system

Abstract

The system according to the embodiment aims to detect abnormalities in a dog's daily behavioral patterns at an early stage and notify humans of the abnormalities. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a detection unit, and a notification unit. The collection unit collects data on the dog's daily behavior. The analysis unit analyzes the data collected by the collection unit and learns the dog's daily behavior patterns. The detection unit compares the data with the normal behavior patterns learned by the analysis unit in real time to detect abnormal behavior patterns. The notification unit notifies a person of the abnormal behavior pattern detected by the detection unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately detect abnormalities in a dog's daily behavioral patterns early and notify humans, so there is room for improvement.

[0005] The system according to the embodiment aims to detect abnormalities in a dog's daily behavioral patterns at an early stage and notify humans of the abnormalities. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a detection unit, and a notification unit. The collection unit collects daily behavioral data of the dog. The analysis unit analyzes the data collected by the collection unit and learns the dog's daily behavioral patterns. The detection unit compares the data with the normal behavioral patterns learned by the analysis unit in real time to detect abnormal behavioral patterns. The notification unit notifies a person of the abnormal behavioral patterns detected by the detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect abnormalities in a dog's daily behavioral patterns at an early stage and notify humans. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An early warning system according to an embodiment of the present invention learns a dog's daily behavioral patterns and notifies humans of any abnormal behavioral patterns. This system collects data on a dog's daily behavior, analyzes it using a generative AI, and learns normal behavioral patterns. It then monitors the dog's behavior in real time and compares it with the learned normal behavioral patterns. If an abnormal behavioral pattern is detected, the system notifies humans. For example, if a dog suddenly loses energy or appetite, the system determines this as an abnormality and notifies the owner. This allows the owner to grasp the dog's health condition early and take appropriate measures. The system also accumulates dog behavioral data, which can be used for long-term health management. Furthermore, the system not only learns a dog's behavioral patterns but also identifies the cause of abnormal behavioral patterns. For example, if a dog exhibits abnormal behavior during a specific time period, it analyzes what is happening during that time period and identifies the cause. This allows the early warning system to support dog health management and improve communication between owners and dogs. For example, if a dog is not feeling well, the system can take the dog to the veterinarian early. Owners can also understand the background of their dog's behavior and take more appropriate measures.

[0029] An early warning system according to an embodiment includes a collection unit, an analysis unit, a detection unit, and a notification unit. The collection unit collects data on the dog's daily behavior. The collected data includes, for example, the dog's movements, voice, body temperature, and heart rate. For example, the collection unit detects the dog's movements using a motion sensor and collects the data. The collection unit can also record the dog's voice using a microphone and collect the data. The collection unit can also measure the dog's body temperature using a body temperature sensor and collect the data. For example, the collection unit detects the dog's movements using an acceleration sensor and collects the dog's walking pattern. The collection unit can also record the dog's barks using a microphone and collect the voice data. The collection unit can also measure the dog's body temperature using an infrared sensor and collect the body temperature data. The analysis unit analyzes the data collected by the collection unit to learn the dog's daily behavioral patterns. The analysis is performed based on, for example, a data preprocessing method and an algorithm used. For example, the analysis unit performs noise removal as data preprocessing to generate clean data. The analysis unit may also use a machine learning algorithm to model the dog's daily behavioral patterns. The analysis unit may also perform data clustering to identify different behavioral patterns. For example, the analysis unit may perform data preprocessing by filtering to remove unnecessary data. The analysis unit may also use a deep learning algorithm to learn the dog's daily behavioral patterns. The analysis unit may also classify the data to distinguish between normal and abnormal behavior. The detection unit may compare the abnormal behavioral patterns with the normal behavioral patterns learned by the analysis unit in real time to detect the abnormal behavioral patterns. The detection may be performed based on, for example, a real-time time range or criteria. For example, the detection unit may analyze data collected in real time and compare it with the normal behavioral patterns. The detection unit may also set a threshold to detect the abnormal behavioral patterns. The detection unit may also generate an alert when an abnormal behavioral pattern is detected. For example, the detection unit may analyze data collected in real time and compare it with the normal behavioral patterns. The detection unit may also use a machine learning model to detect the abnormal behavioral patterns.Furthermore, the detection unit can notify the notification unit when an abnormal behavior pattern is detected. The notification unit notifies a human of the abnormal behavior pattern detected by the detection unit. The notification is performed, for example, via a smartphone app or email. For example, the notification unit can send a notification via a smartphone app when an abnormal behavior pattern is detected. The notification unit can also send a notification via email when an abnormal behavior pattern is detected. Furthermore, the notification unit can generate a voice alert when an abnormal behavior pattern is detected. For example, the notification unit can send a notification via a smartphone app when an abnormal behavior pattern is detected. The notification unit can also send a notification via email when an abnormal behavior pattern is detected. Furthermore, the notification unit can generate a voice alert when an abnormal behavior pattern is detected. As a result, the early warning system according to the embodiment learns the daily behavior patterns of a dog, detects abnormal behavior patterns, and notifies a human, thereby enabling the dog's health condition to be grasped early and appropriate measures to be taken.

[0030] The collection unit can collect data on the dog's movements, voice, body temperature, and heart rate. For example, the collection unit detects the dog's movements using a motion sensor and collects the data. For example, the collection unit detects the dog's movements using an acceleration sensor and collects walking patterns. The collection unit can also record the dog's voice using a microphone and collect the data. For example, the collection unit can record the dog's barks using a microphone and collect the voice data. The collection unit can also measure the dog's body temperature using a body temperature sensor and collect the data. For example, the collection unit can measure the dog's body temperature using an infrared sensor and collect the body temperature data. This allows for the collection of various data on the dog, enabling more accurate analysis of behavioral patterns. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the dog's movement data into the generation AI and have the generation AI analyze the movement data.

[0031] The analysis unit can analyze the collected data and learn the dog's daily behavioral patterns. For example, the analysis unit can perform noise removal as data preprocessing to generate clean data. For example, the analysis unit can perform filtering as data preprocessing to remove unnecessary data. The analysis unit can also model the dog's daily behavioral patterns using a machine learning algorithm. For example, the analysis unit can also learn the dog's daily behavioral patterns using a deep learning algorithm. Furthermore, the analysis unit can cluster the data to identify different behavioral patterns. For example, the analysis unit can classify the data and distinguish between normal and abnormal behavior. This provides a basis for learning the dog's daily behavioral patterns and detecting abnormal behavior by analyzing the collected data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI learn the behavioral patterns.

[0032] The detection unit can detect abnormal behavior patterns by comparing them with learned normal behavior patterns in real time. The detection unit, for example, analyzes data collected in real time and compares the data with normal behavior patterns. For example, the detection unit analyzes data collected in real time and compares the data with normal behavior patterns. The detection unit can also use a machine learning model to detect abnormal behavior patterns. For example, the detection unit can set a threshold to detect abnormal behavior patterns. Furthermore, the detection unit can notify the notification unit when it detects an abnormal behavior pattern. This enables rapid response by detecting abnormal behavior patterns in real time. Some or all of the above-described processing in the detection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the detection unit can input data collected in real time to the generation AI and cause the generation AI to detect abnormal behavior patterns.

[0033] When an abnormal behavior pattern is detected, the notification unit can notify a human via a smartphone app or email. For example, when an abnormal behavior pattern is detected, the notification unit sends a notification via the smartphone app. For example, when an abnormal behavior pattern is detected, the notification unit sends a notification via the smartphone app. The notification unit can also send a notification via email when an abnormal behavior pattern is detected. For example, when an abnormal behavior pattern is detected, the notification unit can also send a notification via email. Furthermore, when an abnormal behavior pattern is detected, the notification unit can generate an audio alert. For example, when an abnormal behavior pattern is detected, the notification unit can generate an audio alert. This allows for prompt notification to a human when an abnormal behavior pattern is detected, thereby prompting an appropriate response. Some or all of the above-described processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, when an abnormal behavior pattern is detected, the notification unit inputs the notification content to the generation AI and causes the generation AI to generate a notification.

[0034] The analysis unit can analyze the collected data to identify the cause of the abnormal behavior pattern. For example, the analysis unit can perform a correlation analysis of the data to identify the cause of the abnormal behavior pattern. For example, the analysis unit can perform a correlation analysis of the data to identify the cause of the abnormal behavior pattern. The analysis unit can also compare the data with past data to identify the cause of the abnormal behavior pattern. For example, the analysis unit can compare the data with past data to identify the cause of the abnormal behavior pattern. Furthermore, the analysis unit can perform clustering of the data to identify the cause of the abnormal behavior pattern. For example, the analysis unit can cluster the data to identify the cause of the abnormal behavior pattern. This allows for more appropriate response by identifying the cause of the abnormal behavior pattern. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input the collected data to the generation AI and have the generation AI identify the cause of the abnormal behavior pattern.

[0035] The collection unit can estimate the dog's emotion and adjust the frequency of data collection based on the estimated emotion. For example, the collection unit captures the dog's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expressions. The collection unit can also record the dog's voice and estimate the emotion using audio analysis technology. For example, the collection unit analyzes the tone and speed of the voice and calculates an emotion score. The collection unit can also collect the dog's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on heart rate fluctuations. This enables more appropriate data collection by adjusting the frequency of data collection according to the dog's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, the generation AI, or without the generation AI. For example, the collection unit can input emotional data of a dog into the generation AI and have the generation AI adjust the frequency of data collection.

[0036] The collection unit can analyze the dog's past behavioral data and select an appropriate data collection method. For example, the collection unit can analyze the dog's past behavioral data and, if the dog was active during a specific time period, concentrate data collection on that time period. For example, the collection unit can analyze the dog's past behavioral data and, if the dog was active during a specific time period, concentrate data collection on that time period. Furthermore, if the dog has previously exhibited abnormal behavior in a specific location, the collection unit can strengthen data collection at that location. For example, if the dog has previously exhibited abnormal behavior in a specific location, the collection unit can strengthen data collection at that location. Furthermore, the collection unit can predict time periods and locations where specific behaviors are likely to occur based on the dog's past behavioral patterns, and optimize data collection. For example, the collection unit predicts time periods and locations where specific behaviors are likely to occur based on the dog's past behavioral patterns, and optimize data collection. This enables efficient data collection by selecting an optimal data collection method based on past behavioral data. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the dog's past behavioral data into the generation AI and have the generation AI select the optimal data collection method.

