system

Abstract

The system according to this embodiment aims to identify livestock with high productivity and health characteristics and provide them to producers. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit monitors the daily behavioral patterns and health status of livestock with specific genetic characteristics. The analysis unit analyzes the behavioral and health data collected by the collection unit to identify livestock with high productivity and health characteristics. The provision unit provides information on the livestock identified by the analysis unit to producers.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to identify individuals with high productivity and health characteristics based on the genetic characteristics of livestock, and there is room for improvement.

[0005] The system according to the embodiment aims to identify livestock with high productivity and health characteristics and provide them to producers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit monitors the daily behavioral patterns and health status of livestock with specific genetic characteristics. The analysis unit analyzes the behavioral and health data collected by the data collection unit to identify livestock with high productivity and health characteristics. The data provision unit provides information on the livestock identified by the analysis unit to producers. [Effects of the Invention]

[0007] The system according to this embodiment can identify livestock with high productivity and health characteristics and provide them to producers. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The livestock management system according to an embodiment of the present invention is a system that identifies livestock with high productivity and health characteristics by monitoring the daily behavioral patterns and health status of livestock with specific genetic characteristics and analyzing this behavioral data with AI. This makes it easier to identify individuals that pass on superior genetic traits, allowing producers to make breeding selections based on more scientific evidence. For example, the livestock management system monitors the daily behavioral patterns and health status of livestock with specific genetic characteristics. For example, it collects behavioral data such as movement, eating, and resting of livestock, as well as health data such as body temperature, heart rate, and respiratory rate, in real time using sensors. Next, the livestock management system uses AI to analyze the collected behavioral and health data. The AI ​​analyzes this data and identifies livestock with high productivity and health characteristics. For example, the AI ​​analyzes behavioral patterns and determines whether a particular behavior is related to high productivity. It also analyzes health data and identifies livestock in good health. Furthermore, the livestock management system provides producers with information on livestock with high productivity and health characteristics identified by AI. Based on this information, producers select individuals that pass on superior genetic traits and perform breeding. This enables scientifically-based breeding selection, improving the productivity and health of livestock. As a result, the livestock management system is expected to increase economic benefits through improved productivity, increase the proportion of healthy livestock, and enable rapid identification of genetically superior livestock. For example, the livestock management system includes AI algorithms based on behavioral pattern analysis, a real-time health monitoring system, and integration with a database of genetic traits. This can meet the needs of producers seeking highly productive livestock and those who want to make scientifically-based breeding selections. This technology is an innovative livestock management method that combines AI and genetics and functions as a data-driven decision support system. Target users include agricultural workers and agricultural companies, and it solves the problem of the difficulty and time-consuming task of identifying genetically superior livestock.This allows livestock management systems to monitor and analyze the behavioral patterns and health status of livestock with specific genetic characteristics, thereby identifying livestock with high productivity and health traits.

[0029] The livestock management system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit monitors the daily behavioral patterns and health status of livestock with specific genetic characteristics. The data collection unit collects behavioral data such as movement, eating, and resting of livestock, as well as health data such as body temperature, heart rate, and respiratory rate, in real time using sensors. For example, the data collection unit can collect movement data of livestock using a GPS sensor. The data collection unit can also collect eating data of livestock using a feed sensor. Furthermore, the data collection unit can also collect resting data of livestock using an acceleration sensor. For example, the data collection unit can collect body temperature data of livestock using a body temperature sensor. The data collection unit can also collect heart rate data of livestock using a heart rate sensor. Furthermore, the data collection unit can also collect respiratory rate data of livestock using a respiratory sensor. The analysis unit analyzes the behavioral and health data collected by the data collection unit to identify livestock with high productivity and health characteristics. For example, the analysis unit analyzes the collected behavioral data to determine whether a particular behavior is related to high productivity. The analysis unit can, for example, analyze livestock movement data and evaluate the impact of movement distance on productivity. It can also analyze livestock diet data and evaluate the impact of feeding frequency on productivity. Furthermore, it can analyze livestock rest data and evaluate the impact of rest time on productivity. The analysis unit can, for example, analyze collected health data to identify livestock in good health. It can, for example, analyze livestock body temperature data and evaluate whether body temperature is within the normal range. It can also analyze livestock heart rate data and evaluate whether heart rate is stable. Furthermore, it can analyze livestock respiratory rate data and evaluate whether respiratory rate is within the normal range. The provision unit provides producers with information on the livestock identified by the analysis unit. The provision unit, for example, provides producers with information for selecting individuals that will pass on superior genetic traits and for breeding based on the analysis results. The provision unit can, for example, notify producers of the analysis results and provide a list of livestock with superior genetic traits. Furthermore, the service provider can also provide information to help formulate breeding plans based on the analysis results.Furthermore, the service provider can also provide information for formulating a livestock health management plan based on the analysis results. As a result, the livestock management system according to this embodiment can identify livestock with high productivity and health characteristics by monitoring and analyzing the behavioral patterns and health status of livestock with specific genetic characteristics.

