System

Abstract

An object of a system according to an embodiment is to determine an emotion from a cry or a behavior of an animal and issue an easy-to-understand instruction to the animal.SOLUTION: A system includes a sound analysis unit, an emotion determination unit, an instruction generation unit, and a sound output unit. The bark analysis part analyzes the bark or behavior of the animal. The emotion determination section determines an emotion of the animal based on the data analyzed by the sound analysis section. The instruction generation unit generates an instruction to the animal based on the emotion determined by the emotion determination unit. The audio output component outputs the instruction generated by the instruction generator as audio that is easy for the animal to understand.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to accurately determine an animal's emotions from its cries and behavior and to provide appropriate instructions.

[0005] The system according to the embodiment aims to determine the emotions of animals from their cries and behaviors and to give them instructions that are easy to understand. [Means for solving the problem]

[0006] The system according to the embodiment includes a cry analysis unit, an emotion determination unit, an instruction generation unit, and an audio output unit. The cry analysis unit analyzes the animal's cry or behavior. The emotion determination unit determines the animal's emotion based on the data analyzed by the cry analysis unit. The instruction generation unit generates instructions for the animal based on the emotion determined by the emotion determination unit. The audio output unit outputs the instructions generated by the instruction generation unit in an audio that is easy for the animal to understand. [Effects of the Invention]

[0007] The system according to the embodiment can determine the emotions of animals from their cries and behaviors and give them instructions that are easy to understand. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The animal emotion determination system according to the embodiment of the present invention analyzes the sounds and behavior of animals, uses a generation AI to determine the emotions, and outputs instructions in a voice that is easy for animals to understand. This allows the animal emotion determination system to understand the emotions of animals and issue appropriate instructions.

[0029] An animal emotion determination system according to an embodiment includes a cry analysis unit, an emotion determination unit, an instruction generation unit, and an audio output unit. The cry analysis unit analyzes the cry or behavior of an animal. For example, the cry analysis unit analyzes the barking or tail wagging of a dog. The cry analysis unit can also analyze the purring of a cat. The cry analysis unit can also analyze the chirping of birds. The emotion determination unit determines the emotion of the animal based on the data analyzed by the cry analysis unit. For example, the emotion determination unit determines whether a dog is happy or alert. The emotion determination unit can also determine whether a cat is relaxed or nervous. The emotion determination unit can also determine whether a bird is excited or calm. The instruction generation unit generates an instruction for the animal based on the emotion determined by the emotion determination unit. For example, the instruction generation unit generates instructions such as "sit" or "wait." The instruction generation unit can also generate instructions such as "calm down" or "be good." The instruction generation unit can also generate instructions such as "Let's play" or "I'll give you a treat." The audio output unit outputs the instructions generated by the instruction generation unit in a voice that is easy for animals to understand. For example, when instructing a dog to "sit," the audio output unit generates a voice in a tone and rhythm that is easy for dogs to understand. When instructing a cat to "be a good boy," the audio output unit can also generate a voice in a tone and rhythm that is easy for cats to understand. When instructing a bird to "play," the audio output unit can also generate a voice in a tone and rhythm that is easy for birds to understand. This allows the animal emotion determination system according to the embodiment to understand the emotions of animals and issue appropriate instructions. For example, the output unit displays the instruction results to the owner via a web application or a mobile application. If the owner desires feedback on paper, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to the owner.

[0030] The cry analysis unit simultaneously collects physiological data such as the animal's body temperature or heart rate, making it possible to comprehensively determine emotions. For example, the cry analysis unit constructs a system that simultaneously monitors the animal's body temperature and heart rate when analyzing the animal's cries and behavior. For example, it analyzes the rise in body temperature and changes in heart rate when a dog barks to determine whether the dog is excited. The cry analysis unit can also analyze the body temperature and heart rate when a cat purrs to determine whether the cat is relaxed. The cry analysis unit can also analyze the body temperature and heart rate when a bird sings to determine whether the bird is excited. This allows for the collection of physiological data such as the animal's body temperature and heart rate to comprehensively determine emotions.

[0031] The bark analysis unit can collect data under different environmental conditions and determine emotions according to the environment. The bark analysis unit, for example, collects animal barks and behavior data under different environmental conditions to build a system that determines emotions according to the environment. For example, the bark analysis unit compares data when a dog barks indoors with data when it barks outdoors. The bark analysis unit can also compare data when a cat purrs during the day with data when it purrs at night. The bark analysis unit can also compare data when a bird sings on a sunny day with data when it sings on a rainy day. This makes it possible to collect data under different environmental conditions and determine emotions according to the environment.

