System

Abstract

An object of the system according to the embodiment is to analyze the cry and behavior of the pet and propose an appropriate countermeasure to the owner.SOLUTION: A system includes a voice analysis part, a behavior analysis part, a life pattern learning part, and an advice part. The sound analysis unit analyzes a cry or bark of the pet. The behavior analysis part analyzes daily behavior of the pet. A life pattern learning part learns the life pattern of the pet based on the data obtained by the voice analysis part and the action analysis part. The advice unit proposes an appropriate countermeasure to the owner based on the information obtained by the life pattern learning unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to accurately understand a pet's emotions and intentions from its cries and behavior, and it took a long time to find an appropriate response.

[0005] The system according to the embodiment aims to analyze the sounds and behavior of pets and propose appropriate countermeasures to pet owners. [Means for solving the problem]

[0006] The system according to the embodiment includes an audio analysis unit, a behavior analysis unit, a lifestyle pattern learning unit, and an advice unit. The audio analysis unit analyzes the cries or barks of the pet. The behavior analysis unit analyzes the pet's daily behavior. The lifestyle pattern learning unit learns the pet's lifestyle patterns based on data obtained by the audio analysis unit and the behavior analysis unit. The advice unit suggests appropriate countermeasures to the owner based on information obtained by the lifestyle pattern learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the cries and behavior of pets and suggest appropriate countermeasures to the owner. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A pet translation system according to an embodiment of the present invention analyzes a pet's cries and behavior, learns its life patterns, and proposes optimal countermeasures. The pet translation system analyzes a pet's cries and barks and interprets their emotions and intentions. It also captures the pet's daily behavior as videos or images and analyzes the data uploaded to an app. It also learns the pet's life patterns and mood changes and proposes optimal countermeasures to the owner. For example, the pet translation system can analyze a pet's cries and identify emotions such as "happy," "anxious," and "alert." Next, the pet translation system captures the pet's daily behavior as videos or images and analyzes the data uploaded to an app. For example, it can analyze basic behaviors such as "I want to go for a walk," "I want to play," and "I'm sleepy," as well as more complex behaviors such as "I'm not feeling well" and "I'm stressed." Furthermore, the pet translation system collects data over a long period of time and learns the pet's life patterns and mood changes. This allows it to detect problems at an early stage, such as when the pet is stressed or showing early signs of illness. Finally, the pet translation system will suggest optimal solutions to owners based on their pet's condition and behavior. For example, it will provide specific advice such as, "If your dog is barking a lot, it may not be getting enough exercise. Try taking it for a long walk." This allows the pet translation system to understand the pet's emotions and intentions and provide appropriate solutions to owners. This can, for example, improve pet health management and communication.

[0029] A pet translation system according to an embodiment includes a voice analysis unit, a behavior analysis unit, a lifestyle pattern learning unit, and an advice unit. The voice analysis unit analyzes the meows or barks of a pet. Pet meows include, but are not limited to, dog barks and cat meows. The voice analysis unit, for example, analyzes the frequency spectrum of the pet's meow to detect changes in a specific frequency band. The voice analysis unit can also analyze changes in the volume of the meow over time to identify sudden changes in volume. The voice analysis unit can also analyze the duration of the meow to identify the difference between short and long meows. For example, the voice analysis unit analyzes the frequency spectrum of the meow to detect changes in a specific frequency band. The voice analysis unit can also analyze changes in the volume of the meow over time to identify sudden changes in volume. The voice analysis unit can also analyze the duration of the meow to identify the difference between short and long meows. The behavior analysis unit captures the pet's daily behavior using video or images and analyzes the data uploaded to the app. The pet's daily behavior includes, but is not limited to, eating, walking, playing, etc. The behavior analysis unit, for example, records the time when the pet's behavior occurs and identifies patterns that occur frequently during specific time periods. The behavior analysis unit can also record the location where the pet's behavior occurs and identify patterns that occur frequently under specific environmental conditions. The behavior analysis unit can also analyze the frequency of the pet's behavior and identify changes in frequency under specific time periods or environmental conditions. For example, the behavior analysis unit records the time when the pet's behavior occurs and identifies patterns that occur frequently during specific time periods. The behavior analysis unit can also record the location where the pet's behavior occurs and identify patterns that occur frequently under specific environmental conditions. The behavior analysis unit can also analyze the frequency of the pet's behavior and identify changes in frequency under specific time periods or environmental conditions. The lifestyle pattern learning unit learns the pet's lifestyle patterns based on the data obtained by the audio analysis unit and the behavior analysis unit. The lifestyle patterns include, but are not limited to, meal times, exercise frequency, etc. The lifestyle pattern learning unit collects, for example, data on the behavior of a pet for one year and identifies seasonal patterns.The lifestyle pattern learning unit can also collect data on pet barks for a year and identify patterns for each time period. The lifestyle pattern learning unit can also collect data on pet behavior and barks for a year and identify overall patterns. For example, the lifestyle pattern learning unit can collect data on pet behavior for a year and identify patterns for each season. The lifestyle pattern learning unit can also collect data on pet barks for a year and identify patterns for each time period. The lifestyle pattern learning unit can also collect data on pet behavior and barks for a year and identify overall patterns. The advice unit suggests appropriate countermeasures to the owner based on the information obtained by the lifestyle pattern learning unit. Appropriate countermeasures include, but are not limited to, changing the pet's diet, increasing or decreasing the amount of exercise, etc. For example, if the pet barks a lot, the advice unit can suggest taking a long walk to compensate for the lack of exercise. If the pet refuses to eat, the advice unit can suggest a change in the pet's diet. If the pet frequently sleeps, the advice unit can suggest a health check. For example, if the pet barks a lot, the advice unit may suggest a long walk to help the pet get enough exercise. If the pet refuses to eat, the advice unit may also suggest a change in the pet's diet. If the pet frequently sleeps, the advice unit may also suggest a health check. This allows the pet translation system according to the embodiment to analyze the pet's cries and behavior, learn its lifestyle patterns, and suggest optimal countermeasures. Some or all of the above-described processing by the advice unit may be performed using, for example, AI, or may be performed without AI. For example, the advice unit may suggest countermeasures using an AI model that inputs information obtained by the lifestyle pattern learning unit and outputs appropriate countermeasures.

[0030] The audio analysis unit can analyze the patterns of a pet's cries and barks and interpret their emotions and intentions. The audio analysis unit, for example, analyzes the frequency spectrum of a pet's cries and detects changes in a specific frequency band. For example, the audio analysis unit can analyze changes in the volume of the pet's cries over time and identify sudden changes in volume. The audio analysis unit can also analyze the duration of the cries and identify the difference between short and long cries. For example, the audio analysis unit analyzes the frequency spectrum of the pet's cries and detects changes in a specific frequency band. The audio analysis unit can analyze changes in the volume of the pet's cries over time and identify sudden changes in volume. The audio analysis unit can also analyze the duration of the cries and identify the difference between short and long cries. In this way, by analyzing the patterns of a pet's cries and barks, it is possible to understand the pet's emotions and intentions. Some or all of the above-mentioned processing in the audio analysis unit may be performed, for example, using AI or without AI. For example, the audio analysis unit can input data on a pet's cries into the generation AI and have the generation AI interpret the emotions and intentions.

[0031] The behavior analysis unit can capture the pet's daily behavior using at least one of video and images and analyze the data uploaded to the app. The behavior analysis unit, for example, records the time when the pet's behavior occurs and identifies patterns that occur frequently during specific time periods. For example, the behavior analysis unit can also record the location where the pet's behavior occurs and identify patterns that occur frequently under specific environmental conditions. The behavior analysis unit can also analyze the frequency of the pet's behavior and identify changes in frequency under specific time periods or environmental conditions. For example, the behavior analysis unit can record the time when the pet's behavior occurs and identify patterns that occur frequently during specific time periods. The behavior analysis unit can also record the location where the pet's behavior occurs and identify patterns that occur frequently under specific environmental conditions. The behavior analysis unit can also analyze the frequency of the pet's behavior and identify changes in frequency under specific time periods or environmental conditions. By analyzing the pet's daily behavior, the meaning of the pet's behavior can be understood. Some or all of the above-described processing in the behavior analysis unit may be performed using, for example, AI, or without AI. For example, the behavior analysis unit can input behavior data of a pet into the generation AI and have the generation AI analyze the meaning of the behavior.

[0032] The lifestyle pattern learning unit can collect data over a long period of time and learn changes in the pet's lifestyle patterns and moods. For example, the lifestyle pattern learning unit can collect pet behavior data for one year and identify seasonal patterns. For example, the lifestyle pattern learning unit can collect pet bark data for one year and identify patterns for each time period. The lifestyle pattern learning unit can also collect pet behavior and bark data for one year and identify overall patterns. For example, the lifestyle pattern learning unit can collect pet behavior data for one year and identify seasonal patterns. The lifestyle pattern learning unit can also collect pet bark data for one year and identify patterns for each time period. The lifestyle pattern learning unit can also collect pet behavior and bark data for one year and identify overall patterns. This allows long-term data collection to learn changes in the pet's lifestyle patterns and moods, enabling early detection of problems. Some or all of the above-mentioned processing in the lifestyle pattern learning unit may be performed using, for example, AI, or may be performed without AI. For example, the lifestyle pattern learning unit can input behavioral data of a pet into the generation AI and cause the generation AI to learn the lifestyle pattern.

[0033] The advice unit can suggest appropriate countermeasures to the owner based on the pet's condition and behavior. For example, if the pet barks a lot, the advice unit can suggest a long walk to compensate for lack of exercise. For example, if the pet refuses to eat, the advice unit can suggest a change in the pet's diet. The advice unit can also suggest a health check if the pet frequently sleeps. For example, if the pet barks a lot, the advice unit can suggest a long walk to compensate for lack of exercise. If the pet refuses to eat, the advice unit can suggest a change in the pet's diet. The advice unit can also suggest a health check if the pet frequently sleeps. This allows the owner to take appropriate action by suggesting optimal countermeasures based on the pet's condition and behavior. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input the pet's behavioral data into the generation AI and cause the generation AI to suggest appropriate countermeasures.

[0034] The audio analysis unit can estimate the pet's emotions and adjust the analysis method for the pet's cries based on the estimated emotions. For example, if the pet is excited, the audio analysis unit can analyze changes in the frequency and volume of the pet's cries in detail to identify subtle changes in emotion. For example, if the pet is relaxed, the audio analysis unit can classify the pet's cries based on the time of day and environmental conditions to improve the accuracy of the analysis. Furthermore, if the pet is anxious, the audio analysis unit can compare the analysis results of the pet's cries with data from other pets to identify abnormal patterns. For example, if the pet is excited, the audio analysis unit can analyze changes in the frequency and volume of the pet's cries in detail to identify subtle changes in emotion. For example, if the pet is relaxed, the audio analysis unit can classify the pet's cries based on the time of day and environmental conditions to improve the accuracy of the analysis. For example, if the pet is anxious, the audio analysis unit can compare the analysis results of the pet's cries with data from other pets to improve the accuracy of the analysis. Thus, by adjusting the analysis method for the pet's cries based on the pet's emotions, the accuracy of the analysis is improved. Some or all of the above-described processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit may input emotional data of a pet into the generation AI and cause the generation AI to adjust the analysis method of the pet's cries.

[0035] The audio analysis unit can analyze changes in the frequency and volume of the bird's cries in detail to identify subtle changes in emotions. The audio analysis unit, for example, analyzes the frequency spectrum of the bird's cries to detect changes in specific frequency bands. For example, the audio analysis unit can analyze changes in the volume of the bird's cries over time to identify sudden changes in volume. The audio analysis unit can also analyze the duration of the bird's cries to identify the difference between short and long cries. For example, the audio analysis unit analyzes the frequency spectrum of the bird's cries to detect changes in specific frequency bands. The audio analysis unit can analyze changes in the volume of the bird's cries over time to identify sudden changes in volume. The audio analysis unit can also analyze the duration of the bird's cries to identify the difference between short and long cries. In this way, by analyzing changes in the frequency and volume of the bird's cries in detail, subtle changes in emotions can be identified. Some or all of the above-described processing in the audio analysis unit may be performed, for example, using AI or without AI. For example, the audio analysis unit can input data on a pet's cries into the generation AI and have the generation AI identify subtle changes in emotions.