[0037] When collecting data, the collection unit can filter the data based on the dog's activity level and environmental conditions. For example, the collection unit collects detailed movement data when the dog is in a high activity state, and collects only basic data when the dog is in a low activity state. For example, the collection unit collects detailed movement data when the dog is in a high activity state, and collects only basic data when the dog is in a low activity state. The collection unit can also temporarily stop collecting audio data when the environment is noisy and resume it in a quiet environment. For example, the collection unit can temporarily stop collecting audio data when the environment is noisy and resume it in a quiet environment. Furthermore, the collection unit can collect GPS data when the dog is outdoors and stop collecting it when the dog is indoors. For example, the collection unit collects GPS data when the dog is outdoors and stops collecting it when the dog is indoors. This allows for efficient collection of only necessary data by filtering data based on the activity level and environmental conditions. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI or without a generation AI. For example, the collection unit can input data about a dog's activity level and environmental conditions into the generation AI and have the generation AI perform data filtering.

[0038] When collecting data, the collection unit can adjust the type of data to be collected depending on the dog's physical condition and age. For example, for young dogs, the collection unit mainly collects activity data, while for older dogs, the collection unit mainly collects health data such as body temperature and heart rate. For example, for young dogs, the collection unit mainly collects activity data, while for older dogs, the collection unit mainly collects health data such as body temperature and heart rate. Furthermore, if the dog is sick, the collection unit can prioritize collecting data related to the dog's physical condition, while collecting normal behavior data if the dog is healthy. For example, if the dog is sick, the collection unit can prioritize collecting data related to the dog's physical condition, while collecting normal behavior data if the dog is healthy. Furthermore, if the dog is in poor health, the collection unit can increase the frequency of data collection to collect detailed health data. For example, if the dog is in poor health, the collection unit increases the frequency of data collection to collect detailed health data. This allows for more appropriate data collection by adjusting the type of data depending on the dog's physical condition and age. Some or all of the above-described processing by the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input data about the dog's physical condition and age into the generation AI and have the generation AI adjust the type of data to be collected.

[0039] The collection unit can estimate the dog's emotions and determine the priority of data to be collected based on the estimated emotions. For example, the collection unit captures the dog's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expressions. The collection unit can also record the dog's voice and estimate the emotions using audio analysis technology. For example, the collection unit analyzes the tone and speed of the voice and calculates an emotion score. The collection unit can also collect the dog's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on heart rate fluctuations. This allows important data to be collected preferentially by determining the priority of data based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, the generation AI, or without the generation AI. For example, the collection unit can input emotional data of a dog into the generation AI and have the generation AI determine the priority of the data to be collected.

[0040] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the dog. For example, when the dog is in a park, the collection unit prioritizes collecting activity data. For example, when the dog is in a park, the collection unit prioritizes collecting activity data. The collection unit can also prioritize collecting data on a relaxed state when the dog is at home. For example, when the dog is at home, the collection unit can prioritize collecting data on a relaxed state. Furthermore, the collection unit can also prioritize collecting health data such as body temperature and heart rate when the dog is at a veterinary clinic. For example, when the dog is at a veterinary clinic, the collection unit prioritizes collecting health data such as body temperature and heart rate. This allows for efficient collection of highly relevant data by taking the geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the geographical location information of the dog to the generation AI and cause the generation AI to prioritize collection of highly relevant data.

[0041] The collection unit can analyze the lifestyle patterns of the dog owner when collecting data and collect relevant data. The collection unit, for example, collects dog behavior data during the time when the owner leaves for work. For example, the collection unit collects dog behavior data during the time when the owner leaves for work. The collection unit can also collect dog reaction data during the time when the owner returns home. For example, the collection unit can collect dog reaction data during the time when the owner returns home. The collection unit can also collect dog behavior data at places where the owner spends their days off. For example, the collection unit collects dog behavior data at places where the owner spends their days off. This makes it possible to efficiently collect highly relevant data by taking the owner's lifestyle patterns into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit may input the owner's lifestyle pattern data into the generation AI and cause the generation AI to collect related data.

[0042] When collecting data, the collection unit can customize the collection method by reflecting the dog's past health record. For example, if the dog has had an illness in the past, the collection unit prioritizes collecting data related to that illness. For example, if the dog has had an illness in the past, the collection unit prioritizes collecting data related to that illness. The collection unit can also focus on collecting specific health indicators based on the dog's past health record. For example, the collection unit can focus on collecting specific health indicators based on the dog's past health record. Furthermore, the collection unit can adjust the frequency and method of data collection by referring to the dog's past health record. For example, the collection unit adjusts the frequency and method of data collection by referring to the dog's past health record. This enables more appropriate data collection by reflecting the past health record. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the dog's past health record data into the generation AI and have the generation AI customize the collection method.

[0043] The analysis unit can estimate the dog's emotions and adjust the analysis algorithm based on the estimated emotions. For example, the analysis unit captures the dog's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expressions. The analysis unit can also record the dog's voice and estimate the emotions using audio analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the dog's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This enables more accurate analysis by adjusting the analysis algorithm based on the emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generative AI, or without the generative AI. For example, the analysis unit can input dog emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit analyzes data with high importance (body temperature, heart rate, etc.) in detail. For example, the analysis unit analyzes data with high importance (body temperature, heart rate, etc.) in detail. The analysis unit can also analyze data with low importance (voice, movement, etc.) in a simplified manner. For example, the analysis unit can also analyze data with low importance (voice, movement, etc.) in a simplified manner. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. For example, the analysis unit optimally allocates analysis resources according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the collected data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis methods depending on the behavioral category of the dog. For example, the analysis unit strengthens the analysis of movement data for the dog's play behavior. For example, the analysis unit strengthens the analysis of movement data for the dog's play behavior. The analysis unit can also analyze appetite and food intake data for the dog's eating behavior. For example, the analysis unit can analyze appetite and food intake data for the dog's eating behavior. The analysis unit can also analyze body temperature and heart rate data for the dog's sleeping behavior. For example, the analysis unit analyzes body temperature and heart rate data for the dog's sleeping behavior. This enables more accurate analysis by applying an analysis method depending on the behavioral category. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input dog behavioral category data into the generation AI and cause the generation AI to apply different analysis methods.

[0046] The analysis unit can improve the accuracy of the analysis by referring to the dog's past behavioral patterns. The analysis unit, for example, improves the accuracy of detecting abnormal behavior based on the dog's past behavioral patterns. For example, the analysis unit improves the accuracy of detecting abnormal behavior based on the dog's past behavioral patterns. The analysis unit can also analyze the dog's past behavioral data and model a normal behavioral pattern. For example, the analysis unit can analyze the dog's past behavioral data and model a normal behavioral pattern. Furthermore, the analysis unit can also optimize the analysis algorithm by referring to the dog's past behavioral patterns. For example, the analysis unit optimizes the analysis algorithm by referring to the dog's past behavioral patterns. By referring to the past behavioral patterns, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the dog's past behavioral pattern data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0047] The analysis unit can estimate the dog's emotions and determine analysis priorities based on the estimated emotions. For example, the analysis unit captures the dog's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expressions. The analysis unit can also record the dog's voice and estimate emotions using audio analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the dog's biometric data (heart rate and electrodermal activity) with a sensor and estimate emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This allows important data to be analyzed preferentially by determining analysis priorities based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generative AI, or without the generative AI. For example, the analysis unit can input emotional data of a dog into the generation AI and have the generation AI determine the priorities of the analysis.

[0048] The analysis unit can adjust the order of analysis based on the time period of the collected data. For example, the analysis unit prioritizes analyzing morning data to understand the state of the dog at the beginning of its day. For example, the analysis unit prioritizes analyzing morning data to understand the state of the dog at the beginning of its day. The analysis unit can also analyze daytime data to understand the dog's activity state. For example, the analysis unit can analyze daytime data to understand the dog's activity state. The analysis unit can also analyze nighttime data to understand the dog's sleeping state. For example, the analysis unit analyzes nighttime data to understand the dog's sleeping state. This enables efficient analysis by adjusting the order of analysis based on the time period of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the time period of the collected data into the generation AI and cause the generation AI to adjust the order of analysis.

[0049] The analysis unit can improve the accuracy of the analysis by referring to the dog's related health data. The analysis unit, for example, refers to the dog's body temperature data and analyzes changes in the health condition. For example, the analysis unit refers to the dog's body temperature data and analyzes changes in the health condition. The analysis unit can also refer to the dog's heart rate data and analyze the stress state. For example, the analysis unit can refer to the dog's heart rate data and analyze the stress state. Furthermore, the analysis unit can also refer to the dog's past health records and identify the cause of abnormal behavior. For example, the analysis unit refers to the dog's past health records and identifies the cause of abnormal behavior. In this way, by referring to the related health data, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the dog's health data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0050] The analysis unit can customize the analysis method by reflecting feedback from the dog's owner. The analysis unit, for example, adjusts the analysis algorithm based on the owner's feedback. For example, the analysis unit adjusts the analysis algorithm based on the owner's feedback. The analysis unit can also reflect additional information provided by the owner in the analysis. For example, the analysis unit can reflect additional information provided by the owner in the analysis. Furthermore, the analysis unit can improve the accuracy of the analysis results based on the owner's feedback. For example, the analysis unit improves the accuracy of the analysis results based on the owner's feedback. In this way, the analysis method is optimized and accuracy is improved by reflecting the owner's feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the owner's feedback data into the generation AI and cause the generation AI to customize the analysis method.

