtained by the communication 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 does not adequately grasp the behavior and health status of livestock in real time and suggest appropriate care methods and environments, so there is room for improvement.

[0005] The system according to the embodiment aims to grasp the behavior and health condition of livestock in real time and propose appropriate care methods and environments. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a communication unit, and a suggestion unit. The collection unit monitors the behavior and health status of livestock. The analysis unit analyzes the data collected by the collection unit. The communication unit communicates with the livestock based on the analysis results obtained by the analysis unit. The suggestion unit suggests appropriate care methods and environments to the manager based on the information obtained by the communication unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the behavior and health condition of livestock in real time and suggest appropriate care methods and environments. [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) A livestock monitoring system according to an embodiment of the present invention monitors the behavior and health of livestock, communicating with them using AI to understand their condition in real time. The livestock monitoring system monitors the behavior and health of livestock, analyzing the collected data using AI to understand their condition. Furthermore, the AI ​​communicates with the livestock and suggests optimal care methods and environments to managers. For example, the livestock monitoring system collects data such as the livestock's movements, feeding patterns, body temperature, and heart rate. The livestock monitoring system then analyzes the collected data using AI to detect signs of stress or illness. Furthermore, the livestock monitoring system issues instructions to the livestock using voice or gestures and checks their responses. Finally, the livestock monitoring system suggests appropriate amounts and types of feed, frequency and methods of exercise, etc., depending on the livestock's health condition. This allows the livestock monitoring system to understand the health condition of livestock in real time and provide appropriate care. This allows the livestock monitoring system to understand the health condition of livestock in real time and provide appropriate care, thereby improving livestock welfare. Managers can also manage livestock more efficiently based on AI suggestions, reducing their workload.

[0029] A livestock monitoring system according to an embodiment includes a collection unit, an analysis unit, a communication unit, and a proposal unit. The collection unit monitors the behavior and health status of livestock. The collection unit collects data such as the movements, eating patterns, body temperature, and heart rate of the livestock. The collection unit can also collect livestock data in real time using sensors. For example, the collection unit detects the movements of the livestock using a motion sensor and collects data. The collection unit can also monitor the eating patterns of the livestock and record their food intake. The collection unit can also measure the body temperature and heart rate of the livestock using a body temperature sensor and a heart rate monitor. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the collected data using AI to detect signs of stress or illness in the livestock. The analysis unit can detect abnormal behavior patterns and body temperature fluctuations and evaluate the health status of the livestock. For example, the analysis unit can analyze the behavior data of the livestock using an AI model to detect signs of stress. The analysis unit can also analyze body temperature data to detect abnormal body temperature fluctuations. Furthermore, the analysis unit can analyze heart rate data and detect abnormal heart rate fluctuations. The communication unit communicates with livestock based on the analysis results obtained by the analysis unit. The communication unit, for example, uses voice or gestures to give instructions to the livestock and checks their reactions. The communication unit can monitor the livestock's reactions in real time and issue appropriate instructions. For example, the communication unit uses voice commands to give instructions to the livestock and checks their reactions. The communication unit can also use gestures to give instructions to the livestock and check their reactions. Furthermore, the communication unit can analyze the livestock's reactions and provide feedback for issuing the next instruction. The suggestion unit suggests optimal care methods and environments to the manager based on the information obtained by the communication unit. For example, the suggestion unit suggests appropriate amounts and types of feed, frequency and methods of exercise, etc., depending on the livestock's health condition. The suggestion unit can evaluate the livestock's health condition and suggest optimal care methods. For example, the suggestion unit suggests appropriate amounts and types of feed based on the livestock's body temperature and heart rate data.The suggestion unit can also suggest appropriate exercise frequency and methods based on the livestock behavior data. Furthermore, the suggestion unit can monitor the health status of livestock in real time and adjust care methods as necessary. This allows the livestock monitoring system according to the embodiment to grasp the health status of livestock in real time and provide appropriate care. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can suggest care methods using an AI model that inputs livestock health data and outputs optimal care methods.

[0030] The collection unit can collect data on the movements of livestock, eating patterns, body temperature, heart rate, and other data. For example, the collection unit detects the movements of livestock using a motion sensor and collects data. The collection unit can also monitor the eating patterns of livestock and record their food intake. The collection unit can also measure the body temperature and heart rate of livestock using a body temperature sensor and a heart rate monitor. For example, the collection unit detects the movements of livestock using a motion sensor and collects data. The collection unit can monitor the eating patterns of livestock and record their food intake. The collection unit can also measure the body temperature and heart rate of livestock using a body temperature sensor and a heart rate monitor. This allows for early detection of abnormalities by monitoring the detailed behavior and health status of livestock. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the movements of livestock, eating patterns, body temperature, heart rate, and other data into the generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze the collected data and detect signs of stress or illness in the livestock. The analysis unit can, for example, use AI to analyze the collected data and detect signs of stress or illness in the livestock. The analysis unit can detect abnormal behavioral patterns and body temperature fluctuations and evaluate the health status of the livestock. For example, the analysis unit can analyze the behavioral data of the livestock using an AI model to detect signs of stress. The analysis unit can also analyze body temperature data and detect abnormal body temperature fluctuations. Furthermore, the analysis unit can analyze heart rate data and detect abnormal heart rate fluctuations. This enables early detection of signs of stress or illness in the livestock, enabling rapid response. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI detect signs of stress or illness.

[0032] The communication unit can issue instructions to the livestock using voice or gestures and check the livestock's reaction. For example, the communication unit can issue instructions to the livestock using voice or gestures and check the livestock's reaction. The communication unit can monitor the livestock's reaction in real time and issue appropriate instructions. For example, the communication unit can issue instructions to the livestock using voice commands and check the livestock's reaction. The communication unit can also issue instructions to the livestock using gestures and check the livestock's reaction. Furthermore, the communication unit can analyze the livestock's reaction and provide feedback for issuing the next instruction. This allows for effective communication with the livestock to appropriately manage the livestock's behavior. Some or all of the above-mentioned processing in the communication unit can be performed using, for example, AI, or can be performed without using AI. For example, the communication unit can input livestock reaction data to the generation AI and cause the generation AI to execute feedback for issuing the next instruction.

[0033] The suggestion unit can suggest appropriate amounts and types of feed, frequency and methods of exercise, etc., depending on the health condition of the livestock. For example, the suggestion unit can suggest appropriate amounts and types of feed, frequency and methods of exercise, etc., depending on the health condition of the livestock. The suggestion unit can evaluate the health condition of the livestock and suggest optimal care methods. For example, the suggestion unit can suggest appropriate amounts and types of feed based on data on the body temperature and heart rate of the livestock. The suggestion unit can also suggest appropriate frequency and methods of exercise based on behavioral data of the livestock. Furthermore, the suggestion unit can monitor the health condition of the livestock in real time and adjust the care method as needed. This improves the welfare of the livestock by suggesting optimal care methods depending on the health condition of the livestock. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can suggest care methods using an AI model that inputs livestock health data into a generation AI and outputs optimal care methods.

[0034] The collection unit can estimate the emotions of the livestock and adjust the frequency of data collection based on the estimated emotions of the livestock. For example, if the livestock are stressed, the collection unit can increase the frequency of data collection to perform detailed monitoring. If the livestock are relaxed, the collection unit can also reduce the frequency of data collection to reduce the burden on the livestock. The collection unit can also adjust the frequency of data collection in real time if the emotions of the livestock change suddenly. This allows for detailed monitoring while reducing the burden on the livestock by adjusting the frequency of data collection according to the emotions of the livestock. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the emotion data of the livestock into the generation AI and have the generation AI adjust the frequency of data collection.