[0036] The audio analysis unit classifies call patterns based on time periods and environmental conditions, thereby improving the accuracy of analysis. The audio analysis unit, for example, records the time at which calls are made and identifies patterns that occur frequently during specific time periods. For example, the audio analysis unit can also record the location at which calls are made and identify patterns that occur frequently under specific environmental conditions. The audio analysis unit can also analyze the frequency at which calls are made and identify changes in frequency under specific time periods and environmental conditions. For example, the audio analysis unit can record the time at which calls are made and identify patterns that occur frequently during specific time periods. The audio analysis unit can also record the location at which calls are made and identify patterns that occur frequently under specific environmental conditions. The audio analysis unit can also analyze the frequency at which calls are made and identify changes in frequency under specific time periods and environmental conditions. In this way, by classifying call patterns based on time periods and environmental conditions, the accuracy of analysis is improved. Some or all of the above-described processing in the audio analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio analysis unit can input pet cry data into the generation AI and have the generation AI classify the cry patterns.

[0037] The audio analysis unit can compare the analysis results of the bark with data of other pets to identify abnormal patterns. The audio analysis unit, for example, compares the frequency spectrum of the bark with data of other pets to identify abnormal frequency bands. For example, the audio analysis unit can compare the time change in the volume of the bark with data of other pets to identify abnormal volume changes. The audio analysis unit can also compare the duration of the bark with data of other pets to identify abnormal durations. For example, the audio analysis unit can compare the frequency spectrum of the bark with data of other pets to identify abnormal frequency bands. The audio analysis unit can also compare the time change in the volume of the bark with data of other pets to identify abnormal volume changes. The audio analysis unit can also compare the duration of the bark with data of other pets to identify abnormal durations. In this way, by comparing with data of other pets, abnormal bark patterns can be identified. Some or all of the above-described processing in the audio analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio analysis unit can input pet cry data into the generation AI and have the generation AI identify abnormal patterns.

[0038] The audio analysis unit can estimate the pet's emotions and adjust the display method of the analysis results based on the estimated pet's emotions. For example, if the pet is excited, the audio analysis unit can visually emphasize and display the analysis results. For example, if the pet is relaxed, the audio analysis unit can simply display the analysis results. Furthermore, if the pet is anxious, the audio analysis unit can display the analysis results in detail. For example, if the pet is excited, the audio analysis unit can visually emphasize and display the analysis results. For example, if the pet is relaxed, the audio analysis unit can simply display the analysis results. For example, if the pet is anxious, the audio analysis unit can display the analysis results in detail. By adjusting the display method of the analysis results based on the pet's emotions, it is possible to provide a display that is easy for the owner to understand. Some or all of the above-described processing in the audio analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio analysis unit can input the pet's emotional data to the generation AI and cause the generation AI to adjust the display method of the analysis results.

[0039] The audio analysis unit can improve the accuracy of the analysis by also using data on the pet's physical condition when analyzing the pet's cries. For example, the audio analysis unit can also use data on the pet's body temperature and analyze changes in the pet's body temperature in association with changes in the pet's cries. For example, the audio analysis unit can also use data on the pet's heart rate and analyze changes in the heart rate in association with changes in the pet's cries. The audio analysis unit can also use data on the pet's activity level and analyze changes in the activity level and changes in the pet's cries in association with changes in the pet's body temperature. For example, the audio analysis unit can also use data on the pet's heart rate and analyze changes in the heart rate in association with changes in the pet's cries. The audio analysis unit can also use data on the pet's activity level and analyze changes in the activity level and changes in the pet's cries in association with changes in the pet's body temperature. In this way, by also using data on the pet's physical condition, the accuracy of the analysis of the pet's cries is improved. Some or all of the above-mentioned processing in the audio analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio analysis unit can input data about a pet's physical condition into the generation AI and have the generation AI analyze the pet's cries.

[0040] When analyzing the pet's cries, the audio analysis unit can perform a comprehensive analysis by combining the pet's behavioral data. For example, the audio analysis unit can combine the pet's walking data and analyze the pattern of the cries made while walking. For example, the audio analysis unit can combine the pet's dietary data and analyze the pattern of the cries made while eating. The audio analysis unit can also combine the pet's sleep data and analyze the pattern of the cries made while sleeping. For example, the audio analysis unit can combine the pet's walking data and analyze the pattern of the cries made while walking. The audio analysis unit can combine the pet's dietary data and analyze the pattern of the cries made while eating. The audio analysis unit can also combine the pet's sleep data and analyze the pattern of the cries made while sleeping. In this way, by combining the pet's behavioral data, a comprehensive analysis is possible. Some or all of the above-described processing in the audio analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio analysis unit can input the pet's behavioral data into a generation AI and cause the generation AI to perform a comprehensive analysis.

[0041] The audio analysis unit can notify the owner's smartphone of the analysis results of the pet bark in real time. The audio analysis unit, for example, pushes the analysis results of the pet bark to the owner's smartphone in real time. For example, the audio analysis unit can also notify the owner's smartphone of the analysis results of the pet bark in real time by email. The audio analysis unit can also notify the owner's smartphone of the analysis results of the pet bark in real time by SMS. For example, the audio analysis unit pushes the analysis results of the pet bark to the owner's smartphone in real time. The audio analysis unit can also notify the owner's smartphone of the analysis results of the pet bark in real time by email. The audio analysis unit can also notify the owner's smartphone of the analysis results of the pet bark in real time by SMS. In this way, by notifying the owner of the analysis results of the pet bark in real time, the owner can respond immediately. Some or all of the above-mentioned processing in the audio analysis unit may be performed, for example, using AI or without using AI. For example, the audio analysis unit can input the results of the bird's cry analysis into the generation AI and have the generation AI execute real-time notifications.

[0042] The behavior analysis unit can estimate the pet's emotions and adjust the behavior analysis algorithm based on the estimated pet emotions. For example, if the pet is excited, the behavior analysis unit can analyze the speed and direction of its movements in detail to identify the intention of its behavior. For example, if the pet is relaxed, the behavior analysis unit can classify its movement patterns based on the time of day and environmental conditions. Furthermore, if the pet is feeling anxious, the behavior analysis unit can compare the behavior analysis results with data of other pets to identify abnormal behavior patterns. For example, if the pet is excited, the behavior analysis unit can analyze the speed and direction of its movements in detail to identify the intention of its behavior. For example, if the pet is relaxed, the behavior analysis unit can classify its movement patterns based on the time of day and environmental conditions. Furthermore, if the pet is feeling anxious, the behavior analysis unit can compare the behavior analysis results with data of other pets to identify abnormal behavior patterns. In this way, by adjusting the behavior analysis algorithm based on the pet's emotions, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the behavior analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior analysis unit can input emotional data of a pet into the generation AI and have the generation AI adjust the behavior analysis algorithm.

[0043] During behavior analysis, the behavior analysis unit can analyze the speed and direction of the pet's movement in detail to identify the intention of the behavior. The behavior analysis unit, for example, analyzes the speed of the pet's movement and identifies sudden changes in speed. For example, the behavior analysis unit can analyze the direction of the pet's movement and identify a movement pattern in a specific direction. The behavior analysis unit can also analyze the duration of the pet's movement and identify the difference between short and long movements. For example, the behavior analysis unit analyzes the speed of the pet's movement and identifies sudden changes in speed. The behavior analysis unit can analyze the direction of the pet's movement and identify a movement pattern in a specific direction. The behavior analysis unit can also analyze the duration of the pet's movement and identify the difference between short and long movements. In this way, by analyzing the speed and direction of the pet's movement in detail, the intention of the behavior can be identified. Some or all of the above-described processing in the behavior analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior analysis unit can input data of the pet's movement to a generation AI and cause the generation AI to identify the intention of the behavior.

[0044] During behavior analysis, the behavior analysis unit can classify the pet's behavioral patterns based on time periods and environmental conditions. For example, the behavior analysis unit may record the time of occurrence of a behavior and identify patterns that frequently occur during a specific time period. For example, the behavior analysis unit may record the location of the behavior and identify patterns that frequently occur under specific environmental conditions. The behavior analysis unit may also analyze the frequency of occurrence of a behavior and identify changes in frequency due to specific time periods and environmental conditions. For example, the behavior analysis unit may record the time of occurrence of a behavior and identify patterns that frequently occur during a specific time period. For example, the behavior analysis unit may record the location of the behavior and identify patterns that frequently occur under specific environmental conditions. The behavior analysis unit may also analyze the frequency of occurrence of a behavior and identify changes in frequency due to specific time periods and environmental conditions. By classifying behavioral patterns based on time periods and environmental conditions, the accuracy of the analysis is improved. Some or all of the above-described processing in the behavior analysis unit may be performed using, or without, AI. For example, the behavior analysis unit may input the pet's behavioral data into a generation AI and cause the generation AI to classify the behavioral patterns.

[0045] The behavior analysis unit can compare the results of the behavior analysis with data of other pets to identify abnormal behavior patterns. The behavior analysis unit, for example, compares the speed and direction of a behavior with data of other pets to identify abnormal speeds and directions. For example, the behavior analysis unit can compare the duration of a behavior with data of other pets to identify abnormal durations. The behavior analysis unit can also compare the frequency of occurrence of a behavior with data of other pets to identify abnormal frequencies. For example, the behavior analysis unit can compare the speed and direction of a behavior with data of other pets to identify abnormal speeds and directions. The behavior analysis unit can also compare the duration of a behavior with data of other pets to identify abnormal durations. The behavior analysis unit can also compare the frequency of occurrence of a behavior with data of other pets to identify abnormal frequencies. In this way, abnormal behavior patterns can be identified by comparing with data of other pets. Some or all of the above-described processing in the behavior analysis unit may be performed using, or without, AI. For example, the behavior analysis unit can input the pet's behavior data to a generation AI and cause the generation AI to identify abnormal behavior patterns.

[0046] The behavior analysis unit can estimate the pet's emotions and adjust the display method of the behavior analysis results based on the estimated pet's emotions. For example, if the pet is excited, the behavior analysis unit visually emphasizes and displays the analysis results. For example, if the pet is relaxed, the behavior analysis unit can simply display the analysis results. Furthermore, if the pet is feeling anxious, the behavior analysis unit can display the analysis results in detail. For example, if the pet is excited, the behavior analysis unit visually emphasizes and displays the analysis results. For example, if the pet is relaxed, the behavior analysis unit can simply display the analysis results. For example, if the pet is feeling anxious, the behavior analysis unit can display the analysis results in detail. This allows the display method of the behavior analysis results to be easily understood by the owner by adjusting the display method based on the pet's emotions. Some or all of the above-described processing in the behavior analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior analysis unit can input the pet's emotional data to the generation AI and cause the generation AI to adjust the display method of the behavior analysis results.

[0047] The behavior analysis unit can improve the accuracy of the analysis by also using data on the pet's physical condition during behavior analysis. The behavior analysis unit, for example, also uses data on the pet's body temperature and analyzes changes in body temperature and changes in behavior by correlating them. For example, the behavior analysis unit can also use data on the pet's heart rate and analyze changes in heart rate and changes in behavior by correlating them. The behavior analysis unit can also use data on the pet's activity level and analyze changes in activity level and changes in behavior by correlating them. For example, the behavior analysis unit also uses data on the pet's body temperature and analyzes changes in body temperature and changes in behavior by correlating them. The behavior analysis unit can also use data on the pet's heart rate and analyze changes in heart rate and changes in behavior by correlating them. The behavior analysis unit can also use data on the pet's activity level and analyze changes in activity level and changes in behavior by correlating them. By using data on the pet's physical condition in combination, the accuracy of the behavior analysis is improved. Some or all of the above-mentioned processing in the behavior analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the behavior analysis unit can input data on the pet's physical condition into the generation AI and have the generation AI perform behavior analysis.