[0051] The detection unit can estimate the dog's emotion and adjust the anomaly detection criteria based on the estimated emotion. For example, the detection unit captures the dog's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the detection unit calculates an emotion score based on changes in facial expression. The detection unit can also record the dog's voice and estimate the emotion using audio analysis technology. For example, the detection unit analyzes the tone and speed of the voice and calculates an emotion score. The detection unit can also collect the dog's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the detection unit calculates an emotion score based on heart rate fluctuations. This enables more accurate anomaly detection by adjusting the anomaly detection criteria based on the emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit can be performed using, for example, the generative AI, or without the generative AI. For example, the detection unit can input emotion data of a dog into the generation AI and cause the generation AI to adjust the criteria for detecting anomalies.

[0052] The detection unit can improve the accuracy of anomaly detection by taking into account the interrelationships between the dog's behaviors. The detection unit, for example, detects abnormal behavior by combining the dog's movement and audio data. For example, the detection unit detects abnormal behavior by combining the dog's movement and audio data. The detection unit can also detect abnormal health conditions by combining the dog's body temperature and heart rate data. For example, the detection unit can detect abnormal health conditions by combining the dog's body temperature and heart rate data. The detection unit can also detect abnormal behavior by combining the dog's behavior data and environmental data. For example, the detection unit detects abnormal behavior by combining the dog's behavior data and environmental data. This improves the accuracy of anomaly detection by taking into account the interrelationships between behaviors. Some or all of the above-described processing in the detection unit may be performed using, or without, a generation AI. For example, the detection unit can input the dog's behavior data and environmental data into the generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0053] The detection unit can detect abnormalities by taking into account attribute information of the dog's owner. The detection unit, for example, detects abnormal behavior by taking into account the owner's lifestyle pattern. For example, the detection unit detects abnormal behavior by taking into account the owner's lifestyle pattern. The detection unit can also detect abnormal behavior by referring to the owner's past feedback. For example, the detection unit can detect abnormal behavior by referring to the owner's past feedback. The detection unit can also detect abnormal behavior by taking into account the owner's health condition. For example, the detection unit detects abnormal behavior by taking into account the owner's health condition. This enables more appropriate abnormality detection by taking into account the owner's attribute information. Some or all of the above-mentioned processing in the detection unit may be performed using, or without, the generation AI. For example, the detection unit can input the owner's attribute information into the generation AI and cause the generation AI to perform abnormality detection.

[0054] The detection unit can weight anomaly detection based on the frequency of the dog's behavior. For example, the detection unit tightens the anomaly detection standard for a behavior that the dog frequently performs. For example, the detection unit tightens the anomaly detection standard for a behavior that the dog frequently performs. The detection unit can also loosen the anomaly detection standard for a behavior that the dog rarely performs. For example, the detection unit can loosen the anomaly detection standard for a behavior that the dog rarely performs. Furthermore, the detection unit can adjust the anomaly detection weighting according to the dog's behavior frequency. For example, the detection unit adjusts the anomaly detection weighting according to the dog's behavior frequency. In this way, weighting based on the behavior frequency improves the accuracy of anomaly detection. Some or all of the above-described processing in the detection unit may be performed using, or without, the generation AI. For example, the detection unit can input the dog's behavior frequency data to the generation AI and cause the generation AI to perform the anomaly detection weighting.

[0055] The detection unit can estimate the dog's emotion and adjust the display order of the anomaly detection results based on the estimated emotion. The detection unit, for example, captures the dog's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the detection unit calculates an emotion score based on changes in facial expression. The detection unit can also record the dog's voice and estimate the emotion using audio analysis technology. For example, the detection unit analyzes the tone and speed of the voice and calculates an emotion score. The detection unit can also collect the dog's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the detection unit calculates an emotion score based on heart rate fluctuations. This allows important anomalies to be prioritized by adjusting the display order based on the emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit can be performed using, for example, the generation AI, or without the generation AI. For example, the detection unit can input emotion data of a dog into the generation AI and cause the generation AI to adjust the display order of the abnormality detection results.

[0056] The detection unit can perform anomaly detection taking into account the geographical distribution of dogs. For example, if a dog is in a specific area, the detection unit performs anomaly detection taking into account environmental data of the area. For example, if a dog is in a specific area, the detection unit performs anomaly detection taking into account environmental data of the area. Furthermore, if a dog is moving, the detection unit can also perform anomaly detection based on the dog's movement path. For example, if a dog is moving, the detection unit can also perform anomaly detection based on the dog's movement path. Furthermore, if a dog stays in a specific location for a long time, the detection unit can perform anomaly detection taking into account environmental data of the location. For example, if a dog stays in a specific location for a long time, the detection unit performs anomaly detection taking into account environmental data of the location. This improves the accuracy of anomaly detection by taking into account the geographical distribution. Some or all of the above-described processing in the detection unit may be performed using, or without, a generation AI. For example, the detection unit can input geographical distribution data of dogs into the generation AI and cause the generation AI to perform anomaly detection.

[0057] The detection unit can improve the accuracy of anomaly detection by referring to dog-related literature. The detection unit, for example, refers to the latest research papers on dog health and updates the criteria for anomaly detection. For example, the detection unit refers to the latest research papers on dog health and updates the criteria for anomaly detection. The detection unit can also improve the accuracy of abnormal behavior detection by referring to past research data on dog behavior. For example, the detection unit can improve the accuracy of abnormal behavior detection by referring to past research data on dog behavior. Furthermore, the detection unit can also refer to literature on a specific dog disease and detect abnormal behavior related to the disease. For example, the detection unit can refer to literature on a specific dog disease and detect abnormal behavior related to the disease. By referring to related literature, the accuracy of anomaly detection is improved. Some or all of the above-described processing in the detection unit may be performed using, or without, the generation AI. For example, the detection unit can input dog-related literature data into the generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0058] The detection unit can perform anomaly detection taking into account the market value of the dog. For example, the detection unit sets stricter anomaly detection standards for expensive dog breeds. For example, the detection unit sets stricter anomaly detection standards for expensive dog breeds. The detection unit can also set standard anomaly detection standards for common dog breeds. For example, the detection unit can also set standard anomaly detection standards for common dog breeds. The detection unit can also adjust the weighting of anomaly detection according to the market value of the dog. For example, the detection unit adjusts the weighting of anomaly detection according to the market value of the dog. This allows the anomaly detection standards to be adjusted by taking market value into account, enabling appropriate response. Some or all of the above-described processing in the detection unit may be performed using, or without, the generation AI. For example, the detection unit can input market value data of the dog into the generation AI and cause the generation AI to perform anomaly detection.

[0059] The notification unit can estimate the dog's emotion and adjust the notification expression method based on the estimated emotion. For example, the notification unit captures the dog's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the notification unit calculates an emotion score based on changes in facial expression. The notification unit can also record the dog's voice and estimate the emotion using voice analysis technology. For example, the notification unit analyzes the tone and speed of the voice and calculates an emotion score. The notification unit can also collect the dog's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the notification unit calculates an emotion score based on heart rate fluctuations. This enables more appropriate notification by adjusting the notification expression method based on the emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, the generation AI, or without the generation AI. For example, the notification unit can input the dog's emotional data into the generation AI and have the generation AI adjust the way the notification is expressed.

[0060] The notification unit can adjust the level of detail of the notification based on the importance of the abnormality. For example, the notification unit transmits a detailed notification when the abnormality is highly important. For example, the notification unit transmits a detailed notification when the abnormality is highly important. The notification unit can also transmit a concise notification when the abnormality is low important. For example, the notification unit can also transmit a concise notification when the abnormality is low important. Furthermore, the notification unit can adjust the level of detail of the notification according to the importance of the abnormality. For example, the notification unit adjusts the level of detail of the notification according to the importance of the abnormality. In this way, appropriate information can be provided by adjusting the level of detail of the notification based on the importance. Some or all of the above-mentioned processing in the notification unit may be performed using, or without, the generation AI. For example, the notification unit can input the importance data of the abnormality to the generation AI and cause the generation AI to adjust the level of detail of the notification.

[0061] The notification unit can apply different notification methods depending on the category of the abnormality. For example, in the case of a health-related abnormality, the notification unit transmits a notification including detailed health data. For example, in the case of a health-related abnormality, the notification unit transmits a notification including detailed health data. Furthermore, in the case of a behavior-related abnormality, the notification unit can transmit a notification including behavioral data. For example, in the case of a behavior-related abnormality, the notification unit can transmit a notification including behavioral data. Furthermore, in the case of an environmental abnormality, the notification unit can transmit a notification including environmental data. For example, in the case of an environmental abnormality, the notification unit transmits a notification including environmental data. In this way, appropriate information can be provided by applying a notification method according to the category. Some or all of the above-mentioned processing in the notification unit may be performed using, or without, a generation AI. For example, the notification unit can input abnormality category data into the generation AI and cause the generation AI to apply different notification methods.

[0062] The notification unit can improve the accuracy of notifications by referring to the dog owner's past notification results. The notification unit, for example, adjusts the content of the notification based on feedback from the owner about notifications received in the past. For example, the notification unit adjusts the content of the notification based on feedback from the owner about notifications received in the past. The notification unit can also analyze the owner's past notification results and suggest an optimal notification method. For example, the notification unit can analyze the owner's past notification results and suggest an optimal notification method. The notification unit can also adjust the timing of notifications by referring to the owner's past notification results. For example, the notification unit adjusts the timing of notifications by referring to the owner's past notification results. In this way, the accuracy of notifications is improved by referring to the past notification results. Some or all of the above-mentioned processing in the notification unit may be performed using, or without, the generation AI. For example, the notification unit can input the owner's past notification result data into the generation AI and cause the generation AI to improve the accuracy of notifications.

[0063] The notification unit can estimate the dog's emotion and adjust the length of the notification based on the estimated emotion. For example, the notification unit captures the dog's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the notification unit calculates an emotion score based on changes in facial expression. The notification unit can also record the dog's voice and estimate the emotion using voice analysis technology. For example, the notification unit analyzes the tone and speed of the voice and calculates an emotion score. The notification unit can also collect the dog's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the notification unit calculates an emotion score based on heart rate fluctuations. This allows appropriate information to be provided by adjusting the length of the notification based on the emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, the generation AI, or without the generation AI. For example, the notification unit can input the dog's emotion data into the generation AI and have the generation AI adjust the length of the notification.