[0032] The emotion determination unit can use the emotion estimation function to evaluate the stress level of an animal based on emotions estimated from the animal's cry or behavior, and make suggestions for stress reduction. The emotion determination unit, for example, analyzes animal cry and behavior data, and uses the emotion estimation function to build a system for evaluating stress levels. For example, the emotion determination unit analyzes the frequency and intensity of a dog's barking to determine its stress level. The emotion determination unit can also analyze the frequency and intensity of a cat's purring to determine its stress level. The emotion determination unit can also analyze the frequency and intensity of birds' chirping to determine its stress level. This makes it possible to evaluate the stress level of an animal and make suggestions for stress reduction.

[0033] The sound analysis unit can be applied to different types of animals and can handle a wide range of animal species. The sound analysis unit will develop a system that collects and analyzes the sounds and behavioral data of not only dogs and cats, but also birds and fish. For example, it will analyze the chirps of birds and the way fish swim to determine their emotions. The sound analysis unit can also collect and analyze behavioral data of reptiles. The sound analysis unit can also collect and analyze the sounds and behavioral data of mammals. This allows it to be applied to different types of animals and handle a wide range of animal species.

[0034] The animal cry analysis unit can notify the owner's smartphone app of the analysis results of the animal's cry or behavior in real time, allowing the owner to respond immediately. For example, the animal cry analysis unit will develop a system that notifies the owner's smartphone app of the analysis results of the animal's cry or behavior in real time. For example, when a dog barks, an alert is sent to the owner's smartphone. The animal cry analysis unit can also send a notification to the owner's smartphone when a cat purrs. The animal cry analysis unit can also send a notification to the owner's smartphone when a bird sings. This allows the owner to receive the analysis results of the animal's cry or behavior in real time, allowing the owner to respond immediately.

[0035] The emotion determination unit uses the emotion estimation function to monitor the health condition of an animal based on emotions estimated from the animal's cries or behavior, and can issue an alert if an abnormality is detected. The emotion determination unit, for example, analyzes animal cries and behavior data and uses the emotion estimation function to build a health condition monitoring system. For example, the emotion determination unit analyzes changes in a dog's barking to detect health abnormalities. The emotion determination unit can also analyze changes in a cat's purring to detect health abnormalities. The emotion determination unit can also analyze changes in bird songs to detect health abnormalities. In this way, the health condition of an animal can be monitored, and an alert can be issued if an abnormality is detected.

[0036] The emotion determination unit can learn the animal's past behavior history or cry data and reflect the emotion pattern of each individual in the model. The emotion determination unit, for example, collects the animal's past behavior history and cry data and builds a system that learns the emotion pattern of each individual. For example, the emotion determination unit trains the emotion determination model based on past barking data of a dog. The emotion determination unit can also train the emotion determination model based on past purring data of a cat. The emotion determination unit can also train the emotion determination model based on past chirping data of a bird. In this way, the animal's past behavior history and cry data can be learned and the emotion pattern of each individual can be reflected in the model.

[0037] The emotion determination unit can take into account the social relationships of animals when determining emotions and perform emotion determination based on social context. The emotion determination unit, for example, builds a system that takes into account relationships with other animals and humans when determining emotions of animals. For example, the emotion determination unit analyzes the barks of a dog when playing with other dogs to determine the emotion. The emotion determination unit can also analyze the purring sounds of a cat when playing with other cats to determine the emotion. The emotion determination unit can also analyze the barks of a bird when singing with other birds to determine the emotion. This makes it possible to take into account the social relationships of animals and perform emotion determination based on social context.

[0038] The emotion determination unit can use the emotion estimation function to suggest environmental settings that will help the animal to relax most based on the emotion determination result of the animal. The emotion determination unit, for example, builds a system that suggests environmental settings that will help the animal to relax most based on the emotion determination result of the animal. For example, music that will help dogs to relax can be automatically played. The emotion determination unit can also automatically adjust lighting that will help cats to relax. The emotion determination unit can also automatically play environmental sounds that will help birds to relax. In this way, environmental settings that will help the animal to relax most can be suggested.