[0048] The behavior analysis unit can combine pet bark data to perform a comprehensive analysis during behavior analysis. The behavior analysis unit, for example, combines pet bark data and analyzes the bark and behavior patterns in association with each other. For example, the behavior analysis unit can combine the frequency spectrum of the pet bark and analyze the bark and behavior changes in association with each other. The behavior analysis unit can also combine the time change in the volume of the pet bark and analyze the bark and behavior changes in association with each other. For example, the behavior analysis unit combines pet bark data and analyzes the bark and behavior patterns in association with each other. The behavior analysis unit can combine the frequency spectrum of the pet bark and analyze the bark and behavior changes in association with each other. The behavior analysis unit can also combine the time change in the volume of the pet bark and analyze the bark and behavior changes in association with each other. In this way, by combining pet bark data, a comprehensive analysis is possible. Some or all of the above-described processing in the behavior analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior analysis unit may input pet cry data into the generation AI and have the generation AI perform a comprehensive analysis.

[0049] The behavior analysis unit can notify the owner's smartphone of the behavior analysis results in real time. The behavior analysis unit, for example, pushes the behavior analysis results to the owner's smartphone in real time. For example, the behavior analysis unit can also notify the owner's smartphone of the behavior analysis results in real time by email. The behavior analysis unit can also notify the owner's smartphone of the behavior analysis results in real time by SMS. For example, the behavior analysis unit pushes the behavior analysis results to the owner's smartphone in real time. The behavior analysis unit can also notify the owner's smartphone of the behavior analysis results in real time by email. The behavior analysis unit can also notify the owner's smartphone of the behavior analysis results in real time by SMS. In this way, by notifying the owner of the behavior analysis results in real time, the owner can respond immediately. Some or all of the above-mentioned processing in the behavior analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior analysis unit can input the behavior analysis results to a generation AI and cause the generation AI to execute a real-time notification.

[0050] The lifestyle pattern learning unit can estimate the pet's emotions and adjust the lifestyle pattern learning method based on the estimated pet's emotions. For example, if the pet is excited, the lifestyle pattern learning unit can increase the frequency of behavioral data collection and identify detailed patterns. For example, if the pet is relaxed, the lifestyle pattern learning unit can decrease the frequency of behavioral data collection and identify long-term patterns. Furthermore, if the pet is anxious, the lifestyle pattern learning unit can adjust the frequency of behavioral data collection and identify abnormal patterns. For example, if the pet is excited, the lifestyle pattern learning unit can increase the frequency of behavioral data collection and identify detailed patterns. For example, if the pet is relaxed, the lifestyle pattern learning unit can decrease the frequency of behavioral data collection and identify long-term patterns. For example, if the pet is anxious, the lifestyle pattern learning unit can adjust the frequency of behavioral data collection and identify abnormal patterns. In this way, adjusting the lifestyle pattern learning method based on the pet's emotions improves the accuracy of learning. Some or all of the above-described processing in the lifestyle pattern learning unit may be performed using AI, for example, or without AI. For example, the lifestyle pattern learning unit can input emotional data of a pet into the generation AI and cause the generation AI to adjust the lifestyle pattern learning method.

[0051] During lifestyle pattern learning, the lifestyle pattern learning unit can collect data on the behavior and cries of a pet over a long period of time and identify detailed patterns. For example, the lifestyle pattern learning unit can collect data on the behavior of a pet for one year and identify seasonal patterns. For example, the lifestyle pattern learning unit can collect data on the cries of a pet for one year and identify patterns for each time period. The lifestyle pattern learning unit can also collect data on the behavior and cries of a pet for one year and identify comprehensive patterns. For example, the lifestyle pattern learning unit can collect data on the behavior of a pet for one year and identify seasonal patterns. The lifestyle pattern learning unit can also collect data on the cries of a pet for one year and identify patterns for each time period. The lifestyle pattern learning unit can also collect data on the behavior and cries of a pet for one year and identify comprehensive patterns. In this way, detailed lifestyle patterns can be identified by collecting data over a long period of time. Some or all of the above-described processing in the lifestyle pattern learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the lifestyle pattern learning unit can input pet behavior data into the generation AI and have the generation AI identify detailed patterns.

[0052] The lifestyle pattern learning unit can improve the accuracy of learning by also using data on the physical condition of the pet when learning the lifestyle pattern. The lifestyle pattern learning unit, for example, also uses data on the body temperature of the pet and learns by associating changes in body temperature with changes in the lifestyle pattern. For example, the lifestyle pattern learning unit can also use data on the heart rate of the pet and learn by associating changes in heart rate with changes in the lifestyle pattern. The lifestyle pattern learning unit can also use data on the activity level of the pet and learn by associating changes in activity level with changes in the lifestyle pattern. For example, the lifestyle pattern learning unit also uses data on the body temperature of the pet and learns by associating changes in body temperature with changes in the lifestyle pattern. The lifestyle pattern learning unit can also use data on the heart rate of the pet and learn by associating changes in heart rate with changes in the lifestyle pattern. The lifestyle pattern learning unit can also use data on the activity level of the pet and learn by associating changes in activity level with changes in the lifestyle pattern. By using data on the physical condition of the pet in combination, the accuracy of lifestyle pattern learning is improved. Some or all of the above-described processing in the lifestyle pattern learning unit may be performed, for example, using AI or without AI. For example, the lifestyle pattern learning unit can input data on a pet's physical condition into the generation AI and cause the generation AI to learn the lifestyle pattern.

[0053] The lifestyle pattern learning unit can compare the lifestyle pattern learning results with data of other pets to identify abnormal patterns. The lifestyle pattern learning unit, for example, compares the lifestyle pattern data with data of other pets to identify abnormal behavioral patterns. For example, the lifestyle pattern learning unit can compare the lifestyle pattern data with data of other pets to identify abnormal vocalization patterns. The lifestyle pattern learning unit can also compare the lifestyle pattern data with data of other pets to identify abnormal physical condition patterns. For example, the lifestyle pattern learning unit can compare the lifestyle pattern data with data of other pets to identify abnormal behavioral patterns. The lifestyle pattern learning unit can compare the lifestyle pattern data with data of other pets to identify abnormal vocalization patterns. The lifestyle pattern learning unit can also compare the lifestyle pattern data with data of other pets to identify abnormal physical condition patterns. In this way, abnormal lifestyle patterns can be identified by comparing with data of other pets. Some or all of the above-described processing in the lifestyle pattern learning unit may be performed using, or without, AI. For example, the lifestyle pattern learning unit can input the pet's lifestyle pattern data to a generation AI and cause the generation AI to identify abnormal patterns.

[0054] The lifestyle pattern learning unit can estimate the pet's emotions and adjust the display method of the lifestyle pattern learning results based on the estimated pet's emotions. For example, if the pet is excited, the lifestyle pattern learning unit can visually emphasize and display the learning results. For example, if the pet is relaxed, the lifestyle pattern learning unit can simply display the learning results. Furthermore, if the pet is anxious, the lifestyle pattern learning unit can display the learning results in detail. For example, if the pet is excited, the lifestyle pattern learning unit can visually emphasize and display the learning results. For example, if the pet is relaxed, the lifestyle pattern learning unit can simply display the learning results. For example, if the pet is anxious, the lifestyle pattern learning unit can display the learning results in detail. By adjusting the display method of the lifestyle pattern learning results based on the pet's emotions, a display that is easy for the owner to understand can be achieved. Some or all of the above-described processing in the lifestyle pattern learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the lifestyle pattern learning unit can input the pet's emotional data to the generation AI and cause the generation AI to adjust the display method of the learning results.

[0055] The lifestyle pattern learning unit can improve the accuracy of learning by also using pet environmental data when learning lifestyle patterns. The lifestyle pattern learning unit, for example, also uses pet room temperature data and learns by associating changes in room temperature with changes in lifestyle patterns. For example, the lifestyle pattern learning unit can also use pet humidity data and learn by associating changes in humidity with changes in lifestyle patterns. The lifestyle pattern learning unit can also also use pet illuminance data and learn by associating changes in illuminance with changes in lifestyle patterns. For example, the lifestyle pattern learning unit also uses pet room temperature data and learns by associating changes in room temperature with changes in lifestyle patterns. The lifestyle pattern learning unit can also use pet humidity data and learn by associating changes in humidity with changes in lifestyle patterns. The lifestyle pattern learning unit can also use pet illuminance data and learn by associating changes in illuminance with changes in lifestyle patterns. In this way, by also using pet environmental data, the accuracy of lifestyle pattern learning is improved. Some or all of the above-mentioned processing in the lifestyle pattern learning unit may be performed, for example, using AI or without AI. For example, the lifestyle pattern learning unit can input environmental data about a pet into the generation AI and cause the generation AI to learn the lifestyle pattern.

[0056] The lifestyle pattern learning unit can combine pet diet data to perform comprehensive learning during lifestyle pattern learning. For example, the lifestyle pattern learning unit combines pet diet data and learns by associating meal timing with changes in lifestyle patterns. For example, the lifestyle pattern learning unit can combine pet diet content data and learn by associating changes in diet content with changes in lifestyle patterns. The lifestyle pattern learning unit can also combine pet diet amount data and learn by associating changes in diet amount with changes in lifestyle patterns. For example, the lifestyle pattern learning unit combines pet diet data and learns by associating meal timing with changes in lifestyle patterns. The lifestyle pattern learning unit can also combine pet diet content data and learn by associating changes in diet content with changes in lifestyle patterns. The lifestyle pattern learning unit can also combine pet diet amount data and learn by associating changes in diet amount with changes in lifestyle patterns. In this way, by combining pet diet data, comprehensive lifestyle pattern learning is possible. Some or all of the above-described processing in the lifestyle pattern learning unit may be performed, for example, using AI or without AI. For example, the lifestyle pattern learning unit can input pet dietary data into the generation AI and have the generation AI perform comprehensive learning.

[0057] The lifestyle pattern learning unit can notify the owner's smartphone of the lifestyle pattern learning results in real time. The lifestyle pattern learning unit, for example, pushes the lifestyle pattern learning results to the owner's smartphone in real time. For example, the lifestyle pattern learning unit can notify the owner's smartphone of the lifestyle pattern learning results in real time by email. The lifestyle pattern learning unit can also notify the owner's smartphone of the lifestyle pattern learning results in real time by SMS. For example, the lifestyle pattern learning unit pushes the lifestyle pattern learning results to the owner's smartphone in real time. The lifestyle pattern learning unit can also notify the owner's smartphone of the lifestyle pattern learning results in real time by email. The lifestyle pattern learning unit can also notify the owner's smartphone of the lifestyle pattern learning results in real time by SMS. This allows the owner to respond immediately by notifying the owner of the lifestyle pattern learning results in real time. Some or all of the above-described processing in the lifestyle pattern learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the lifestyle pattern learning unit can input the lifestyle pattern learning results to a generation AI and cause the generation AI to execute a real-time notification.