[0064] The notification unit can determine the priority of notifications based on the time when the abnormality occurred. For example, the notification unit transmits a notification immediately after the abnormality occurred. For example, the notification unit transmits a notification immediately after the abnormality occurred. The notification unit can also transmit a notification a certain time after the abnormality occurred. For example, the notification unit can transmit a notification a certain time after the abnormality occurred. Furthermore, the notification unit can adjust the priority of notifications depending on the time when the abnormality occurred. For example, the notification unit adjusts the priority of notifications depending on the time when the abnormality occurred. This enables a rapid response by determining the priority of notifications based on the time of occurrence. Some or all of the above-described processing in the notification unit may be performed using, or without, the generation AI. For example, the notification unit can input data on the time when the abnormality occurred into the generation AI and have the generation AI determine the priority of notifications.

[0065] The notification unit can adjust the order of notifications based on the relevance of the anomalies. For example, the notification unit gives top priority to notifying anomalies with high importance. For example, the notification unit gives top priority to notifying anomalies with high importance. The notification unit can also postpone notifying anomalies with low importance. For example, the notification unit can postpone notifying anomalies with low importance. Furthermore, the notification unit can also adjust the order of notifications based on the relevance of the anomalies. For example, the notification unit adjusts the order of notifications based on the relevance. This makes it possible to provide important information preferentially. Some or all of the above-described processing in the notification unit may be performed using, or without, the generation AI. For example, the notification unit can input anomaly relevance data to the generation AI and cause the generation AI to adjust the order of notifications.

[0066] The notification unit can adjust the use of technical terminology in the notification depending on the expertise level of the dog owner. For example, if the owner has specialized knowledge, the notification unit sends a notification using detailed technical terminology. For example, if the owner has specialized knowledge, the notification unit sends a notification using detailed technical terminology. The notification unit can also send a concise and easy-to-understand notification if the owner only has general knowledge. For example, if the owner only has general knowledge, the notification unit can also send a concise and easy-to-understand notification. Furthermore, the notification unit can adjust the content of the notification depending on the expertise level of the owner. For example, the notification unit adjusts the content of the notification depending on the expertise level. As a result, appropriate information can be provided by adjusting the content of the notification depending on the expertise level. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the notification unit can input the owner's expertise level data into the generation AI and cause the generation AI to use technical terminology in the notification.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] When analyzing dog behavior data, the analysis unit can take into account individual information about the dog, such as its age and weight. For example, for young dogs, the criteria for abnormal behavior can be set more leniently because they are more active. On the other hand, for heavier dogs, the criteria for abnormal behavior can be set more strictly because they typically move less. Furthermore, the analysis unit can adjust the analysis algorithm according to the dog's health condition. For example, sick dogs exhibit different behavioral patterns from healthy dogs, so the criteria for abnormal behavior can be set individually. By taking individual information into account, abnormal behavior can be detected more accurately.

[0069] When abnormal behavior is detected, the notification unit can suggest specific countermeasures to the owner. For example, if a dog is listless, a notification can be sent recommending taking the dog to a veterinarian. If the dog barks excessively, the notification unit can also suggest checking for environmental changes or stressors. Furthermore, the notification unit can send questions to the owner to identify the cause of the abnormal behavior and suggest further countermeasures based on the answers. This allows the owner to take appropriate action quickly.

[0070] When collecting the dog's behavioral data, the collection unit can link with the owner's smartphone or wearable device. For example, the dog's behavioral data can be collected in real time through an app installed on the owner's smartphone. The collection unit can also collect the dog's biological data, such as heart rate and body temperature, through a wearable device worn by the owner. Furthermore, the collection unit can collect the dog's location information using the GPS function of the owner's smartphone. By linking with the owner's device, a wider variety of data can be collected, improving the accuracy of analysis.

[0071] When analyzing dog behavior data, the analysis unit can take into account environmental factors such as season and weather. For example, since dogs generally become less active in the summer, the criteria for abnormal behavior can be adjusted. Also, since dogs spend more time indoors during rainy weather, analysis can be focused on indoor behavior patterns. Furthermore, the analysis unit can identify the cause of abnormal behavior based on environmental factors. For example, if a dog is shivering due to cold weather, the analysis unit can identify the cause and suggest appropriate measures. By taking environmental factors into account, more accurate analysis becomes possible.

[0072] When analyzing the dog's behavioral data, the detection unit can compare it with the behavioral data of other pets or family members. For example, if there are multiple dogs in the same household, abnormal behavior can be detected by comparing it with the behavioral patterns of other dogs. It can also detect whether the dog is showing an abnormal reaction to a specific family member by comparing it with the behavioral data of family members. Furthermore, the detection unit can identify the cause of the abnormal behavior by comparing it with the behavioral data of other pets. For example, if a cat is showing aggressive behavior toward a dog, the abnormal behavior can be detected by taking into account the influence of that behavior. This allows for more accurate detection of abnormal behavior by taking into account the behavioral data of other pets and family members.

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

[0074] Step 1: The collection unit collects data on the dog's daily behavior. The collected data includes, for example, the dog's movements, voice, body temperature, heart rate, etc. The collection unit, for example, detects the dog's movements with a motion sensor and collects the data. The collection unit can also record the dog's voice with a microphone and collect the data. The collection unit can also measure the dog's body temperature with a body temperature sensor and collect the data. For example, the collection unit detects the dog's movements with an acceleration sensor and collects its walking pattern. The collection unit can also record the dog's barking with a microphone and collect the voice data. The collection unit can also measure the dog's body temperature with an infrared sensor and collect the body temperature data. Step 2: The analysis unit analyzes the data collected by the collection unit and learns the dog's daily behavioral patterns. The analysis is performed, for example, based on the data preprocessing method and the algorithm used. For example, the analysis unit performs noise removal as data preprocessing to generate clean data. The analysis unit can also use a machine learning algorithm to model the dog's daily behavioral patterns. The analysis unit can also cluster the data to identify different behavioral patterns. For example, the analysis unit performs filtering as data preprocessing to remove unnecessary data. The analysis unit can also use a deep learning algorithm to learn the dog's daily behavioral patterns. The analysis unit can also classify the data to distinguish between normal and abnormal behavior. Step 3: The detection unit compares the abnormal behavior pattern with the normal behavior pattern learned by the analysis unit in real time to detect the abnormal behavior pattern. The detection is performed, for example, based on a real-time time range or criteria. For example, the detection unit analyzes data collected in real time and compares it with the normal behavior pattern. The detection unit can also set a threshold to detect the abnormal behavior pattern. Furthermore, the detection unit can generate an alert when it detects an abnormal behavior pattern. For example, the detection unit analyzes data collected in real time and compares it with the normal behavior pattern. The detection unit can also use a machine learning model to detect the abnormal behavior pattern. Furthermore, the detection unit can notify the notification unit when it detects an abnormal behavior pattern. Step 4: The notification unit notifies a person of the abnormal behavior pattern detected by the detection unit. The notification is performed, for example, by a smartphone app or email. For example, the notification unit sends a notification by smartphone app when an abnormal behavior pattern is detected. The notification unit can also send a notification by email when an abnormal behavior pattern is detected. Furthermore, the notification unit can generate an audio alert when an abnormal behavior pattern is detected. For example, the notification unit sends a notification by smartphone app when an abnormal behavior pattern is detected. The notification unit can also send a notification by email when an abnormal behavior pattern is detected. Furthermore, the notification unit can generate an audio alert when an abnormal behavior pattern is detected.

[0075] (Example 2) An early warning system according to an embodiment of the present invention learns a dog's daily behavioral patterns and notifies humans of any abnormal behavioral patterns. This system collects data on a dog's daily behavior, analyzes it using a generative AI, and learns normal behavioral patterns. It then monitors the dog's behavior in real time and compares it with the learned normal behavioral patterns. If an abnormal behavioral pattern is detected, the system notifies humans. For example, if a dog suddenly loses energy or appetite, the system determines this as an abnormality and notifies the owner. This allows the owner to grasp the dog's health condition early and take appropriate measures. The system also accumulates dog behavioral data, which can be used for long-term health management. Furthermore, the system not only learns a dog's behavioral patterns but also identifies the cause of abnormal behavioral patterns. For example, if a dog exhibits abnormal behavior during a specific time period, it analyzes what is happening during that time period and identifies the cause. This allows the early warning system to support dog health management and improve communication between owners and dogs. For example, if a dog is not feeling well, the system can take the dog to the veterinarian early. Owners can also understand the background of their dog's behavior and take more appropriate measures.