[0039] The emotion determination unit reflects the emotion determination result in the animal's training program, and can provide the optimal training method for each individual animal. The emotion determination unit, for example, builds a system that provides the optimal training method for each individual animal based on the emotion determination result. For example, the emotion determination unit proposes a training program according to the emotional state of a dog. The emotion determination unit can also propose a training program according to the emotional state of a cat. The emotion determination unit can also propose a training program according to the emotional state of a bird. In this way, the emotion determination result can be reflected in the animal's training program, and the optimal training method for each individual animal can be provided.

[0040] The emotion determination unit can provide specific advice to the owner based on the emotion determination result to help improve the animal's living environment. The emotion determination unit, for example, builds a system that provides specific advice useful for improving the animal's living environment based on the emotion determination result. For example, the emotion determination unit makes suggestions for improving the living environment according to the emotional state of a dog. The emotion determination unit can also make suggestions for improving the living environment according to the emotional state of a cat. The emotion determination unit can also make suggestions for improving the living environment according to the emotional state of a bird. This makes it possible to provide specific advice useful for improving the animal's living environment.

[0041] The emotion determination unit can use the emotion estimation function to suggest the most enjoyable game or toy for the animal based on the result of the emotion determination of the animal. The emotion determination unit, for example, uses the emotion estimation function to build a system that suggests the most enjoyable game or toy based on the result of the emotion determination of the animal. For example, an appropriate game is suggested when a dog is excited. The emotion determination unit can also suggest a suitable toy when a cat is relaxed. The emotion determination unit can also suggest a suitable game when a bird is excited. In this way, it is possible to suggest the most enjoyable game or toy for the animal.

[0042] The instruction generation unit can learn past reaction data of animals and generate optimal instruction content for each individual animal. The instruction generation unit, for example, collects past reaction data of animals and builds a system that generates optimal instruction content for each individual animal. For example, optimal instructions are generated based on data of instructions that a dog has followed in the past. The instruction generation unit can also generate optimal instructions based on data of instructions that a cat has followed in the past. The instruction generation unit can also generate optimal instructions based on data of instructions that a bird has followed in the past. In this way, it is possible to learn past reaction data of animals and generate optimal instruction content for each individual animal.

[0043] The instruction generation unit can generate instructions according to the emotion, taking into account the current emotional state of the animal. The instruction generation unit, for example, builds a system that analyzes the current emotional state of the animal in real time and generates instructions according to the emotion. For example, if a dog is excited, it generates an instruction to calm the dog. The instruction generation unit can also generate an instruction to praise a cat if the cat is relaxed. The instruction generation unit can also generate an instruction to calm a bird if the bird is excited. In this way, it is possible to generate instructions according to the emotion, taking into account the current emotional state of the animal.

[0044] The instruction generation unit can use the emotion estimation function to select instruction content that will elicit the most positive response from the animal when generating instructions for the animal. The instruction generation unit, for example, uses the emotion estimation function to build a system that selects instruction content that will elicit the most positive response from the animal. For example, instruction content that will please a dog is generated. The instruction generation unit can also generate instruction content that will please a cat. The instruction generation unit can also generate instruction content that will please a bird. In this way, instruction content that will elicit the most positive response from the animal can be selected.

[0045] The instruction generation unit can generate instructions for the animal in different languages ​​or dialects to accommodate international pet owners. For example, the instruction generation unit can develop a system that generates instructions for the animal in different languages ​​or dialects to accommodate international pet owners. For example, instructions can be generated in English or French. The instruction generation unit can also generate instructions in Spanish or German. The instruction generation unit can also generate instructions in a Kansai dialect or a New York accent. This allows instructions for the animal to be generated in different languages ​​or dialects to accommodate international pet owners.

[0046] The instruction generation unit can generate instructions for the animal in a voice that imitates the owner's voice, making the animal feel more familiar. For example, the instruction generation unit develops a system that generates instructions for the animal in a voice that imitates the owner's voice. For example, the owner's voice is recorded and instructions are generated using that voice. The instruction generation unit can also imitate the owner's voice using voice synthesis technology and generate instructions using that voice. The instruction generation unit can also imitate the owner's voice using recording and playback technology and generate instructions using that voice. In this way, instructions for the animal can be generated in a voice that imitates the owner's voice, making the animal feel more familiar.

