System

Abstract

An object of the system according to the embodiment is to lower the hurdle that young generations are engaged in in the livestock and dairy industry.SOLUTION: A system includes a health condition analysis unit, a behavior data analysis unit, a breeding timing proposal unit, and a methane gas management unit. The health condition analysis unit analyzes a health condition of a cow. The behavior data analysis unit analyzes behavior data of the cow on the basis of the data analyzed by the health condition analysis unit. A breeding timing proposal part proposes the optimum timing of breeding and childbirth on the basis of the data analyzed by the action data analysis part. The methane gas management unit manages the amount of methane gas discharged by the burping of the cow.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 in the livestock and dairy industry relies on experience and skills, and there is a serious problem of finding successors.

[0005] The system of the embodiment aims to lower the barriers for younger generations to enter the livestock and dairy industry. [Means for solving the problem]

[0006] The system according to the embodiment includes a health condition analysis unit, a behavioral data analysis unit, a breeding timing suggestion unit, and a methane gas management unit. The health condition analysis unit analyzes the health condition of the cows. The behavioral data analysis unit analyzes the behavioral data of the cows based on the data analyzed by the health condition analysis unit. The breeding timing suggestion unit suggests optimal timing for breeding and birth based on the data analyzed by the behavioral data analysis unit. The methane gas management unit manages the amount of methane gas emitted by the cows' burps. [Effects of the Invention]

[0007] The system according to the embodiment can lower the barriers for young people to enter the livestock and dairy industry. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

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

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

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) The automation system for the livestock and dairy industry according to an embodiment of the present invention analyzes cow health and behavior data, proposes optimal breeding and birth timing, and manages methane gas emissions from cow burps. As a result, the automation system for the livestock and dairy industry can automate work in the livestock and dairy industry and solve the succession problem.

[0029] An automated system for the livestock and dairy industry according to an embodiment includes a health condition analysis unit, a behavioral data analysis unit, a breeding timing suggestion unit, and a methane gas management unit. The health condition analysis unit analyzes the health condition of cows. For example, the health condition analysis unit measures the body temperature of the cows and evaluates their health condition. The health condition analysis unit can also monitor their heart rate and analyze their health condition. The health condition analysis unit can also measure their activity level and evaluate their health condition. For example, the health condition analysis unit measures the body temperature of the cows in real time using a body temperature sensor and issues an alert if an abnormality is detected. Heart rate monitoring is performed using a heart rate sensor and issues an alert if an abnormal heart rate pattern is detected. Activity level measurement is performed using an acceleration sensor, and a decrease in activity level indicates an abnormal health condition. The behavioral data analysis unit analyzes the behavioral data of the cows based on the data analyzed by the health condition analysis unit. For example, the behavioral data analysis unit analyzes the movement patterns of the cows and detects abnormal behavior. The behavioral data analysis unit can also monitor their eating behavior and detect abnormalities. The behavioral data analysis unit can also analyze resting time and detect abnormal behavior. For example, the behavioral data analysis unit can analyze the movement patterns of cows using GPS data and issue an alert if abnormal movement is detected. Feeding behavior is monitored using a feed intake sensor to detect changes in food intake. Resting time is analyzed using an acceleration sensor and an alert is issued if the resting time is abnormally short. The breeding timing suggestion unit suggests the optimal timing for breeding and birth based on the data analyzed by the behavioral data analysis unit. For example, the breeding timing suggestion unit analyzes the body temperature and behavioral patterns of cows to suggest the optimal breeding timing. The breeding timing suggestion unit can also suggest the optimal timing based on past breeding data. The breeding timing suggestion unit can also analyze environmental data and suggest the optimal breeding timing. For example, the breeding timing suggestion unit suggests the optimal breeding timing based on body temperature data and analyzes behavioral patterns to predict the breeding success rate. Based on past breeding data, the breeding timing suggestion unit suggests the timing for breeding when similar conditions are met. The environmental data is analyzed based on temperature and humidity to suggest the optimal breeding timing.The methane gas management unit manages methane gas emissions from cow burps. For example, the methane gas management unit analyzes cow feed data and proposes optimal feed to reduce methane gas emissions. The methane gas management unit can also analyze the amount of exercise and rest time of cows and propose optimal rearing methods to reduce methane gas emissions. The methane gas management unit can also analyze intestinal flora and propose feed that reduces methane gas emissions by optimizing the intestinal environment. For example, the methane gas management unit proposes feed that reduces methane gas emissions based on feed data, and proposes optimal rearing methods by analyzing the amount of exercise and rest time. The analysis of intestinal flora suggests feed to balance intestinal bacteria. This allows the automation system for the livestock and dairy farming industry according to the embodiment to automate tasks in the livestock and dairy farming industry and solve the succession problem. For example, the output unit displays proposed breeding timing and rearing methods to the farmer via a web application or mobile application. If feedback on paper is desired, the results can be printed using a printer. Emailing provides immediate feedback by sending results directly to the breeder.