[0058] The advice unit can estimate the pet's emotions and adjust the content of the advice based on the estimated pet's emotions. For example, if the pet is excited, the advice unit provides advice to eliminate lack of exercise. For example, if the pet is relaxed, the advice unit can provide advice to maintain relaxation. Furthermore, if the pet is feeling anxious, the advice unit can provide advice to reduce anxiety. For example, if the pet is excited, the advice unit provides advice to eliminate lack of exercise. If the pet is relaxed, the advice unit can provide advice to maintain relaxation. If the pet is feeling anxious, the advice unit can provide advice to reduce anxiety. In this way, by adjusting the content of the advice based on the pet's emotions, more appropriate advice can be provided. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input pet emotion data to the generation AI and cause the generation AI to adjust the content of the advice.

[0059] When providing advice, the advice unit can suggest specific countermeasures based on data on the pet's behavior and cries. For example, if the pet barks a lot, the advice unit can suggest a long walk to compensate for lack of exercise. For example, if the pet refuses to eat, the advice unit can suggest a change in the pet's diet. Furthermore, if the pet frequently sleeps, the advice unit can suggest a health check. For example, if the pet barks a lot, the advice unit can suggest a long walk to compensate for lack of exercise. If the pet refuses to eat, the advice unit can suggest a change in the pet's diet. If the pet frequently sleeps, the advice unit can suggest a health check. In this way, by suggesting specific countermeasures based on data on the pet's behavior and cries, the owner can take appropriate action. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the pet's behavior data into the generation AI and cause the generation AI to execute specific countermeasure suggestions.

[0060] When providing advice, the advice unit can propose optimal countermeasures by also using the pet's physical condition data. For example, if the pet's body temperature is high, the advice unit can propose a cooling method. For example, if the pet's heart rate is high, the advice unit can also propose a method of resting. Furthermore, if the pet's activity level is low, the advice unit can also propose a method of encouraging exercise. For example, if the pet's body temperature is high, the advice unit can propose a cooling method. If the pet's heart rate is high, the advice unit can also propose a method of encouraging exercise. If the pet's activity level is low, the advice unit can propose an optimal countermeasure by also using the pet's physical condition data. Some or all of the above-mentioned processing by the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the pet's physical condition data into the generation AI and cause the generation AI to execute the optimal countermeasure proposal.

[0061] The advice unit can compare the advice results with data of other pets and identify countermeasures for abnormal patterns. The advice unit, for example, compares the advice results with data of other pets and identify countermeasures for abnormal behavioral patterns. For example, the advice unit can compare the advice results with data of other pets and identify countermeasures for abnormal vocalization patterns. The advice unit can also compare the advice results with data of other pets and identify countermeasures for abnormal physical condition patterns. For example, the advice unit can compare the advice results with data of other pets and identify countermeasures for abnormal behavioral patterns. The advice unit can compare the advice results with data of other pets and identify countermeasures for abnormal vocalization patterns. The advice unit can also compare the advice results with data of other pets and identify countermeasures for abnormal physical condition patterns. In this way, by comparing with data of other pets, it is possible to identify countermeasures for abnormal patterns. Some or all of the above-described processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input data of other pets to the generation AI and cause the generation AI to identify countermeasures for abnormal patterns.

[0062] The advice unit can estimate the pet's emotions and adjust the way the advice is displayed based on the estimated pet's emotions. For example, if the pet is excited, the advice unit can visually emphasize the advice when it is displayed. For example, if the pet is relaxed, the advice unit can simply display the advice when it is displayed. Furthermore, if the pet is anxious, the advice unit can display detailed advice when it is displayed. For example, if the pet is excited, the advice unit can visually emphasize the advice when it is displayed. For example, if the pet is relaxed, the advice unit can simply display the advice when it is displayed. Furthermore, if the pet is anxious, the advice unit can display detailed advice when it is displayed. In this way, by adjusting the way the advice is displayed based on the pet's emotions, it is possible to provide a display that is easy for the owner to understand. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the pet's emotion data to the generation AI and cause the generation AI to adjust the way the advice is displayed.

[0063] When providing advice, the advice unit can propose an optimal countermeasure by also using the pet's environmental data. For example, if the pet's room temperature is high, the advice unit can propose a cooling method. For example, if the pet's humidity is high, the advice unit can also propose a humidity adjustment method. Furthermore, the advice unit can also propose a lighting adjustment method when the pet's illuminance is low. For example, if the pet's room temperature is high, the advice unit can propose a cooling method. If the pet's humidity is high, the advice unit can also propose a humidity adjustment method. If the pet's illuminance is low, the advice unit can also propose a lighting adjustment method. In this way, by also using the pet's environmental data, an optimal countermeasure is proposed. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the pet's environmental data into the generation AI and cause the generation AI to execute the proposal of the optimal countermeasure.

[0064] When providing advice, the advice unit can combine the pet's dietary data to propose a comprehensive response. The advice unit, for example, proposes a nutritionally balanced diet based on the pet's dietary content. For example, the advice unit can also propose an appropriate dietary amount based on the pet's dietary amount. The advice unit can also propose an appropriate dietary timing based on the pet's dietary timing. For example, the advice unit proposes a nutritionally balanced diet based on the pet's dietary content. The advice unit can also propose an appropriate dietary amount based on the pet's dietary amount. The advice unit can also propose an appropriate dietary timing based on the pet's dietary timing. In this way, a comprehensive response is proposed by combining the pet's dietary data. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the pet's dietary data into the generation AI and cause the generation AI to propose a comprehensive response.

[0065] The advice unit can notify the owner's smartphone of the advice result in real time. The advice unit, for example, pushes the advice result to the owner's smartphone in real time. For example, the advice unit can also notify the owner's smartphone of the advice result in real time by email. The advice unit can also notify the owner's smartphone of the advice result in real time by SMS. For example, the advice unit pushes the advice result to the owner's smartphone in real time. The advice unit can also notify the owner's smartphone of the advice result in real time by email. The advice unit can also notify the owner's smartphone of the advice result in real time by SMS. In this way, by notifying the advice result in real time, the owner can respond immediately. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input the advice result to a generation AI and cause the generation AI to execute a real-time notification.

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

[0067] The pet translation system can further include a health management unit that monitors the pet's health. The health management unit collects data such as the pet's body temperature, heart rate, and activity level, and detects abnormalities. For example, if the pet's body temperature is higher than normal, the health management unit can suggest cooling methods to the owner. Also, if the pet's heart rate is abnormally high, it can suggest ways to keep the pet calm. Furthermore, if the pet's activity level is decreasing, it can suggest ways to encourage exercise. This allows the pet's health to be monitored in real time, abnormalities to be detected early, and appropriate measures to be taken.

[0068] The pet translation system can further include a diet management unit that manages the pet's diet. The diet management unit records the pet's dietary content, amount, and timing, and manages nutritional balance. For example, if the pet is deficient in a particular nutrient, the diet management unit can suggest a diet containing that nutrient. Also, if the amount of food is inappropriate, it can suggest an appropriate amount of food. Furthermore, if the timing of meals is irregular, it can suggest an appropriate timing of meals. This enables appropriate diet management to maintain the pet's health.

[0069] The pet translation system can further include an exercise management unit that manages the pet's exercise. The exercise management unit records the amount, frequency, and duration of the pet's exercise and proposes an appropriate exercise plan. For example, if the pet is not getting enough exercise, the exercise management unit can suggest a long walk or playtime. If the pet is exercising too much, it can also suggest ways to encourage rest. Furthermore, if the exercise frequency is irregular, it can also suggest an appropriate exercise frequency. This enables appropriate exercise management to maintain the pet's health.

[0070] The pet translation system may further include a stress management unit that manages stress in pets. The stress management unit analyzes data on the pet's behavior and cries to evaluate the stress level. For example, if the pet barks frequently, the stress management unit can suggest ways to reduce stress. Also, if the pet refuses to eat, stress may be the cause, so the stress management unit can suggest ways to relax. Furthermore, if the pet sleeps frequently, stress may be the cause, so the stress management unit can suggest ways to reduce stress. In this way, the pet's stress can be managed and its health can be maintained.

[0071] The pet translation system may further include a sleep management unit that manages the pet's sleep. The sleep management unit records the pet's sleep time, sleep quality, and sleep patterns, and suggests an appropriate sleeping environment. For example, if the pet's sleep time is short, the sleep management unit can suggest a way to provide a quiet environment. Also, if the sleep quality is low, it can suggest a way to provide a comfortable bed. Furthermore, if the sleep pattern is irregular, it can suggest a regular sleep schedule. This enables appropriate sleep management to maintain the pet's health.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The audio analysis unit analyzes the pet's cry or bark. Specifically, it analyzes the frequency spectrum of the pet's cry and detects changes in specific frequency bands. It can also analyze the change in the volume of the cry over time to identify sudden changes in volume. It can also analyze the duration of the cry to identify the difference between short and long cries. Step 2: The behavior analysis unit captures your pet's daily behavior via video or images and analyzes the data uploaded to the app. Specifically, it records the time when your pet's behavior occurs and identifies patterns that occur more frequently at certain times of the day. It can also record the location where the behavior occurs and identify patterns that occur more frequently under certain environmental conditions. It can also analyze the frequency of behavior and identify changes in frequency at certain times of day or under certain environmental conditions. Step 3: The lifestyle pattern learning unit learns the pet's lifestyle patterns based on the data obtained by the audio analysis unit and behavior analysis unit. Specifically, it collects data on the pet's behavior and cries over a long period of time and identifies seasonal and time-of-day patterns. It also comprehensively analyzes the behavior and cries data to identify the pet's lifestyle patterns. Step 4: The advice section proposes appropriate measures to the owner based on the information obtained by the lifestyle pattern learning section. Specifically, if the pet barks a lot, it suggests taking the owner on a long walk to get some exercise, if the pet refuses to eat, it suggests changing the pet's diet, and if the pet sleeps frequently, it can also suggest a health check.

[0074] (Example 2) A pet translation system according to an embodiment of the present invention analyzes a pet's cries and behavior, learns its life patterns, and proposes optimal countermeasures. The pet translation system analyzes a pet's cries and barks and interprets their emotions and intentions. It also captures the pet's daily behavior as videos or images and analyzes the data uploaded to an app. It also learns the pet's life patterns and mood changes and proposes optimal countermeasures to the owner. For example, the pet translation system can analyze a pet's cries and identify emotions such as "happy," "anxious," and "alert." Next, the pet translation system captures the pet's daily behavior as videos or images and analyzes the data uploaded to an app. For example, it can analyze basic behaviors such as "I want to go for a walk," "I want to play," and "I'm sleepy," as well as more complex behaviors such as "I'm not feeling well" and "I'm stressed." Furthermore, the pet translation system collects data over a long period of time and learns the pet's life patterns and mood changes. This allows it to detect problems at an early stage, such as when the pet is stressed or showing early signs of illness. Finally, the pet translation system will suggest optimal solutions to owners based on their pet's condition and behavior. For example, it will provide specific advice such as, "If your dog is barking a lot, it may not be getting enough exercise. Try taking it for a long walk." This allows the pet translation system to understand the pet's emotions and intentions and provide appropriate solutions to owners. This can, for example, improve pet health management and communication.