[0035] The collection unit can analyze past behavioral data of livestock and select the optimal timing for data collection. For example, if livestock have been active during a specific time period in the past, the collection unit can concentrate data collection during that time period. If livestock have been resting during a specific time period in the past, the collection unit can also refrain from collecting data during that time period. The collection unit can also analyze past behavioral patterns of livestock and select the most efficient timing for data collection. This enables efficient monitoring by selecting the optimal timing for data collection based on past behavioral data of livestock. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input past behavioral data of livestock into a generation AI and have the generation AI select the optimal timing for data collection.

[0036] The collection unit can customize the type of data to be collected depending on the type and age of livestock when collecting data. For example, the collection unit can focus on collecting growth data for young livestock. The collection unit can also focus on collecting health data for older livestock. The collection unit can also collect specific health indicators and behavioral patterns depending on the type of livestock. This enables more accurate monitoring by collecting data depending on the type and age of livestock. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can cause the generation AI to customize data collection depending on the type and age of livestock.

[0037] The collection unit can correct the collected data when collecting data, taking into account the livestock's environment (temperature, humidity, light intensity, and other environmental factors). For example, when the temperature is high, the collection unit corrects the livestock's body temperature data to accurately determine their health condition. When the humidity is high, the collection unit can also correct the livestock's respiration data to accurately determine their health condition. When the amount of light fluctuates, the collection unit can also correct the livestock's behavior data to accurately determine their behavioral patterns. This makes it possible to more accurately determine their health condition by correcting the data taking into account the livestock's environment. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input livestock's environmental data into the generation AI and have the generation AI correct the collected data.

[0038] The collection unit can estimate the emotions of the livestock and determine the priority of data to be collected based on the estimated emotions of the livestock. For example, if the livestock are feeling stressed, the collection unit can prioritize collecting stress-related data. If the livestock are relaxed, the collection unit can also prioritize collecting data related to their health. If the livestock's emotions change suddenly, the collection unit can also prioritize collecting data related to that change. This allows important data to be collected preferentially by determining the priority of data according to the livestock's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the livestock's emotion data into the generation AI and have the generation AI determine the priority of the data to be collected.

[0039] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the livestock. For example, if the livestock are in a specific location, the collection unit prioritizes collecting data related to that location. If the livestock are on the move, the collection unit can also prioritize collecting data related to their movement. If the livestock are staying in a specific area for a long time, the collection unit can also prioritize collecting data related to that area. In this way, by collecting data by taking into account the geographical location information of the livestock, more relevant data can be obtained. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the livestock into the generation AI and cause the generation AI to prioritize the collection of highly relevant data.

[0040] The collection unit can analyze the social behavior of livestock (relationships with other livestock) during data collection and collect related data. For example, if livestock frequently come into contact with other livestock, the collection unit collects data related to that contact. If livestock are isolated, the collection unit can also collect data related to that isolation. The collection unit can also analyze the social behavior patterns of livestock and collect the most relevant data. This makes it possible to collect more detailed behavioral data by analyzing the social behavior of livestock. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input social behavior data of livestock into a generation AI and have the generation AI collect related data.

[0041] When collecting data, the collection unit can customize the collection method by reflecting the past health history of the livestock. For example, if the livestock has had a disease in the past, the collection unit will focus on collecting data related to that disease. If the livestock has had a specific health problem in the past, the collection unit can also collect data related to that problem. The collection unit can also analyze the past health history of the livestock and select the most appropriate data collection method. This makes it possible to collect data that reflects the past health history of the livestock, thereby enabling a more accurate understanding of the health condition. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past health history data of the livestock into the generation AI and have the generation AI customize the collection method.

[0042] The analysis unit can estimate the emotions of the livestock and adjust the analysis algorithm based on the estimated emotions of the livestock. For example, if the livestock are stressed, the analysis unit applies an analysis algorithm that emphasizes stress-related data. If the livestock are relaxed, the analysis unit can also apply an analysis algorithm that emphasizes health-related data. If the livestock's emotions change suddenly, the analysis unit can also apply an analysis algorithm that corresponds to the change. This allows for more accurate analysis results to be obtained by adjusting the analysis algorithm according to the livestock's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input livestock emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0043] During analysis, the analysis unit can predict fluctuations in the health status of livestock and detect abnormalities early. The analysis unit can, for example, analyze fluctuations in the body temperature and heart rate of livestock and detect abnormalities early. The analysis unit can also analyze fluctuations in the behavioral patterns of livestock and detect abnormalities early. The analysis unit can also analyze fluctuations in the eating patterns of livestock and detect abnormalities early. This makes it possible to predict fluctuations in the health status of livestock and detect abnormalities early. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input livestock health data into the generation AI and have the generation AI predict fluctuations in the health status and detect abnormalities.

[0044] During analysis, the analysis unit can apply different analysis methods depending on the type and age of the livestock. For example, the analysis unit applies an analysis method that emphasizes growth data to young livestock. The analysis unit can also apply an analysis method that emphasizes health status data to older livestock. The analysis unit can also apply an analysis method that emphasizes specific health indicators or behavioral patterns depending on the type of livestock. In this way, more accurate analysis results can be obtained by applying an analysis method depending on the type and age of the livestock. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to apply an analysis method depending on the type and age of the livestock.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past data on the livestock. For example, the analysis unit can improve the accuracy of the analysis by referring to past health data on the livestock. The analysis unit can also improve the accuracy of the analysis by referring to past behavioral data on the livestock. The analysis unit can also improve the accuracy of the analysis by referring to past dietary data on the livestock. In this way, by referring to past data on the livestock, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input past data on the livestock into the generation AI and have the generation AI improve the accuracy of the analysis.

[0046] The analysis unit can estimate the emotions of the livestock and adjust the display method of the analysis results based on the estimated emotions of the livestock. For example, if the livestock are feeling stressed, the analysis unit can highlight stress-related data. If the livestock are relaxed, the analysis unit can also highlight data related to the livestock's health. If the livestock's emotions change suddenly, the analysis unit can highlight data related to the change. This allows for a more understandable display by adjusting the display method of the analysis results according to the livestock's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input livestock emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0047] The analysis unit can perform the analysis while taking into account the geographical distribution of livestock. For example, if livestock are concentrated in a specific area, the analysis unit will focus on data related to that area. If livestock are distributed over a wide area, the analysis unit can also analyze data for each area individually. The analysis unit can also select the optimal analysis method while taking into account the geographical distribution of livestock. In this way, more accurate analysis results can be obtained by performing the analysis while taking into account the geographical distribution of livestock. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input livestock geographical distribution data into the generation AI and have the generation AI perform the analysis.

[0048] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to livestock. The analysis unit can improve the accuracy of the analysis by referring to, for example, the latest research papers on livestock health. The analysis unit can also improve the accuracy of the analysis by referring to past research data on livestock behavior. The analysis unit can also improve the accuracy of the analysis by referring to literature on livestock breeding. In this way, the accuracy of the analysis is improved by referring to literature related to livestock. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input livestock-related literature data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0049] The analysis unit can perform the analysis taking into account the market value of the livestock. For example, the analysis unit performs the analysis with emphasis on the health condition and growth rate based on the market value of the livestock. The analysis unit can also perform the analysis to propose the optimal breeding method based on the market value of the livestock. The analysis unit can also perform the analysis with emphasis on early detection of abnormalities based on the market value of the livestock. In this way, by performing the analysis taking into account the market value of the livestock, it is possible to propose a more economical breeding method. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input livestock market value data into the generation AI and have the generation AI perform the analysis.

[0050] The communication unit can estimate the emotions of the livestock and adjust the communication method based on the estimated emotions. For example, if the livestock are stressed, the communication unit can issue instructions in a calm voice. If the livestock are relaxed, the communication unit can also issue instructions in a normal voice. If the livestock's emotions change suddenly, the communication unit can also apply a communication method that corresponds to the change. This reduces the stress of the livestock by adjusting the communication method according to the livestock's emotions, enabling more effective communication. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the communication unit can be performed using an AI, for example, or without an AI. For example, the communication unit can input emotional data of the livestock into the generation AI and have the generation AI adjust the communication method.