[0030] The data collection unit monitors the daily behavioral patterns and health status of livestock with specific genetic characteristics. For example, it collects behavioral data such as movement, eating, and resting, as well as health data such as body temperature, heart rate, and respiratory rate, in real time using sensors. Specifically, to collect movement data, GPS sensors are attached to continuously track the livestock's location. This allows for understanding how livestock move within the pasture and which areas they stay in for extended periods. Feed sensors are used to collect feeding data, recording the frequency and amount of feed consumed. This allows for monitoring of changes in the livestock's eating patterns and intake. Accelerometers are used to measure how long livestock spend resting, providing detailed information on their resting patterns and activity levels. Body temperature sensors, heart rate sensors, and respiratory sensors are used to collect health data. Body temperature sensors continuously measure the livestock's body temperature and detect abnormal temperature fluctuations. Heart rate sensors monitor the livestock's heart rate in real time to detect stress and changes in health status. Respiratory sensors measure the respiratory rate of livestock and detect abnormal respiratory patterns. Data collected from these sensors is transmitted to a central database using wireless communication technology and monitored in real time. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and accessed by the analysis and provisioning units. Furthermore, the frequency and accuracy of data collection can be adjusted, allowing for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes behavioral and health data collected by the collection unit to identify livestock with high productivity and health characteristics. Specifically, it analyzes collected behavioral data to determine whether specific behaviors are associated with high productivity. For example, it can analyze livestock movement data to evaluate the impact of movement distance on productivity. Livestock that move long distances are likely to have a high level of exercise and good health. It can also analyze livestock diet data to evaluate the impact of feeding frequency and quantity on productivity. Livestock that eat frequently and in appropriate amounts are predicted to have good nutritional status and high productivity. Furthermore, it can analyze livestock rest data to evaluate the impact of rest time on productivity. Livestock that have adequate rest time are likely to have less stress and good health. The analysis unit analyzes collected health data to identify livestock with good health. For example, it can analyze livestock body temperature data to evaluate whether the body temperature is within the normal range. Livestock with a body temperature within the normal range are judged to be in good health. It can also analyze livestock heart rate data to evaluate whether the heart rate is stable. Livestock with stable heart rates are considered to be under less stress and in good health. Furthermore, respiratory rate data can be analyzed to evaluate whether the respiratory rate is within the normal range. Livestock with a normal respiratory rate are judged to be in good health. This allows the analysis unit to quickly and accurately analyze the collected data and identify livestock with high productivity and health characteristics. In addition, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on historical data, it can evaluate the impact of specific behavioral patterns or health conditions on productivity and formulate future countermeasures. Moreover, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The service provider provides producers with information on livestock identified by the analysis unit. Specifically, based on the analysis results, it provides producers with information to select individuals that will pass on superior genetic traits and to carry out breeding. For example, it can notify producers of the analysis results and provide a list of livestock with superior genetic traits. This allows producers to select livestock with superior genetic traits and formulate a breeding plan. The service provider can also provide information for formulating a breeding plan based on the analysis results. For example, breeding livestock with specific genetic traits can improve the productivity and health characteristics of the next generation of livestock. Furthermore, the service provider can also provide information for formulating a livestock health management plan based on the analysis results. For example, it can suggest appropriate breeding methods and health management methods for livestock with specific health conditions. This allows producers to take appropriate measures to maintain the health of their livestock and improve productivity. The service provider can use various means to provide this information to producers. For example, it can notify producers of the analysis results via email or a dedicated web portal. The service provider can also use graphs and charts to display the analysis results in an easy-to-understand visual format. This allows producers to easily understand the analysis results and make appropriate decisions. Furthermore, the service provider can collect feedback from producers and continuously improve the accuracy and delivery methods of the analysis results. This enables the service provider to provide producers with timely and accurate information, improving the efficiency and effectiveness of livestock management.

[0033] The data collection unit can collect behavioral data such as movement, eating, and resting of livestock, as well as health data such as body temperature, heart rate, and respiratory rate, in real time using sensors. For example, the data collection unit can collect livestock movement data using a GPS sensor. For example, the data collection unit can collect livestock eating data using a feed sensor. For example, the data collection unit can collect livestock resting data using an accelerometer. For example, the data collection unit can collect livestock body temperature data using a body temperature sensor. For example, the data collection unit can collect livestock heart rate data using a heart rate sensor. For example, the data collection unit can collect livestock respiratory rate data using a respiratory sensor. This allows for accurate data to be obtained by collecting livestock behavioral and health data in real time. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can acquire livestock behavioral and health data using sensors and input it into AI for analysis.

[0034] The analysis unit can analyze collected behavioral data and determine whether a particular behavior is associated with high productivity. For example, the analysis unit can analyze livestock movement data and evaluate the impact of movement distance on productivity. For example, the analysis unit can analyze livestock feeding data and evaluate the impact of feeding frequency on productivity. For example, the analysis unit can analyze livestock resting data and evaluate the impact of resting time on productivity. In this way, behaviors associated with high productivity can be identified by analyzing behavioral data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input livestock behavioral data into AI, and the AI ​​can analyze the behavioral data to identify behaviors associated with high productivity.

[0035] The analysis unit can analyze the collected health data and identify livestock in good health. For example, the analysis unit can analyze the body temperature data of livestock and evaluate whether the body temperature is within the normal range. For example, the analysis unit can analyze the heart rate data of livestock and evaluate whether the heart rate is stable. For example, the analysis unit can analyze the respiratory rate data of livestock and evaluate whether the respiratory rate is within the normal range. In this way, livestock in good health can be identified by analyzing the health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the health data of livestock into AI, and the AI ​​can analyze the health data to identify livestock in good health.

[0036] The service provider can provide producers with information to select individuals that will pass on superior genetic traits and to carry out breeding based on the analysis results. For example, the service provider can notify producers of the analysis results and provide a list of livestock with superior genetic traits. For example, the service provider can provide information to formulate a breeding plan based on the analysis results. For example, the service provider can provide information to formulate a livestock health management plan based on the analysis results. This makes it possible to make scientifically based breeding selections by providing information to select individuals that will pass on superior genetic traits and to carry out breeding based on the analysis results. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the analysis results into AI and provide the AI ​​with information to formulate a breeding plan based on the analysis results.

[0037] The analysis unit may include an AI algorithm based on behavioral pattern analysis, a real-time health monitoring system, and integration with a genetic characteristics database. For example, the analysis unit analyzes livestock behavioral data using an AI algorithm based on behavioral pattern analysis. For example, the analysis unit monitors livestock health data in real time using a real-time health monitoring system. For example, the analysis unit performs analysis based on the genetic characteristics of livestock by integrating with a genetic characteristics database. This improves the accuracy of the analysis through behavioral pattern analysis, real-time health monitoring, and integration with a genetic characteristics database. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input an AI algorithm based on behavioral pattern analysis into an AI, which can then analyze the behavioral data.

[0038] The data collection unit can analyze past behavioral data of livestock and select the optimal collection method. For example, the data collection unit can prioritize collecting data from livestock that exhibit active behavior during specific time periods based on past behavioral data. For example, the data collection unit can place dedicated sensors on livestock that exhibit specific behavioral patterns based on past data. For example, the data collection unit can analyze past data and focus on collecting behavioral data under specific environmental conditions. This allows the optimal collection method to be selected by analyzing past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past behavioral data into AI, which can then analyze the data and select the optimal collection method.