[0030] The breeding timing suggestion unit analyzes body temperature, behavioral patterns, and environmental data to propose more accurate breeding and birthing timing. For example, the generation AI collects environmental data such as cow body temperature data, behavioral patterns, ambient temperature, humidity, and sound, and analyzes this data comprehensively. For example, when a cow's body temperature rises and a specific behavioral pattern is observed, it proposes breeding timing if the temperature and humidity are appropriate. The breeding timing suggestion unit also monitors the cow's body temperature and behavioral patterns in real time, as well as surrounding environmental data, and the generation AI predicts the optimal breeding and birthing timing based on this data. For example, it proposes breeding timing by selecting a day with appropriate temperature and low humidity. The breeding timing suggestion unit also analyzes the cow's body temperature, behavioral patterns, and ambient environmental data, and compares it with past data to propose the optimal breeding and birthing timing. For example, it proposes breeding timing based on past success stories when similar environmental conditions are met. This improves the accuracy of breeding and birthing timing.

[0031] The breeding timing suggestion unit can analyze genetic information and suggest genetically optimal breeding timing. In the breeding timing suggestion unit, for example, the generation AI analyzes the genetic information of a cow and suggests the genetically optimal breeding timing. For example, it suggests the optimal breeding period for cows with specific genetic markers. In addition, the breeding timing suggestion unit predicts the breeding success rate based on the genetic information of the cow and suggests the genetically optimal timing. For example, it suggests the timing of breeding by selecting a period with a high probability of giving birth to a genetically healthy calf. In addition, the breeding timing suggestion unit analyzes the genetic information of a cow and suggests the optimal timing by comparing it with past breeding data. For example, it suggests the breeding timing for cows with genetically superior characteristics. This makes it possible to suggest the genetically optimal breeding timing.

[0032] The breeding timing suggestion unit can also be applied to optimizing the breeding and birthing timing of other livestock. For example, the breeding timing suggestion unit uses generative AI to develop a system that optimizes the breeding and birthing timing of pigs and sheep. For example, it analyzes the body temperature and behavioral patterns of pigs and sheep to propose the optimal breeding timing. In addition, to make the breeding timing suggestion unit applicable to livestock other than cows, the generative AI analyzes health and behavioral data of pigs and sheep to propose the optimal breeding and birthing timing. For example, it proposes breeding timing based on specific behavioral patterns of pigs and sheep. In addition, the breeding timing suggestion unit uses generative AI to build a data collection and analysis system for optimizing the breeding and birthing timing of pigs and sheep. For example, it collects body temperature, behavioral patterns, and environmental data of pigs and sheep to propose the optimal timing. This can also be applied to optimizing the breeding and birthing timing of other livestock.

[0033] The breeding timing suggestion unit can optimize not only the timing of breeding and birthing, but also overall health management. For example, the generative AI analyzes cow health data and suggests the optimal timing for disease prevention and treatment. For example, it suggests early disease detection and preventative measures based on body temperature and behavior patterns. In addition, to optimize overall cow health management, the generative AI analyzes health data and suggests the optimal timing for disease prevention and treatment. For example, it suggests appropriate treatment if specific symptoms are observed. In addition, the breeding timing suggestion unit builds a system in which the generative AI suggests the optimal timing for disease prevention and treatment based on cow health data. For example, it monitors health data in real time and suggests treatment if an abnormality is detected. This makes it possible to optimize overall cow health management.

[0034] The methane gas management unit analyzes feed data, exercise volume, and rest time, and can propose optimal rearing methods to further reduce methane gas emissions. For example, the generation AI analyzes cattle feed data, exercise volume, and rest time to propose optimal rearing methods to reduce methane gas emissions. For example, feeding specific feed to cattle that exercise a lot reduces methane gas emissions. The methane gas management unit also monitors cattle exercise volume and rest time in real time, and the generation AI uses this data to propose optimal rearing methods to reduce methane gas emissions. For example, it suggests increasing rest time for cattle that exercise less. The methane gas management unit also analyzes cattle feed data, exercise volume, and rest time, and compares them with past data to propose optimal rearing methods to reduce methane gas emissions. For example, it suggests feeding specific feed during periods when exercise volume is high. This makes it possible to propose optimal rearing methods to further reduce methane gas emissions.