[0075] A pet translation system according to an embodiment includes a voice analysis unit, a behavior analysis unit, a lifestyle pattern learning unit, and an advice unit. The voice analysis unit analyzes the meows or barks of a pet. Pet meows include, but are not limited to, dog barks and cat meows. The voice analysis unit, for example, analyzes the frequency spectrum of the pet's meow to detect changes in a specific frequency band. The voice analysis unit can also analyze changes in the volume of the meow over time to identify sudden changes in volume. The voice analysis unit can also analyze the duration of the meow to identify the difference between short and long meows. For example, the voice analysis unit analyzes the frequency spectrum of the meow to detect changes in a specific frequency band. The voice analysis unit can also analyze changes in the volume of the meow over time to identify sudden changes in volume. The voice analysis unit can also analyze the duration of the meow to identify the difference between short and long meows. The behavior analysis unit captures the pet's daily behavior using video or images and analyzes the data uploaded to the app. The pet's daily behavior includes, but is not limited to, eating, walking, playing, etc. The behavior analysis unit, for example, records the time when the pet's behavior occurs and identifies patterns that occur frequently during specific time periods. The behavior analysis unit can also record the location where the pet's behavior occurs and identify patterns that occur frequently under specific environmental conditions. The behavior analysis unit can also analyze the frequency of the pet's behavior and identify changes in frequency under specific time periods or environmental conditions. For example, the behavior analysis unit records the time when the pet's behavior occurs and identifies patterns that occur frequently during specific time periods. The behavior analysis unit can also record the location where the pet's behavior occurs and identify patterns that occur frequently under specific environmental conditions. The behavior analysis unit can also analyze the frequency of the pet's behavior and identify changes in frequency under specific time periods or environmental conditions. The lifestyle pattern learning unit learns the pet's lifestyle patterns based on the data obtained by the audio analysis unit and the behavior analysis unit. The lifestyle patterns include, but are not limited to, meal times, exercise frequency, etc. The lifestyle pattern learning unit collects, for example, data on the behavior of a pet for one year and identifies seasonal patterns.The lifestyle pattern learning unit can also collect data on pet barks for a year and identify patterns for each time period. The lifestyle pattern learning unit can also collect data on pet behavior and barks for a year and identify overall patterns. For example, the lifestyle pattern learning unit can collect data on pet behavior for a year and identify patterns for each season. The lifestyle pattern learning unit can also collect data on pet barks for a year and identify patterns for each time period. The lifestyle pattern learning unit can also collect data on pet behavior and barks for a year and identify overall patterns. The advice unit suggests appropriate countermeasures to the owner based on the information obtained by the lifestyle pattern learning unit. Appropriate countermeasures include, but are not limited to, changing the pet's diet, increasing or decreasing the amount of exercise, etc. For example, if the pet barks a lot, the advice unit can suggest taking a long walk to compensate for the lack of exercise. If the pet refuses to eat, the advice unit can suggest a change in the pet's diet. If the pet frequently sleeps, the advice unit can suggest a health check. For example, if the pet barks a lot, the advice unit may suggest a long walk to help the pet get enough exercise. If the pet refuses to eat, the advice unit may also suggest a change in the pet's diet. If the pet frequently sleeps, the advice unit may also suggest a health check. This allows the pet translation system according to the embodiment to analyze the pet's cries and behavior, learn its lifestyle patterns, and suggest optimal countermeasures. Some or all of the above-described processing by the advice unit may be performed using, for example, AI, or may be performed without AI. For example, the advice unit may suggest countermeasures using an AI model that inputs information obtained by the lifestyle pattern learning unit and outputs appropriate countermeasures.

[0076] The audio analysis unit can analyze the patterns of a pet's cries and barks and interpret their emotions and intentions. The audio analysis unit, for example, analyzes the frequency spectrum of a pet's cries and detects changes in a specific frequency band. For example, the audio analysis unit can analyze changes in the volume of the pet's cries over time and identify sudden changes in volume. The audio analysis unit can also analyze the duration of the cries and identify the difference between short and long cries. For example, the audio analysis unit analyzes the frequency spectrum of the pet's cries and detects changes in a specific frequency band. The audio analysis unit can analyze changes in the volume of the pet's cries over time and identify sudden changes in volume. The audio analysis unit can also analyze the duration of the cries and identify the difference between short and long cries. In this way, by analyzing the patterns of a pet's cries and barks, it is possible to understand the pet's emotions and intentions. Some or all of the above-mentioned processing in the audio analysis unit may be performed, for example, using AI or without AI. For example, the audio analysis unit can input data on a pet's cries into the generation AI and have the generation AI interpret the emotions and intentions.

[0077] The behavior analysis unit can capture the pet's daily behavior using at least one of video and images and analyze the data uploaded to the app. The behavior analysis unit, for example, records the time when the pet's behavior occurs and identifies patterns that occur frequently during specific time periods. For example, the behavior analysis unit can also record the location where the pet's behavior occurs and identify patterns that occur frequently under specific environmental conditions. The behavior analysis unit can also analyze the frequency of the pet's behavior and identify changes in frequency under specific time periods or environmental conditions. For example, the behavior analysis unit can record the time when the pet's behavior occurs and identify patterns that occur frequently during specific time periods. The behavior analysis unit can also record the location where the pet's behavior occurs and identify patterns that occur frequently under specific environmental conditions. The behavior analysis unit can also analyze the frequency of the pet's behavior and identify changes in frequency under specific time periods or environmental conditions. By analyzing the pet's daily behavior, the meaning of the pet's behavior can be understood. Some or all of the above-described processing in the behavior analysis unit may be performed using, for example, AI, or without AI. For example, the behavior analysis unit can input behavior data of a pet into the generation AI and have the generation AI analyze the meaning of the behavior.

[0078] The lifestyle pattern learning unit can collect data over a long period of time and learn changes in the pet's lifestyle patterns and moods. For example, the lifestyle pattern learning unit can collect pet behavior data for one year and identify seasonal patterns. For example, the lifestyle pattern learning unit can collect pet bark data for one year and identify patterns for each time period. The lifestyle pattern learning unit can also collect pet behavior and bark data for one year and identify overall patterns. For example, the lifestyle pattern learning unit can collect pet behavior data for one year and identify seasonal patterns. The lifestyle pattern learning unit can also collect pet bark data for one year and identify patterns for each time period. The lifestyle pattern learning unit can also collect pet behavior and bark data for one year and identify overall patterns. This allows long-term data collection to learn changes in the pet's lifestyle patterns and moods, enabling early detection of problems. Some or all of the above-mentioned processing in the lifestyle pattern learning unit may be performed using, for example, AI, or may be performed without AI. For example, the lifestyle pattern learning unit can input behavioral data of a pet into the generation AI and cause the generation AI to learn the lifestyle pattern.

[0079] The advice unit can suggest appropriate countermeasures to the owner based on the pet's condition and behavior. For example, if the pet barks a lot, the advice unit can suggest a long walk to compensate for lack of exercise. For example, if the pet refuses to eat, the advice unit can suggest a change in the pet's diet. The advice unit can also suggest a health check if the pet frequently sleeps. For example, if the pet barks a lot, the advice unit can suggest a long walk to compensate for lack of exercise. If the pet refuses to eat, the advice unit can suggest a change in the pet's diet. The advice unit can also suggest a health check if the pet frequently sleeps. This allows the owner to take appropriate action by suggesting optimal countermeasures based on the pet's condition and behavior. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input the pet's behavioral data into the generation AI and cause the generation AI to suggest appropriate countermeasures.

[0080] The audio analysis unit can estimate the pet's emotions and adjust the analysis method for the pet's cries based on the estimated emotions. For example, if the pet is excited, the audio analysis unit can analyze changes in the frequency and volume of the pet's cries in detail to identify subtle changes in emotion. For example, if the pet is relaxed, the audio analysis unit can classify the pet's cries based on the time of day and environmental conditions to improve the accuracy of the analysis. Furthermore, if the pet is anxious, the audio analysis unit can compare the analysis results of the pet's cries with data from other pets to identify abnormal patterns. For example, if the pet is excited, the audio analysis unit can analyze changes in the frequency and volume of the pet's cries in detail to identify subtle changes in emotion. For example, if the pet is relaxed, the audio analysis unit can classify the pet's cries based on the time of day and environmental conditions to improve the accuracy of the analysis. For example, if the pet is anxious, the audio analysis unit can compare the analysis results of the pet's cries with data from other pets to improve the accuracy of the analysis. Thus, by adjusting the analysis method for the pet's cries based on the pet's emotions, the accuracy of the analysis is improved. Some or all of the above-described processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit may input emotional data of a pet into the generation AI and cause the generation AI to adjust the analysis method of the pet's cries.

[0081] The audio analysis unit can analyze changes in the frequency and volume of the bird's cries in detail to identify subtle changes in emotions. The audio analysis unit, for example, analyzes the frequency spectrum of the bird's cries to detect changes in specific frequency bands. For example, the audio analysis unit can analyze changes in the volume of the bird's cries over time to identify sudden changes in volume. The audio analysis unit can also analyze the duration of the bird's cries to identify the difference between short and long cries. For example, the audio analysis unit analyzes the frequency spectrum of the bird's cries to detect changes in specific frequency bands. The audio analysis unit can analyze changes in the volume of the bird's cries over time to identify sudden changes in volume. The audio analysis unit can also analyze the duration of the bird's cries to identify the difference between short and long cries. In this way, by analyzing changes in the frequency and volume of the bird's cries in detail, subtle changes in emotions can be identified. Some or all of the above-described processing in the audio analysis unit may be performed, for example, using AI or without AI. For example, the audio analysis unit can input data on a pet's cries into the generation AI and have the generation AI identify subtle changes in emotions.

[0082] The audio analysis unit classifies call patterns based on time periods and environmental conditions, thereby improving the accuracy of analysis. The audio analysis unit, for example, records the time at which calls are made and identifies patterns that occur frequently during specific time periods. For example, the audio analysis unit can also record the location at which calls are made and identify patterns that occur frequently under specific environmental conditions. The audio analysis unit can also analyze the frequency at which calls are made and identify changes in frequency under specific time periods and environmental conditions. For example, the audio analysis unit can record the time at which calls are made and identify patterns that occur frequently during specific time periods. The audio analysis unit can also record the location at which calls are made and identify patterns that occur frequently under specific environmental conditions. The audio analysis unit can also analyze the frequency at which calls are made and identify changes in frequency under specific time periods and environmental conditions. In this way, by classifying call patterns based on time periods and environmental conditions, the accuracy of analysis is improved. Some or all of the above-described processing in the audio analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio analysis unit can input pet cry data into the generation AI and have the generation AI classify the cry patterns.

[0083] The audio analysis unit can compare the analysis results of the bark with data of other pets to identify abnormal patterns. The audio analysis unit, for example, compares the frequency spectrum of the bark with data of other pets to identify abnormal frequency bands. For example, the audio analysis unit can compare the time change in the volume of the bark with data of other pets to identify abnormal volume changes. The audio analysis unit can also compare the duration of the bark with data of other pets to identify abnormal durations. For example, the audio analysis unit can compare the frequency spectrum of the bark with data of other pets to identify abnormal frequency bands. The audio analysis unit can also compare the time change in the volume of the bark with data of other pets to identify abnormal volume changes. The audio analysis unit can also compare the duration of the bark with data of other pets to identify abnormal durations. In this way, by comparing with data of other pets, abnormal bark patterns can be identified. Some or all of the above-described processing in the audio analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio analysis unit can input pet cry data into the generation AI and have the generation AI identify abnormal patterns.

[0084] The audio analysis unit can estimate the pet's emotions and adjust the display method of the analysis results based on the estimated pet's emotions. For example, if the pet is excited, the audio analysis unit can visually emphasize and display the analysis results. For example, if the pet is relaxed, the audio analysis unit can simply display the analysis results. Furthermore, if the pet is anxious, the audio analysis unit can display the analysis results in detail. For example, if the pet is excited, the audio analysis unit can visually emphasize and display the analysis results. For example, if the pet is relaxed, the audio analysis unit can simply display the analysis results. For example, if the pet is anxious, the audio analysis unit can display the analysis results in detail. By adjusting the display method of the analysis results based on the pet's emotions, it is possible to provide a display that is easy for the owner to understand. Some or all of the above-described processing in the audio analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio analysis unit can input the pet's emotional data to the generation AI and cause the generation AI to adjust the display method of the analysis results.

