System

Abstract

An object of a system according to an embodiment is to efficiently identify an individual having livestock productivity and health characteristics.SOLUTION: A system includes a monitoring unit, an analysis unit, and an identification unit. The monitoring unit monitors a behavior pattern and a health condition of livestock. The analysis unit analyzes the data collected by the monitoring unit. The identification unit identifies a livestock animal having productivity and health characteristics on the basis of a result analyzed by the analysis 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 techniques have had the problem of making it difficult to efficiently identify livestock individuals with productivity and health characteristics.

[0005] The system according to the embodiment aims to efficiently identify livestock individuals with productivity and health characteristics. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, an analysis unit, and an identification unit. The monitoring unit monitors the behavioral patterns and health conditions of livestock. The analysis unit analyzes data collected by the monitoring unit. The identification unit identifies livestock with productivity and health characteristics based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently identify livestock individuals with productivity and health characteristics. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The livestock monitoring system according to an embodiment of the present invention is a system that monitors the behavioral patterns and health conditions of livestock and uses AI analysis to identify livestock with high productivity and health characteristics. This allows the livestock monitoring system to identify livestock with superior genetic qualities, enabling producers to make scientifically based breeding selections.

[0029] A livestock monitoring system according to an embodiment includes a monitoring unit, an analysis unit, and an identification unit. The monitoring unit monitors the behavioral patterns and health conditions of livestock. For example, the monitoring unit collects data such as the distance traveled, frequency of meals, sleeping time, body temperature, and heart rate of the livestock. The monitoring unit can also collect data using sensors and cameras. For example, the monitoring unit uses an infrared sensor to measure the body temperature of the livestock. The monitoring unit also uses a heart rate sensor to measure the heart rate of the livestock. The monitoring unit also uses a camera to monitor the behavioral patterns of the livestock. The analysis unit analyzes the data collected by the monitoring unit. For example, the analysis unit analyzes the behavioral patterns and health conditions of the livestock using AI. The analysis unit can also analyze whether a specific behavioral pattern is associated with high productivity or health characteristics. The analysis unit also analyzes data using AI to analyze the health conditions of the livestock. For example, the analysis unit uses AI to perform analysis based on the behavioral data and health data of the livestock. The identification unit identifies livestock with high productivity or health characteristics based on the results of the analysis by the analysis unit. For example, the identification unit identifies individuals whose specific behavioral patterns or health conditions are associated with high productivity or health characteristics. The identification unit can also identify livestock with superior genetic qualities based on the results of the AI ​​analysis. Thus, the livestock monitoring system according to the embodiment can identify livestock with high productivity or health characteristics by monitoring and analyzing the behavioral patterns and health conditions of livestock. For example, the output unit provides information about the identified livestock to producers. The output unit can display the information through a web application or a mobile application. The output unit can also print the information using a printer. Sending the information via email provides quick feedback by sending the information directly to producers.

[0030] The monitoring unit can monitor the behavioral patterns of livestock using drones. The monitoring unit, for example, monitors the behavioral patterns of livestock using drones. For example, drones fly over a wide area and record the migration routes of livestock and the movements of herds in real time. This makes it possible to collect detailed information about behavioral patterns that are difficult to grasp from the ground. As a result, by using drones, it is possible to monitor the behavioral patterns of livestock over a wide area in detail.

[0031] The monitoring unit can monitor the health of livestock using non-invasive biosensors. For example, the monitoring unit attaches non-invasive biosensors to livestock to measure blood components and hormone levels in real time. For example, a sensor that can be attached to the skin can be used to monitor blood glucose levels and cortisol levels. In this way, the health of livestock can be monitored in real time using non-invasive biosensors.

[0032] The monitoring unit can analyze the health condition or stress level from the sounds or voices of livestock using voice recognition technology. The monitoring unit, for example, uses voice recognition technology to analyze the sounds of livestock and estimate the health condition or stress level. For example, the monitoring unit analyzes the frequency and volume of the sounds and detects abnormal sounds. In this way, the health condition and stress level can be analyzed from the sounds or voices of livestock using voice recognition technology.