[0047] The instruction generation unit can use the emotion estimation function to generate audio in a tone or rhythm that is most relaxing for the animal when generating instructions for the animal. The instruction generation unit, for example, uses the emotion estimation function to build a system that generates audio in a tone or rhythm that is most relaxing for the animal. For example, instructions are generated in a tone that is relaxing for a dog. The instruction generation unit can also generate instructions in a tone that is relaxing for a cat. The instruction generation unit can also generate instructions in a tone that is relaxing for a bird. This makes it possible to generate audio in a tone or rhythm that is most relaxing for the animal.

[0048] The audio output unit can generate audio that takes into account the auditory characteristics of animals in order to generate audio that is easy for animals to understand. The audio output unit, for example, constructs an audio generation system that takes into account the auditory characteristics of animals and generates audio that is easy for animals to understand. For example, audio is generated in a frequency range that matches the auditory characteristics of dogs. The audio output unit can also generate audio at a volume that matches the auditory characteristics of cats. The audio output unit can also generate audio with a tone that matches the auditory characteristics of birds. This makes it possible to generate audio that takes into account the auditory characteristics of animals in order to generate audio that is easy for animals to understand.

[0049] The audio output unit can learn past reaction data of animals and generate optimal audio patterns for each individual animal in order to generate audio that is easy for animals to understand. The audio output unit, for example, collects past reaction data of animals and builds a system that generates optimal audio patterns for each individual animal. For example, the audio output unit generates optimal audio based on audio patterns to which dogs have responded in the past. The audio output unit can also generate optimal audio based on audio patterns to which cats have responded in the past. The audio output unit can also generate optimal audio based on audio patterns to which birds have responded in the past. In this way, it is possible to learn past reaction data of animals and generate optimal audio patterns for each individual animal in order to generate audio that is easy for animals to understand.

[0050] The audio output unit uses the emotion estimation function to consider the current emotional state of the animal and select an audio tone that corresponds to the emotion when generating audio that is easy for animals to understand. The audio output unit, for example, uses the emotion estimation function to analyze the current emotional state of the animal in real time and build a system that selects an audio tone that corresponds to the emotion. For example, if a dog is relaxed, the audio output unit generates an audio in a relaxing tone. The audio output unit can also generate an audio in a calming tone if a cat is excited. The audio output unit can also generate an audio in a relaxing tone if a bird is relaxed. This makes it possible to consider the current emotional state of the animal and select an audio tone that corresponds to the emotion when generating audio that is easy for animals to understand.

[0051] The audio output unit can customize sounds that are easy for animals to understand to accommodate different animal species. The audio output unit will develop a system that collects and analyzes the calls and behavioral data of not only dogs and cats, but also birds and fish. For example, it analyzes the chirps of birds and the swimming patterns of fish to determine emotions. The audio output unit can also collect and analyze behavioral data of reptiles. The audio output unit can also collect and analyze the calls and behavioral data of mammals. This makes it possible to customize sounds that are easy for animals to understand to accommodate different animal species.

[0052] The audio output unit generates audio that is easy for animals to understand by imitating the owner's voice, making the animal feel more familiar with it. For example, a system is developed in which the audio output unit generates instructions for the animal by imitating the owner's voice. For example, the owner's voice is recorded and instructions are generated using that voice. The audio output unit can also imitate the owner's voice using voice synthesis technology and generate instructions using that voice. The audio output unit can also imitate the owner's voice using recording and playback technology and generate instructions using that voice. In this way, instructions for the animal can be generated in a voice that imitates the owner's voice, making the animal feel more familiar with it.

[0053] The audio output unit can use the emotion estimation function to generate audio that is easy for animals to understand, using a tone or rhythm that is most relaxing for animals. The audio output unit, for example, uses the emotion estimation function to build a system that generates audio in a tone or rhythm that is most relaxing for animals. For example, instructions are generated in a tone that is relaxing for dogs. The audio output unit can also generate instructions in a tone that is relaxing for cats. The audio output unit can also generate instructions in a tone that is relaxing for birds. This makes it possible to generate audio in a tone or rhythm that is most relaxing for animals.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The animal emotion detection system can also monitor an animal's eating patterns and determine its emotions based on the timing and amount of food it eats. For example, if a dog eats its meal quickly, it may be feeling stressed. If a cat leaves its food uneaten, it may be a sign of poor health or stress. If a bird refuses to eat, it may be showing anxiety about a change in its environment. This makes it possible to monitor an animal's eating patterns and use them to determine its emotions.