[0085] The audio analysis unit can improve the accuracy of the analysis by also using data on the pet's physical condition when analyzing the pet's cries. For example, the audio analysis unit can also use data on the pet's body temperature and analyze changes in the pet's body temperature in association with changes in the pet's cries. For example, the audio analysis unit can also use data on the pet's heart rate and analyze changes in the heart rate in association with changes in the pet's cries. The audio analysis unit can also use data on the pet's activity level and analyze changes in the activity level and changes in the pet's cries in association with changes in the pet's body temperature. For example, the audio analysis unit can also use data on the pet's heart rate and analyze changes in the heart rate in association with changes in the pet's cries. The audio analysis unit can also use data on the pet's activity level and analyze changes in the activity level and changes in the pet's cries in association with changes in the pet's body temperature. In this way, by also using data on the pet's physical condition, the accuracy of the analysis of the pet's cries is improved. Some or all of the above-mentioned processing in the audio analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio analysis unit can input data about a pet's physical condition into the generation AI and have the generation AI analyze the pet's cries.

[0086] When analyzing the pet's cries, the audio analysis unit can perform a comprehensive analysis by combining the pet's behavioral data. For example, the audio analysis unit can combine the pet's walking data and analyze the pattern of the cries made while walking. For example, the audio analysis unit can combine the pet's dietary data and analyze the pattern of the cries made while eating. The audio analysis unit can also combine the pet's sleep data and analyze the pattern of the cries made while sleeping. For example, the audio analysis unit can combine the pet's walking data and analyze the pattern of the cries made while walking. The audio analysis unit can combine the pet's dietary data and analyze the pattern of the cries made while eating. The audio analysis unit can also combine the pet's sleep data and analyze the pattern of the cries made while sleeping. In this way, by combining the pet's behavioral data, a comprehensive analysis is possible. Some or all of the above-described processing in the audio analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio analysis unit can input the pet's behavioral data into a generation AI and cause the generation AI to perform a comprehensive analysis.

[0087] The audio analysis unit can notify the owner's smartphone of the analysis results of the pet bark in real time. The audio analysis unit, for example, pushes the analysis results of the pet bark to the owner's smartphone in real time. For example, the audio analysis unit can also notify the owner's smartphone of the analysis results of the pet bark in real time by email. The audio analysis unit can also notify the owner's smartphone of the analysis results of the pet bark in real time by SMS. For example, the audio analysis unit pushes the analysis results of the pet bark to the owner's smartphone in real time. The audio analysis unit can also notify the owner's smartphone of the analysis results of the pet bark in real time by email. The audio analysis unit can also notify the owner's smartphone of the analysis results of the pet bark in real time by SMS. In this way, by notifying the owner of the analysis results of the pet bark in real time, the owner can respond immediately. Some or all of the above-mentioned processing in the audio analysis unit may be performed, for example, using AI or without using AI. For example, the audio analysis unit can input the results of the bird's cry analysis into the generation AI and have the generation AI execute real-time notifications.

[0088] The behavior analysis unit can estimate the pet's emotions and adjust the behavior analysis algorithm based on the estimated pet emotions. For example, if the pet is excited, the behavior analysis unit can analyze the speed and direction of its movements in detail to identify the intention of its behavior. For example, if the pet is relaxed, the behavior analysis unit can classify its movement patterns based on the time of day and environmental conditions. Furthermore, if the pet is feeling anxious, the behavior analysis unit can compare the behavior analysis results with data of other pets to identify abnormal behavior patterns. For example, if the pet is excited, the behavior analysis unit can analyze the speed and direction of its movements in detail to identify the intention of its behavior. For example, if the pet is relaxed, the behavior analysis unit can classify its movement patterns based on the time of day and environmental conditions. Furthermore, if the pet is feeling anxious, the behavior analysis unit can compare the behavior analysis results with data of other pets to identify abnormal behavior patterns. In this way, by adjusting the behavior analysis algorithm based on the pet's emotions, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the behavior analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior analysis unit can input emotional data of a pet into the generation AI and have the generation AI adjust the behavior analysis algorithm.

[0089] During behavior analysis, the behavior analysis unit can analyze the speed and direction of the pet's movement in detail to identify the intention of the behavior. The behavior analysis unit, for example, analyzes the speed of the pet's movement and identifies sudden changes in speed. For example, the behavior analysis unit can analyze the direction of the pet's movement and identify a movement pattern in a specific direction. The behavior analysis unit can also analyze the duration of the pet's movement and identify the difference between short and long movements. For example, the behavior analysis unit analyzes the speed of the pet's movement and identifies sudden changes in speed. The behavior analysis unit can analyze the direction of the pet's movement and identify a movement pattern in a specific direction. The behavior analysis unit can also analyze the duration of the pet's movement and identify the difference between short and long movements. In this way, by analyzing the speed and direction of the pet's movement in detail, the intention of the behavior can be identified. Some or all of the above-described processing in the behavior analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior analysis unit can input data of the pet's movement to a generation AI and cause the generation AI to identify the intention of the behavior.

[0090] During behavior analysis, the behavior analysis unit can classify the pet's behavioral patterns based on time periods and environmental conditions. For example, the behavior analysis unit may record the time of occurrence of a behavior and identify patterns that frequently occur during a specific time period. For example, the behavior analysis unit may record the location of the behavior and identify patterns that frequently occur under specific environmental conditions. The behavior analysis unit may also analyze the frequency of occurrence of a behavior and identify changes in frequency due to specific time periods and environmental conditions. For example, the behavior analysis unit may record the time of occurrence of a behavior and identify patterns that frequently occur during a specific time period. For example, the behavior analysis unit may record the location of the behavior and identify patterns that frequently occur under specific environmental conditions. The behavior analysis unit may also analyze the frequency of occurrence of a behavior and identify changes in frequency due to specific time periods and environmental conditions. By classifying behavioral patterns based on time periods and environmental conditions, the accuracy of the analysis is improved. Some or all of the above-described processing in the behavior analysis unit may be performed using, or without, AI. For example, the behavior analysis unit may input the pet's behavioral data into a generation AI and cause the generation AI to classify the behavioral patterns.

[0091] The behavior analysis unit can compare the results of the behavior analysis with data of other pets to identify abnormal behavior patterns. The behavior analysis unit, for example, compares the speed and direction of a behavior with data of other pets to identify abnormal speeds and directions. For example, the behavior analysis unit can compare the duration of a behavior with data of other pets to identify abnormal durations. The behavior analysis unit can also compare the frequency of occurrence of a behavior with data of other pets to identify abnormal frequencies. For example, the behavior analysis unit can compare the speed and direction of a behavior with data of other pets to identify abnormal speeds and directions. The behavior analysis unit can also compare the duration of a behavior with data of other pets to identify abnormal durations. The behavior analysis unit can also compare the frequency of occurrence of a behavior with data of other pets to identify abnormal frequencies. In this way, abnormal behavior patterns can be identified by comparing with data of other pets. Some or all of the above-described processing in the behavior analysis unit may be performed using, or without, AI. For example, the behavior analysis unit can input the pet's behavior data to a generation AI and cause the generation AI to identify abnormal behavior patterns.

[0092] The behavior analysis unit can estimate the pet's emotions and adjust the display method of the behavior analysis results based on the estimated pet's emotions. For example, if the pet is excited, the behavior analysis unit visually emphasizes and displays the analysis results. For example, if the pet is relaxed, the behavior analysis unit can simply display the analysis results. Furthermore, if the pet is feeling anxious, the behavior analysis unit can display the analysis results in detail. For example, if the pet is excited, the behavior analysis unit visually emphasizes and displays the analysis results. For example, if the pet is relaxed, the behavior analysis unit can simply display the analysis results. For example, if the pet is feeling anxious, the behavior analysis unit can display the analysis results in detail. This allows the display method of the behavior analysis results to be easily understood by the owner by adjusting the display method based on the pet's emotions. Some or all of the above-described processing in the behavior analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior analysis unit can input the pet's emotional data to the generation AI and cause the generation AI to adjust the display method of the behavior analysis results.

[0093] The behavior analysis unit can improve the accuracy of the analysis by also using data on the pet's physical condition during behavior analysis. The behavior analysis unit, for example, also uses data on the pet's body temperature and analyzes changes in body temperature and changes in behavior by correlating them. For example, the behavior analysis unit can also use data on the pet's heart rate and analyze changes in heart rate and changes in behavior by correlating them. The behavior analysis unit can also use data on the pet's activity level and analyze changes in activity level and changes in behavior by correlating them. For example, the behavior analysis unit also uses data on the pet's body temperature and analyzes changes in body temperature and changes in behavior by correlating them. The behavior analysis unit can also use data on the pet's heart rate and analyze changes in heart rate and changes in behavior by correlating them. The behavior analysis unit can also use data on the pet's activity level and analyze changes in activity level and changes in behavior by correlating them. By using data on the pet's physical condition in combination, the accuracy of the behavior analysis is improved. Some or all of the above-mentioned processing in the behavior analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the behavior analysis unit can input data on the pet's physical condition into the generation AI and have the generation AI perform behavior analysis.

[0094] The behavior analysis unit can combine pet bark data to perform a comprehensive analysis during behavior analysis. The behavior analysis unit, for example, combines pet bark data and analyzes the bark and behavior patterns in association with each other. For example, the behavior analysis unit can combine the frequency spectrum of the pet bark and analyze the bark and behavior changes in association with each other. The behavior analysis unit can also combine the time change in the volume of the pet bark and analyze the bark and behavior changes in association with each other. For example, the behavior analysis unit combines pet bark data and analyzes the bark and behavior patterns in association with each other. The behavior analysis unit can combine the frequency spectrum of the pet bark and analyze the bark and behavior changes in association with each other. The behavior analysis unit can also combine the time change in the volume of the pet bark and analyze the bark and behavior changes in association with each other. In this way, by combining pet bark data, a comprehensive analysis is possible. Some or all of the above-described processing in the behavior analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior analysis unit may input pet cry data into the generation AI and have the generation AI perform a comprehensive analysis.

[0095] The behavior analysis unit can notify the owner's smartphone of the behavior analysis results in real time. The behavior analysis unit, for example, pushes the behavior analysis results to the owner's smartphone in real time. For example, the behavior analysis unit can also notify the owner's smartphone of the behavior analysis results in real time by email. The behavior analysis unit can also notify the owner's smartphone of the behavior analysis results in real time by SMS. For example, the behavior analysis unit pushes the behavior analysis results to the owner's smartphone in real time. The behavior analysis unit can also notify the owner's smartphone of the behavior analysis results in real time by email. The behavior analysis unit can also notify the owner's smartphone of the behavior analysis results in real time by SMS. In this way, by notifying the owner of the behavior analysis results in real time, the owner can respond immediately. Some or all of the above-mentioned processing in the behavior analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior analysis unit can input the behavior analysis results to a generation AI and cause the generation AI to execute a real-time notification.

[0096] The lifestyle pattern learning unit can estimate the pet's emotions and adjust the lifestyle pattern learning method based on the estimated pet's emotions. For example, if the pet is excited, the lifestyle pattern learning unit can increase the frequency of behavioral data collection and identify detailed patterns. For example, if the pet is relaxed, the lifestyle pattern learning unit can decrease the frequency of behavioral data collection and identify long-term patterns. Furthermore, if the pet is anxious, the lifestyle pattern learning unit can adjust the frequency of behavioral data collection and identify abnormal patterns. For example, if the pet is excited, the lifestyle pattern learning unit can increase the frequency of behavioral data collection and identify detailed patterns. For example, if the pet is relaxed, the lifestyle pattern learning unit can decrease the frequency of behavioral data collection and identify long-term patterns. For example, if the pet is anxious, the lifestyle pattern learning unit can adjust the frequency of behavioral data collection and identify abnormal patterns. In this way, adjusting the lifestyle pattern learning method based on the pet's emotions improves the accuracy of learning. Some or all of the above-described processing in the lifestyle pattern learning unit may be performed using AI, for example, or without AI. For example, the lifestyle pattern learning unit can input emotional data of a pet into the generation AI and cause the generation AI to adjust the lifestyle pattern learning method.