[0035] The methane gas management unit analyzes the intestinal flora and can propose feed that reduces methane gas emissions by optimizing the intestinal environment. For example, the methane gas management unit uses a generation AI to analyze the intestinal flora of cows and propose feed that optimizes the intestinal environment. For example, feeding feed containing specific probiotics can reduce methane gas emissions. The methane gas management unit also uses a generation AI to propose feed that optimizes the intestinal environment based on the intestinal flora data of cows. For example, it can propose feed that balances intestinal bacteria. The methane gas management unit also uses a generation AI to analyze the intestinal flora of cows and propose feed that optimizes the intestinal environment by comparing it with past data. For example, it can propose feed that improves the composition of intestinal bacteria. This makes it possible to propose feed that reduces methane gas emissions by optimizing the intestinal environment.

[0036] The methane gas management unit also manages greenhouse gas emissions, reducing the overall environmental impact. For example, the generation AI in the methane gas management unit analyzes carbon dioxide and nitrous oxide emissions from cattle breeding and proposes breeding methods to reduce the overall environmental impact. For example, it selects feed and improves the breeding environment. The methane gas management unit also monitors greenhouse gas emissions from cattle breeding in real time, and the generation AI makes suggestions to reduce the overall environmental impact. For example, it proposes changes to feed and adjustments to breeding methods. The methane gas management unit also analyzes greenhouse gas emissions from cattle breeding and compares them with past data to make suggestions to reduce the overall environmental impact. For example, it proposes improvements to the breeding environment and optimizing feed. This reduces the overall environmental impact.

[0037] The methane gas management unit also manages methane gas emissions from manure, making it possible to reduce the environmental impact of the entire farming process. For example, the generation AI in the methane gas management unit analyzes methane gas emissions from cow manure and makes suggestions to reduce the environmental impact of the entire farming process. For example, it makes suggestions to improve the manure treatment method. The methane gas management unit also monitors methane gas emissions from cow manure in real time, making suggestions to reduce the environmental impact of the entire farming process. For example, it makes suggestions to change the manure treatment method. The methane gas management unit also analyzes methane gas emissions from cow manure and makes suggestions to reduce the environmental impact of the entire farming process by comparing it with past data. For example, it makes suggestions to optimize the manure treatment method. This makes it possible to reduce the environmental impact of the entire farming process.

[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] Automated systems for the livestock and dairy industries can also be equipped with a social behavior analysis unit that analyzes the social behavior of cows. For example, the social behavior analysis unit can analyze the frequency of contact between cows and the composition of the herd to evaluate the level of social stress. The social behavior analysis unit can also analyze the cows' moosing and ear movements to understand their communication patterns. Furthermore, the social behavior analysis unit can analyze the leadership and ranking of cows within the herd to evaluate their social stability. This can help understand the social behavior of cows and help reduce stress and manage their health.

[0040] Automated systems for the livestock and dairy industries can also be equipped with a nutritional analysis unit that analyzes the nutritional status of cows. For example, the nutritional analysis unit can analyze cow blood samples to evaluate nutrient balance. The nutritional analysis unit can also monitor the cow's diet and detect nutrient deficiencies or excesses. Furthermore, the nutritional analysis unit can measure the cow's weight and body fat percentage to comprehensively evaluate the nutritional status. This allows for optimal nutritional management of cows and maintaining their health.

[0041] Automated systems for the livestock and dairy industries can also be equipped with a sleep analysis unit that analyzes the sleep patterns of cows. For example, the sleep analysis unit can analyze the cow's brain wave data to evaluate the quality of sleep. The sleep analysis unit can also monitor the cow's body movement data and analyze the sleep duration and depth. Furthermore, the sleep analysis unit can analyze the cow's environmental data and suggest environmental conditions suitable for sleep. This can improve the quality of cow's sleep and maintain their health.