[0051] The communication unit can analyze the livestock's reactions in real time during communication and issue optimal instructions. For example, if the livestock do not follow an instruction, the communication unit can change the content of the instruction and issue it again. If the livestock follow the instruction, the communication unit can also issue the next instruction. The communication unit can also analyze the livestock's reactions in real time and issue instructions at the optimal timing. In this way, by analyzing the livestock's reactions in real time, more appropriate instructions can be issued. Some or all of the above-mentioned processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input livestock reaction data into a generation AI and have the generation AI output optimal instructions.

[0052] The communication unit can apply different communication methods depending on the type and age of the livestock when communicating. For example, the communication unit applies a playful communication method to young livestock. The communication unit can also apply a gentle communication method to older livestock. The communication unit can also select the optimal communication method depending on the type of livestock. This enables more effective communication by applying a communication method depending on the type and age of the livestock. Some or all of the above-mentioned processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input data on the type and age of the livestock into the generation AI and have the generation AI apply the communication method.

[0053] The communication unit can improve the accuracy of communication by referring to past reaction data of the livestock when communicating. For example, the communication unit refers to past reaction data of the livestock and selects the optimal communication method. The communication unit can also re-apply a method that the livestock responded favorably to in the past. The communication unit can also analyze past reaction data of the livestock and apply the most effective communication method. In this way, by referring to past reaction data of the livestock, the accuracy of communication is improved. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input past reaction data of the livestock into the generation AI and have the generation AI select the communication method.

[0054] The communication unit can estimate the emotions of the livestock and adjust the frequency of communication based on the estimated emotions. For example, if the livestock are stressed, the communication unit can increase the frequency of communication to give them a sense of security. If the livestock are relaxed, the communication unit can also reduce the frequency of communication to reduce their burden. If the emotions of the livestock change suddenly, the communication unit can adjust the frequency of communication in response to the change. This reduces the stress of the livestock by adjusting the frequency of communication according to their emotions, enabling more effective communication. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the communication unit can be performed using AI, for example, or without AI. For example, the communication unit can input emotional data of the livestock into the generation AI and have the generation AI adjust the communication frequency.

[0055] The communication unit can select the optimal communication method by taking into account the geographical location information of the livestock when communicating. For example, if the livestock are in a specific location, the communication unit selects a communication method appropriate for that location. If the livestock are on the move, the communication unit can also select a communication method appropriate for movement. The communication unit can also select the optimal communication method by taking into account the geographical location information of the livestock. In this way, more effective communication is possible by selecting a communication method by taking into account the geographical location information of the livestock. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the geographical location information of the livestock into the generation AI and cause the generation AI to select the optimal communication method.

[0056] The communication unit can analyze the social behavior of the livestock during communication and perform relevant communication. For example, if the livestock are in frequent contact with other livestock, the communication unit can perform communication related to that contact. If the livestock are isolated, the communication unit can also perform communication related to the isolation. The communication unit can also analyze the social behavior patterns of the livestock and perform the most relevant communication. This enables more relevant communication by analyzing the social behavior of the livestock. Some or all of the above-mentioned processing in the communication unit can be performed using AI, for example, or without AI. For example, the communication unit can input social behavior data of the livestock into a generation AI and have the generation AI perform relevant communication.

[0057] The communication unit can customize the communication method to reflect the health status of the livestock when communicating. For example, if the livestock are healthy, the communication unit applies a normal communication method. If the livestock are sick, the communication unit can also apply a communication method that corresponds to the illness. The communication unit can also select the optimal communication method to reflect the health status of the livestock. This enables more appropriate communication by customizing the communication method to reflect the health status of the livestock. Some or all of the above-mentioned processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input livestock health data into the generation AI and have the generation AI customize the communication method.

[0058] The suggestion unit can estimate the emotions of the livestock and adjust the content of the suggestion based on the estimated emotions of the livestock. For example, if the livestock are feeling stressed, the suggestion unit can make suggestions to reduce stress. If the livestock are relaxed, the suggestion unit can also make suggestions to maintain health. If the emotions of the livestock change suddenly, the suggestion unit can also make suggestions that correspond to the change. This makes it possible to suggest more appropriate care methods by adjusting the content of the suggestion according to the emotions of the livestock. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input emotional data of the livestock into the generation AI and have the generation AI adjust the content of the suggestion.

[0059] When making a proposal, the proposal unit can predict fluctuations in the health condition of the livestock and propose the optimal care method. The proposal unit can, for example, predict fluctuations in the body temperature and heart rate of the livestock and propose the optimal care method. The proposal unit can also predict fluctuations in the behavioral patterns of the livestock and propose the optimal care method. The proposal unit can also predict fluctuations in the eating patterns of the livestock and propose the optimal care method. In this way, by predicting fluctuations in the health condition of the livestock, more appropriate care methods can be proposed. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can input livestock health data into the generation AI and cause the generation AI to predict fluctuations in the health condition and propose care methods.

[0060] When making a proposal, the proposal unit can apply different proposal methods depending on the type and age of the livestock. For example, the proposal unit makes proposals regarding growth for young livestock. The proposal unit can also make proposals regarding health maintenance for older livestock. The proposal unit can also propose the optimal care method depending on the type of livestock. In this way, by applying a proposal method depending on the type and age of the livestock, a more appropriate care method can be proposed. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the type and age of livestock into the generation AI and cause the generation AI to apply the proposal method.

[0061] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to the livestock's past care history. The proposal unit, for example, refers to the livestock's past care history and proposes the optimal care method. The proposal unit can also analyze the effects of care that the livestock have received in the past and propose the optimal care method. The proposal unit can also propose the most effective care method based on the livestock's past care history. In this way, by referring to the livestock's past care history, the accuracy of the proposal is improved. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input livestock's past care history data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0062] The suggestion unit can estimate the emotions of the livestock and prioritize suggestions based on the estimated emotions of the livestock. For example, if the livestock are stressed, the suggestion unit prioritizes suggestions for stress reduction. If the livestock are relaxed, the suggestion unit can also prioritize suggestions for maintaining health. If the livestock's emotions change suddenly, the suggestion unit can also prioritize suggestions that correspond to the change. This allows more important care methods to be prioritized by prioritizing suggestions based on the livestock's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input livestock emotion data into the generation AI and have the generation AI determine the priority of suggestions.

[0063] When making a proposal, the proposal unit can propose the optimal care method by taking into account the geographical location information of the livestock. For example, if the livestock are in a specific location, the proposal unit can propose a care method appropriate for that location. If the livestock are on the move, the proposal unit can also propose a care method appropriate for the move. The proposal unit can also propose the optimal care method by taking into account the geographical location information of the livestock. In this way, by proposing a care method by taking into account the geographical location information of the livestock, it is possible to propose a more appropriate care method. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input the geographical location information of the livestock into the generation AI and cause the generation AI to propose the optimal care method.

[0064] When making a suggestion, the suggestion unit can analyze the social behavior of the livestock and suggest a relevant care method. For example, if the livestock is in frequent contact with other livestock, the suggestion unit can suggest a care method related to that contact. If the livestock is isolated, the suggestion unit can also suggest a care method related to the isolation. The suggestion unit can also analyze the social behavior patterns of the livestock and suggest the most relevant care method. In this way, by analyzing the social behavior of the livestock, more relevant care methods can be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input social behavior data of the livestock into the generation AI and cause the generation AI to suggest relevant care methods.