[0039] The data collection unit can filter behavioral data based on the livestock's current health status and environmental conditions. For example, if a livestock's body temperature is high, the data collection unit can filter the collected data to identify abnormal behavior. For example, if environmental conditions are poor, the data collection unit can filter the collected data to identify stress behavior. For example, the data collection unit can prioritize the collection of data from livestock in good health and eliminate abnormal behavior. In this way, abnormal behavior can be identified by filtering the data based on health status and environmental conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the livestock's health status and environmental conditions into the AI, which can then filter the data to identify abnormal behavior.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of livestock when collecting behavioral data. For example, if livestock are in a specific area, the data collection unit will prioritize the collection of behavioral data in that area. For example, if livestock are on the move, the data collection unit will prioritize the collection of behavioral data along the movement route. For example, if livestock exhibit a behavioral pattern at a specific location, the data collection unit will focus on collecting data at that location. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of livestock into AI, which can then analyze the location information and prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze the social media activity of livestock and collect relevant data when collecting behavioral data. For example, the data collection unit can collect data showing specific behavioral patterns from the social media activity of livestock. For example, the data collection unit can collect information on the health status of livestock on social media. For example, the data collection unit can collect feedback on the behavior of livestock on social media and reflect it in the data. This allows relevant data to be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the social media activity data of livestock into AI, and the AI ​​can analyze the activity data and collect relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the livestock behavior data during the analysis. For example, the analysis unit performs a detailed analysis for important behavior data. For example, the analysis unit performs a simplified analysis for general behavior data. For example, the analysis unit performs a focused analysis for specific behavior patterns. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the behavior data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the behavior data into the AI, and the AI ​​can adjust the level of detail of the analysis based on the importance.

[0043] The analysis unit can apply different analysis algorithms depending on the behavioral category of the livestock during analysis. For example, the analysis unit applies a movement analysis algorithm to movement behavior. For example, the analysis unit applies a feeding analysis algorithm to feeding behavior. For example, the analysis unit applies a resting analysis algorithm to resting behavior. This enables efficient data analysis by applying different analysis algorithms depending on the behavioral category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input behavioral categories into the AI, and the AI ​​can apply different analysis algorithms depending on the category.

[0044] The analysis unit can determine the priority of analysis based on the submission timing of livestock behavioral data during analysis. For example, the analysis unit may prioritize the analysis of the most recent behavioral data. For example, the analysis unit may postpone the analysis of older data. For example, the analysis unit may prioritize the analysis of important data based on the submission timing. This enables efficient data analysis by determining the priority of analysis based on the submission timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission timing of behavioral data into the AI, and the AI ​​can determine the priority of analysis based on the submission timing.

[0045] The analysis unit can adjust the order of analysis based on the relevance of livestock behavioral data during analysis. For example, the analysis unit prioritizes the analysis of highly relevant data. For example, the analysis unit postpones the analysis of less relevant data. For example, the analysis unit prioritizes the analysis of important data based on relevance. This allows for efficient data analysis by adjusting the order of analysis based on relevance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of behavioral data into the AI, and the AI ​​can adjust the order of analysis based on relevance.

[0046] The information provider can adjust the level of detail of the information provided based on the importance of the genetic characteristics of the livestock. For example, the provider provides detailed information on important genetic characteristics. For example, it provides simplified information on general genetic characteristics. For example, it provides focused information on specific genetic characteristics. This allows for efficient information provision by adjusting the level of detail based on the importance of the genetic characteristics. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the importance of genetic characteristics into the AI, and the AI ​​can adjust the level of detail of the information based on the importance.

[0047] The information provider can apply different information provision algorithms depending on the category of the livestock's genetic characteristics at the time of provision. For example, the information provider can apply a productivity improvement algorithm to genetic characteristics related to productivity. For example, the information provider can apply a health management algorithm to genetic characteristics related to health status. For example, the information provider can apply a reproductive management algorithm to genetic characteristics related to reproductive capacity. This enables efficient information provision by applying different information provision algorithms depending on the category of genetic characteristics. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the categories of genetic characteristics into the AI, and the AI ​​can apply different information provision algorithms depending on the category.

[0048] The information provider can prioritize the information provided based on the submission date of the livestock's genetic characteristics. For example, the provider may prioritize providing the most recent information on genetic characteristics. For example, it may postpone providing older information. For example, the provider may prioritize providing important information based on the submission date. This enables efficient information provision by prioritizing information based on the submission date. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the submission date of the genetic characteristics into the AI, and the AI ​​can determine the priority of information based on the submission date.

[0049] The information provider can adjust the order of the information provided based on the relevance of the genetic characteristics of the livestock. For example, the provider may prioritize providing highly relevant information. For example, it may postpone providing less relevant information. For example, the provider may prioritize providing important information based on relevance. This allows for efficient information provision by adjusting the order of information based on relevance. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the relevance of genetic characteristics into the AI, and the AI ​​can adjust the order of the information based on relevance.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can add individual identification information to livestock when collecting behavioral and health data. For example, the unit can acquire individual identification information from livestock ear tags or microchips and link it to the collected data. This allows for accurate management of data for individual livestock. The data collection unit can also use the individual identification information to prioritize the collection of data from specific livestock. For example, by focusing on collecting data from livestock with specific genetic characteristics, the accuracy of the analysis can be improved. Furthermore, the data collection unit can use the individual identification information to prevent data duplication. For example, it can prevent data from being collected multiple times for the same livestock, thus maintaining data consistency.

[0052] The analysis unit can consider the age and sex of livestock when analyzing collected behavioral and health data. For example, the analysis unit can set criteria for behavioral patterns and health status according to the age of the livestock and adjust the analysis results accordingly. This enables accurate analysis that takes age-related differences into account. The analysis unit can also change the analysis algorithm according to the sex of the livestock. For example, by considering the differences in behavioral patterns and health status between males and females, more accurate results can be obtained. Furthermore, the analysis unit can issue warnings about specific behaviors and health conditions based on the age and sex of the livestock. For example, it can notify elderly livestock or pregnant females if they require special care.

[0053] The service provider can include individual livestock history information when providing analysis results to producers. For example, the service provider can generate and provide reports that include past behavioral and health data. This allows producers to understand the long-term trends of their livestock. The service provider can also use individual livestock history information to predict future behavior and health status. For example, based on past data, it can predict what kind of behavior a particular livestock will exhibit in the future and how its health status will change, and notify the producer. Furthermore, the service provider can use individual livestock history information to propose customized care plans for specific livestock. For example, it can propose appropriate care methods for livestock with specific health problems.