[0042] Automated systems for the livestock and dairy industries can also be equipped with an individual identification unit that identifies individual cows. For example, the individual identification unit can use facial recognition technology to identify individuals and manage their individual health and behavior data. The individual identification unit can also identify individuals using ear tags or microchips and centrally manage data. Furthermore, the individual identification unit can analyze the patterns and characteristics of the cow's body surface to identify individual cows. This allows for accurate management of data for each individual cow, optimizing health and breeding management.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The health condition analysis unit analyzes the health of the cow. For example, the health condition analysis unit measures the cow's body temperature and evaluates its health condition. It can also monitor heart rate and measure activity level. A body temperature sensor is used to measure the cow's body temperature in real time, and an alert is issued if an abnormality is detected. Heart rate monitoring is performed using a heart rate sensor, and a warning is issued if an abnormal heart rate pattern is detected. Activity level is measured using an acceleration sensor, and a decrease in activity level indicates an abnormal health condition. Step 2: The behavioral data analysis unit analyzes the behavioral data of the cows based on the data analyzed by the health condition analysis unit. For example, the behavioral data analysis unit analyzes the movement patterns of the cows and detects abnormalities in their behavior. It can also monitor eating behavior and analyze resting time. It uses GPS data to analyze the movement patterns of the cows and issues an alert if abnormal movement is detected. Eating behavior is monitored using a feed intake sensor to detect changes in food intake. Resting time is analyzed using an acceleration sensor and an alert is issued if the resting time is abnormally short. Step 3: The breeding timing suggestion unit suggests the optimal timing for breeding and birth based on the data analyzed by the behavioral data analysis unit. For example, the breeding timing suggestion unit analyzes the cow's body temperature and behavioral patterns to suggest the optimal breeding timing. It can also suggest the optimal timing based on past breeding data. It can also analyze environmental data to suggest the optimal breeding timing. It suggests the optimal breeding timing based on body temperature data and analyzes behavioral patterns to predict the breeding success rate. It suggests the timing for breeding when similar conditions are met based on past breeding data. It analyzes environmental data based on temperature and humidity to suggest the optimal breeding timing. Step 4: The methane gas management department manages the amount of methane gas emitted by cows' burps. For example, the methane gas management department analyzes cow feed data and suggests the optimal feed to reduce methane gas emissions. It can also analyze the amount of exercise and rest time of cows and suggest the optimal rearing method to reduce methane gas emissions. It can also analyze the intestinal flora and suggest feed that reduces methane gas emissions by optimizing the intestinal environment. Based on the feed data, it suggests feed that reduces methane gas emissions, and analyzes the amount of exercise and rest time to suggest the optimal rearing method. Analysis of the intestinal flora suggests feed that will balance the intestinal bacteria.

[0045] (Example 2) The automation system for the livestock and dairy industry according to an embodiment of the present invention analyzes cow health and behavior data, proposes optimal breeding and birth timing, and manages methane gas emissions from cow burps. As a result, the automation system for the livestock and dairy industry can automate work in the livestock and dairy industry and solve the succession problem.

[0046] An automated system for the livestock and dairy industry according to an embodiment includes a health condition analysis unit, a behavioral data analysis unit, a breeding timing suggestion unit, and a methane gas management unit. The health condition analysis unit analyzes the health condition of cows. For example, the health condition analysis unit measures the body temperature of the cows and evaluates their health condition. The health condition analysis unit can also monitor their heart rate and analyze their health condition. The health condition analysis unit can also measure their activity level and evaluate their health condition. For example, the health condition analysis unit measures the body temperature of the cows in real time using a body temperature sensor and issues an alert if an abnormality is detected. Heart rate monitoring is performed using a heart rate sensor and issues an alert if an abnormal heart rate pattern is detected. Activity level measurement is performed using an acceleration sensor, and a decrease in activity level indicates an abnormal health condition. The behavioral data analysis unit analyzes the behavioral data of the cows based on the data analyzed by the health condition analysis unit. For example, the behavioral data analysis unit analyzes the movement patterns of the cows and detects abnormal behavior. The behavioral data analysis unit can also monitor their eating behavior and detect abnormalities. The behavioral data analysis unit can also analyze resting time and detect abnormal behavior. For example, the behavioral data analysis unit can analyze the movement patterns of cows using GPS data and issue an alert if abnormal movement is detected. Feeding behavior is monitored using a feed intake sensor to detect changes in food intake. Resting time is analyzed using an acceleration sensor and an alert is issued if the resting time is abnormally short. The breeding timing suggestion unit suggests the optimal timing for breeding and birth based on the data analyzed by the behavioral data analysis unit. For example, the breeding timing suggestion unit analyzes the body temperature and behavioral patterns of cows to suggest the optimal breeding timing. The breeding timing suggestion unit can also suggest the optimal timing based on past breeding data. The breeding timing suggestion unit can also analyze environmental data and suggest the optimal breeding timing. For example, the breeding timing suggestion unit suggests the optimal breeding timing based on body temperature data and analyzes behavioral patterns to predict the breeding success rate. Based on past breeding data, the breeding timing suggestion unit suggests the timing for breeding when similar conditions are met. The environmental data is analyzed based on temperature and humidity to suggest the optimal breeding timing.The methane gas management unit manages methane gas emissions from cow burps. For example, the methane gas management unit analyzes cow feed data and proposes optimal feed to reduce methane gas emissions. The methane gas management unit can also analyze the amount of exercise and rest time of cows and propose optimal rearing methods to reduce methane gas emissions. The methane gas management unit can also analyze intestinal flora and propose feed that reduces methane gas emissions by optimizing the intestinal environment. For example, the methane gas management unit proposes feed that reduces methane gas emissions based on feed data, and proposes optimal rearing methods by analyzing the amount of exercise and rest time. The analysis of intestinal flora suggests feed to balance intestinal bacteria. This allows the automation system for the livestock and dairy farming industry according to the embodiment to automate tasks in the livestock and dairy farming industry and solve the succession problem. For example, the output unit displays proposed breeding timing and rearing methods to the farmer via a web application or mobile application. If feedback on paper is desired, the results can be printed using a printer. Emailing provides immediate feedback by sending results directly to the breeder.