[0065] When making a proposal, the proposal unit can propose an optimal care method taking into account the market value of the livestock. For example, the proposal unit proposes a care method that emphasizes the health condition and growth rate based on the market value of the livestock. The proposal unit can also propose an optimal rearing method based on the market value of the livestock. The proposal unit can also propose a care method that emphasizes early detection of abnormalities based on the market value of the livestock. In this way, by proposing a care method taking into account the market value of the livestock, it is possible to propose a more economical rearing method. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without using, AI, for example. For example, the proposal unit can input livestock market value data into the generation AI and cause the generation AI to propose an optimal care method.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The suggestion unit can evaluate the productivity of livestock in addition to their health conditions and propose optimal care methods. For example, the suggestion unit can monitor the weight and growth rate of livestock and propose optimal amounts and types of feed. The suggestion unit can also monitor the milk and egg production of livestock and propose adjustments to exercise and environment to improve productivity. Furthermore, the suggestion unit can consider the balance between the health conditions and productivity of livestock and propose optimal care methods. This makes it possible to provide optimal care that balances the health conditions and productivity of livestock.

[0068] In addition to livestock behavior data, the collection unit can collect detailed data on livestock diets. For example, the collection unit records what type of feed the livestock eat and how much. The collection unit can also monitor how livestock respond to specific feeds and analyze eating patterns. Furthermore, the collection unit can record the timing and frequency of livestock meals and suggest optimal feeding schedules. This allows for the collection of detailed data on livestock diets and appropriate nutritional management.

[0069] The analysis unit can analyze the amount of exercise the livestock performs based on the livestock behavior data and suggest an appropriate exercise program. For example, the analysis unit can analyze the distance the livestock has traveled and detect signs of insufficient or excessive exercise. The analysis unit can also analyze the livestock's exercise patterns and suggest optimal exercise frequency and methods. Furthermore, the analysis unit can analyze the relationship between the livestock's health status and the amount of exercise performed, and suggest an exercise program based on the livestock's health status. This allows the livestock's exercise volume to be appropriately managed and their health to be maintained.

[0070] The communication unit can evaluate the learning ability of livestock based on the behavioral data of the livestock and suggest an optimal training program. For example, the communication unit can evaluate how quickly livestock learn new instructions and suggest a training program based on the learning speed. The communication unit can also evaluate how accurately livestock perform specific tasks and adjust the content of training. Furthermore, the communication unit can suggest the frequency and method of training based on the learning ability of the livestock. This makes it possible to provide optimal training based on the learning ability of livestock.

[0071] The proposal unit can evaluate the reproductive ability of livestock in addition to their health condition and propose an optimal breeding program. For example, the proposal unit can monitor the reproductive cycle of livestock and propose the optimal timing for mating. The proposal unit can also analyze the relationship between the health condition of livestock and their reproductive ability and propose care methods to maintain a health condition suitable for breeding. Furthermore, the proposal unit can refer to the reproductive history of livestock and propose the most effective breeding program. This enables optimal care to maximize the reproductive ability of livestock.

[0072] In addition to the behavioral data of livestock, the collection unit can monitor the sleep patterns of livestock. For example, the collection unit can record how much time livestock spend sleeping and detect signs of sleep deprivation or excessive sleep. The collection unit can also evaluate the quality of sleep of livestock and suggest improvements to the sleep environment. Furthermore, the collection unit can analyze the relationship between livestock sleep patterns and health status and suggest an optimal sleep schedule. This allows livestock sleep patterns to be monitored and appropriate sleep management to be carried out.

[0073] The processing flow of the first embodiment will be briefly explained below.

[0074] Step 1: The collection unit monitors the behavior and health of livestock. The collection unit collects data such as the livestock's movements, eating patterns, body temperature, and heart rate. The collection unit can also collect livestock data in real time using sensors. For example, the collection unit detects the livestock's movements using a motion sensor and collects data. The collection unit can also monitor the livestock's eating patterns and record their food intake. The collection unit can also measure the livestock's body temperature and heart rate using a body temperature sensor and a heart rate monitor. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit may, for example, use AI to analyze the collected data and detect signs of stress or illness in the livestock. The analysis unit may detect abnormal behavioral patterns and body temperature fluctuations and evaluate the health of the livestock. For example, the analysis unit may use an AI model to analyze the behavioral data of the livestock and detect signs of stress. The analysis unit may also analyze body temperature data and detect abnormal body temperature fluctuations. Furthermore, the analysis unit may analyze heart rate data and detect abnormal heart rate fluctuations. Step 3: The communication unit communicates with the livestock based on the analysis results obtained by the analysis unit. The communication unit issues instructions to the livestock using, for example, voice or gestures and checks the livestock's response. The communication unit can monitor the livestock's response in real time and issue appropriate instructions. For example, the communication unit issues instructions to the livestock using voice commands and checks the livestock's response. The communication unit can also issue instructions to the livestock using gestures and check the livestock's response. Furthermore, the communication unit can analyze the livestock's response and provide feedback to issue the next instruction. Step 4: The suggestion unit suggests optimal care methods and environments to the manager based on the information obtained by the communication unit. For example, the suggestion unit suggests the appropriate amount and type of feed, frequency and method of exercise, etc., depending on the health condition of the livestock. The suggestion unit can evaluate the health condition of the livestock and suggest the optimal care method. For example, the suggestion unit suggests the appropriate amount and type of feed based on data on the body temperature and heart rate of the livestock. The suggestion unit can also suggest the appropriate frequency and method of exercise based on data on the behavior of the livestock. Furthermore, the suggestion unit can monitor the health condition of the livestock in real time and adjust the care method as needed.

[0075] (Example 2) A livestock monitoring system according to an embodiment of the present invention monitors the behavior and health of livestock, communicating with them using AI to understand their condition in real time. The livestock monitoring system monitors the behavior and health of livestock, analyzing the collected data using AI to understand their condition. Furthermore, the AI ​​communicates with the livestock and suggests optimal care methods and environments to managers. For example, the livestock monitoring system collects data such as the livestock's movements, feeding patterns, body temperature, and heart rate. The livestock monitoring system then analyzes the collected data using AI to detect signs of stress or illness. Furthermore, the livestock monitoring system issues instructions to the livestock using voice or gestures and checks their responses. Finally, the livestock monitoring system suggests appropriate amounts and types of feed, frequency and methods of exercise, etc., depending on the livestock's health condition. This allows the livestock monitoring system to understand the health condition of livestock in real time and provide appropriate care. This allows the livestock monitoring system to understand the health condition of livestock in real time and provide appropriate care, thereby improving livestock welfare. Managers can also manage livestock more efficiently based on AI suggestions, reducing their workload.

[0076] A livestock monitoring system according to an embodiment includes a collection unit, an analysis unit, a communication unit, and a proposal unit. The collection unit monitors the behavior and health status of livestock. The collection unit collects data such as the movements, eating patterns, body temperature, and heart rate of the livestock. The collection unit can also collect livestock data in real time using sensors. For example, the collection unit detects the movements of the livestock using a motion sensor and collects data. The collection unit can also monitor the eating patterns of the livestock and record their food intake. The collection unit can also measure the body temperature and heart rate of the livestock using a body temperature sensor and a heart rate monitor. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the collected data using AI to detect signs of stress or illness in the livestock. The analysis unit can detect abnormal behavior patterns and body temperature fluctuations and evaluate the health status of the livestock. For example, the analysis unit can analyze the behavior data of the livestock using an AI model to detect signs of stress. The analysis unit can also analyze body temperature data to detect abnormal body temperature fluctuations. Furthermore, the analysis unit can analyze heart rate data and detect abnormal heart rate fluctuations. The communication unit communicates with livestock based on the analysis results obtained by the analysis unit. The communication unit, for example, uses voice or gestures to give instructions to the livestock and checks their reactions. The communication unit can monitor the livestock's reactions in real time and issue appropriate instructions. For example, the communication unit uses voice commands to give instructions to the livestock and checks their reactions. The communication unit can also use gestures to give instructions to the livestock and check their reactions. Furthermore, the communication unit can analyze the livestock's reactions and provide feedback for issuing the next instruction. The suggestion unit suggests optimal care methods and environments to the manager based on the information obtained by the communication unit. For example, the suggestion unit suggests appropriate amounts and types of feed, frequency and methods of exercise, etc., depending on the livestock's health condition. The suggestion unit can evaluate the livestock's health condition and suggest optimal care methods. For example, the suggestion unit suggests appropriate amounts and types of feed based on the livestock's body temperature and heart rate data.The suggestion unit can also suggest appropriate exercise frequency and methods based on the livestock behavior data. Furthermore, the suggestion unit can monitor the health status of livestock in real time and adjust care methods as necessary. This allows the livestock monitoring system according to the embodiment to grasp the health status of livestock in real time and provide appropriate care. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can suggest care methods using an AI model that inputs livestock health data and outputs optimal care methods.