[0097] During lifestyle pattern learning, the lifestyle pattern learning unit can collect data on the behavior and cries of a pet over a long period of time and identify detailed patterns. For example, the lifestyle pattern learning unit can collect data on the behavior of a pet for one year and identify seasonal patterns. For example, the lifestyle pattern learning unit can collect data on the cries of a pet for one year and identify patterns for each time period. The lifestyle pattern learning unit can also collect data on the behavior and cries of a pet for one year and identify comprehensive patterns. For example, the lifestyle pattern learning unit can collect data on the behavior of a pet for one year and identify seasonal patterns. The lifestyle pattern learning unit can also collect data on the cries of a pet for one year and identify patterns for each time period. The lifestyle pattern learning unit can also collect data on the behavior and cries of a pet for one year and identify comprehensive patterns. In this way, detailed lifestyle patterns can be identified by collecting data over a long period of time. Some or all of the above-described processing in the lifestyle pattern learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the lifestyle pattern learning unit can input pet behavior data into the generation AI and have the generation AI identify detailed patterns.

[0098] The lifestyle pattern learning unit can improve the accuracy of learning by also using data on the physical condition of the pet when learning the lifestyle pattern. The lifestyle pattern learning unit, for example, also uses data on the body temperature of the pet and learns by associating changes in body temperature with changes in the lifestyle pattern. For example, the lifestyle pattern learning unit can also use data on the heart rate of the pet and learn by associating changes in heart rate with changes in the lifestyle pattern. The lifestyle pattern learning unit can also use data on the activity level of the pet and learn by associating changes in activity level with changes in the lifestyle pattern. For example, the lifestyle pattern learning unit also uses data on the body temperature of the pet and learns by associating changes in body temperature with changes in the lifestyle pattern. The lifestyle pattern learning unit can also use data on the heart rate of the pet and learn by associating changes in heart rate with changes in the lifestyle pattern. The lifestyle pattern learning unit can also use data on the activity level of the pet and learn by associating changes in activity level with changes in the lifestyle pattern. By using data on the physical condition of the pet in combination, the accuracy of lifestyle pattern learning is improved. Some or all of the above-described processing in the lifestyle pattern learning unit may be performed, for example, using AI or without AI. For example, the lifestyle pattern learning unit can input data on a pet's physical condition into the generation AI and cause the generation AI to learn the lifestyle pattern.

[0099] The lifestyle pattern learning unit can compare the lifestyle pattern learning results with data of other pets to identify abnormal patterns. The lifestyle pattern learning unit, for example, compares the lifestyle pattern data with data of other pets to identify abnormal behavioral patterns. For example, the lifestyle pattern learning unit can compare the lifestyle pattern data with data of other pets to identify abnormal vocalization patterns. The lifestyle pattern learning unit can also compare the lifestyle pattern data with data of other pets to identify abnormal physical condition patterns. For example, the lifestyle pattern learning unit can compare the lifestyle pattern data with data of other pets to identify abnormal behavioral patterns. The lifestyle pattern learning unit can compare the lifestyle pattern data with data of other pets to identify abnormal vocalization patterns. The lifestyle pattern learning unit can also compare the lifestyle pattern data with data of other pets to identify abnormal physical condition patterns. In this way, abnormal lifestyle patterns can be identified by comparing with data of other pets. Some or all of the above-described processing in the lifestyle pattern learning unit may be performed using, or without, AI. For example, the lifestyle pattern learning unit can input the pet's lifestyle pattern data to a generation AI and cause the generation AI to identify abnormal patterns.

[0100] The lifestyle pattern learning unit can estimate the pet's emotions and adjust the display method of the lifestyle pattern learning results based on the estimated pet's emotions. For example, if the pet is excited, the lifestyle pattern learning unit can visually emphasize and display the learning results. For example, if the pet is relaxed, the lifestyle pattern learning unit can simply display the learning results. Furthermore, if the pet is anxious, the lifestyle pattern learning unit can display the learning results in detail. For example, if the pet is excited, the lifestyle pattern learning unit can visually emphasize and display the learning results. For example, if the pet is relaxed, the lifestyle pattern learning unit can simply display the learning results. For example, if the pet is anxious, the lifestyle pattern learning unit can display the learning results in detail. By adjusting the display method of the lifestyle pattern learning results based on the pet's emotions, a display that is easy for the owner to understand can be achieved. Some or all of the above-described processing in the lifestyle pattern learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the lifestyle pattern learning unit can input the pet's emotional data to the generation AI and cause the generation AI to adjust the display method of the learning results.

[0101] The lifestyle pattern learning unit can improve the accuracy of learning by also using pet environmental data when learning lifestyle patterns. The lifestyle pattern learning unit, for example, also uses pet room temperature data and learns by associating changes in room temperature with changes in lifestyle patterns. For example, the lifestyle pattern learning unit can also use pet humidity data and learn by associating changes in humidity with changes in lifestyle patterns. The lifestyle pattern learning unit can also also use pet illuminance data and learn by associating changes in illuminance with changes in lifestyle patterns. For example, the lifestyle pattern learning unit also uses pet room temperature data and learns by associating changes in room temperature with changes in lifestyle patterns. The lifestyle pattern learning unit can also use pet humidity data and learn by associating changes in humidity with changes in lifestyle patterns. The lifestyle pattern learning unit can also use pet illuminance data and learn by associating changes in illuminance with changes in lifestyle patterns. In this way, by also using pet environmental data, the accuracy of lifestyle pattern learning is improved. Some or all of the above-mentioned processing in the lifestyle pattern learning unit may be performed, for example, using AI or without AI. For example, the lifestyle pattern learning unit can input environmental data about a pet into the generation AI and cause the generation AI to learn the lifestyle pattern.

[0102] The lifestyle pattern learning unit can combine pet diet data to perform comprehensive learning during lifestyle pattern learning. For example, the lifestyle pattern learning unit combines pet diet data and learns by associating meal timing with changes in lifestyle patterns. For example, the lifestyle pattern learning unit can combine pet diet content data and learn by associating changes in diet content with changes in lifestyle patterns. The lifestyle pattern learning unit can also combine pet diet amount data and learn by associating changes in diet amount with changes in lifestyle patterns. For example, the lifestyle pattern learning unit combines pet diet data and learns by associating meal timing with changes in lifestyle patterns. The lifestyle pattern learning unit can also combine pet diet content data and learn by associating changes in diet content with changes in lifestyle patterns. The lifestyle pattern learning unit can also combine pet diet amount data and learn by associating changes in diet amount with changes in lifestyle patterns. In this way, by combining pet diet data, comprehensive lifestyle pattern learning is possible. Some or all of the above-described processing in the lifestyle pattern learning unit may be performed, for example, using AI or without AI. For example, the lifestyle pattern learning unit can input pet dietary data into the generation AI and have the generation AI perform comprehensive learning.

[0103] The lifestyle pattern learning unit can notify the owner's smartphone of the lifestyle pattern learning results in real time. The lifestyle pattern learning unit, for example, pushes the lifestyle pattern learning results to the owner's smartphone in real time. For example, the lifestyle pattern learning unit can notify the owner's smartphone of the lifestyle pattern learning results in real time by email. The lifestyle pattern learning unit can also notify the owner's smartphone of the lifestyle pattern learning results in real time by SMS. For example, the lifestyle pattern learning unit pushes the lifestyle pattern learning results to the owner's smartphone in real time. The lifestyle pattern learning unit can also notify the owner's smartphone of the lifestyle pattern learning results in real time by email. The lifestyle pattern learning unit can also notify the owner's smartphone of the lifestyle pattern learning results in real time by SMS. This allows the owner to respond immediately by notifying the owner of the lifestyle pattern learning results in real time. Some or all of the above-described processing in the lifestyle pattern learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the lifestyle pattern learning unit can input the lifestyle pattern learning results to a generation AI and cause the generation AI to execute a real-time notification.

[0104] The advice unit can estimate the pet's emotions and adjust the content of the advice based on the estimated pet's emotions. For example, if the pet is excited, the advice unit provides advice to eliminate lack of exercise. For example, if the pet is relaxed, the advice unit can provide advice to maintain relaxation. Furthermore, if the pet is feeling anxious, the advice unit can provide advice to reduce anxiety. For example, if the pet is excited, the advice unit provides advice to eliminate lack of exercise. If the pet is relaxed, the advice unit can provide advice to maintain relaxation. If the pet is feeling anxious, the advice unit can provide advice to reduce anxiety. In this way, by adjusting the content of the advice based on the pet's emotions, more appropriate advice can be provided. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input pet emotion data to the generation AI and cause the generation AI to adjust the content of the advice.

[0105] When providing advice, the advice unit can suggest specific countermeasures based on data on the pet's behavior and cries. For example, if the pet barks a lot, the advice unit can suggest a long walk to compensate for lack of exercise. For example, if the pet refuses to eat, the advice unit can suggest a change in the pet's diet. Furthermore, if the pet frequently sleeps, the advice unit can suggest a health check. For example, if the pet barks a lot, the advice unit can suggest a long walk to compensate for lack of exercise. If the pet refuses to eat, the advice unit can suggest a change in the pet's diet. If the pet frequently sleeps, the advice unit can suggest a health check. In this way, by suggesting specific countermeasures based on data on the pet's behavior and cries, the owner can take appropriate action. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the pet's behavior data into the generation AI and cause the generation AI to execute specific countermeasure suggestions.

[0106] When providing advice, the advice unit can propose optimal countermeasures by also using the pet's physical condition data. For example, if the pet's body temperature is high, the advice unit can propose a cooling method. For example, if the pet's heart rate is high, the advice unit can also propose a method of resting. Furthermore, if the pet's activity level is low, the advice unit can also propose a method of encouraging exercise. For example, if the pet's body temperature is high, the advice unit can propose a cooling method. If the pet's heart rate is high, the advice unit can also propose a method of encouraging exercise. If the pet's activity level is low, the advice unit can propose an optimal countermeasure by also using the pet's physical condition data. Some or all of the above-mentioned processing by the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the pet's physical condition data into the generation AI and cause the generation AI to execute the optimal countermeasure proposal.

[0107] The advice unit can compare the advice results with data of other pets and identify countermeasures for abnormal patterns. The advice unit, for example, compares the advice results with data of other pets and identify countermeasures for abnormal behavioral patterns. For example, the advice unit can compare the advice results with data of other pets and identify countermeasures for abnormal vocalization patterns. The advice unit can also compare the advice results with data of other pets and identify countermeasures for abnormal physical condition patterns. For example, the advice unit can compare the advice results with data of other pets and identify countermeasures for abnormal behavioral patterns. The advice unit can compare the advice results with data of other pets and identify countermeasures for abnormal vocalization patterns. The advice unit can also compare the advice results with data of other pets and identify countermeasures for abnormal physical condition patterns. In this way, by comparing with data of other pets, it is possible to identify countermeasures for abnormal patterns. Some or all of the above-described processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input data of other pets to the generation AI and cause the generation AI to identify countermeasures for abnormal patterns.

[0108] The advice unit can estimate the pet's emotions and adjust the way the advice is displayed based on the estimated pet's emotions. For example, if the pet is excited, the advice unit can visually emphasize the advice when it is displayed. For example, if the pet is relaxed, the advice unit can simply display the advice when it is displayed. Furthermore, if the pet is anxious, the advice unit can display detailed advice when it is displayed. For example, if the pet is excited, the advice unit can visually emphasize the advice when it is displayed. For example, if the pet is relaxed, the advice unit can simply display the advice when it is displayed. Furthermore, if the pet is anxious, the advice unit can display detailed advice when it is displayed. In this way, by adjusting the way the advice is displayed based on the pet's emotions, it is possible to provide a display that is easy for the owner to understand. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the pet's emotion data to the generation AI and cause the generation AI to adjust the way the advice is displayed.