[0047] The breeding timing suggestion unit analyzes body temperature, behavioral patterns, and environmental data to propose more accurate breeding and birthing timing. For example, the generation AI collects environmental data such as cow body temperature data, behavioral patterns, ambient temperature, humidity, and sound, and analyzes this data comprehensively. For example, when a cow's body temperature rises and a specific behavioral pattern is observed, it proposes breeding timing if the temperature and humidity are appropriate. The breeding timing suggestion unit also monitors the cow's body temperature and behavioral patterns in real time, as well as surrounding environmental data, and the generation AI predicts the optimal breeding and birthing timing based on this data. For example, it proposes breeding timing by selecting a day with appropriate temperature and low humidity. The breeding timing suggestion unit also analyzes the cow's body temperature, behavioral patterns, and ambient environmental data, and compares it with past data to propose the optimal breeding and birthing timing. For example, it proposes breeding timing based on past success stories when similar environmental conditions are met. This improves the accuracy of breeding and birthing timing.

[0048] The breeding timing suggestion unit can analyze genetic information and suggest genetically optimal breeding timing. In the breeding timing suggestion unit, for example, the generation AI analyzes the genetic information of a cow and suggests the genetically optimal breeding timing. For example, it suggests the optimal breeding period for cows with specific genetic markers. In addition, the breeding timing suggestion unit predicts the breeding success rate based on the genetic information of the cow and suggests the genetically optimal timing. For example, it suggests the timing of breeding by selecting a period with a high probability of giving birth to a genetically healthy calf. In addition, the breeding timing suggestion unit analyzes the genetic information of a cow and suggests the optimal timing by comparing it with past breeding data. For example, it suggests the breeding timing for cows with genetically superior characteristics. This makes it possible to suggest the genetically optimal breeding timing.

[0049] The breeding timing suggestion unit can use the emotion estimation function to estimate stress levels and suggest breeding and birthing times when stress is low. For example, the breeding timing suggestion unit can use the emotion estimation function to monitor the stress levels of cows in real time and suggest breeding and birthing times when stress is low. For example, it can analyze the behavior and facial expressions of cows and suggest breeding times when stress is low. In addition, to estimate the stress levels of cows, the generative AI analyzes the behavioral data and physiological data of the cows and suggests breeding and birthing times when stress is low. For example, it estimates stress levels based on heart rate and respiratory rate. In addition, the breeding timing suggestion unit can use the emotion estimation function to analyze the stress levels of cows and suggest breeding and birthing times when stress is low by comparing them with past data. For example, it can suggest breeding times by selecting a time when stress is low. This allows breeding and birthing to occur when stress is low in the cows.

[0050] The breeding timing suggestion unit can also be applied to optimizing the breeding and birthing timing of other livestock. For example, the breeding timing suggestion unit uses generative AI to develop a system that optimizes the breeding and birthing timing of pigs and sheep. For example, it analyzes the body temperature and behavioral patterns of pigs and sheep to propose the optimal breeding timing. In addition, to make the breeding timing suggestion unit applicable to livestock other than cows, the generative AI analyzes health and behavioral data of pigs and sheep to propose the optimal breeding and birthing timing. For example, it proposes breeding timing based on specific behavioral patterns of pigs and sheep. In addition, the breeding timing suggestion unit uses generative AI to build a data collection and analysis system for optimizing the breeding and birthing timing of pigs and sheep. For example, it collects body temperature, behavioral patterns, and environmental data of pigs and sheep to propose the optimal timing. This can also be applied to optimizing the breeding and birthing timing of other livestock.

[0051] The breeding timing suggestion unit can optimize not only the timing of breeding and birthing, but also overall health management. For example, the generative AI analyzes cow health data and suggests the optimal timing for disease prevention and treatment. For example, it suggests early disease detection and preventative measures based on body temperature and behavior patterns. In addition, to optimize overall cow health management, the generative AI analyzes health data and suggests the optimal timing for disease prevention and treatment. For example, it suggests appropriate treatment if specific symptoms are observed. In addition, the breeding timing suggestion unit builds a system in which the generative AI suggests the optimal timing for disease prevention and treatment based on cow health data. For example, it monitors health data in real time and suggests treatment if an abnormality is detected. This makes it possible to optimize overall cow health management.