[0077] The collection unit can collect data on the movements of livestock, eating patterns, body temperature, heart rate, and other data. For example, the collection unit detects the movements of livestock using a motion sensor and collects data. The collection unit can also monitor the eating patterns of livestock and record their food intake. The collection unit can also measure the body temperature and heart rate of livestock using a body temperature sensor and a heart rate monitor. For example, the collection unit detects the movements of livestock using a motion sensor and collects data. The collection unit can monitor the eating patterns of livestock and record their food intake. The collection unit can also measure the body temperature and heart rate of livestock using a body temperature sensor and a heart rate monitor. This allows for early detection of abnormalities by monitoring the detailed behavior and health status of livestock. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the movements of livestock, eating patterns, body temperature, heart rate, and other data into the generation AI and have the generation AI analyze the data.

[0078] The analysis unit can analyze the collected data and detect signs of stress or illness in the livestock. The analysis unit can, for example, use AI to analyze the collected data and detect signs of stress or illness in the livestock. The analysis unit can detect abnormal behavioral patterns and body temperature fluctuations and evaluate the health status of the livestock. For example, the analysis unit can analyze the behavioral data of the livestock using an AI model to detect signs of stress. The analysis unit can also analyze body temperature data and detect abnormal body temperature fluctuations. Furthermore, the analysis unit can analyze heart rate data and detect abnormal heart rate fluctuations. This enables early detection of signs of stress or illness in the livestock, enabling rapid response. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI detect signs of stress or illness.

[0079] The communication unit can issue instructions to the livestock using voice or gestures and check the livestock's reaction. For example, the communication unit can issue instructions to the livestock using voice or gestures and check the livestock's reaction. The communication unit can monitor the livestock's reaction in real time and issue appropriate instructions. For example, the communication unit can issue instructions to the livestock using voice commands and check the livestock's reaction. The communication unit can also issue instructions to the livestock using gestures and check the livestock's reaction. Furthermore, the communication unit can analyze the livestock's reaction and provide feedback for issuing the next instruction. This allows for effective communication with the livestock to appropriately manage the livestock's behavior. Some or all of the above-mentioned processing in the communication unit can be performed using, for example, AI, or can be performed without using AI. For example, the communication unit can input livestock reaction data to the generation AI and cause the generation AI to execute feedback for issuing the next instruction.

[0080] The suggestion unit can suggest appropriate amounts and types of feed, frequency and methods of exercise, etc., depending on the health condition of the livestock. For example, the suggestion unit can suggest appropriate amounts and types of feed, frequency and methods of exercise, etc., depending on the health condition of the livestock. The suggestion unit can evaluate the health condition of the livestock and suggest optimal care methods. For example, the suggestion unit can suggest appropriate amounts and types of feed based on data on the body temperature and heart rate of the livestock. The suggestion unit can also suggest appropriate frequency and methods of exercise based on behavioral data of the livestock. Furthermore, the suggestion unit can monitor the health condition of the livestock in real time and adjust the care method as needed. This improves the welfare of the livestock by suggesting optimal care methods depending on the health condition of the livestock. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can suggest care methods using an AI model that inputs livestock health data into a generation AI and outputs optimal care methods.

[0081] The collection unit can estimate the emotions of the livestock and adjust the frequency of data collection based on the estimated emotions of the livestock. For example, if the livestock are stressed, the collection unit can increase the frequency of data collection to perform detailed monitoring. If the livestock are relaxed, the collection unit can also reduce the frequency of data collection to reduce the burden on the livestock. The collection unit can also adjust the frequency of data collection in real time if the emotions of the livestock change suddenly. This allows for detailed monitoring while reducing the burden on the livestock by adjusting the frequency of data collection according to the emotions of the livestock. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the emotion data of the livestock into the generation AI and have the generation AI adjust the frequency of data collection.

[0082] The collection unit can analyze past behavioral data of livestock and select the optimal timing for data collection. For example, if livestock have been active during a specific time period in the past, the collection unit can concentrate data collection during that time period. If livestock have been resting during a specific time period in the past, the collection unit can also refrain from collecting data during that time period. The collection unit can also analyze past behavioral patterns of livestock and select the most efficient timing for data collection. This enables efficient monitoring by selecting the optimal timing for data collection based on past behavioral data of livestock. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input past behavioral data of livestock into a generation AI and have the generation AI select the optimal timing for data collection.

[0083] The collection unit can customize the type of data to be collected depending on the type and age of livestock when collecting data. For example, the collection unit can focus on collecting growth data for young livestock. The collection unit can also focus on collecting health data for older livestock. The collection unit can also collect specific health indicators and behavioral patterns depending on the type of livestock. This enables more accurate monitoring by collecting data depending on the type and age of livestock. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can cause the generation AI to customize data collection depending on the type and age of livestock.

[0084] The collection unit can correct the collected data when collecting data, taking into account the livestock's environment (temperature, humidity, light intensity, and other environmental factors). For example, when the temperature is high, the collection unit corrects the livestock's body temperature data to accurately determine their health condition. When the humidity is high, the collection unit can also correct the livestock's respiration data to accurately determine their health condition. When the amount of light fluctuates, the collection unit can also correct the livestock's behavior data to accurately determine their behavioral patterns. This makes it possible to more accurately determine their health condition by correcting the data taking into account the livestock's environment. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input livestock's environmental data into the generation AI and have the generation AI correct the collected data.

[0085] The collection unit can estimate the emotions of the livestock and determine the priority of data to be collected based on the estimated emotions of the livestock. For example, if the livestock are feeling stressed, the collection unit can prioritize collecting stress-related data. If the livestock are relaxed, the collection unit can also prioritize collecting data related to their health. If the livestock's emotions change suddenly, the collection unit can also prioritize collecting data related to that change. This allows important data to be collected preferentially by determining the priority of data according to the livestock's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the livestock's emotion data into the generation AI and have the generation AI determine the priority of the data to be collected.

[0086] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the livestock. For example, if the livestock are in a specific location, the collection unit prioritizes collecting data related to that location. If the livestock are on the move, the collection unit can also prioritize collecting data related to their movement. If the livestock are staying in a specific area for a long time, the collection unit can also prioritize collecting data related to that area. In this way, by collecting data by taking into account the geographical location information of the livestock, more relevant data can be obtained. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the livestock into the generation AI and cause the generation AI to prioritize the collection of highly relevant data.

[0087] The collection unit can analyze the social behavior of livestock (relationships with other livestock) during data collection and collect related data. For example, if livestock frequently come into contact with other livestock, the collection unit collects data related to that contact. If livestock are isolated, the collection unit can also collect data related to that isolation. The collection unit can also analyze the social behavior patterns of livestock and collect the most relevant data. This makes it possible to collect more detailed behavioral data by analyzing the social behavior of livestock. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input social behavior data of livestock into a generation AI and have the generation AI collect related data.