[0054] The data collection unit can collect environmental data simultaneously with livestock behavioral and health data. For example, the unit uses sensors to collect environmental data such as temperature, humidity, and atmospheric pressure in the location where the livestock are. This allows for the identification of environmental factors that affect the behavior and health of the livestock. The data collection unit can also use the environmental data to detect abnormalities in the behavior and health of the livestock at an early stage. For example, it can predict that the body temperature of livestock will rise when the temperature is high and take appropriate measures. Furthermore, the data collection unit can use the environmental data to provide information for optimizing the livestock rearing environment. For example, it can suggest improvements to environmental conditions, such as encouraging ventilation when the humidity is high.

[0055] The analysis unit can consider the genetic information of livestock when analyzing collected behavioral and health data. For example, the analysis unit can evaluate the impact of specific genetic traits on behavior and health status based on the livestock's genetic information. This enables highly accurate analysis based on genetic traits. Furthermore, the analysis unit can use the livestock's genetic information to predict the behavior and health status of livestock with specific genetic traits. For example, it can predict what kind of behavior livestock with specific genetic traits will exhibit in the future and how their health status will change, and notify producers. In addition, the analysis unit can use the livestock's genetic information to propose customized care plans for livestock with specific genetic traits. For example, it can propose appropriate care methods for livestock with specific genetic traits.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit monitors the daily behavioral patterns and health status of livestock with specific genetic characteristics. The data collection unit collects behavioral data such as movement, eating, and resting, as well as health data such as body temperature, heart rate, and respiratory rate, in real time using sensors. The data collection unit collects movement data using GPS sensors, eating data using feed sensors, and resting data using accelerometers. Furthermore, it collects body temperature data using body temperature sensors, heart rate data using heart rate sensors, and respiratory rate data using respiratory sensors. Step 2: The analysis unit analyzes the behavioral and health data collected by the collection unit to identify livestock with high productivity and health characteristics. The analysis unit analyzes the collected behavioral data to determine whether specific behaviors are associated with high productivity. For example, it analyzes movement data to evaluate the impact of distance traveled on productivity, analyzes diet data to evaluate the impact of feeding frequency on productivity, and analyzes rest data to evaluate the impact of rest time on productivity. Furthermore, it analyzes the collected health data to identify livestock with good health. For example, it analyzes body temperature data to evaluate whether body temperature is within the normal range, analyzes heart rate data to evaluate whether heart rate is stable, and analyzes respiratory rate data to evaluate whether respiratory rate is within the normal range. Step 3: The supply unit provides producers with information on livestock identified by the analysis unit. Based on the analysis results, the supply unit provides producers with information to select individuals that will pass on superior genetic traits and to carry out breeding. For example, it notifies producers of the analysis results, provides a list of livestock with superior genetic traits, provides information to formulate a breeding plan, and provides information to formulate a livestock health management plan.

[0058] (Example of form 2) The livestock management system according to an embodiment of the present invention is a system that identifies livestock with high productivity and health characteristics by monitoring the daily behavioral patterns and health status of livestock with specific genetic characteristics and analyzing this behavioral data with AI. This makes it easier to identify individuals that pass on superior genetic traits, allowing producers to make breeding selections based on more scientific evidence. For example, the livestock management system monitors the daily behavioral patterns and health status of livestock with specific genetic characteristics. For example, it collects behavioral data such as movement, eating, and resting of livestock, as well as health data such as body temperature, heart rate, and respiratory rate, in real time using sensors. Next, the livestock management system uses AI to analyze the collected behavioral and health data. The AI ​​analyzes this data and identifies livestock with high productivity and health characteristics. For example, the AI ​​analyzes behavioral patterns and determines whether a particular behavior is related to high productivity. It also analyzes health data and identifies livestock in good health. Furthermore, the livestock management system provides producers with information on livestock with high productivity and health characteristics identified by AI. Based on this information, producers select individuals that pass on superior genetic traits and perform breeding. This enables scientifically-based breeding selection, improving the productivity and health of livestock. As a result, the livestock management system is expected to increase economic benefits through improved productivity, increase the proportion of healthy livestock, and enable rapid identification of genetically superior livestock. For example, the livestock management system includes AI algorithms based on behavioral pattern analysis, a real-time health monitoring system, and integration with a database of genetic traits. This can meet the needs of producers seeking highly productive livestock and those who want to make scientifically-based breeding selections. This technology is an innovative livestock management method that combines AI and genetics and functions as a data-driven decision support system. Target users include agricultural workers and agricultural companies, and it solves the problem of the difficulty and time-consuming task of identifying genetically superior livestock.This allows livestock management systems to monitor and analyze the behavioral patterns and health status of livestock with specific genetic characteristics, thereby identifying livestock with high productivity and health traits.

[0059] The livestock management system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit monitors the daily behavioral patterns and health status of livestock with specific genetic characteristics. The data collection unit collects behavioral data such as movement, eating, and resting of livestock, as well as health data such as body temperature, heart rate, and respiratory rate, in real time using sensors. For example, the data collection unit can collect movement data of livestock using a GPS sensor. The data collection unit can also collect eating data of livestock using a feed sensor. Furthermore, the data collection unit can also collect resting data of livestock using an acceleration sensor. For example, the data collection unit can collect body temperature data of livestock using a body temperature sensor. The data collection unit can also collect heart rate data of livestock using a heart rate sensor. Furthermore, the data collection unit can also collect respiratory rate data of livestock using a respiratory sensor. The analysis unit analyzes the behavioral and health data collected by the data collection unit to identify livestock with high productivity and health characteristics. For example, the analysis unit analyzes the collected behavioral data to determine whether a particular behavior is related to high productivity. The analysis unit can, for example, analyze livestock movement data and evaluate the impact of movement distance on productivity. It can also analyze livestock diet data and evaluate the impact of feeding frequency on productivity. Furthermore, it can analyze livestock rest data and evaluate the impact of rest time on productivity. The analysis unit can, for example, analyze collected health data to identify livestock in good health. It can, for example, analyze livestock body temperature data and evaluate whether body temperature is within the normal range. It can also analyze livestock heart rate data and evaluate whether heart rate is stable. Furthermore, it can analyze livestock respiratory rate data and evaluate whether respiratory rate is within the normal range. The provision unit provides producers with information on the livestock identified by the analysis unit. The provision unit, for example, provides producers with information for selecting individuals that will pass on superior genetic traits and for breeding based on the analysis results. The provision unit can, for example, notify producers of the analysis results and provide a list of livestock with superior genetic traits. Furthermore, the service provider can also provide information to help formulate breeding plans based on the analysis results.Furthermore, the service provider can also provide information for formulating a livestock health management plan based on the analysis results. As a result, the livestock management system according to this embodiment can identify livestock with high productivity and health characteristics by monitoring and analyzing the behavioral patterns and health status of livestock with specific genetic characteristics.