[0052] The breeding timing suggestion unit can use the emotion estimation function to analyze the emotions of the keeper and suggest tasks at times when the keeper feels the least stress. The breeding timing suggestion unit, for example, uses the emotion estimation function to monitor the keeper's emotions in real time and suggest tasks at times when stress is low. For example, it analyzes the keeper's facial expressions and voice and suggests tasks when stress is low. The breeding timing suggestion unit also analyzes the keeper's emotion data, and the generation AI estimates the keeper's stress level and suggests tasks at times when stress is lowest. For example, it estimates the stress level based on the keeper's heart rate and respiratory rate. The breeding timing suggestion unit also uses the emotion estimation function to analyze the keeper's emotions and suggest tasks at times when stress is lowest by comparing them with past data. For example, it suggests the timing of tasks by selecting times when stress is lowest. This makes it possible to suggest tasks at times when the keeper feels the least stress.

[0053] The methane gas management unit analyzes feed data, exercise volume, and rest time, and can propose optimal rearing methods to further reduce methane gas emissions. For example, the generation AI analyzes cattle feed data, exercise volume, and rest time to propose optimal rearing methods to reduce methane gas emissions. For example, feeding specific feed to cattle that exercise a lot reduces methane gas emissions. The methane gas management unit also monitors cattle exercise volume and rest time in real time, and the generation AI uses this data to propose optimal rearing methods to reduce methane gas emissions. For example, it suggests increasing rest time for cattle that exercise less. The methane gas management unit also analyzes cattle feed data, exercise volume, and rest time, and compares them with past data to propose optimal rearing methods to reduce methane gas emissions. For example, it suggests feeding specific feed during periods when exercise volume is high. This makes it possible to propose optimal rearing methods to further reduce methane gas emissions.

[0054] The methane gas management unit analyzes the intestinal flora and can propose feed that reduces methane gas emissions by optimizing the intestinal environment. For example, the methane gas management unit uses a generation AI to analyze the intestinal flora of cows and propose feed that optimizes the intestinal environment. For example, feeding feed containing specific probiotics can reduce methane gas emissions. The methane gas management unit also uses a generation AI to propose feed that optimizes the intestinal environment based on the intestinal flora data of cows. For example, it can propose feed that balances intestinal bacteria. The methane gas management unit also uses a generation AI to analyze the intestinal flora of cows and propose feed that optimizes the intestinal environment by comparing it with past data. For example, it can propose feed that improves the composition of intestinal bacteria. This makes it possible to propose feed that reduces methane gas emissions by optimizing the intestinal environment.

[0055] The methane gas management unit uses the emotion estimation function to estimate stress levels and recommend feed that will produce low-stress conditions, thereby reducing methane gas emissions. For example, the methane gas management unit uses the emotion estimation function to monitor cows' stress levels in real time and recommend feed that will produce low-stress conditions. For example, feeding specific feed when stress is low reduces methane gas emissions. To estimate cows' stress levels, the methane gas management unit uses a generative AI to analyze the cows' behavioral and physiological data and recommend feed that will produce low-stress conditions. For example, it estimates stress levels based on heart rate and respiratory rate. The methane gas management unit also uses the emotion estimation function to analyze cows' stress levels and compare them with past data to recommend feed that will produce low-stress conditions. For example, it recommends feeding specific feed when stress is low. This makes it possible to reduce methane gas emissions by recommending feed that will produce low-stress conditions.

[0056] The methane gas management unit also manages greenhouse gas emissions, reducing the overall environmental impact. For example, the generation AI in the methane gas management unit analyzes carbon dioxide and nitrous oxide emissions from cattle breeding and proposes breeding methods to reduce the overall environmental impact. For example, it selects feed and improves the breeding environment. The methane gas management unit also monitors greenhouse gas emissions from cattle breeding in real time, and the generation AI makes suggestions to reduce the overall environmental impact. For example, it proposes changes to feed and adjustments to breeding methods. The methane gas management unit also analyzes greenhouse gas emissions from cattle breeding and compares them with past data to make suggestions to reduce the overall environmental impact. For example, it proposes improvements to the breeding environment and optimizing feed. This reduces the overall environmental impact.

[0057] The methane gas management unit also manages methane gas emissions from manure, making it possible to reduce the environmental impact of the entire farming process. For example, the generation AI in the methane gas management unit analyzes methane gas emissions from cow manure and makes suggestions to reduce the environmental impact of the entire farming process. For example, it makes suggestions to improve the manure treatment method. The methane gas management unit also monitors methane gas emissions from cow manure in real time, making suggestions to reduce the environmental impact of the entire farming process. For example, it makes suggestions to change the manure treatment method. The methane gas management unit also analyzes methane gas emissions from cow manure and makes suggestions to reduce the environmental impact of the entire farming process by comparing it with past data. For example, it makes suggestions to optimize the manure treatment method. This makes it possible to reduce the environmental impact of the entire farming process.