[0088] When collecting data, the collection unit can customize the collection method by reflecting the past health history of the livestock. For example, if the livestock has had a disease in the past, the collection unit will focus on collecting data related to that disease. If the livestock has had a specific health problem in the past, the collection unit can also collect data related to that problem. The collection unit can also analyze the past health history of the livestock and select the most appropriate data collection method. This makes it possible to collect data that reflects the past health history of the livestock, thereby enabling a more accurate understanding of the health condition. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past health history data of the livestock into the generation AI and have the generation AI customize the collection method.

[0089] The analysis unit can estimate the emotions of the livestock and adjust the analysis algorithm based on the estimated emotions of the livestock. For example, if the livestock are stressed, the analysis unit applies an analysis algorithm that emphasizes stress-related data. If the livestock are relaxed, the analysis unit can also apply an analysis algorithm that emphasizes health-related data. If the livestock's emotions change suddenly, the analysis unit can also apply an analysis algorithm that corresponds to the change. This allows for more accurate analysis results to be obtained by adjusting the analysis algorithm according to the livestock's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input livestock emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0090] During analysis, the analysis unit can predict fluctuations in the health status of livestock and detect abnormalities early. The analysis unit can, for example, analyze fluctuations in the body temperature and heart rate of livestock and detect abnormalities early. The analysis unit can also analyze fluctuations in the behavioral patterns of livestock and detect abnormalities early. The analysis unit can also analyze fluctuations in the eating patterns of livestock and detect abnormalities early. This makes it possible to predict fluctuations in the health status of livestock and detect abnormalities early. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input livestock health data into the generation AI and have the generation AI predict fluctuations in the health status and detect abnormalities.

[0091] During analysis, the analysis unit can apply different analysis methods depending on the type and age of the livestock. For example, the analysis unit applies an analysis method that emphasizes growth data to young livestock. The analysis unit can also apply an analysis method that emphasizes health status data to older livestock. The analysis unit can also apply an analysis method that emphasizes specific health indicators or behavioral patterns depending on the type of livestock. In this way, more accurate analysis results can be obtained by applying an analysis method depending on the type and age of the livestock. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to apply an analysis method depending on the type and age of the livestock.

[0092] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past data on the livestock. For example, the analysis unit can improve the accuracy of the analysis by referring to past health data on the livestock. The analysis unit can also improve the accuracy of the analysis by referring to past behavioral data on the livestock. The analysis unit can also improve the accuracy of the analysis by referring to past dietary data on the livestock. In this way, by referring to past data on the livestock, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input past data on the livestock into the generation AI and have the generation AI improve the accuracy of the analysis.

[0093] The analysis unit can estimate the emotions of the livestock and adjust the display method of the analysis results based on the estimated emotions of the livestock. For example, if the livestock are feeling stressed, the analysis unit can highlight stress-related data. If the livestock are relaxed, the analysis unit can also highlight data related to the livestock's health. If the livestock's emotions change suddenly, the analysis unit can highlight data related to the change. This allows for a more understandable display by adjusting the display method of the analysis results according to the livestock's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input livestock emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0094] The analysis unit can perform the analysis while taking into account the geographical distribution of livestock. For example, if livestock are concentrated in a specific area, the analysis unit will focus on data related to that area. If livestock are distributed over a wide area, the analysis unit can also analyze data for each area individually. The analysis unit can also select the optimal analysis method while taking into account the geographical distribution of livestock. In this way, more accurate analysis results can be obtained by performing the analysis while taking into account the geographical distribution of livestock. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input livestock geographical distribution data into the generation AI and have the generation AI perform the analysis.

[0095] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to livestock. The analysis unit can improve the accuracy of the analysis by referring to, for example, the latest research papers on livestock health. The analysis unit can also improve the accuracy of the analysis by referring to past research data on livestock behavior. The analysis unit can also improve the accuracy of the analysis by referring to literature on livestock breeding. In this way, the accuracy of the analysis is improved by referring to literature related to livestock. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input livestock-related literature data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0096] The analysis unit can perform the analysis taking into account the market value of the livestock. For example, the analysis unit performs the analysis with emphasis on the health condition and growth rate based on the market value of the livestock. The analysis unit can also perform the analysis to propose the optimal breeding method based on the market value of the livestock. The analysis unit can also perform the analysis with emphasis on early detection of abnormalities based on the market value of the livestock. In this way, by performing the analysis taking into account the market value of the livestock, it is possible to propose a more economical breeding method. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input livestock market value data into the generation AI and have the generation AI perform the analysis.

[0097] The communication unit can estimate the emotions of the livestock and adjust the communication method based on the estimated emotions. For example, if the livestock are stressed, the communication unit can issue instructions in a calm voice. If the livestock are relaxed, the communication unit can also issue instructions in a normal voice. If the livestock's emotions change suddenly, the communication unit can also apply a communication method that corresponds to the change. This reduces the stress of the livestock by adjusting the communication method according to the livestock's emotions, enabling more effective communication. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the communication unit can be performed using an AI, for example, or without an AI. For example, the communication unit can input emotional data of the livestock into the generation AI and have the generation AI adjust the communication method.

[0098] The communication unit can analyze the livestock's reactions in real time during communication and issue optimal instructions. For example, if the livestock do not follow an instruction, the communication unit can change the content of the instruction and issue it again. If the livestock follow the instruction, the communication unit can also issue the next instruction. The communication unit can also analyze the livestock's reactions in real time and issue instructions at the optimal timing. In this way, by analyzing the livestock's reactions in real time, more appropriate instructions can be issued. Some or all of the above-mentioned processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input livestock reaction data into a generation AI and have the generation AI output optimal instructions.

[0099] The communication unit can apply different communication methods depending on the type and age of the livestock when communicating. For example, the communication unit applies a playful communication method to young livestock. The communication unit can also apply a gentle communication method to older livestock. The communication unit can also select the optimal communication method depending on the type of livestock. This enables more effective communication by applying a communication method depending on the type and age of the livestock. Some or all of the above-mentioned processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input data on the type and age of the livestock into the generation AI and have the generation AI apply the communication method.

[0100] The communication unit can improve the accuracy of communication by referring to past reaction data of the livestock when communicating. For example, the communication unit refers to past reaction data of the livestock and selects the optimal communication method. The communication unit can also re-apply a method that the livestock responded favorably to in the past. The communication unit can also analyze past reaction data of the livestock and apply the most effective communication method. In this way, by referring to past reaction data of the livestock, the accuracy of communication is improved. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input past reaction data of the livestock into the generation AI and have the generation AI select the communication method.

[0101] The communication unit can estimate the emotions of the livestock and adjust the frequency of communication based on the estimated emotions. For example, if the livestock are stressed, the communication unit can increase the frequency of communication to give them a sense of security. If the livestock are relaxed, the communication unit can also reduce the frequency of communication to reduce their burden. If the emotions of the livestock change suddenly, the communication unit can adjust the frequency of communication in response to the change. This reduces the stress of the livestock by adjusting the frequency of communication according to their emotions, enabling more effective communication. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the communication unit can be performed using AI, for example, or without AI. For example, the communication unit can input emotional data of the livestock into the generation AI and have the generation AI adjust the communication frequency.

[0102] The communication unit can select the optimal communication method by taking into account the geographical location information of the livestock when communicating. For example, if the livestock are in a specific location, the communication unit selects a communication method appropriate for that location. If the livestock are on the move, the communication unit can also select a communication method appropriate for movement. The communication unit can also select the optimal communication method by taking into account the geographical location information of the livestock. In this way, more effective communication is possible by selecting a communication method by taking into account the geographical location information of the livestock. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the geographical location information of the livestock into the generation AI and cause the generation AI to select the optimal communication method.