[0060] The data collection unit monitors the daily behavioral patterns and health status of livestock with specific genetic characteristics. For example, it collects behavioral data such as movement, eating, and resting, as well as health data such as body temperature, heart rate, and respiratory rate, in real time using sensors. Specifically, to collect movement data, GPS sensors are attached to continuously track the livestock's location. This allows for understanding how livestock move within the pasture and which areas they stay in for extended periods. Feed sensors are used to collect feeding data, recording the frequency and amount of feed consumed. This allows for monitoring of changes in the livestock's eating patterns and intake. Accelerometers are used to measure how long livestock spend resting, providing detailed information on their resting patterns and activity levels. Body temperature sensors, heart rate sensors, and respiratory sensors are used to collect health data. Body temperature sensors continuously measure the livestock's body temperature and detect abnormal temperature fluctuations. Heart rate sensors monitor the livestock's heart rate in real time to detect stress and changes in health status. Respiratory sensors measure the respiratory rate of livestock and detect abnormal respiratory patterns. Data collected from these sensors is transmitted to a central database using wireless communication technology and monitored in real time. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and accessed by the analysis and provisioning units. Furthermore, the frequency and accuracy of data collection can be adjusted, allowing for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0061] The analysis unit analyzes behavioral and health data collected by the collection unit to identify livestock with high productivity and health characteristics. Specifically, it analyzes collected behavioral data to determine whether specific behaviors are associated with high productivity. For example, it can analyze livestock movement data to evaluate the impact of movement distance on productivity. Livestock that move long distances are likely to have a high level of exercise and good health. It can also analyze livestock diet data to evaluate the impact of feeding frequency and quantity on productivity. Livestock that eat frequently and in appropriate amounts are predicted to have good nutritional status and high productivity. Furthermore, it can analyze livestock rest data to evaluate the impact of rest time on productivity. Livestock that have adequate rest time are likely to have less stress and good health. The analysis unit analyzes collected health data to identify livestock with good health. For example, it can analyze livestock body temperature data to evaluate whether the body temperature is within the normal range. Livestock with a body temperature within the normal range are judged to be in good health. It can also analyze livestock heart rate data to evaluate whether the heart rate is stable. Livestock with stable heart rates are considered to be under less stress and in good health. Furthermore, respiratory rate data can be analyzed to evaluate whether the respiratory rate is within the normal range. Livestock with a normal respiratory rate are judged to be in good health. This allows the analysis unit to quickly and accurately analyze the collected data and identify livestock with high productivity and health characteristics. In addition, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on historical data, it can evaluate the impact of specific behavioral patterns or health conditions on productivity and formulate future countermeasures. Moreover, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0062] The service provider provides producers with information on livestock identified by the analysis unit. Specifically, based on the analysis results, it provides producers with information to select individuals that will pass on superior genetic traits and to carry out breeding. For example, it can notify producers of the analysis results and provide a list of livestock with superior genetic traits. This allows producers to select livestock with superior genetic traits and formulate a breeding plan. The service provider can also provide information for formulating a breeding plan based on the analysis results. For example, breeding livestock with specific genetic traits can improve the productivity and health characteristics of the next generation of livestock. Furthermore, the service provider can also provide information for formulating a livestock health management plan based on the analysis results. For example, it can suggest appropriate breeding methods and health management methods for livestock with specific health conditions. This allows producers to take appropriate measures to maintain the health of their livestock and improve productivity. The service provider can use various means to provide this information to producers. For example, it can notify producers of the analysis results via email or a dedicated web portal. The service provider can also use graphs and charts to display the analysis results in an easy-to-understand visual format. This allows producers to easily understand the analysis results and make appropriate decisions. Furthermore, the service provider can collect feedback from producers and continuously improve the accuracy and delivery methods of the analysis results. This enables the service provider to provide producers with timely and accurate information, improving the efficiency and effectiveness of livestock management.

[0063] The data collection unit can collect behavioral data such as movement, eating, and resting of livestock, as well as health data such as body temperature, heart rate, and respiratory rate, in real time using sensors. For example, the data collection unit can collect livestock movement data using a GPS sensor. For example, the data collection unit can collect livestock eating data using a feed sensor. For example, the data collection unit can collect livestock resting data using an accelerometer. For example, the data collection unit can collect livestock body temperature data using a body temperature sensor. For example, the data collection unit can collect livestock heart rate data using a heart rate sensor. For example, the data collection unit can collect livestock respiratory rate data using a respiratory sensor. This allows for accurate data to be obtained by collecting livestock behavioral and health data in real time. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can acquire livestock behavioral and health data using sensors and input it into AI for analysis.

[0064] The analysis unit can analyze collected behavioral data and determine whether a particular behavior is associated with high productivity. For example, the analysis unit can analyze livestock movement data and evaluate the impact of movement distance on productivity. For example, the analysis unit can analyze livestock feeding data and evaluate the impact of feeding frequency on productivity. For example, the analysis unit can analyze livestock resting data and evaluate the impact of resting time on productivity. In this way, behaviors associated with high productivity can be identified by analyzing behavioral data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input livestock behavioral data into AI, and the AI ​​can analyze the behavioral data to identify behaviors associated with high productivity.

[0065] The analysis unit can analyze the collected health data and identify livestock in good health. For example, the analysis unit can analyze the body temperature data of livestock and evaluate whether the body temperature is within the normal range. For example, the analysis unit can analyze the heart rate data of livestock and evaluate whether the heart rate is stable. For example, the analysis unit can analyze the respiratory rate data of livestock and evaluate whether the respiratory rate is within the normal range. In this way, livestock in good health can be identified by analyzing the health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the health data of livestock into AI, and the AI ​​can analyze the health data to identify livestock in good health.

[0066] The service provider can provide producers with information to select individuals that will pass on superior genetic traits and to carry out breeding based on the analysis results. For example, the service provider can notify producers of the analysis results and provide a list of livestock with superior genetic traits. For example, the service provider can provide information to formulate a breeding plan based on the analysis results. For example, the service provider can provide information to formulate a livestock health management plan based on the analysis results. This makes it possible to make scientifically based breeding selections by providing information to select individuals that will pass on superior genetic traits and to carry out breeding based on the analysis results. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the analysis results into AI and provide the AI ​​with information to formulate a breeding plan based on the analysis results.