[0076] An early warning system according to an embodiment includes a collection unit, an analysis unit, a detection unit, and a notification unit. The collection unit collects data on the dog's daily behavior. The collected data includes, for example, the dog's movements, voice, body temperature, and heart rate. For example, the collection unit detects the dog's movements using a motion sensor and collects the data. The collection unit can also record the dog's voice using a microphone and collect the data. The collection unit can also measure the dog's body temperature using a body temperature sensor and collect the data. For example, the collection unit detects the dog's movements using an acceleration sensor and collects the dog's walking pattern. The collection unit can also record the dog's barks using a microphone and collect the voice data. The collection unit can also measure the dog's body temperature using an infrared sensor and collect the body temperature data. The analysis unit analyzes the data collected by the collection unit to learn the dog's daily behavioral patterns. The analysis is performed based on, for example, a data preprocessing method and an algorithm used. For example, the analysis unit performs noise removal as data preprocessing to generate clean data. The analysis unit may also use a machine learning algorithm to model the dog's daily behavioral patterns. The analysis unit may also perform data clustering to identify different behavioral patterns. For example, the analysis unit may perform data preprocessing by filtering to remove unnecessary data. The analysis unit may also use a deep learning algorithm to learn the dog's daily behavioral patterns. The analysis unit may also classify the data to distinguish between normal and abnormal behavior. The detection unit may compare the abnormal behavioral patterns with the normal behavioral patterns learned by the analysis unit in real time to detect the abnormal behavioral patterns. The detection may be performed based on, for example, a real-time time range or criteria. For example, the detection unit may analyze data collected in real time and compare it with the normal behavioral patterns. The detection unit may also set a threshold to detect the abnormal behavioral patterns. The detection unit may also generate an alert when an abnormal behavioral pattern is detected. For example, the detection unit may analyze data collected in real time and compare it with the normal behavioral patterns. The detection unit may also use a machine learning model to detect the abnormal behavioral patterns.Furthermore, the detection unit can notify the notification unit when an abnormal behavior pattern is detected. The notification unit notifies a human of the abnormal behavior pattern detected by the detection unit. The notification is performed, for example, via a smartphone app or email. For example, the notification unit can send a notification via a smartphone app when an abnormal behavior pattern is detected. The notification unit can also send a notification via email when an abnormal behavior pattern is detected. Furthermore, the notification unit can generate a voice alert when an abnormal behavior pattern is detected. For example, the notification unit can send a notification via a smartphone app when an abnormal behavior pattern is detected. The notification unit can also send a notification via email when an abnormal behavior pattern is detected. Furthermore, the notification unit can generate a voice alert when an abnormal behavior pattern is detected. As a result, the early warning system according to the embodiment learns the daily behavior patterns of a dog, detects abnormal behavior patterns, and notifies a human, thereby enabling the dog's health condition to be grasped early and appropriate measures to be taken.

[0077] The collection unit can collect data on the dog's movements, voice, body temperature, and heart rate. For example, the collection unit detects the dog's movements using a motion sensor and collects the data. For example, the collection unit detects the dog's movements using an acceleration sensor and collects walking patterns. The collection unit can also record the dog's voice using a microphone and collect the data. For example, the collection unit can record the dog's barks using a microphone and collect the voice data. The collection unit can also measure the dog's body temperature using a body temperature sensor and collect the data. For example, the collection unit can measure the dog's body temperature using an infrared sensor and collect the body temperature data. This allows for the collection of various data on the dog, enabling more accurate analysis of behavioral patterns. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the dog's movement data into the generation AI and have the generation AI analyze the movement data.

[0078] The analysis unit can analyze the collected data and learn the dog's daily behavioral patterns. For example, the analysis unit can perform noise removal as data preprocessing to generate clean data. For example, the analysis unit can perform filtering as data preprocessing to remove unnecessary data. The analysis unit can also model the dog's daily behavioral patterns using a machine learning algorithm. For example, the analysis unit can also learn the dog's daily behavioral patterns using a deep learning algorithm. Furthermore, the analysis unit can cluster the data to identify different behavioral patterns. For example, the analysis unit can classify the data and distinguish between normal and abnormal behavior. This provides a basis for learning the dog's daily behavioral patterns and detecting abnormal behavior by analyzing the collected data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI learn the behavioral patterns.

[0079] The detection unit can detect abnormal behavior patterns by comparing them with learned normal behavior patterns in real time. The detection unit, for example, analyzes data collected in real time and compares the data with normal behavior patterns. For example, the detection unit analyzes data collected in real time and compares the data with normal behavior patterns. The detection unit can also use a machine learning model to detect abnormal behavior patterns. For example, the detection unit can set a threshold to detect abnormal behavior patterns. Furthermore, the detection unit can notify the notification unit when it detects an abnormal behavior pattern. This enables rapid response by detecting abnormal behavior patterns in real time. Some or all of the above-described processing in the detection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the detection unit can input data collected in real time to the generation AI and cause the generation AI to detect abnormal behavior patterns.

[0080] When an abnormal behavior pattern is detected, the notification unit can notify a human via a smartphone app or email. For example, when an abnormal behavior pattern is detected, the notification unit sends a notification via the smartphone app. For example, when an abnormal behavior pattern is detected, the notification unit sends a notification via the smartphone app. The notification unit can also send a notification via email when an abnormal behavior pattern is detected. For example, when an abnormal behavior pattern is detected, the notification unit can also send a notification via email. Furthermore, when an abnormal behavior pattern is detected, the notification unit can generate an audio alert. For example, when an abnormal behavior pattern is detected, the notification unit can generate an audio alert. This allows for prompt notification to a human when an abnormal behavior pattern is detected, thereby prompting an appropriate response. Some or all of the above-described processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, when an abnormal behavior pattern is detected, the notification unit inputs the notification content to the generation AI and causes the generation AI to generate a notification.

[0081] The analysis unit can analyze the collected data to identify the cause of the abnormal behavior pattern. For example, the analysis unit can perform a correlation analysis of the data to identify the cause of the abnormal behavior pattern. For example, the analysis unit can perform a correlation analysis of the data to identify the cause of the abnormal behavior pattern. The analysis unit can also compare the data with past data to identify the cause of the abnormal behavior pattern. For example, the analysis unit can compare the data with past data to identify the cause of the abnormal behavior pattern. Furthermore, the analysis unit can perform clustering of the data to identify the cause of the abnormal behavior pattern. For example, the analysis unit can cluster the data to identify the cause of the abnormal behavior pattern. This allows for more appropriate response by identifying the cause of the abnormal behavior pattern. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input the collected data to the generation AI and have the generation AI identify the cause of the abnormal behavior pattern.

[0082] The collection unit can estimate the dog's emotion and adjust the frequency of data collection based on the estimated emotion. For example, the collection unit captures the dog's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expressions. The collection unit can also record the dog's voice and estimate the emotion using audio analysis technology. For example, the collection unit analyzes the tone and speed of the voice and calculates an emotion score. The collection unit can also collect the dog's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on heart rate fluctuations. This enables more appropriate data collection by adjusting the frequency of data collection according to the dog's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, the generation AI, or without the generation AI. For example, the collection unit can input emotional data of a dog into the generation AI and have the generation AI adjust the frequency of data collection.

[0083] The collection unit can analyze the dog's past behavioral data and select an appropriate data collection method. For example, the collection unit can analyze the dog's past behavioral data and, if the dog was active during a specific time period, concentrate data collection on that time period. For example, the collection unit can analyze the dog's past behavioral data and, if the dog was active during a specific time period, concentrate data collection on that time period. Furthermore, if the dog has previously exhibited abnormal behavior in a specific location, the collection unit can strengthen data collection at that location. For example, if the dog has previously exhibited abnormal behavior in a specific location, the collection unit can strengthen data collection at that location. Furthermore, the collection unit can predict time periods and locations where specific behaviors are likely to occur based on the dog's past behavioral patterns, and optimize data collection. For example, the collection unit predicts time periods and locations where specific behaviors are likely to occur based on the dog's past behavioral patterns, and optimize data collection. This enables efficient data collection by selecting an optimal data collection method based on past behavioral data. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the dog's past behavioral data into the generation AI and have the generation AI select the optimal data collection method.

[0084] When collecting data, the collection unit can filter the data based on the dog's activity level and environmental conditions. For example, the collection unit collects detailed movement data when the dog is in a high activity state, and collects only basic data when the dog is in a low activity state. For example, the collection unit collects detailed movement data when the dog is in a high activity state, and collects only basic data when the dog is in a low activity state. The collection unit can also temporarily stop collecting audio data when the environment is noisy and resume it in a quiet environment. For example, the collection unit can temporarily stop collecting audio data when the environment is noisy and resume it in a quiet environment. Furthermore, the collection unit can collect GPS data when the dog is outdoors and stop collecting it when the dog is indoors. For example, the collection unit collects GPS data when the dog is outdoors and stops collecting it when the dog is indoors. This allows for efficient collection of only necessary data by filtering data based on the activity level and environmental conditions. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI or without a generation AI. For example, the collection unit can input data about a dog's activity level and environmental conditions into the generation AI and have the generation AI perform data filtering.

[0085] When collecting data, the collection unit can adjust the type of data to be collected depending on the dog's physical condition and age. For example, for young dogs, the collection unit mainly collects activity data, while for older dogs, the collection unit mainly collects health data such as body temperature and heart rate. For example, for young dogs, the collection unit mainly collects activity data, while for older dogs, the collection unit mainly collects health data such as body temperature and heart rate. Furthermore, if the dog is sick, the collection unit can prioritize collecting data related to the dog's physical condition, while collecting normal behavior data if the dog is healthy. For example, if the dog is sick, the collection unit can prioritize collecting data related to the dog's physical condition, while collecting normal behavior data if the dog is healthy. Furthermore, if the dog is in poor health, the collection unit can increase the frequency of data collection to collect detailed health data. For example, if the dog is in poor health, the collection unit increases the frequency of data collection to collect detailed health data. This allows for more appropriate data collection by adjusting the type of data depending on the dog's physical condition and age. Some or all of the above-described processing by the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input data about the dog's physical condition and age into the generation AI and have the generation AI adjust the type of data to be collected.

[0086] The collection unit can estimate the dog's emotions and determine the priority of data to be collected based on the estimated emotions. For example, the collection unit captures the dog's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expressions. The collection unit can also record the dog's voice and estimate the emotions using audio analysis technology. For example, the collection unit analyzes the tone and speed of the voice and calculates an emotion score. The collection unit can also collect the dog's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on heart rate fluctuations. This allows important data to be collected preferentially by determining the priority of data based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, the generation AI, or without the generation AI. For example, the collection unit can input emotional data of a dog into the generation AI and have the generation AI determine the priority of the data to be collected.

[0087] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the dog. For example, when the dog is in a park, the collection unit prioritizes collecting activity data. For example, when the dog is in a park, the collection unit prioritizes collecting activity data. The collection unit can also prioritize collecting data on a relaxed state when the dog is at home. For example, when the dog is at home, the collection unit can prioritize collecting data on a relaxed state. Furthermore, the collection unit can also prioritize collecting health data such as body temperature and heart rate when the dog is at a veterinary clinic. For example, when the dog is at a veterinary clinic, the collection unit prioritizes collecting health data such as body temperature and heart rate. This allows for efficient collection of highly relevant data by taking the geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the geographical location information of the dog to the generation AI and cause the generation AI to prioritize collection of highly relevant data.