[0103] The communication unit can analyze the social behavior of the livestock during communication and perform relevant communication. For example, if the livestock are in frequent contact with other livestock, the communication unit can perform communication related to that contact. If the livestock are isolated, the communication unit can also perform communication related to the isolation. The communication unit can also analyze the social behavior patterns of the livestock and perform the most relevant communication. This enables more relevant communication by analyzing the social behavior of the livestock. Some or all of the above-mentioned processing in the communication unit can be performed using AI, for example, or without AI. For example, the communication unit can input social behavior data of the livestock into a generation AI and have the generation AI perform relevant communication.

[0104] The communication unit can customize the communication method to reflect the health status of the livestock when communicating. For example, if the livestock are healthy, the communication unit applies a normal communication method. If the livestock are sick, the communication unit can also apply a communication method that corresponds to the illness. The communication unit can also select the optimal communication method to reflect the health status of the livestock. This enables more appropriate communication by customizing the communication method to reflect the health status of the livestock. Some or all of the above-mentioned processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input livestock health data into the generation AI and have the generation AI customize the communication method.

[0105] The suggestion unit can estimate the emotions of the livestock and adjust the content of the suggestion based on the estimated emotions of the livestock. For example, if the livestock are feeling stressed, the suggestion unit can make suggestions to reduce stress. If the livestock are relaxed, the suggestion unit can also make suggestions to maintain health. If the emotions of the livestock change suddenly, the suggestion unit can also make suggestions that correspond to the change. This makes it possible to suggest more appropriate care methods by adjusting the content of the suggestion according to the emotions of the livestock. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input emotional data of the livestock into the generation AI and have the generation AI adjust the content of the suggestion.

[0106] When making a proposal, the proposal unit can predict fluctuations in the health condition of the livestock and propose the optimal care method. The proposal unit can, for example, predict fluctuations in the body temperature and heart rate of the livestock and propose the optimal care method. The proposal unit can also predict fluctuations in the behavioral patterns of the livestock and propose the optimal care method. The proposal unit can also predict fluctuations in the eating patterns of the livestock and propose the optimal care method. In this way, by predicting fluctuations in the health condition of the livestock, more appropriate care methods can be proposed. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can input livestock health data into the generation AI and cause the generation AI to predict fluctuations in the health condition and propose care methods.

[0107] When making a proposal, the proposal unit can apply different proposal methods depending on the type and age of the livestock. For example, the proposal unit makes proposals regarding growth for young livestock. The proposal unit can also make proposals regarding health maintenance for older livestock. The proposal unit can also propose the optimal care method depending on the type of livestock. In this way, by applying a proposal method depending on the type and age of the livestock, a more appropriate care method can be proposed. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the type and age of livestock into the generation AI and cause the generation AI to apply the proposal method.

[0108] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to the livestock's past care history. The proposal unit, for example, refers to the livestock's past care history and proposes the optimal care method. The proposal unit can also analyze the effects of care that the livestock have received in the past and propose the optimal care method. The proposal unit can also propose the most effective care method based on the livestock's past care history. In this way, by referring to the livestock's past care history, the accuracy of the proposal is improved. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input livestock's past care history data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0109] The suggestion unit can estimate the emotions of the livestock and prioritize suggestions based on the estimated emotions of the livestock. For example, if the livestock are stressed, the suggestion unit prioritizes suggestions for stress reduction. If the livestock are relaxed, the suggestion unit can also prioritize suggestions for maintaining health. If the livestock's emotions change suddenly, the suggestion unit can also prioritize suggestions that correspond to the change. This allows more important care methods to be prioritized by prioritizing suggestions based on the livestock's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input livestock emotion data into the generation AI and have the generation AI determine the priority of suggestions.

[0110] When making a proposal, the proposal unit can propose the optimal care method by taking into account the geographical location information of the livestock. For example, if the livestock are in a specific location, the proposal unit can propose a care method appropriate for that location. If the livestock are on the move, the proposal unit can also propose a care method appropriate for the move. The proposal unit can also propose the optimal care method by taking into account the geographical location information of the livestock. In this way, by proposing a care method by taking into account the geographical location information of the livestock, it is possible to propose a more appropriate care method. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input the geographical location information of the livestock into the generation AI and cause the generation AI to propose the optimal care method.

[0111] When making a suggestion, the suggestion unit can analyze the social behavior of the livestock and suggest a relevant care method. For example, if the livestock is in frequent contact with other livestock, the suggestion unit can suggest a care method related to that contact. If the livestock is isolated, the suggestion unit can also suggest a care method related to the isolation. The suggestion unit can also analyze the social behavior patterns of the livestock and suggest the most relevant care method. In this way, by analyzing the social behavior of the livestock, more relevant care methods can be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input social behavior data of the livestock into the generation AI and cause the generation AI to suggest relevant care methods.

[0112] When making a proposal, the proposal unit can propose an optimal care method taking into account the market value of the livestock. For example, the proposal unit proposes a care method that emphasizes the health condition and growth rate based on the market value of the livestock. The proposal unit can also propose an optimal rearing method based on the market value of the livestock. The proposal unit can also propose a care method that emphasizes early detection of abnormalities based on the market value of the livestock. In this way, by proposing a care method taking into account the market value of the livestock, it is possible to propose a more economical rearing method. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without using, AI, for example. For example, the proposal unit can input livestock market value data into the generation AI and cause the generation AI to propose an optimal care method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, communication unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect data such as livestock movements, body temperature, and heart rate using the camera 42 and sensors of the smart device 14. The analysis unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data using AI and detects signs of stress or illness in the livestock. The communication unit issues instructions to the livestock by voice or gesture using the control unit 46A of the smart device 14 and checks the livestock's response. The suggestion unit, realized by the specific processing unit 290 of the data processing device 12, suggests optimal care methods and environments to the manager based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, communication unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect data such as livestock movements, body temperature, and heart rate using the camera 42 and sensors of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI to detect signs of stress or illness in the livestock. The communication unit issues instructions to the livestock by voice or gesture using the control unit 46A of the smart glasses 214 and checks the livestock's reactions. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal care methods and environments to the manager based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, communication unit, and suggestion unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect data such as livestock movements, body temperature, and heart rate using the camera 42 and sensors of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI to detect signs of stress or illness in the livestock. The communication unit issues instructions to the livestock by voice or gesture using the control unit 46A of the headset terminal 314 and checks the livestock's response. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal care methods and environments to the manager based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, communication unit, and suggestion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect data such as the movements, body temperature, and heart rate of livestock using the camera 42 and sensors of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI to detect signs of stress or illness in the livestock. The communication unit uses the control unit 46A of the robot 414 to issue instructions to the livestock by voice or gesture and check the livestock's reaction. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal care methods and environments to the manager based on the analysis results.

[0113] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0114] In addition to livestock behavior data, the collection unit can also collect surrounding environmental sounds. For example, the collection unit can use a microphone to collect sounds in the environment where the livestock are present and detect sounds that may affect the livestock's behavior or health. The collection unit can also identify sounds that cause stress to livestock and record the livestock's reactions when those sounds occur. Furthermore, the collection unit can identify sounds that relax livestock and play those sounds to reduce the livestock's stress. This makes it possible to monitor environmental sounds that affect the livestock's behavior or health and take appropriate measures.

[0115] The analysis unit can analyze the social relationships of livestock based on the behavioral data of the livestock. For example, the analysis unit can analyze how livestock interact with other livestock and detect signs of social stress or isolation. If livestock frequently come into contact with specific companions, the analysis unit can make suggestions to strengthen those relationships. Furthermore, if livestock are isolated, the analysis unit can suggest measures to alleviate that isolation. This allows the social relationships of livestock to be analyzed and appropriate care to be provided.