[0056] The animal emotion detection system can also monitor an animal's sleep patterns and determine its emotions based on the quality and quantity of sleep. For example, if a dog wakes up frequently during the night, it may be feeling anxious or stressed. If a cat sleeps for a long time during the day, it may be that it is excessively active at night. If a bird sleeps during the day, it may be suggesting that it is not adapting well to changes in its environment. This makes it possible to monitor an animal's sleep patterns and use them to determine its emotions.

[0057] The animal emotion detection system can also monitor the amount of exercise an animal does and determine its emotions based on the frequency and intensity of its exercise. For example, if a dog exercises excessively, it may be experiencing stress or anxiety. If a cat avoids exercise, it may be a sign of poor health or stress. If a bird avoids flying, it may be indicating anxiety about changes in its environment. This makes it possible to monitor an animal's exercise volume and use it to determine its emotions.

[0058] The animal emotion determination system can also monitor the social interactions of animals and determine their emotions based on their relationships with other animals and humans. For example, it can analyze the behavior of a dog when playing with other dogs to determine its emotions. It can also analyze the behavior of a cat when playing with its owner to determine its emotions. It can also analyze the behavior of a bird when singing with other birds to determine its emotions. This makes it possible to monitor the social interactions of animals and use it to determine their emotions.

[0059] The animal emotion determination system can also monitor the health data of animals and determine their emotions based on their health condition. For example, it can analyze changes in a dog's weight and body temperature to determine its emotions. It can also analyze changes in a cat's coat condition and appetite to determine its emotions. It can also analyze changes in a bird's feather condition and appetite to determine its emotions. This makes it possible to monitor an animal's health data and use it to determine its emotions.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The sound analyzer analyzes animal sounds or behaviors, such as a dog's bark or tail wagging, a cat's purring, or a bird's chirp. Step 2: The emotion determination unit determines the animal's emotion based on the data analyzed by the sound analysis unit. For example, it determines whether a dog is happy or alert, a cat is relaxed or nervous, and a bird is excited or calm. Step 3: The instruction generator generates instructions for the animal based on the emotion determined by the emotion determiner, such as "sit," "wait," "calm down," "be good," "let's play," or "here's a treat." Step 4: The voice output unit outputs the instruction generated by the instruction generation unit in a voice that is easy for animals to understand. For example, when instructing a dog to "sit," a voice is generated in a tone and rhythm that is easy for dogs to understand, when instructing a cat to "be a good boy," a voice is generated in a tone and rhythm that is easy for cats to understand, and when instructing a bird to "let's play," a voice is generated in a tone and rhythm that is easy for birds to understand.

[0062] (Example 2) The animal emotion determination system according to the embodiment of the present invention analyzes the sounds and behavior of animals, uses a generation AI to determine the emotions, and outputs instructions in a voice that is easy for animals to understand. This allows the animal emotion determination system to understand the emotions of animals and issue appropriate instructions.

[0063] An animal emotion determination system according to an embodiment includes a cry analysis unit, an emotion determination unit, an instruction generation unit, and an audio output unit. The cry analysis unit analyzes the cry or behavior of an animal. For example, the cry analysis unit analyzes the barking or tail wagging of a dog. The cry analysis unit can also analyze the purring of a cat. The cry analysis unit can also analyze the chirping of birds. The emotion determination unit determines the emotion of the animal based on the data analyzed by the cry analysis unit. For example, the emotion determination unit determines whether a dog is happy or alert. The emotion determination unit can also determine whether a cat is relaxed or nervous. The emotion determination unit can also determine whether a bird is excited or calm. The instruction generation unit generates an instruction for the animal based on the emotion determined by the emotion determination unit. For example, the instruction generation unit generates instructions such as "sit" or "wait." The instruction generation unit can also generate instructions such as "calm down" or "be good." The instruction generation unit can also generate instructions such as "Let's play" or "I'll give you a treat." The audio output unit outputs the instructions generated by the instruction generation unit in a voice that is easy for animals to understand. For example, when instructing a dog to "sit," the audio output unit generates a voice in a tone and rhythm that is easy for dogs to understand. When instructing a cat to "be a good boy," the audio output unit can also generate a voice in a tone and rhythm that is easy for cats to understand. When instructing a bird to "play," the audio output unit can also generate a voice in a tone and rhythm that is easy for birds to understand. This allows the animal emotion determination system according to the embodiment to understand the emotions of animals and issue appropriate instructions. For example, the output unit displays the instruction results to the owner via a web application or a mobile application. If the owner desires feedback on paper, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to the owner.