[0058] The methane gas management unit can use the emotion estimation function to analyze the emotions of the keeper and suggest the least stressful animal rearing method for the keeper. For example, the methane gas management unit can use the emotion estimation function to monitor the keeper's emotions in real time and suggest the least stressful animal rearing method. For example, it can analyze the keeper's facial expressions and voice and suggest animal rearing tasks when stress is low. The methane gas management unit also analyzes the keeper's emotional data, and the generation AI estimates the keeper's stress level and suggests the least stressful animal rearing method. For example, it can estimate the stress level based on the keeper's heart rate and respiratory rate. The methane gas management unit also uses the emotion estimation function to analyze the keeper's emotions and suggest the least stressful animal rearing method by comparing it with past data. For example, it can suggest the timing of animal rearing tasks by selecting a time when stress is low. This makes it possible to suggest the least stressful animal rearing method for the keeper.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] Automated systems for the livestock and dairy industries can also be equipped with a social behavior analysis unit that analyzes the social behavior of cows. For example, the social behavior analysis unit can analyze the frequency of contact between cows and the composition of the herd to evaluate the level of social stress. The social behavior analysis unit can also analyze the cows' moosing and ear movements to understand their communication patterns. Furthermore, the social behavior analysis unit can analyze the leadership and ranking of cows within the herd to evaluate their social stability. This can help understand the social behavior of cows and help reduce stress and manage their health.

[0061] Automated systems for the livestock and dairy industries can also be equipped with a nutritional analysis unit that analyzes the nutritional status of cows. For example, the nutritional analysis unit can analyze cow blood samples to evaluate nutrient balance. The nutritional analysis unit can also monitor the cow's diet and detect nutrient deficiencies or excesses. Furthermore, the nutritional analysis unit can measure the cow's weight and body fat percentage to comprehensively evaluate the nutritional status. This allows for optimal nutritional management of cows and maintaining their health.

[0062] Automated systems for the livestock and dairy industries can also be equipped with a sleep analysis unit that analyzes the sleep patterns of cows. For example, the sleep analysis unit can analyze the cow's brain wave data to evaluate the quality of sleep. The sleep analysis unit can also monitor the cow's body movement data and analyze the sleep duration and depth. Furthermore, the sleep analysis unit can analyze the cow's environmental data and suggest environmental conditions suitable for sleep. This can improve the quality of cow's sleep and maintain their health.

[0063] Automated systems in the livestock and dairy industries can further estimate cow emotions and adjust rearing methods based on their emotions. For example, the emotion estimation function can be used to monitor cow stress levels and happiness in real time and suggest rearing methods that keep them under low stress. The emotion estimation function can also be used to analyze cow behavioral and physiological data and adjust the rearing environment based on their emotions. Furthermore, the emotion estimation function can be used to analyze cow emotions and compare them with past data to suggest optimal rearing methods. This makes it possible to adjust rearing methods based on the cow's emotions and maintain its health.

[0064] Automated systems for the livestock and dairy industries can also be equipped with an individual identification unit that identifies individual cows. For example, the individual identification unit can use facial recognition technology to identify individuals and manage their individual health and behavior data. The individual identification unit can also identify individuals using ear tags or microchips and centrally manage data. Furthermore, the individual identification unit can analyze the patterns and characteristics of the cow's body surface to identify individual cows. This allows for accurate management of data for each individual cow, optimizing health and breeding management.

[0065] Automated systems in the livestock and dairy industries can further estimate cow emotions and suggest breeding timing based on their emotions. For example, the emotion estimation function can be used to monitor cow stress levels and happiness in real time and suggest breeding timing when stress levels are low. The emotion estimation function can also be used to analyze cow behavioral and physiological data and suggest optimal breeding timing based on their emotions. Furthermore, the emotion estimation function can be used to analyze cow emotions and compare them with past data to suggest optimal breeding timing. This makes it possible to suggest breeding timing based on cow emotions and improve breeding success rates.

[0066] Automated systems in the livestock and dairy industries can also estimate cow emotions and select feed based on those emotions. For example, the emotion estimation function can be used to monitor cow stress levels and happiness in real time and recommend feed that will keep cows low in stress. The emotion estimation function can also be used to analyze cow behavioral and physiological data and select feed based on emotions. Furthermore, the emotion estimation function can be used to analyze cow emotions and compare them with past data to recommend the optimal feed. This allows feed to be selected based on the cow's emotions, helping to maintain its health.