[0033] The monitoring unit can use environmental sensors to collect data on the temperature, humidity, and air quality of the rearing environment and analyze the correlation with the health condition. The monitoring unit, for example, uses environmental sensors to monitor the temperature and humidity of the rearing environment in real time. For example, it analyzes the impact that fluctuations in temperature and humidity have on the health condition of livestock. In this way, by using environmental sensors, it is possible to collect data on the rearing environment and analyze the correlation with the health condition.

[0034] The analysis unit can also combine and analyze the genetic data of livestock to clarify the relationship between genetic characteristics and behavioral patterns and health conditions. For example, the analysis unit uses AI to combine and analyze the genetic data and behavioral data of livestock. For example, it can analyze whether specific genetic characteristics are associated with high productivity or health characteristics. In this way, by combining and analyzing genetic data, it is possible to clarify the relationship between genetic characteristics and behavioral patterns and health conditions.

[0035] The analysis unit can track changes in livestock behavior patterns and health conditions by comparing them with past data and analyze long-term trends. For example, the analysis unit uses AI to compare past behavior data with current data and analyze changes in livestock behavior patterns and health conditions. For example, it analyzes seasonal fluctuations in behavior patterns. By comparing with past data, it is possible to analyze long-term trends in livestock behavior patterns and health conditions.

[0036] The analysis unit can integrate data from different types of livestock and perform comparative analysis of productivity and health characteristics between species. For example, AI can integrate data from different types of livestock and perform comparative analysis of productivity and health characteristics between species. For example, it can compare the behavior patterns and health conditions of cows and pigs. In this way, by integrating data from different types of livestock, comparative analysis of productivity and health characteristics between species can be performed.

[0037] The analysis unit can also combine and analyze data on the rearing environment to identify optimal rearing conditions. For example, AI can combine and analyze data on the rearing environment and behavioral data to identify optimal rearing conditions. For example, it can analyze the impact of feed type and rearing method on productivity and health characteristics. This allows optimal rearing conditions to be identified by combining and analyzing data on the rearing environment.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The monitoring unit can monitor not only the behavioral patterns and health status of livestock, but also their social interactions. For example, the monitoring unit can record the frequency and duration of contact between livestock and analyze their social networks. This can clarify the impact of livestock social behavior on productivity and health characteristics. The monitoring unit can also monitor whether livestock frequently come into contact with specific individuals and estimate social stress and feelings of isolation. Furthermore, the monitoring unit can analyze the leadership and hierarchical structure of livestock herds and clarify the impact of leadership on productivity and health characteristics.

[0040] The monitoring unit not only monitors the health status of livestock using non-invasive biosensors, but can also monitor the metabolic status of livestock using biosensors. For example, the monitoring unit can measure metabolic products of livestock in real time and analyze the metabolic status. This can clarify the impact of the metabolic status of livestock on productivity and health characteristics. The monitoring unit can also monitor changes in the metabolic status of livestock and detect abnormal metabolic conditions early. Furthermore, the monitoring unit can analyze the metabolic status of livestock to determine whether a specific metabolic pattern is associated with high productivity or health characteristics.

[0041] The monitoring unit not only uses environmental sensors to collect data on the temperature, humidity, and air quality of the rearing environment, but can also use environmental sensors to monitor the light intensity and sound level of the rearing environment. For example, the monitoring unit can measure the light intensity of the rearing environment in real time and analyze the effect of the light intensity on the health of the livestock. The monitoring unit can also monitor the sound level of the rearing environment and analyze the effect of the sound level on the stress level of the livestock. Furthermore, the monitoring unit can adjust the light intensity and sound level of the rearing environment to optimize the health and stress level of the livestock.

[0042] The analysis unit can not only combine and analyze genetic data of livestock, but also combine and analyze microbiome data of livestock. For example, the analysis unit can collect data on the intestinal bacteria of livestock and analyze it in combination with genetic data. This makes it possible to clarify the impact of the composition of intestinal bacteria on productivity and health characteristics. The analysis unit can also analyze microbiome data on the skin and oral cavity of livestock and clarify the correlation with health status. Furthermore, the analysis unit can propose optimal feed and breeding methods based on the microbiome data of livestock.