[0116] The communication unit can learn the likes and dislikes of livestock based on their behavioral data and issue individually customized instructions. For example, the communication unit can learn how livestock respond to specific voice commands and use the most effective voice commands. The communication unit can also learn how livestock respond to specific gestures and use the most effective gestures. Furthermore, the communication unit can analyze livestock responses in real time and provide feedback to issue the next instruction. This enables more effective communication based on livestock likes and dislikes.

[0117] The suggestion unit can evaluate the productivity of livestock in addition to their health conditions and propose optimal care methods. For example, the suggestion unit can monitor the weight and growth rate of livestock and propose optimal amounts and types of feed. The suggestion unit can also monitor the milk and egg production of livestock and propose adjustments to exercise and environment to improve productivity. Furthermore, the suggestion unit can consider the balance between the health conditions and productivity of livestock and propose optimal care methods. This makes it possible to provide optimal care that balances the health conditions and productivity of livestock.

[0118] In addition to livestock behavior data, the collection unit can collect detailed data on livestock diets. For example, the collection unit records what type of feed the livestock eat and how much. The collection unit can also monitor how livestock respond to specific feeds and analyze eating patterns. Furthermore, the collection unit can record the timing and frequency of livestock meals and suggest optimal feeding schedules. This allows for the collection of detailed data on livestock diets and appropriate nutritional management.

[0119] The analysis unit can analyze the amount of exercise the livestock performs based on the livestock behavior data and suggest an appropriate exercise program. For example, the analysis unit can analyze the distance the livestock has traveled and detect signs of insufficient or excessive exercise. The analysis unit can also analyze the livestock's exercise patterns and suggest optimal exercise frequency and methods. Furthermore, the analysis unit can analyze the relationship between the livestock's health status and the amount of exercise performed, and suggest an exercise program based on the livestock's health status. This allows the livestock's exercise volume to be appropriately managed and their health to be maintained.

[0120] The communication unit can evaluate the learning ability of livestock based on the behavioral data of the livestock and suggest an optimal training program. For example, the communication unit can evaluate how quickly livestock learn new instructions and suggest a training program based on the learning speed. The communication unit can also evaluate how accurately livestock perform specific tasks and adjust the content of training. Furthermore, the communication unit can suggest the frequency and method of training based on the learning ability of the livestock. This makes it possible to provide optimal training based on the learning ability of livestock.

[0121] The proposal unit can evaluate the reproductive ability of livestock in addition to their health condition and propose an optimal breeding program. For example, the proposal unit can monitor the reproductive cycle of livestock and propose the optimal timing for mating. The proposal unit can also analyze the relationship between the health condition of livestock and their reproductive ability and propose care methods to maintain a health condition suitable for breeding. Furthermore, the proposal unit can refer to the reproductive history of livestock and propose the most effective breeding program. This enables optimal care to maximize the reproductive ability of livestock.

[0122] In addition to the behavioral data of livestock, the collection unit can monitor the sleep patterns of livestock. For example, the collection unit can record how much time livestock spend sleeping and detect signs of sleep deprivation or excessive sleep. The collection unit can also evaluate the quality of sleep of livestock and suggest improvements to the sleep environment. Furthermore, the collection unit can analyze the relationship between livestock sleep patterns and health status and suggest an optimal sleep schedule. This allows livestock sleep patterns to be monitored and appropriate sleep management to be carried out.

[0123] The analysis unit can evaluate the stress levels of livestock in real time based on the livestock behavior data and propose measures to reduce stress. For example, the analysis unit can analyze the behavioral patterns and physiological data of livestock to detect signs of stress. If livestock are feeling stressed, the analysis unit can identify the cause and propose environmental adjustments to reduce stress. Furthermore, the analysis unit can continuously monitor the stress levels of livestock and evaluate the effectiveness of stress reduction. This makes it possible to evaluate the stress levels of livestock in real time and take appropriate measures.

[0124] The processing flow of the second embodiment will be briefly explained below.

[0125] Step 1: The collection unit monitors the behavior and health of livestock. The collection unit collects data such as the livestock's movements, eating patterns, body temperature, and heart rate. The collection unit can also collect livestock data in real time using sensors. For example, the collection unit detects the livestock's movements using a motion sensor and collects data. The collection unit can also monitor the livestock's eating patterns and record their food intake. The collection unit can also measure the livestock's body temperature and heart rate using a body temperature sensor and a heart rate monitor. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit may, for example, use AI to analyze the collected data and detect signs of stress or illness in the livestock. The analysis unit may detect abnormal behavioral patterns and body temperature fluctuations and evaluate the health of the livestock. For example, the analysis unit may use an AI model to analyze the behavioral data of the livestock and detect signs of stress. The analysis unit may also analyze body temperature data and detect abnormal body temperature fluctuations. Furthermore, the analysis unit may analyze heart rate data and detect abnormal heart rate fluctuations. Step 3: The communication unit communicates with the livestock based on the analysis results obtained by the analysis unit. The communication unit issues instructions to the livestock using, for example, voice or gestures and checks the livestock's response. The communication unit can monitor the livestock's response in real time and issue appropriate instructions. For example, the communication unit issues instructions to the livestock using voice commands and checks the livestock's response. The communication unit can also issue instructions to the livestock using gestures and check the livestock's response. Furthermore, the communication unit can analyze the livestock's response and provide feedback to issue the next instruction. Step 4: The suggestion unit suggests optimal care methods and environments to the manager based on the information obtained by the communication unit. For example, the suggestion unit suggests the appropriate amount and type of feed, frequency and method of exercise, etc., depending on the health condition of the livestock. The suggestion unit can evaluate the health condition of the livestock and suggest the optimal care method. For example, the suggestion unit suggests the appropriate amount and type of feed based on data on the body temperature and heart rate of the livestock. The suggestion unit can also suggest the appropriate frequency and method of exercise based on data on the behavior of the livestock. Furthermore, the suggestion unit can monitor the health condition of the livestock in real time and adjust the care method as needed.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0130] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0131] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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 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.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0146] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0147] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type 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.

[0154] 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.

[0155] 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.

[0156] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0157] 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.

[0158] 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.

[0159] 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 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.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0162] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0174] 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.

[0175] 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.

[0176] 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 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.

[0177] 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.

[0178] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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, to avoid confusion and 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.

[0196] 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.

[0197] [Explanation of symbols]

[0198] 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 collection department that monitors the behavior and health of livestock; an analysis unit that analyzes the data collected by the collection unit; a communication unit that communicates with livestock based on the analysis results obtained by the analysis unit; a suggestion unit that suggests an appropriate care method and environment to the manager based on the information obtained by the communication unit; Equipped with A system characterized by:

2. The collecting unit Collecting data on animal movements, feeding patterns, body temperature, heart rate, and other data 2. The system of claim 1.

3. The analysis unit Analyzing the collected data to detect signs of stress or illness in livestock 2. The system of claim 1.

4. The communication unit Give commands to livestock using voice or gestures and check their response 2. The system of claim 1.

5. The proposal unit We suggest the appropriate amount and type of feed, frequency and method of exercise, etc. depending on the health condition of the livestock.

2. The system of claim 1.

6. The collecting unit Estimate the emotions of livestock and adjust the frequency of data collection based on the estimated emotions of livestock.

2. The system of claim 1.

7. The collecting unit Analyzing past behavioral data of livestock and selecting the optimal timing for data collection 2. The system of claim 1.

8. The collecting unit During data collection, customize the type of data collected based on the breed and age of livestock.

2. The system of claim 1.

9. The collecting unit When collecting data, correct the collected data by taking into account the livestock's environment (temperature, humidity, light level, and other environmental factors).

2. The system of claim 1.

Images