[0067] The analysis unit may include an AI algorithm based on behavioral pattern analysis, a real-time health monitoring system, and integration with a genetic characteristics database. For example, the analysis unit analyzes livestock behavioral data using an AI algorithm based on behavioral pattern analysis. For example, the analysis unit monitors livestock health data in real time using a real-time health monitoring system. For example, the analysis unit performs analysis based on the genetic characteristics of livestock by integrating with a genetic characteristics database. This improves the accuracy of the analysis through behavioral pattern analysis, real-time health monitoring, and integration with a genetic characteristics database. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input an AI algorithm based on behavioral pattern analysis into an AI, which can then analyze the behavioral data.

[0068] The data collection unit can estimate the user's emotions and adjust the timing of livestock behavior data collection based on the estimated user emotions. For example, if the user is stressed, the data collection unit can increase the collection frequency to collect more detailed data. For example, if the user is relaxed, the data collection unit can decrease the collection frequency to reduce the burden of data collection. For example, if the user is in a hurry, the data collection unit can prioritize collecting only important data. This allows for efficient data collection by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's emotion data into an AI, which can analyze the emotion data and adjust the collection timing.

[0069] The data collection unit can analyze past behavioral data of livestock and select the optimal collection method. For example, the data collection unit can prioritize collecting data from livestock that exhibit active behavior during specific time periods based on past behavioral data. For example, the data collection unit can place dedicated sensors on livestock that exhibit specific behavioral patterns based on past data. For example, the data collection unit can analyze past data and focus on collecting behavioral data under specific environmental conditions. This allows the optimal collection method to be selected by analyzing past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past behavioral data into AI, which can then analyze the data and select the optimal collection method.

[0070] The data collection unit can filter behavioral data based on the livestock's current health status and environmental conditions. For example, if a livestock's body temperature is high, the data collection unit can filter the collected data to identify abnormal behavior. For example, if environmental conditions are poor, the data collection unit can filter the collected data to identify stress behavior. For example, the data collection unit can prioritize the collection of data from livestock in good health and eliminate abnormal behavior. In this way, abnormal behavior can be identified by filtering the data based on health status and environmental conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the livestock's health status and environmental conditions into the AI, which can then filter the data to identify abnormal behavior.

[0071] The data collection unit can estimate the user's emotions and determine the priority of behavioral data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important behavioral data. If the user is relaxed, the data collection unit will collect overall behavioral data in a balanced manner. If the user is in a hurry, the data collection unit will quickly collect only specific behavioral data. This allows for the priority collection of important data by prioritizing behavioral data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can analyze the emotion data to determine the priority of behavioral data.

[0072] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of livestock when collecting behavioral data. For example, if livestock are in a specific area, the data collection unit will prioritize the collection of behavioral data in that area. For example, if livestock are on the move, the data collection unit will prioritize the collection of behavioral data along the movement route. For example, if livestock exhibit a behavioral pattern at a specific location, the data collection unit will focus on collecting data at that location. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of livestock into AI, which can then analyze the location information and prioritize the collection of highly relevant data.

[0073] The data collection unit can analyze the social media activity of livestock and collect relevant data when collecting behavioral data. For example, the data collection unit can collect data showing specific behavioral patterns from the social media activity of livestock. For example, the data collection unit can collect information on the health status of livestock on social media. For example, the data collection unit can collect feedback on the behavior of livestock on social media and reflect it in the data. This allows relevant data to be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the social media activity data of livestock into AI, and the AI ​​can analyze the activity data and collect relevant data.

[0074] The analysis unit can estimate the user's emotions and adjust the analysis method of behavioral data based on the estimated user emotions. For example, if the user is stressed, the analysis unit applies a concise and rapid analysis method. For example, if the user is relaxed, the analysis unit applies a detailed analysis method. For example, if the user is in a hurry, the analysis unit prioritizes analyzing only the important data. This allows for efficient data analysis by adjusting the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into an AI, and the AI ​​can analyze the emotion data and adjust the analysis method.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the livestock behavior data during the analysis. For example, the analysis unit performs a detailed analysis for important behavior data. For example, the analysis unit performs a simplified analysis for general behavior data. For example, the analysis unit performs a focused analysis for specific behavior patterns. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the behavior data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the behavior data into the AI, and the AI ​​can adjust the level of detail of the analysis based on the importance.

[0076] The analysis unit can apply different analysis algorithms depending on the behavioral category of the livestock during analysis. For example, the analysis unit applies a movement analysis algorithm to movement behavior. For example, the analysis unit applies a feeding analysis algorithm to feeding behavior. For example, the analysis unit applies a resting analysis algorithm to resting behavior. This enables efficient data analysis by applying different analysis algorithms depending on the behavioral category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input behavioral categories into the AI, and the AI ​​can apply different analysis algorithms depending on the category.

[0077] The analysis unit can estimate the user's emotions and adjust the length of the analysis of behavioral data based on the estimated user emotions. For example, if the user is stressed, the analysis unit will complete the analysis in a short time. For example, if the user is relaxed, the analysis unit will perform a detailed analysis and provide results over time. For example, if the user is in a hurry, the analysis unit will quickly analyze only the important data. This allows for efficient data analysis by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into an AI, which can analyze the emotion data and adjust the length of the analysis.

[0078] The analysis unit can determine the priority of analysis based on the submission timing of livestock behavioral data during analysis. For example, the analysis unit may prioritize the analysis of the most recent behavioral data. For example, the analysis unit may postpone the analysis of older data. For example, the analysis unit may prioritize the analysis of important data based on the submission timing. This enables efficient data analysis by determining the priority of analysis based on the submission timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission timing of behavioral data into the AI, and the AI ​​can determine the priority of analysis based on the submission timing.

[0079] The analysis unit can adjust the order of analysis based on the relevance of livestock behavioral data during analysis. For example, the analysis unit prioritizes the analysis of highly relevant data. For example, the analysis unit postpones the analysis of less relevant data. For example, the analysis unit prioritizes the analysis of important data based on relevance. This allows for efficient data analysis by adjusting the order of analysis based on relevance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of behavioral data into the AI, and the AI ​​can adjust the order of analysis based on relevance.

[0080] The information provider can estimate the user's emotions and adjust the way the information is presented based on the estimated emotions. For example, if the user is stressed, the provider will provide concise and easy-to-understand information. For example, if the user is relaxed, the provider will provide detailed information. For example, if the user is in a hurry, the provider will quickly provide only the essential information. This enables efficient information delivery by adjusting the way information is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into an AI, and the AI ​​can analyze the emotion data and adjust the way the information is presented.