[0043] The analytics unit not only tracks changes in livestock behavior patterns and health conditions by comparing them with past data, but can also predict future behavior patterns and health conditions based on past data. For example, the analytics unit can use AI to learn from past data and build a model that predicts future behavior patterns and health conditions. This allows for early prediction of deterioration in livestock health and the implementation of preventative measures. The analytics unit can also predict seasonal fluctuations in behavior patterns and optimize breeding schedules. Furthermore, the analytics unit can predict future trends in livestock growth and productivity and create long-term breeding plans.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The monitoring unit monitors the behavioral patterns and health conditions of the livestock. For example, the monitoring unit collects data such as the distance traveled by the livestock, frequency of meals, sleeping hours, body temperature, and heart rate. The monitoring unit can also collect data using sensors and cameras. For example, the monitoring unit uses an infrared sensor to measure the body temperature of the livestock. The monitoring unit also uses a heart rate sensor to measure the heart rate of the livestock. The monitoring unit also uses a camera to monitor the behavioral patterns of the livestock. Step 2: The analysis unit analyzes the data collected by the monitoring unit. For example, the analysis unit uses AI to analyze the behavioral patterns and health conditions of the livestock. The analysis unit can also analyze whether a specific behavioral pattern is associated with high productivity or health characteristics. The analysis unit also uses AI to analyze the data in order to analyze the health conditions of the livestock. For example, the analysis unit uses AI to perform analysis based on the behavioral data and health data of the livestock. Step 3: The identification unit identifies livestock with high productivity and health characteristics based on the results of the analysis by the analysis unit. For example, the identification unit identifies individuals whose specific behavioral patterns or health conditions are associated with high productivity or health characteristics. The identification unit can also identify livestock with excellent genetic qualities based on the results of the AI ​​analysis.

[0046] (Example 2) The livestock monitoring system according to an embodiment of the present invention is a system that monitors the behavioral patterns and health conditions of livestock and uses AI analysis to identify livestock with high productivity and health characteristics. This allows the livestock monitoring system to identify livestock with superior genetic qualities, enabling producers to make scientifically based breeding selections.

[0047] A livestock monitoring system according to an embodiment includes a monitoring unit, an analysis unit, and an identification unit. The monitoring unit monitors the behavioral patterns and health conditions of livestock. For example, the monitoring unit collects data such as the distance traveled, frequency of meals, sleeping time, body temperature, and heart rate of the livestock. The monitoring unit can also collect data using sensors and cameras. For example, the monitoring unit uses an infrared sensor to measure the body temperature of the livestock. The monitoring unit also uses a heart rate sensor to measure the heart rate of the livestock. The monitoring unit also uses a camera to monitor the behavioral patterns of the livestock. The analysis unit analyzes the data collected by the monitoring unit. For example, the analysis unit analyzes the behavioral patterns and health conditions of the livestock using AI. The analysis unit can also analyze whether a specific behavioral pattern is associated with high productivity or health characteristics. The analysis unit also analyzes data using AI to analyze the health conditions of the livestock. For example, the analysis unit uses AI to perform analysis based on the behavioral data and health data of the livestock. The identification unit identifies livestock with high productivity or health characteristics based on the results of the analysis by the analysis unit. For example, the identification unit identifies individuals whose specific behavioral patterns or health conditions are associated with high productivity or health characteristics. The identification unit can also identify livestock with superior genetic qualities based on the results of the AI ​​analysis. Thus, the livestock monitoring system according to the embodiment can identify livestock with high productivity or health characteristics by monitoring and analyzing the behavioral patterns and health conditions of livestock. For example, the output unit provides information about the identified livestock to producers. The output unit can display the information through a web application or a mobile application. The output unit can also print the information using a printer. Sending the information via email provides quick feedback by sending the information directly to producers.

[0048] The monitoring unit can monitor the behavioral patterns of livestock using drones. The monitoring unit, for example, monitors the behavioral patterns of livestock using drones. For example, drones fly over a wide area and record the migration routes of livestock and the movements of herds in real time. This makes it possible to collect detailed information about behavioral patterns that are difficult to grasp from the ground. As a result, by using drones, it is possible to monitor the behavioral patterns of livestock over a wide area in detail.