[0088] The collection unit can analyze the lifestyle patterns of the dog owner when collecting data and collect relevant data. The collection unit, for example, collects dog behavior data during the time when the owner leaves for work. For example, the collection unit collects dog behavior data during the time when the owner leaves for work. The collection unit can also collect dog reaction data during the time when the owner returns home. For example, the collection unit can collect dog reaction data during the time when the owner returns home. The collection unit can also collect dog behavior data at places where the owner spends their days off. For example, the collection unit collects dog behavior data at places where the owner spends their days off. This makes it possible to efficiently collect highly relevant data by taking the owner's lifestyle patterns into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit may input the owner's lifestyle pattern data into the generation AI and cause the generation AI to collect related data.

[0089] When collecting data, the collection unit can customize the collection method by reflecting the dog's past health record. For example, if the dog has had an illness in the past, the collection unit prioritizes collecting data related to that illness. For example, if the dog has had an illness in the past, the collection unit prioritizes collecting data related to that illness. The collection unit can also focus on collecting specific health indicators based on the dog's past health record. For example, the collection unit can focus on collecting specific health indicators based on the dog's past health record. Furthermore, the collection unit can adjust the frequency and method of data collection by referring to the dog's past health record. For example, the collection unit adjusts the frequency and method of data collection by referring to the dog's past health record. This enables more appropriate data collection by reflecting the past health record. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the dog's past health record data into the generation AI and have the generation AI customize the collection method.

[0090] The analysis unit can estimate the dog's emotions and adjust the analysis algorithm based on the estimated emotions. For example, the analysis unit captures the dog's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expressions. The analysis unit can also record the dog's voice and estimate the emotions using audio analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the dog's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This enables more accurate analysis by adjusting the analysis algorithm based on the emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generative AI, or without the generative AI. For example, the analysis unit can input dog emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit analyzes data with high importance (body temperature, heart rate, etc.) in detail. For example, the analysis unit analyzes data with high importance (body temperature, heart rate, etc.) in detail. The analysis unit can also analyze data with low importance (voice, movement, etc.) in a simplified manner. For example, the analysis unit can also analyze data with low importance (voice, movement, etc.) in a simplified manner. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. For example, the analysis unit optimally allocates analysis resources according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the collected data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0092] The analysis unit can apply different analysis methods depending on the behavioral category of the dog. For example, the analysis unit strengthens the analysis of movement data for the dog's play behavior. For example, the analysis unit strengthens the analysis of movement data for the dog's play behavior. The analysis unit can also analyze appetite and food intake data for the dog's eating behavior. For example, the analysis unit can analyze appetite and food intake data for the dog's eating behavior. The analysis unit can also analyze body temperature and heart rate data for the dog's sleeping behavior. For example, the analysis unit analyzes body temperature and heart rate data for the dog's sleeping behavior. This enables more accurate analysis by applying an analysis method depending on the behavioral category. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input dog behavioral category data into the generation AI and cause the generation AI to apply different analysis methods.

[0093] The analysis unit can improve the accuracy of the analysis by referring to the dog's past behavioral patterns. The analysis unit, for example, improves the accuracy of detecting abnormal behavior based on the dog's past behavioral patterns. For example, the analysis unit improves the accuracy of detecting abnormal behavior based on the dog's past behavioral patterns. The analysis unit can also analyze the dog's past behavioral data and model a normal behavioral pattern. For example, the analysis unit can analyze the dog's past behavioral data and model a normal behavioral pattern. Furthermore, the analysis unit can also optimize the analysis algorithm by referring to the dog's past behavioral patterns. For example, the analysis unit optimizes the analysis algorithm by referring to the dog's past behavioral patterns. By referring to the past behavioral patterns, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the dog's past behavioral pattern data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0094] The analysis unit can estimate the dog's emotions and determine analysis priorities based on the estimated emotions. For example, the analysis unit captures the dog's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expressions. The analysis unit can also record the dog's voice and estimate emotions using audio analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the dog's biometric data (heart rate and electrodermal activity) with a sensor and estimate emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This allows important data to be analyzed preferentially by determining analysis priorities based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generative AI, or without the generative AI. For example, the analysis unit can input emotional data of a dog into the generation AI and have the generation AI determine the priorities of the analysis.

[0095] The analysis unit can adjust the order of analysis based on the time period of the collected data. For example, the analysis unit prioritizes analyzing morning data to understand the state of the dog at the beginning of its day. For example, the analysis unit prioritizes analyzing morning data to understand the state of the dog at the beginning of its day. The analysis unit can also analyze daytime data to understand the dog's activity state. For example, the analysis unit can analyze daytime data to understand the dog's activity state. The analysis unit can also analyze nighttime data to understand the dog's sleeping state. For example, the analysis unit analyzes nighttime data to understand the dog's sleeping state. This enables efficient analysis by adjusting the order of analysis based on the time period of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the time period of the collected data into the generation AI and cause the generation AI to adjust the order of analysis.

[0096] The analysis unit can improve the accuracy of the analysis by referring to the dog's related health data. The analysis unit, for example, refers to the dog's body temperature data and analyzes changes in the health condition. For example, the analysis unit refers to the dog's body temperature data and analyzes changes in the health condition. The analysis unit can also refer to the dog's heart rate data and analyze the stress state. For example, the analysis unit can refer to the dog's heart rate data and analyze the stress state. Furthermore, the analysis unit can also refer to the dog's past health records and identify the cause of abnormal behavior. For example, the analysis unit refers to the dog's past health records and identifies the cause of abnormal behavior. In this way, by referring to the related health data, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the dog's health data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0097] The analysis unit can customize the analysis method by reflecting feedback from the dog's owner. The analysis unit, for example, adjusts the analysis algorithm based on the owner's feedback. For example, the analysis unit adjusts the analysis algorithm based on the owner's feedback. The analysis unit can also reflect additional information provided by the owner in the analysis. For example, the analysis unit can reflect additional information provided by the owner in the analysis. Furthermore, the analysis unit can improve the accuracy of the analysis results based on the owner's feedback. For example, the analysis unit improves the accuracy of the analysis results based on the owner's feedback. In this way, the analysis method is optimized and accuracy is improved by reflecting the owner's feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the owner's feedback data into the generation AI and cause the generation AI to customize the analysis method.

[0098] The detection unit can estimate the dog's emotion and adjust the anomaly detection criteria based on the estimated emotion. For example, the detection unit captures the dog's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the detection unit calculates an emotion score based on changes in facial expression. The detection unit can also record the dog's voice and estimate the emotion using audio analysis technology. For example, the detection unit analyzes the tone and speed of the voice and calculates an emotion score. The detection unit can also collect the dog's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the detection unit calculates an emotion score based on heart rate fluctuations. This enables more accurate anomaly detection by adjusting the anomaly detection criteria based on the emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit can be performed using, for example, the generative AI, or without the generative AI. For example, the detection unit can input emotion data of a dog into the generation AI and cause the generation AI to adjust the criteria for detecting anomalies.

[0099] The detection unit can improve the accuracy of anomaly detection by taking into account the interrelationships between the dog's behaviors. The detection unit, for example, detects abnormal behavior by combining the dog's movement and audio data. For example, the detection unit detects abnormal behavior by combining the dog's movement and audio data. The detection unit can also detect abnormal health conditions by combining the dog's body temperature and heart rate data. For example, the detection unit can detect abnormal health conditions by combining the dog's body temperature and heart rate data. The detection unit can also detect abnormal behavior by combining the dog's behavior data and environmental data. For example, the detection unit detects abnormal behavior by combining the dog's behavior data and environmental data. This improves the accuracy of anomaly detection by taking into account the interrelationships between behaviors. Some or all of the above-described processing in the detection unit may be performed using, or without, a generation AI. For example, the detection unit can input the dog's behavior data and environmental data into the generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0100] The detection unit can detect abnormalities by taking into account attribute information of the dog's owner. The detection unit, for example, detects abnormal behavior by taking into account the owner's lifestyle pattern. For example, the detection unit detects abnormal behavior by taking into account the owner's lifestyle pattern. The detection unit can also detect abnormal behavior by referring to the owner's past feedback. For example, the detection unit can detect abnormal behavior by referring to the owner's past feedback. The detection unit can also detect abnormal behavior by taking into account the owner's health condition. For example, the detection unit detects abnormal behavior by taking into account the owner's health condition. This enables more appropriate abnormality detection by taking into account the owner's attribute information. Some or all of the above-mentioned processing in the detection unit may be performed using, or without, the generation AI. For example, the detection unit can input the owner's attribute information into the generation AI and cause the generation AI to perform abnormality detection.

[0101] The detection unit can weight anomaly detection based on the frequency of the dog's behavior. For example, the detection unit tightens the anomaly detection standard for a behavior that the dog frequently performs. For example, the detection unit tightens the anomaly detection standard for a behavior that the dog frequently performs. The detection unit can also loosen the anomaly detection standard for a behavior that the dog rarely performs. For example, the detection unit can loosen the anomaly detection standard for a behavior that the dog rarely performs. Furthermore, the detection unit can adjust the anomaly detection weighting according to the dog's behavior frequency. For example, the detection unit adjusts the anomaly detection weighting according to the dog's behavior frequency. In this way, weighting based on the behavior frequency improves the accuracy of anomaly detection. Some or all of the above-described processing in the detection unit may be performed using, or without, the generation AI. For example, the detection unit can input the dog's behavior frequency data to the generation AI and cause the generation AI to perform the anomaly detection weighting.

[0102] The detection unit can estimate the dog's emotion and adjust the display order of the anomaly detection results based on the estimated emotion. The detection unit, for example, captures the dog's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the detection unit calculates an emotion score based on changes in facial expression. The detection unit can also record the dog's voice and estimate the emotion using audio analysis technology. For example, the detection unit analyzes the tone and speed of the voice and calculates an emotion score. The detection unit can also collect the dog's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the detection unit calculates an emotion score based on heart rate fluctuations. This allows important anomalies to be prioritized by adjusting the display order based on the emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit can be performed using, for example, the generation AI, or without the generation AI. For example, the detection unit can input emotion data of a dog into the generation AI and cause the generation AI to adjust the display order of the abnormality detection results.