[0064] The cry analysis unit simultaneously collects physiological data such as the animal's body temperature or heart rate, making it possible to comprehensively determine emotions. For example, the cry analysis unit constructs a system that simultaneously monitors the animal's body temperature and heart rate when analyzing the animal's cries and behavior. For example, it analyzes the rise in body temperature and changes in heart rate when a dog barks to determine whether the dog is excited. The cry analysis unit can also analyze the body temperature and heart rate when a cat purrs to determine whether the cat is relaxed. The cry analysis unit can also analyze the body temperature and heart rate when a bird sings to determine whether the bird is excited. This allows for the collection of physiological data such as the animal's body temperature and heart rate to comprehensively determine emotions.

[0065] The bark analysis unit can collect data under different environmental conditions and determine emotions according to the environment. The bark analysis unit, for example, collects animal barks and behavior data under different environmental conditions to build a system that determines emotions according to the environment. For example, the bark analysis unit compares data when a dog barks indoors with data when it barks outdoors. The bark analysis unit can also compare data when a cat purrs during the day with data when it purrs at night. The bark analysis unit can also compare data when a bird sings on a sunny day with data when it sings on a rainy day. This makes it possible to collect data under different environmental conditions and determine emotions according to the environment.

[0066] The emotion determination unit can use the emotion estimation function to evaluate the stress level of an animal based on emotions estimated from the animal's cry or behavior, and make suggestions for stress reduction. The emotion determination unit, for example, analyzes animal cry and behavior data, and uses the emotion estimation function to build a system for evaluating stress levels. For example, the emotion determination unit analyzes the frequency and intensity of a dog's barking to determine its stress level. The emotion determination unit can also analyze the frequency and intensity of a cat's purring to determine its stress level. The emotion determination unit can also analyze the frequency and intensity of birds' chirping to determine its stress level. This makes it possible to evaluate the stress level of an animal and make suggestions for stress reduction.

[0067] The sound analysis unit can be applied to different types of animals and can handle a wide range of animal species. The sound analysis unit will develop a system that collects and analyzes the sounds and behavioral data of not only dogs and cats, but also birds and fish. For example, it will analyze the chirps of birds and the way fish swim to determine their emotions. The sound analysis unit can also collect and analyze behavioral data of reptiles. The sound analysis unit can also collect and analyze the sounds and behavioral data of mammals. This allows it to be applied to different types of animals and handle a wide range of animal species.

[0068] The animal cry analysis unit can notify the owner's smartphone app of the analysis results of the animal's cry or behavior in real time, allowing the owner to respond immediately. For example, the animal cry analysis unit will develop a system that notifies the owner's smartphone app of the analysis results of the animal's cry or behavior in real time. For example, when a dog barks, an alert is sent to the owner's smartphone. The animal cry analysis unit can also send a notification to the owner's smartphone when a cat purrs. The animal cry analysis unit can also send a notification to the owner's smartphone when a bird sings. This allows the owner to receive the analysis results of the animal's cry or behavior in real time, allowing the owner to respond immediately.

[0069] The emotion determination unit uses the emotion estimation function to monitor the health condition of an animal based on emotions estimated from the animal's cries or behavior, and can issue an alert if an abnormality is detected. The emotion determination unit, for example, analyzes animal cries and behavior data and uses the emotion estimation function to build a health condition monitoring system. For example, the emotion determination unit analyzes changes in a dog's barking to detect health abnormalities. The emotion determination unit can also analyze changes in a cat's purring to detect health abnormalities. The emotion determination unit can also analyze changes in bird songs to detect health abnormalities. In this way, the health condition of an animal can be monitored, and an alert can be issued if an abnormality is detected.

[0070] The emotion determination unit can learn the animal's past behavior history or cry data and reflect the emotion pattern of each individual in the model. The emotion determination unit, for example, collects the animal's past behavior history and cry data and builds a system that learns the emotion pattern of each individual. For example, the emotion determination unit trains the emotion determination model based on past barking data of a dog. The emotion determination unit can also train the emotion determination model based on past purring data of a cat. The emotion determination unit can also train the emotion determination model based on past chirping data of a bird. In this way, the animal's past behavior history and cry data can be learned and the emotion pattern of each individual can be reflected in the model.