[0049] The monitoring unit can monitor the health of livestock using non-invasive biosensors. For example, the monitoring unit attaches non-invasive biosensors to livestock to measure blood components and hormone levels in real time. For example, a sensor that can be attached to the skin can be used to monitor blood glucose levels and cortisol levels. In this way, the health of livestock can be monitored in real time using non-invasive biosensors.

[0050] The monitoring unit can estimate the stress level or happiness of the livestock using the emotion estimation function. The monitoring unit, for example, uses the emotion estimation function to estimate the stress level of the livestock in real time. For example, the monitoring unit analyzes whether the livestock are feeling stressed based on behavioral data and physiological data. In this way, the emotion estimation function can estimate the stress level and happiness of the livestock in real time.

[0051] The monitoring unit can analyze the health condition or stress level from the sounds or voices of livestock using voice recognition technology. The monitoring unit, for example, uses voice recognition technology to analyze the sounds of livestock and estimate the health condition or stress level. For example, the monitoring unit analyzes the frequency and volume of the sounds and detects abnormal sounds. In this way, the health condition and stress level can be analyzed from the sounds or voices of livestock using voice recognition technology.

[0052] The monitoring unit can use environmental sensors to collect data on the temperature, humidity, and air quality of the rearing environment and analyze the correlation with the health condition. The monitoring unit, for example, uses environmental sensors to monitor the temperature and humidity of the rearing environment in real time. For example, it analyzes the impact that fluctuations in temperature and humidity have on the health condition of livestock. In this way, by using environmental sensors, it is possible to collect data on the rearing environment and analyze the correlation with the health condition.

[0053] The monitoring unit can simultaneously analyze the emotions of the keeper using the emotion estimation function and make suggestions to reduce the keeper's stress and fatigue level. The monitoring unit, for example, uses the emotion estimation function to analyze the keeper's emotional state in real time. For example, it analyzes the keeper's facial expressions and voice to estimate the keeper's stress and fatigue level. In this way, by using the emotion estimation function, the monitoring unit can analyze the keeper's emotions and make suggestions to reduce the keeper's stress and fatigue level.

[0054] The analysis unit can also combine and analyze the genetic data of livestock to clarify the relationship between genetic characteristics and behavioral patterns and health conditions. For example, the analysis unit uses AI to combine and analyze the genetic data and behavioral data of livestock. For example, it can analyze whether specific genetic characteristics are associated with high productivity or health characteristics. In this way, by combining and analyzing genetic data, it is possible to clarify the relationship between genetic characteristics and behavioral patterns and health conditions.

[0055] The analysis unit can track changes in livestock behavior patterns and health conditions by comparing them with past data and analyze long-term trends. For example, the analysis unit uses AI to compare past behavior data with current data and analyze changes in livestock behavior patterns and health conditions. For example, it analyzes seasonal fluctuations in behavior patterns. By comparing with past data, it is possible to analyze long-term trends in livestock behavior patterns and health conditions.

[0056] The analysis unit uses the emotion estimation function to analyze the emotion data of the livestock and can clarify the impact that a positive emotional state has on productivity and health characteristics. The analysis unit, for example, uses the emotion estimation function to analyze the emotion data of the livestock. For example, it analyzes the behavioral patterns of the livestock when they are in a positive emotional state. In this way, by using the emotion estimation function, it is possible to clarify the impact that a positive emotional state of the livestock has on productivity and health characteristics.

[0057] The analysis unit can integrate data from different types of livestock and perform comparative analysis of productivity and health characteristics between species. For example, AI can integrate data from different types of livestock and perform comparative analysis of productivity and health characteristics between species. For example, it can compare the behavior patterns and health conditions of cows and pigs. In this way, by integrating data from different types of livestock, comparative analysis of productivity and health characteristics between species can be performed.

[0058] The analysis unit can also combine and analyze data on the rearing environment to identify optimal rearing conditions. For example, AI can combine and analyze data on the rearing environment and behavioral data to identify optimal rearing conditions. For example, it can analyze the impact of feed type and rearing method on productivity and health characteristics. This allows optimal rearing conditions to be identified by combining and analyzing data on the rearing environment.