[0081] The information provider can adjust the level of detail of the information provided based on the importance of the genetic characteristics of the livestock. For example, the provider provides detailed information on important genetic characteristics. For example, it provides simplified information on general genetic characteristics. For example, it provides focused information on specific genetic characteristics. This allows for efficient information provision by adjusting the level of detail based on the importance of the genetic characteristics. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the importance of genetic characteristics into the AI, and the AI ​​can adjust the level of detail of the information based on the importance.

[0082] The information provider can apply different information provision algorithms depending on the category of the livestock's genetic characteristics at the time of provision. For example, the information provider can apply a productivity improvement algorithm to genetic characteristics related to productivity. For example, the information provider can apply a health management algorithm to genetic characteristics related to health status. For example, the information provider can apply a reproductive management algorithm to genetic characteristics related to reproductive capacity. This enables efficient information provision by applying different information provision algorithms depending on the category of genetic characteristics. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the categories of genetic characteristics into the AI, and the AI ​​can apply different information provision algorithms depending on the category.

[0083] The information provider can estimate the user's emotions and adjust the length of the information provided based on the estimated emotions. For example, if the user is stressed, the provider will provide short, concise information. If the user is relaxed, the provider will provide longer information including detailed explanations. If the user is in a hurry, the provider will quickly provide only the essential information. This allows for efficient information delivery by adjusting the length of information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input user emotion data into an AI, which can analyze the emotion data and adjust the length of the information.

[0084] The information provider can prioritize the information provided based on the submission date of the livestock's genetic characteristics. For example, the provider may prioritize providing the most recent information on genetic characteristics. For example, it may postpone providing older information. For example, the provider may prioritize providing important information based on the submission date. This enables efficient information provision by prioritizing information based on the submission date. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the submission date of the genetic characteristics into the AI, and the AI ​​can determine the priority of information based on the submission date.

[0085] The information provider can adjust the order of the information provided based on the relevance of the genetic characteristics of the livestock. For example, the provider may prioritize providing highly relevant information. For example, it may postpone providing less relevant information. For example, the provider may prioritize providing important information based on relevance. This allows for efficient information provision by adjusting the order of information based on relevance. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the relevance of genetic characteristics into the AI, and the AI ​​can adjust the order of the information based on relevance.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The data collection unit can add individual identification information to livestock when collecting behavioral and health data. For example, the unit can acquire individual identification information from livestock ear tags or microchips and link it to the collected data. This allows for accurate management of data for individual livestock. The data collection unit can also use the individual identification information to prioritize the collection of data from specific livestock. For example, by focusing on collecting data from livestock with specific genetic characteristics, the accuracy of the analysis can be improved. Furthermore, the data collection unit can use the individual identification information to prevent data duplication. For example, it can prevent data from being collected multiple times for the same livestock, thus maintaining data consistency.

[0088] The analysis unit can consider the age and sex of livestock when analyzing collected behavioral and health data. For example, the analysis unit can set criteria for behavioral patterns and health status according to the age of the livestock and adjust the analysis results accordingly. This enables accurate analysis that takes age-related differences into account. The analysis unit can also change the analysis algorithm according to the sex of the livestock. For example, by considering the differences in behavioral patterns and health status between males and females, more accurate results can be obtained. Furthermore, the analysis unit can issue warnings about specific behaviors and health conditions based on the age and sex of the livestock. For example, it can notify elderly livestock or pregnant females if they require special care.

[0089] The service provider can include individual livestock history information when providing analysis results to producers. For example, the service provider can generate and provide reports that include past behavioral and health data. This allows producers to understand the long-term trends of their livestock. The service provider can also use individual livestock history information to predict future behavior and health status. For example, based on past data, it can predict what kind of behavior a particular livestock will exhibit in the future and how its health status will change, and notify the producer. Furthermore, the service provider can use individual livestock history information to propose customized care plans for specific livestock. For example, it can propose appropriate care methods for livestock with specific health problems.

[0090] The data collection unit can collect environmental data simultaneously with livestock behavioral and health data. For example, the unit uses sensors to collect environmental data such as temperature, humidity, and atmospheric pressure in the location where the livestock are. This allows for the identification of environmental factors that affect the behavior and health of the livestock. The data collection unit can also use the environmental data to detect abnormalities in the behavior and health of the livestock at an early stage. For example, it can predict that the body temperature of livestock will rise when the temperature is high and take appropriate measures. Furthermore, the data collection unit can use the environmental data to provide information for optimizing the livestock rearing environment. For example, it can suggest improvements to environmental conditions, such as encouraging ventilation when the humidity is high.

[0091] The analysis unit can consider the genetic information of livestock when analyzing collected behavioral and health data. For example, the analysis unit can evaluate the impact of specific genetic traits on behavior and health status based on the livestock's genetic information. This enables highly accurate analysis based on genetic traits. Furthermore, the analysis unit can use the livestock's genetic information to predict the behavior and health status of livestock with specific genetic traits. For example, it can predict what kind of behavior livestock with specific genetic traits will exhibit in the future and how their health status will change, and notify producers. In addition, the analysis unit can use the livestock's genetic information to propose customized care plans for livestock with specific genetic traits. For example, it can propose appropriate care methods for livestock with specific genetic traits.

[0092] The data collection unit can estimate the user's emotions and adjust the method of collecting livestock behavior data based on the estimated user emotions. For example, if the user is stressed, the collection method can be simplified to reduce the burden of data collection. If the user is relaxed, for example, the data collection unit can complicate the collection method to collect detailed data. If the user is in a hurry, for example, the data collection unit can quickly collect only the important data. This allows for efficient data collection by adjusting the collection method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into an AI, which can analyze the emotion data and adjust the collection method.

[0093] The analysis unit can estimate the user's emotions and determine the priority of analyzing behavioral data based on the estimated user emotions. For example, if the user is stressed, it will prioritize the analysis of important data. If the user is relaxed, the analysis unit will analyze the overall data in a balanced manner. If the user is in a hurry, the analysis unit will quickly analyze only specific data. This enables efficient data analysis by determining the priority of analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI, which can analyze the emotion data and determine the priority of analysis.