[0109] When providing advice, the advice unit can propose an optimal countermeasure by also using the pet's environmental data. For example, if the pet's room temperature is high, the advice unit can propose a cooling method. For example, if the pet's humidity is high, the advice unit can also propose a humidity adjustment method. Furthermore, the advice unit can also propose a lighting adjustment method when the pet's illuminance is low. For example, if the pet's room temperature is high, the advice unit can propose a cooling method. If the pet's humidity is high, the advice unit can also propose a humidity adjustment method. If the pet's illuminance is low, the advice unit can also propose a lighting adjustment method. In this way, by also using the pet's environmental data, an optimal countermeasure is proposed. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the pet's environmental data into the generation AI and cause the generation AI to execute the proposal of the optimal countermeasure.

[0110] When providing advice, the advice unit can combine the pet's dietary data to propose a comprehensive response. The advice unit, for example, proposes a nutritionally balanced diet based on the pet's dietary content. For example, the advice unit can also propose an appropriate dietary amount based on the pet's dietary amount. The advice unit can also propose an appropriate dietary timing based on the pet's dietary timing. For example, the advice unit proposes a nutritionally balanced diet based on the pet's dietary content. The advice unit can also propose an appropriate dietary amount based on the pet's dietary amount. The advice unit can also propose an appropriate dietary timing based on the pet's dietary timing. In this way, a comprehensive response is proposed by combining the pet's dietary data. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the pet's dietary data into the generation AI and cause the generation AI to propose a comprehensive response.

[0111] The advice unit can notify the owner's smartphone of the advice result in real time. The advice unit, for example, pushes the advice result to the owner's smartphone in real time. For example, the advice unit can also notify the owner's smartphone of the advice result in real time by email. The advice unit can also notify the owner's smartphone of the advice result in real time by SMS. For example, the advice unit pushes the advice result to the owner's smartphone in real time. The advice unit can also notify the owner's smartphone of the advice result in real time by email. The advice unit can also notify the owner's smartphone of the advice result in real time by SMS. In this way, by notifying the advice result in real time, the owner can respond immediately. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input the advice result to a generation AI and cause the generation AI to execute a real-time notification. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned voice analysis unit, behavior analysis unit, lifestyle pattern learning unit, and advice unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the voice analysis unit is realized by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the behavior analysis unit is realized by the camera 42 or the processor 46 of the smart device 14, or the specific processing unit 290 of the data processing device 12. For example, the lifestyle pattern learning unit is realized by the specific processing unit 290 of the data processing device 12. For example, the advice unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned voice analysis unit, behavior analysis unit, lifestyle pattern learning unit, and advice unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the voice analysis unit is realized by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the behavior analysis unit is realized by the camera 42 or the processor 46 of the smart glasses 214, or the specific processing unit 290 of the data processing device 12. For example, the lifestyle pattern learning unit is realized by the specific processing unit 290 of the data processing device 12. For example, the advice unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-described voice analysis unit, behavior analysis unit, lifestyle pattern learning unit, and advice unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the voice analysis unit is realized by the processor 46 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the behavior analysis unit is realized by the camera 42 or processor 46 of the headset type terminal 314, or the specific processing unit 290 of the data processing device 12. For example, the lifestyle pattern learning unit is realized by the specific processing unit 290 of the data processing device 12. For example, the advice unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned voice analysis unit, behavior analysis unit, lifestyle pattern learning unit, and advice unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the voice analysis unit is realized by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the behavior analysis unit is realized by the camera 42 or processor 46 of the robot 414, or the specific processing unit 290 of the data processing device 12. For example, the lifestyle pattern learning unit is realized by the specific processing unit 290 of the data processing device 12. For example, the advice unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] The pet translation system can further include a health management unit that monitors the pet's health. The health management unit collects data such as the pet's body temperature, heart rate, and activity level, and detects abnormalities. For example, if the pet's body temperature is higher than normal, the health management unit can suggest cooling methods to the owner. Also, if the pet's heart rate is abnormally high, it can suggest ways to keep the pet calm. Furthermore, if the pet's activity level is decreasing, it can suggest ways to encourage exercise. This allows the pet's health to be monitored in real time, abnormalities to be detected early, and appropriate measures to be taken.

[0114] The pet translation system can further include a diet management unit that manages the pet's diet. The diet management unit records the pet's dietary content, amount, and timing, and manages nutritional balance. For example, if the pet is deficient in a particular nutrient, the diet management unit can suggest a diet containing that nutrient. Also, if the amount of food is inappropriate, it can suggest an appropriate amount of food. Furthermore, if the timing of meals is irregular, it can suggest an appropriate timing of meals. This enables appropriate diet management to maintain the pet's health.

[0115] The pet translation system can further include an exercise management unit that manages the pet's exercise. The exercise management unit records the amount, frequency, and duration of the pet's exercise and proposes an appropriate exercise plan. For example, if the pet is not getting enough exercise, the exercise management unit can suggest a long walk or playtime. If the pet is exercising too much, it can also suggest ways to encourage rest. Furthermore, if the exercise frequency is irregular, it can also suggest an appropriate exercise frequency. This enables appropriate exercise management to maintain the pet's health.

[0116] The pet translation system may further include a stress management unit that manages stress in pets. The stress management unit analyzes data on the pet's behavior and cries to evaluate the stress level. For example, if the pet barks frequently, the stress management unit can suggest ways to reduce stress. Also, if the pet refuses to eat, stress may be the cause, so the stress management unit can suggest ways to relax. Furthermore, if the pet sleeps frequently, stress may be the cause, so the stress management unit can suggest ways to reduce stress. In this way, the pet's stress can be managed and its health can be maintained.

[0117] The pet translation system may further include a sleep management unit that manages the pet's sleep. The sleep management unit records the pet's sleep time, sleep quality, and sleep patterns, and suggests an appropriate sleeping environment. For example, if the pet's sleep time is short, the sleep management unit can suggest a way to provide a quiet environment. Also, if the sleep quality is low, it can suggest a way to provide a comfortable bed. Furthermore, if the sleep pattern is irregular, it can suggest a regular sleep schedule. This enables appropriate sleep management to maintain the pet's health.

[0118] The pet translation system may further include a behavior prediction unit that estimates the pet's emotions and predicts the pet's behavior based on the estimated emotions. The behavior prediction unit analyzes the pet's emotional data and predicts the pet's next likely behavior. For example, if the pet is excited, the behavior prediction unit may predict that the pet wants to play and suggest play activities to the owner. If the pet is anxious, the behavior prediction unit may predict that the pet wants to hide and suggest ways to reassure the owner. If the pet is relaxed, the behavior prediction unit may predict that the pet will take a rest and suggest ways to provide the owner with a quiet environment. This makes it possible to predict behavior based on the pet's emotions and take appropriate measures.

[0119] The pet translation system may further include a health assessment unit that estimates the pet's emotions and assesses the pet's health condition based on the estimated emotions. The health assessment unit analyzes the pet's emotional data and assesses the pet's health condition. For example, if the pet frequently feels anxious, the health assessment unit may assess that stress is the cause and suggest stress reduction methods to the owner. If the pet frequently becomes excited, the health assessment unit may assess that lack of exercise is the cause and suggest methods to encourage the owner to exercise. If the pet frequently becomes relaxed, the health assessment unit may assess that the pet is in good health and suggest methods to maintain the current state to the owner. This allows the health condition to be assessed based on the pet's emotions and appropriate measures to be taken.

[0120] The pet translation system may further include a behavior reinforcement unit that estimates the pet's emotions and reinforces the pet's behavior based on the estimated emotions. The behavior reinforcement unit analyzes the pet's emotional data and suggests a method for reinforcing desirable behavior. For example, if the pet is relaxed, the behavior reinforcement unit can suggest a method for maintaining that state. Also, if the pet is excited, the behavior reinforcement unit can suggest a method for releasing the pet's energy. Furthermore, if the pet is anxious, the behavior reinforcement unit can suggest a method for reducing the pet's anxiety. In this way, behavior can be reinforced based on the pet's emotions, and desirable behavior can be promoted.

[0121] The pet translation system may further include a behavior modification unit that estimates the pet's emotions and modifies the pet's behavior based on the estimated emotions. The behavior modification unit analyzes the pet's emotional data and suggests a method for modifying undesirable behavior. For example, if the pet is excited, the behavior modification unit can suggest a method for releasing the pet's energy. If the pet is anxious, the behavior modification unit can suggest a method for reducing the pet's anxiety. Furthermore, if the pet is relaxed, the behavior modification unit can suggest a method for maintaining that state. In this way, the behavior of the pet can be modified based on the pet's emotions, and undesirable behavior can be reduced.

[0122] The pet translation system may further include a behavior prevention unit that estimates the pet's emotions and prevents the pet from engaging in behavior based on the estimated emotions. The behavior prevention unit analyzes the pet's emotional data and suggests methods for preventing problem behavior before it occurs. For example, if the pet is excited, the behavior prevention unit can suggest methods for releasing the pet's energy. If the pet is anxious, the behavior prevention unit can also suggest methods for reducing the pet's anxiety. Furthermore, if the pet is relaxed, the behavior prevention unit can also suggest methods for maintaining that state. This makes it possible to prevent problem behavior before it occurs based on the pet's emotions and maintain the pet's health and happiness.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The audio analysis unit analyzes the pet's cry or bark. Specifically, it analyzes the frequency spectrum of the pet's cry and detects changes in specific frequency bands. It can also analyze the change in the volume of the cry over time to identify sudden changes in volume. It can also analyze the duration of the cry to identify the difference between short and long cries. Step 2: The behavior analysis unit captures your pet's daily behavior via video or images and analyzes the data uploaded to the app. Specifically, it records the time when your pet's behavior occurs and identifies patterns that occur more frequently at certain times of the day. It can also record the location where the behavior occurs and identify patterns that occur more frequently under certain environmental conditions. It can also analyze the frequency of behavior and identify changes in frequency at certain times of day or under certain environmental conditions. Step 3: The lifestyle pattern learning unit learns the pet's lifestyle patterns based on the data obtained by the audio analysis unit and behavior analysis unit. Specifically, it collects data on the pet's behavior and cries over a long period of time and identifies seasonal and time-of-day patterns. It also comprehensively analyzes the behavior and cries data to identify the pet's lifestyle patterns. Step 4: The advice section proposes appropriate measures to the owner based on the information obtained by the lifestyle pattern learning section. Specifically, if the pet barks a lot, it suggests taking the owner on a long walk to get some exercise, if the pet refuses to eat, it suggests changing the pet's diet, and if the pet sleeps frequently, it can also suggest a health check.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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 AI 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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 AI 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.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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 AI 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.

[0176] 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.

[0177] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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, in order to avoid confusion and to 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.

[0195] 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.

[0196] [Explanation of symbols]

[0197] 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. an audio analysis unit that analyzes the pet's cry or bark; A behavior analysis unit that analyzes the daily behavior of pets; a life pattern learning unit that learns the life pattern of a pet based on the data obtained by the voice analysis unit and the behavior analysis unit; an advice unit that proposes appropriate countermeasures to the owner based on the information obtained by the life pattern learning unit; Equipped with A system characterized by:

2. The voice analysis unit Analyze your pet's meow and bark patterns to interpret their emotions and intentions 2. The system of claim 1.

3. The behavior analysis unit Capture your pet's daily behavior through video and / or images, and analyze the data uploaded to the app.

2. The system of claim 1.

4. The lifestyle pattern learning unit Collect data over time to learn about your pet's lifestyle patterns and mood changes 2. The system of claim 1.

5. The advice unit Providing appropriate advice to owners based on their pet's condition and behavior 2. The system of claim 1.

6. The voice analysis unit Estimate your pet's emotions and adjust how your pet's meows are analyzed based on the estimated emotions.

2. The system of claim 1.

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