[0059] When analyzing the behavioral data and health data of livestock using the emotion estimation function, the analysis unit also combines and analyzes the emotion data of the keeper, thereby clarifying the impact of the emotion of the keeper on the productivity and health characteristics of the livestock. For example, the analysis unit uses the emotion estimation function to collect emotion data of the keeper and analyze it in combination with the behavioral data and health data of the livestock. For example, the analysis unit analyzes the impact of the stress level of the keeper on the productivity and health characteristics of the livestock. In this way, by combining and analyzing the emotion data of the keeper, it is possible to clarify the impact of the emotion of the keeper on the productivity and health characteristics of the livestock.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The monitoring unit can monitor not only the behavioral patterns and health status of livestock, but also their social interactions. For example, the monitoring unit can record the frequency and duration of contact between livestock and analyze their social networks. This can clarify the impact of livestock social behavior on productivity and health characteristics. The monitoring unit can also monitor whether livestock frequently come into contact with specific individuals and estimate social stress and feelings of isolation. Furthermore, the monitoring unit can analyze the leadership and hierarchical structure of livestock herds and clarify the impact of leadership on productivity and health characteristics.

[0062] The monitoring department not only uses drones to monitor livestock behavior patterns, but also collects livestock sounds using microphones mounted on the drones. For example, drones can collect livestock sounds in real time and analyze the audio data to estimate the livestock's health and stress levels. Drones can also monitor changes in livestock sounds and detect abnormal sounds. Furthermore, drones can analyze livestock sound patterns to determine whether a particular sound is associated with a specific health condition or behavior pattern.

[0063] The monitoring unit not only monitors the health status of livestock using non-invasive biosensors, but can also monitor the metabolic status of livestock using biosensors. For example, the monitoring unit can measure metabolic products of livestock in real time and analyze the metabolic status. This can clarify the impact of the metabolic status of livestock on productivity and health characteristics. The monitoring unit can also monitor changes in the metabolic status of livestock and detect abnormal metabolic conditions early. Furthermore, the monitoring unit can analyze the metabolic status of livestock to determine whether a specific metabolic pattern is associated with high productivity or health characteristics.

[0064] The monitoring unit can not only estimate the stress level or happiness of the livestock using the emotion estimation function, but also adjust the rearing environment based on the emotional state of the livestock. For example, the monitoring unit can adjust the temperature and humidity of the rearing environment if the livestock are feeling stressed. The monitoring unit can also provide relaxation measures, such as playing music in the rearing environment, if the livestock are not happy. Furthermore, the monitoring unit can adjust the type and amount of feed based on the emotional state of the livestock to reduce stress in the livestock.

[0065] The monitoring unit can use voice recognition technology to analyze the health status or stress level of livestock from their sounds or voices, as well as identify individual livestock based on the voice data. For example, the monitoring unit can analyze the characteristics of livestock sounds and identify the sound patterns of each individual livestock. This makes it possible to identify individual livestock based on their sounds and analyze their health status and stress level. The monitoring unit can also monitor changes in the sounds of livestock and detect whether a particular individual is making an abnormal sound. Furthermore, the monitoring unit can analyze the patterns of livestock sounds to determine whether a particular sound is associated with a particular health status or behavior pattern.

[0066] The monitoring unit not only uses environmental sensors to collect data on the temperature, humidity, and air quality of the rearing environment, but can also use environmental sensors to monitor the light intensity and sound level of the rearing environment. For example, the monitoring unit can measure the light intensity of the rearing environment in real time and analyze the effect of the light intensity on the health of the livestock. The monitoring unit can also monitor the sound level of the rearing environment and analyze the effect of the sound level on the stress level of the livestock. Furthermore, the monitoring unit can adjust the light intensity and sound level of the rearing environment to optimize the health and stress level of the livestock.

[0067] The monitoring unit not only simultaneously analyzes the emotions of the keeper using the emotion estimation function, but can also adjust the breeding schedule based on the keeper's emotional state. For example, if the keeper is feeling stressed, the monitoring unit can adjust the breeding work schedule to reduce stress. The monitoring unit can also issue an alert to encourage the keeper to take a break if the keeper is tired. Furthermore, the monitoring unit can adjust the priority of breeding work based on the keeper's emotional state to support efficient work.