[0094] The information provider can estimate the user's emotions and adjust the format of the information provided based on the estimated emotions. For example, if the user is stressed, it can provide concise and visually easy-to-understand information. If the user is relaxed, it can provide detailed text information. If the user is in a hurry, it can provide only the important points in bullet points. This allows for efficient information delivery by adjusting the format of the information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input user emotion data into an AI, which can analyze the emotion data and adjust the format of the information.

[0095] The information delivery unit can estimate the user's emotions and adjust the timing of information delivery based on the estimated emotions. For example, if the user is stressed, it can quickly provide important information. If the user is relaxed, it can provide detailed information over time. If the user is in a hurry, it can immediately provide only the necessary information. This allows for efficient information delivery by adjusting the timing of information delivery based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input user emotion data into an AI, which can analyze the emotion data and adjust the timing of information delivery.

[0096] The information provider can estimate the user's emotions and adjust the content of the information provided based on the estimated emotions. For example, if the user is stressed, only essential information is provided. If the user is relaxed, the information provider provides information including detailed background information. If the user is in a hurry, the information provider quickly provides concise information. This allows for efficient information provision by adjusting the content of information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input user emotion data into an AI, which can analyze the emotion data and adjust the content of the information.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection unit monitors the daily behavioral patterns and health status of livestock with specific genetic characteristics. The data collection unit collects behavioral data such as movement, eating, and resting, as well as health data such as body temperature, heart rate, and respiratory rate, in real time using sensors. The data collection unit collects movement data using GPS sensors, eating data using feed sensors, and resting data using accelerometers. Furthermore, it collects body temperature data using body temperature sensors, heart rate data using heart rate sensors, and respiratory rate data using respiratory sensors. Step 2: The analysis unit analyzes the behavioral and health data collected by the collection unit to identify livestock with high productivity and health characteristics. The analysis unit analyzes the collected behavioral data to determine whether specific behaviors are associated with high productivity. For example, it analyzes movement data to evaluate the impact of distance traveled on productivity, analyzes diet data to evaluate the impact of feeding frequency on productivity, and analyzes rest data to evaluate the impact of rest time on productivity. Furthermore, it analyzes the collected health data to identify livestock with good health. For example, it analyzes body temperature data to evaluate whether body temperature is within the normal range, analyzes heart rate data to evaluate whether heart rate is stable, and analyzes respiratory rate data to evaluate whether respiratory rate is within the normal range. Step 3: The supply unit provides producers with information on livestock identified by the analysis unit. Based on the analysis results, the supply unit provides producers with information to select individuals that will pass on superior genetic traits and to carry out breeding. For example, it notifies producers of the analysis results, provides a list of livestock with superior genetic traits, provides information to formulate a breeding plan, and provides information to formulate a livestock health management plan.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects livestock behavioral and health data in real time using the sensors of the smart device 14. The analysis unit analyzes the collected data, for example, by the identification processing unit 290 of the data processing unit 12, to identify livestock with high productivity and health characteristics. The provision unit provides the analysis results to the producer, for example, by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects livestock behavioral and health data in real time using the sensors of the smart glasses 214. The analysis unit analyzes the collected data, for example, by the identification processing unit 290 of the data processing unit 12, to identify livestock with high productivity and health characteristics. The data provision unit provides the analysis results to the producer, for example, by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects livestock behavioral and health data in real time using the sensors of the headset terminal 314. The analysis unit analyzes the collected data by, for example, the identification processing unit 290 of the data processing unit 12 to identify livestock with high productivity and health characteristics. The provision unit provides the analysis results to the producer by, for example, the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects livestock behavioral and health data in real time using the sensors of the robot 414. The analysis unit analyzes the collected data, for example, by the identification processing unit 290 of the data processing unit 12, to identify livestock with high productivity and health characteristics. The data provision unit provides the analysis results to the producer, for example, by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A collection unit that monitors the daily behavioral patterns and health status of livestock with specific genetic characteristics, An analysis unit analyzes behavioral data and health data collected by the aforementioned collection unit to identify livestock with high productivity and health characteristics, The system includes a providing unit that provides producers with information on livestock identified by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Behavioral data such as livestock movement, eating, and resting, as well as health data such as body temperature, heart rate, and respiratory rate, are collected in real time using sensors. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected behavioral data is analyzed to determine whether specific behaviors are associated with high productivity. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The collected health data is analyzed to identify livestock that are in good health. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Based on the analysis results, we select individuals with superior genetic traits and provide producers with information for breeding. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, This includes AI algorithms based on behavioral pattern analysis, a real-time health monitoring system, and integration with a database of genetic traits. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of livestock behavior data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past behavioral data of livestock and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting behavioral data, filtering is performed based on the current health status and environmental conditions of the livestock. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of behavioral data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting behavioral data, the geographical location of livestock is taken into consideration, and the collection of highly relevant data is prioritized. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting behavioral data, analyze the social media activity of livestock and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis method of behavioral data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the livestock behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the livestock behavior category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the behavioral data analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of submission of livestock behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of livestock behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing information, adjust the level of detail based on the importance of the livestock's genetic characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing livestock, different provisioning algorithms are applied depending on the category of the livestock's genetic characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the length of the information provided based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing information, the priority of the information provided will be determined based on the timing of the submission of the livestock's genetic characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing information, the order of the information provided will be adjusted based on the relevance of the genetic characteristics of the livestock. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that monitors the daily behavioral patterns and health status of livestock with specific genetic characteristics, An analysis unit analyzes behavioral data and health data collected by the aforementioned collection unit to identify livestock with high productivity and health characteristics, The system includes a providing unit that provides producers with information on livestock identified by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Behavioral data such as livestock movement, eating, and resting, as well as health data such as body temperature, heart rate, and respiratory rate, are collected in real time using sensors. The system according to feature 1.

3. The aforementioned analysis unit, The collected behavioral data is analyzed to determine whether specific behaviors are associated with high productivity. The system according to feature 1.

4. The aforementioned analysis unit, The collected health data is analyzed to identify livestock that are in good health. The system according to feature 1.

5. The aforementioned supply unit is, Based on the analysis results, we select individuals with superior genetic traits and provide producers with information for breeding. The system according to feature 1.

6. The aforementioned analysis unit, This includes AI algorithms based on behavioral pattern analysis, a real-time health monitoring system, and integration with a database of genetic traits. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of livestock behavior data collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past behavioral data of livestock and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting behavioral data, filtering is performed based on the current health status and environmental conditions of the livestock. The system according to feature 1.

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