[0067] Automated systems in the livestock and dairy industries can also estimate the emotions of cows and adjust the amount of exercise based on their emotions. For example, the emotion estimation function can be used to monitor the stress levels and happiness of cows in real time and suggest the amount of exercise required to maintain low stress. The emotion estimation function can also be used to analyze the behavioral and physiological data of cows and adjust the amount of exercise based on their emotions. Furthermore, the emotion estimation function can be used to analyze the emotions of cows and compare them with past data to suggest the optimal amount of exercise. This allows the amount of exercise to be adjusted based on the emotions of the cows, helping to maintain their health.

[0068] Automated systems in the livestock and dairy industries can further estimate cow emotions and adjust rest times based on their emotions. For example, the emotion estimation function can be used to monitor cow stress levels and happiness in real time and suggest rest times that are low in stress. The emotion estimation function can also be used to analyze cow behavioral and physiological data and adjust rest times based on their emotions. Furthermore, the emotion estimation function can be used to analyze cow emotions and compare them with past data to suggest optimal rest times. This allows rest times to be adjusted based on the cow's emotions, helping to maintain its health.

[0069] Automated systems in the livestock and dairy industries can further estimate the emotions of cows and adjust their rearing environment based on their emotions. For example, the emotion estimation function can be used to monitor the stress levels and happiness of cows in real time and suggest rearing environments that keep them low-stress. The emotion estimation function can also be used to analyze the behavioral and physiological data of cows and adjust their rearing environment based on their emotions. Furthermore, the emotion estimation function can be used to analyze the emotions of cows and compare them with past data to suggest the optimal rearing environment. This makes it possible to adjust the rearing environment based on the emotions of cows and maintain their health.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The health condition analysis unit analyzes the health of the cow. For example, the health condition analysis unit measures the cow's body temperature and evaluates its health condition. It can also monitor heart rate and measure activity level. A body temperature sensor is used to measure the cow's body temperature in real time, and an alert is issued if an abnormality is detected. Heart rate monitoring is performed using a heart rate sensor, and a warning is issued if an abnormal heart rate pattern is detected. Activity level is measured using an acceleration sensor, and a decrease in activity level indicates an abnormal health condition. Step 2: The behavioral data analysis unit analyzes the behavioral data of the cows based on the data analyzed by the health condition analysis unit. For example, the behavioral data analysis unit analyzes the movement patterns of the cows and detects abnormalities in their behavior. It can also monitor eating behavior and analyze resting time. It uses GPS data to analyze the movement patterns of the cows and issues an alert if abnormal movement is detected. Eating behavior is monitored using a feed intake sensor to detect changes in food intake. Resting time is analyzed using an acceleration sensor and an alert is issued if the resting time is abnormally short. Step 3: The breeding timing suggestion unit suggests the optimal timing for breeding and birth based on the data analyzed by the behavioral data analysis unit. For example, the breeding timing suggestion unit analyzes the cow's body temperature and behavioral patterns to suggest the optimal breeding timing. It can also suggest the optimal timing based on past breeding data. It can also analyze environmental data to suggest the optimal breeding timing. It suggests the optimal breeding timing based on body temperature data and analyzes behavioral patterns to predict the breeding success rate. It suggests the timing for breeding when similar conditions are met based on past breeding data. It analyzes environmental data based on temperature and humidity to suggest the optimal breeding timing. Step 4: The methane gas management department manages the amount of methane gas emitted by cows' burps. For example, the methane gas management department analyzes cow feed data and suggests the optimal feed to reduce methane gas emissions. It can also analyze the amount of exercise and rest time of cows and suggest the optimal rearing method to reduce methane gas emissions. It can also analyze the intestinal flora and suggest feed that reduces methane gas emissions by optimizing the intestinal environment. Based on the feed data, it suggests feed that reduces methane gas emissions, and analyzes the amount of exercise and rest time to suggest the optimal rearing method. Analysis of the intestinal flora suggests feed that will balance the intestinal bacteria.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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]

[0139] 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 health condition analysis unit that analyzes the health condition of the cows; a behavioral data analysis unit that analyzes behavioral data of cattle based on the data analyzed by the health condition analysis unit; a breeding timing suggestion unit that suggests optimal timing for breeding and birth based on the data analyzed by the behavioral data analysis unit; and a methane gas management unit that manages methane gas emissions due to cow burps. A system characterized by:

2. The breeding timing suggestion unit Analyzing body temperature, behavioral patterns, and environmental data to suggest more accurate breeding and birth timing 2. The system of claim 1.

3. The breeding timing suggestion unit Analyzing genetic information and proposing optimal breeding timing 2. The system of claim 1.

4. The breeding timing suggestion unit Estimate stress levels and suggest breeding and birthing times when stress levels are low 2. The system of claim 1.

5. The breeding timing suggestion unit It can also be applied to optimizing the timing of reproduction and birth for other livestock.

2. The system of claim 1.

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