[0068] The analysis unit can not only combine and analyze genetic data of livestock, but also combine and analyze microbiome data of livestock. For example, the analysis unit can collect data on the intestinal bacteria of livestock and analyze it in combination with genetic data. This makes it possible to clarify the impact of the composition of intestinal bacteria on productivity and health characteristics. The analysis unit can also analyze microbiome data on the skin and oral cavity of livestock and clarify the correlation with health status. Furthermore, the analysis unit can propose optimal feed and breeding methods based on the microbiome data of livestock.

[0069] The analytics unit not only tracks changes in livestock behavior patterns and health conditions by comparing them with past data, but can also predict future behavior patterns and health conditions based on past data. For example, the analytics unit can use AI to learn from past data and build a model that predicts future behavior patterns and health conditions. This allows for early prediction of deterioration in livestock health and the implementation of preventative measures. The analytics unit can also predict seasonal fluctuations in behavior patterns and optimize breeding schedules. Furthermore, the analytics unit can predict future trends in livestock growth and productivity and create long-term breeding plans.

[0070] The analysis unit can not only analyze the emotional data of livestock using the emotion estimation function, but also optimize rearing methods based on the emotional data of the livestock. For example, the analysis unit can identify rearing methods used when livestock are in a positive emotional state and apply those rearing methods to other livestock. The analysis unit can also suggest adjustments to the rearing environment to reduce stress based on the emotional data of the livestock. Furthermore, the analysis unit can adjust the type and amount of feed based on the emotional data of the livestock to improve the happiness of the livestock.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The monitoring unit monitors the behavioral patterns and health conditions of the livestock. For example, the monitoring unit collects data such as the distance traveled by the livestock, frequency of meals, sleeping hours, body temperature, and heart rate. The monitoring unit can also collect data using sensors and cameras. For example, the monitoring unit uses an infrared sensor to measure the body temperature of the livestock. The monitoring unit also uses a heart rate sensor to measure the heart rate of the livestock. The monitoring unit also uses a camera to monitor the behavioral patterns of the livestock. Step 2: The analysis unit analyzes the data collected by the monitoring unit. For example, the analysis unit uses AI to analyze the behavioral patterns and health conditions of the livestock. The analysis unit can also analyze whether a specific behavioral pattern is associated with high productivity or health characteristics. The analysis unit also uses AI to analyze the data in order to analyze the health conditions of the livestock. For example, the analysis unit uses AI to perform analysis based on the behavioral data and health data of the livestock. Step 3: The identification unit identifies livestock with high productivity and health characteristics based on the results of the analysis by the analysis unit. For example, the identification unit identifies individuals whose specific behavioral patterns or health conditions are associated with high productivity or health characteristics. The identification unit can also identify livestock with excellent genetic qualities based on the results of the AI ​​analysis.

[0073] 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.

[0074] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0086] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0140] 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 surveillance department that monitors the behavioral patterns and health of livestock; an analysis unit that analyzes the data collected by the monitoring unit; and an identification unit that identifies livestock having productivity and health characteristics based on the results of the analysis by the analysis unit. A system characterized by:

2. The monitoring unit Using an emotion estimation function to estimate the stress level or happiness of the livestock 2. The system of claim 1.

3. The monitoring unit Environmental sensors are used to collect data on temperature, humidity, and air quality in the rearing environment, and the correlation with the health status is analyzed.

2. The system of claim 1.

4. The analysis unit The genetic data of the livestock will also be analyzed to clarify the relationship between genetic characteristics and the behavioral patterns and health conditions.

2. The system of claim 1.

5. The analysis unit Analyzing the emotional data of the livestock using an emotion estimation function and clarifying the impact of a positive emotional state on the productivity and health characteristics.

2. The system of claim 1.

6. The analysis unit Integrate data from different types of livestock to perform comparative analysis of productivity and health characteristics between species.

2. The system of claim 1.

7. The analysis unit When analyzing the behavioral data and health data of the livestock using the emotion estimation function, the emotion data of the breeder is also analyzed in combination, and the impact of the emotion of the breeder on the productivity and health characteristics of the livestock is clarified.

2. The system of claim 1.

8. The monitoring unit The emotion estimation function simultaneously analyzes the emotions of the caregiver and makes suggestions to reduce the caregiver's stress and fatigue.

2. The system of claim 1.

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