[0103] The detection unit can perform anomaly detection taking into account the geographical distribution of dogs. For example, if a dog is in a specific area, the detection unit performs anomaly detection taking into account environmental data of the area. For example, if a dog is in a specific area, the detection unit performs anomaly detection taking into account environmental data of the area. Furthermore, if a dog is moving, the detection unit can also perform anomaly detection based on the dog's movement path. For example, if a dog is moving, the detection unit can also perform anomaly detection based on the dog's movement path. Furthermore, if a dog stays in a specific location for a long time, the detection unit can perform anomaly detection taking into account environmental data of the location. For example, if a dog stays in a specific location for a long time, the detection unit performs anomaly detection taking into account environmental data of the location. This improves the accuracy of anomaly detection by taking into account the geographical distribution. Some or all of the above-described processing in the detection unit may be performed using, or without, a generation AI. For example, the detection unit can input geographical distribution data of dogs into the generation AI and cause the generation AI to perform anomaly detection.

[0104] The detection unit can improve the accuracy of anomaly detection by referring to dog-related literature. The detection unit, for example, refers to the latest research papers on dog health and updates the criteria for anomaly detection. For example, the detection unit refers to the latest research papers on dog health and updates the criteria for anomaly detection. The detection unit can also improve the accuracy of abnormal behavior detection by referring to past research data on dog behavior. For example, the detection unit can improve the accuracy of abnormal behavior detection by referring to past research data on dog behavior. Furthermore, the detection unit can also refer to literature on a specific dog disease and detect abnormal behavior related to the disease. For example, the detection unit can refer to literature on a specific dog disease and detect abnormal behavior related to the disease. By referring to related literature, the accuracy of anomaly detection is improved. Some or all of the above-described processing in the detection unit may be performed using, or without, the generation AI. For example, the detection unit can input dog-related literature data into the generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0105] The detection unit can perform anomaly detection taking into account the market value of the dog. For example, the detection unit sets stricter anomaly detection standards for expensive dog breeds. For example, the detection unit sets stricter anomaly detection standards for expensive dog breeds. The detection unit can also set standard anomaly detection standards for common dog breeds. For example, the detection unit can also set standard anomaly detection standards for common dog breeds. The detection unit can also adjust the weighting of anomaly detection according to the market value of the dog. For example, the detection unit adjusts the weighting of anomaly detection according to the market value of the dog. This allows the anomaly detection standards to be adjusted by taking market value into account, enabling appropriate response. Some or all of the above-described processing in the detection unit may be performed using, or without, the generation AI. For example, the detection unit can input market value data of the dog into the generation AI and cause the generation AI to perform anomaly detection.

[0106] The notification unit can estimate the dog's emotion and adjust the notification expression method based on the estimated emotion. For example, the notification unit captures the dog's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the notification unit calculates an emotion score based on changes in facial expression. The notification unit can also record the dog's voice and estimate the emotion using voice analysis technology. For example, the notification unit analyzes the tone and speed of the voice and calculates an emotion score. The notification unit can also collect the dog's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the notification unit calculates an emotion score based on heart rate fluctuations. This enables more appropriate notification by adjusting the notification expression method based on the emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, the generation AI, or without the generation AI. For example, the notification unit can input the dog's emotional data into the generation AI and have the generation AI adjust the way the notification is expressed.

[0107] The notification unit can adjust the level of detail of the notification based on the importance of the abnormality. For example, the notification unit transmits a detailed notification when the abnormality is highly important. For example, the notification unit transmits a detailed notification when the abnormality is highly important. The notification unit can also transmit a concise notification when the abnormality is low important. For example, the notification unit can also transmit a concise notification when the abnormality is low important. Furthermore, the notification unit can adjust the level of detail of the notification according to the importance of the abnormality. For example, the notification unit adjusts the level of detail of the notification according to the importance of the abnormality. In this way, appropriate information can be provided by adjusting the level of detail of the notification based on the importance. Some or all of the above-mentioned processing in the notification unit may be performed using, or without, the generation AI. For example, the notification unit can input the importance data of the abnormality to the generation AI and cause the generation AI to adjust the level of detail of the notification.

[0108] The notification unit can apply different notification methods depending on the category of the abnormality. For example, in the case of a health-related abnormality, the notification unit transmits a notification including detailed health data. For example, in the case of a health-related abnormality, the notification unit transmits a notification including detailed health data. Furthermore, in the case of a behavior-related abnormality, the notification unit can transmit a notification including behavioral data. For example, in the case of a behavior-related abnormality, the notification unit can transmit a notification including behavioral data. Furthermore, in the case of an environmental abnormality, the notification unit can transmit a notification including environmental data. For example, in the case of an environmental abnormality, the notification unit transmits a notification including environmental data. In this way, appropriate information can be provided by applying a notification method according to the category. Some or all of the above-mentioned processing in the notification unit may be performed using, or without, a generation AI. For example, the notification unit can input abnormality category data into the generation AI and cause the generation AI to apply different notification methods.

[0109] The notification unit can improve the accuracy of notifications by referring to the dog owner's past notification results. The notification unit, for example, adjusts the content of the notification based on feedback from the owner about notifications received in the past. For example, the notification unit adjusts the content of the notification based on feedback from the owner about notifications received in the past. The notification unit can also analyze the owner's past notification results and suggest an optimal notification method. For example, the notification unit can analyze the owner's past notification results and suggest an optimal notification method. The notification unit can also adjust the timing of notifications by referring to the owner's past notification results. For example, the notification unit adjusts the timing of notifications by referring to the owner's past notification results. In this way, the accuracy of notifications is improved by referring to the past notification results. Some or all of the above-mentioned processing in the notification unit may be performed using, or without, the generation AI. For example, the notification unit can input the owner's past notification result data into the generation AI and cause the generation AI to improve the accuracy of notifications.

[0110] The notification unit can estimate the dog's emotion and adjust the length of the notification based on the estimated emotion. For example, the notification unit captures the dog's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the notification unit calculates an emotion score based on changes in facial expression. The notification unit can also record the dog's voice and estimate the emotion using voice analysis technology. For example, the notification unit analyzes the tone and speed of the voice and calculates an emotion score. The notification unit can also collect the dog's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the notification unit calculates an emotion score based on heart rate fluctuations. This allows appropriate information to be provided by adjusting the length of the notification based on the emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, the generation AI, or without the generation AI. For example, the notification unit can input the dog's emotion data into the generation AI and have the generation AI adjust the length of the notification.

[0111] The notification unit can determine the priority of notifications based on the time when the abnormality occurred. For example, the notification unit transmits a notification immediately after the abnormality occurred. For example, the notification unit transmits a notification immediately after the abnormality occurred. The notification unit can also transmit a notification a certain time after the abnormality occurred. For example, the notification unit can transmit a notification a certain time after the abnormality occurred. Furthermore, the notification unit can adjust the priority of notifications depending on the time when the abnormality occurred. For example, the notification unit adjusts the priority of notifications depending on the time when the abnormality occurred. This enables a rapid response by determining the priority of notifications based on the time of occurrence. Some or all of the above-described processing in the notification unit may be performed using, or without, the generation AI. For example, the notification unit can input data on the time when the abnormality occurred into the generation AI and have the generation AI determine the priority of notifications.

[0112] The notification unit can adjust the order of notifications based on the relevance of the anomalies. For example, the notification unit gives top priority to notifying anomalies with high importance. For example, the notification unit gives top priority to notifying anomalies with high importance. The notification unit can also postpone notifying anomalies with low importance. For example, the notification unit can postpone notifying anomalies with low importance. Furthermore, the notification unit can also adjust the order of notifications based on the relevance of the anomalies. For example, the notification unit adjusts the order of notifications based on the relevance. This makes it possible to provide important information preferentially. Some or all of the above-described processing in the notification unit may be performed using, or without, the generation AI. For example, the notification unit can input anomaly relevance data to the generation AI and cause the generation AI to adjust the order of notifications.

[0113] The notification unit can adjust the use of technical terminology in the notification depending on the expertise level of the dog owner. For example, if the owner has specialized knowledge, the notification unit sends a notification using detailed technical terminology. For example, if the owner has specialized knowledge, the notification unit sends a notification using detailed technical terminology. The notification unit can also send a concise and easy-to-understand notification if the owner only has general knowledge. For example, if the owner only has general knowledge, the notification unit can also send a concise and easy-to-understand notification. Furthermore, the notification unit can adjust the content of the notification depending on the expertise level of the owner. For example, the notification unit adjusts the content of the notification depending on the expertise level. As a result, appropriate information can be provided by adjusting the content of the notification depending on the expertise level. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the notification unit can input the owner's expertise level data into the generation AI and cause the generation AI to use technical terminology in the notification. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, detection unit, and notification unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects dog behavior data using the camera 42 and microphone 38B of the smart device 14 and processes the data using the control unit 46A. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to learn the dog's daily behavior patterns. The detection unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data in real time to detect abnormal behavior patterns. The notification unit, for example, is realized by the control unit 46A of the smart device 14 and sends a notification via a smartphone app or email when an abnormal behavior pattern is detected. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, detection unit, and notification unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects dog behavior data using the camera 42 and microphone 238 of the smart glasses 214 and processes the data using the control unit 46A. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to learn the dog's daily behavior patterns. The detection unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data in real time to detect abnormal behavior patterns. The notification unit, for example, is realized by the control unit 46A of the smart glasses 214 and sends a notification via a smartphone app or email when an abnormal behavior pattern is detected. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, detection unit, and notification unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects behavioral data of the dog using the camera 42 and microphone 238 of the headset-type terminal 314 and processes the data using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data to learn the dog's daily behavioral patterns. The detection unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data in real time and detects abnormal behavioral patterns. The notification unit, realized, for example, by the control unit 46A of the headset-type terminal 314, sends a notification via a smartphone app or email when an abnormal behavioral pattern is detected. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, detection unit, and notification unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects behavioral data of the dog using the camera 42 and microphone 238 of the robot 414 and processes the data using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data to learn the dog's daily behavioral patterns. The detection unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data in real time and detects abnormal behavioral patterns. The notification unit, realized, for example, by the control unit 46A of the robot 414, sends a notification via a smartphone app or email when an abnormal behavioral pattern is detected.

[0114] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0115] When analyzing dog behavior data, the analysis unit can take into account individual information about the dog, such as its age and weight. For example, for young dogs, the criteria for abnormal behavior can be set more leniently because they are more active. On the other hand, for heavier dogs, the criteria for abnormal behavior can be set more strictly because they typically move less. Furthermore, the analysis unit can adjust the analysis algorithm according to the dog's health condition. For example, sick dogs exhibit different behavioral patterns from healthy dogs, so the criteria for abnormal behavior can be set individually. By taking individual information into account, abnormal behavior can be detected more accurately.

[0116] When abnormal behavior is detected, the notification unit can suggest specific countermeasures to the owner. For example, if a dog is listless, a notification can be sent recommending taking the dog to a veterinarian. If the dog barks excessively, the notification unit can also suggest checking for environmental changes or stressors. Furthermore, the notification unit can send questions to the owner to identify the cause of the abnormal behavior and suggest further countermeasures based on the answers. This allows the owner to take appropriate action quickly.

[0117] When collecting the dog's behavioral data, the collection unit can link with the owner's smartphone or wearable device. For example, the dog's behavioral data can be collected in real time through an app installed on the owner's smartphone. The collection unit can also collect the dog's biological data, such as heart rate and body temperature, through a wearable device worn by the owner. Furthermore, the collection unit can collect the dog's location information using the GPS function of the owner's smartphone. By linking with the owner's device, a wider variety of data can be collected, improving the accuracy of analysis.

[0118] When analyzing dog behavior data, the analysis unit can take into account environmental factors such as season and weather. For example, since dogs generally become less active in the summer, the criteria for abnormal behavior can be adjusted. Also, since dogs spend more time indoors during rainy weather, analysis can be focused on indoor behavior patterns. Furthermore, the analysis unit can identify the cause of abnormal behavior based on environmental factors. For example, if a dog is shivering due to cold weather, the analysis unit can identify the cause and suggest appropriate measures. By taking environmental factors into account, more accurate analysis becomes possible.

[0119] When analyzing the dog's behavioral data, the detection unit can compare it with the behavioral data of other pets or family members. For example, if there are multiple dogs in the same household, abnormal behavior can be detected by comparing it with the behavioral patterns of other dogs. It can also detect whether the dog is showing an abnormal reaction to a specific family member by comparing it with the behavioral data of family members. Furthermore, the detection unit can identify the cause of the abnormal behavior by comparing it with the behavioral data of other pets. For example, if a cat is showing aggressive behavior toward a dog, the abnormal behavior can be detected by taking into account the influence of that behavior. This allows for more accurate detection of abnormal behavior by taking into account the behavioral data of other pets and family members.

[0120] The analysis unit can estimate the dog's emotions and determine the priority of analysis based on the estimated emotions of the dog. For example, if the dog is feeling stressed, that data can be analyzed first to identify the cause of the stress. Alternatively, if the dog is relaxed, that data can be analyzed later. Furthermore, the analysis unit can adjust the level of detail of analysis based on the dog's emotions. For example, if the dog is feeling anxious, a detailed analysis can be performed to identify the cause of abnormal behavior. In this way, by determining the priority of analysis based on emotions, important data can be analyzed first.

[0121] The notification unit can estimate the dog's emotions and customize the content of the notification based on the estimated dog's emotions. For example, if the dog is feeling anxious, the notification unit can send the owner a notification suggesting specific ways to reassure them. Also, if the dog is excited, the notification unit can suggest ways to calm the owner. Furthermore, the notification unit can adjust the urgency of the notification based on the dog's emotions. For example, if the dog is feeling highly stressed, the notification unit can send a high-urgency notification to encourage a quick response. In this way, customizing the content of the notification based on the dog's emotions allows the owner to take appropriate action.

[0122] The detection unit can estimate the dog's emotions and adjust the detection criteria for abnormal behavior based on the estimated emotions of the dog. For example, if the dog is feeling stressed, the criteria for abnormal behavior can be set looser to detect abnormalities early. Alternatively, if the dog is relaxed, the criteria for abnormal behavior can be set stricter. Furthermore, the detection unit can identify the cause of abnormal behavior based on the dog's emotions. For example, if the dog is feeling anxious, the cause can be identified and an appropriate response can be suggested. This allows for more accurate detection of abnormal behavior by adjusting the detection criteria for abnormal behavior based on emotions.

[0123] The analysis unit can estimate the dog's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the dog is feeling stressed, the analysis can prioritize stress-related data. Alternatively, if the dog is relaxed, the analysis can prioritize normal behavioral patterns. Furthermore, the analysis unit can optimally allocate analysis resources based on the dog's emotions. For example, if the dog is feeling anxious, a detailed analysis can be performed to identify the cause of abnormal behavior. This allows for more accurate analysis by adjusting the analysis algorithm based on emotions.

[0124] The notification unit can estimate the dog's emotions and adjust the timing of notifications based on the estimated dog's emotions. For example, if the dog is stressed, the notification unit can send a prompt notification so that the owner can respond immediately. Alternatively, if the dog is relaxed, the notification unit can send a delayed notification. Furthermore, the notification unit can adjust the frequency of notifications based on the dog's emotions. For example, if the dog is anxious, the notification unit can send frequent notifications to allow the owner to understand the situation. In this way, adjusting the timing of notifications based on the dog's emotions allows the owner to take appropriate action.

[0125] The processing flow of the second embodiment will be briefly explained below.

[0126] Step 1: The collection unit collects data on the dog's daily behavior. The collected data includes, for example, the dog's movements, voice, body temperature, heart rate, etc. The collection unit, for example, detects the dog's movements with a motion sensor and collects the data. The collection unit can also record the dog's voice with a microphone and collect the data. The collection unit can also measure the dog's body temperature with a body temperature sensor and collect the data. For example, the collection unit detects the dog's movements with an acceleration sensor and collects its walking pattern. The collection unit can also record the dog's barking with a microphone and collect the voice data. The collection unit can also measure the dog's body temperature with an infrared sensor and collect the body temperature data. Step 2: The analysis unit analyzes the data collected by the collection unit and learns the dog's daily behavioral patterns. The analysis is performed, for example, based on the data preprocessing method and the algorithm used. For example, the analysis unit performs noise removal as data preprocessing to generate clean data. The analysis unit can also use a machine learning algorithm to model the dog's daily behavioral patterns. The analysis unit can also cluster the data to identify different behavioral patterns. For example, the analysis unit performs filtering as data preprocessing to remove unnecessary data. The analysis unit can also use a deep learning algorithm to learn the dog's daily behavioral patterns. The analysis unit can also classify the data to distinguish between normal and abnormal behavior. Step 3: The detection unit compares the abnormal behavior pattern with the normal behavior pattern learned by the analysis unit in real time to detect the abnormal behavior pattern. The detection is performed, for example, based on a real-time time range or criteria. For example, the detection unit analyzes data collected in real time and compares it with the normal behavior pattern. The detection unit can also set a threshold to detect the abnormal behavior pattern. Furthermore, the detection unit can generate an alert when it detects an abnormal behavior pattern. For example, the detection unit analyzes data collected in real time and compares it with the normal behavior pattern. The detection unit can also use a machine learning model to detect the abnormal behavior pattern. Furthermore, the detection unit can notify the notification unit when it detects an abnormal behavior pattern. Step 4: The notification unit notifies a person of the abnormal behavior pattern detected by the detection unit. The notification is performed, for example, by a smartphone app or email. For example, the notification unit sends a notification by smartphone app when an abnormal behavior pattern is detected. The notification unit can also send a notification by email when an abnormal behavior pattern is detected. Furthermore, the notification unit can generate an audio alert when an abnormal behavior pattern is detected. For example, the notification unit sends a notification by smartphone app when an abnormal behavior pattern is detected. The notification unit can also send a notification by email when an abnormal behavior pattern is detected. Furthermore, the notification unit can generate an audio alert when an abnormal behavior pattern is detected.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0132] 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.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0148] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0154] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0164] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0165] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0166] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0167] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0169] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0170] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0171] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0172] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0173] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0174] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0175] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0176] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0177] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0178] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0179] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0180] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0181] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0182] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0183] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0184] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0185] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0186] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0187] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0188] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0189] 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.

[0190] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0191] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0192] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0193] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0194] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0195] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0196] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0197] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0198] [Explanation of symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects daily behavioral data of dogs; an analysis unit that analyzes the data collected by the collection unit and learns the dog's daily behavior patterns; a detection unit that compares the normal behavioral patterns learned by the analysis unit in real time to detect abnormal behavioral patterns; a notification unit that notifies a person of the abnormal behavior pattern detected by the detection unit; Equipped with A system characterized by:

2. The collecting unit Collects data on the dog's movements, sounds, temperature, and heart rate 2. The system of claim 1.

3. The analysis unit Analyze the collected data and learn the dog's daily behavior patterns 2. The system of claim 1.

4. The detection unit Detects abnormal behavior patterns by comparing them with learned normal behavior patterns in real time 2. The system of claim 1.

5. The notification unit If an abnormal behavior pattern is detected, a human is notified via a smartphone app or email.

2. The system of claim 1.

6. The analysis unit Analyzing collected data to identify causes of abnormal patterns of behavior 2. The system of claim 1.

7. The collecting unit Estimate the dog's emotions and adjust the frequency of data collection based on the estimated emotions.

2. The system of claim 1.

8. The collecting unit Analyze past behavioral data of your dog and select the appropriate data collection method 2. The system of claim 1.

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