[0071] The emotion determination unit can take into account the social relationships of animals when determining emotions and perform emotion determination based on social context. The emotion determination unit, for example, builds a system that takes into account relationships with other animals and humans when determining emotions of animals. For example, the emotion determination unit analyzes the barks of a dog when playing with other dogs to determine the emotion. The emotion determination unit can also analyze the purring sounds of a cat when playing with other cats to determine the emotion. The emotion determination unit can also analyze the barks of a bird when singing with other birds to determine the emotion. This makes it possible to take into account the social relationships of animals and perform emotion determination based on social context.

[0072] The emotion determination unit can use the emotion estimation function to suggest environmental settings that will help the animal to relax most based on the emotion determination result of the animal. The emotion determination unit, for example, builds a system that suggests environmental settings that will help the animal to relax most based on the emotion determination result of the animal. For example, music that will help dogs to relax can be automatically played. The emotion determination unit can also automatically adjust lighting that will help cats to relax. The emotion determination unit can also automatically play environmental sounds that will help birds to relax. In this way, environmental settings that will help the animal to relax most can be suggested.

[0073] The emotion determination unit reflects the emotion determination result in the animal's training program, and can provide the optimal training method for each individual animal. The emotion determination unit, for example, builds a system that provides the optimal training method for each individual animal based on the emotion determination result. For example, the emotion determination unit proposes a training program according to the emotional state of a dog. The emotion determination unit can also propose a training program according to the emotional state of a cat. The emotion determination unit can also propose a training program according to the emotional state of a bird. In this way, the emotion determination result can be reflected in the animal's training program, and the optimal training method for each individual animal can be provided.

[0074] The emotion determination unit can provide specific advice to the owner based on the emotion determination result to help improve the animal's living environment. The emotion determination unit, for example, builds a system that provides specific advice useful for improving the animal's living environment based on the emotion determination result. For example, the emotion determination unit makes suggestions for improving the living environment according to the emotional state of a dog. The emotion determination unit can also make suggestions for improving the living environment according to the emotional state of a cat. The emotion determination unit can also make suggestions for improving the living environment according to the emotional state of a bird. This makes it possible to provide specific advice useful for improving the animal's living environment.

[0075] The emotion determination unit can use the emotion estimation function to suggest the most enjoyable game or toy for the animal based on the result of the emotion determination of the animal. The emotion determination unit, for example, uses the emotion estimation function to build a system that suggests the most enjoyable game or toy based on the result of the emotion determination of the animal. For example, an appropriate game is suggested when a dog is excited. The emotion determination unit can also suggest a suitable toy when a cat is relaxed. The emotion determination unit can also suggest a suitable game when a bird is excited. In this way, it is possible to suggest the most enjoyable game or toy for the animal.

[0076] The instruction generation unit can learn past reaction data of animals and generate optimal instruction content for each individual animal. The instruction generation unit, for example, collects past reaction data of animals and builds a system that generates optimal instruction content for each individual animal. For example, optimal instructions are generated based on data of instructions that a dog has followed in the past. The instruction generation unit can also generate optimal instructions based on data of instructions that a cat has followed in the past. The instruction generation unit can also generate optimal instructions based on data of instructions that a bird has followed in the past. In this way, it is possible to learn past reaction data of animals and generate optimal instruction content for each individual animal.

[0077] The instruction generation unit can generate instructions according to the emotion, taking into account the current emotional state of the animal. The instruction generation unit, for example, builds a system that analyzes the current emotional state of the animal in real time and generates instructions according to the emotion. For example, if a dog is excited, it generates an instruction to calm the dog. The instruction generation unit can also generate an instruction to praise a cat if the cat is relaxed. The instruction generation unit can also generate an instruction to calm a bird if the bird is excited. In this way, it is possible to generate instructions according to the emotion, taking into account the current emotional state of the animal.

[0078] The instruction generation unit can use the emotion estimation function to select instruction content that will elicit the most positive response from the animal when generating instructions for the animal. The instruction generation unit, for example, uses the emotion estimation function to build a system that selects instruction content that will elicit the most positive response from the animal. For example, instruction content that will please a dog is generated. The instruction generation unit can also generate instruction content that will please a cat. The instruction generation unit can also generate instruction content that will please a bird. In this way, instruction content that will elicit the most positive response from the animal can be selected.

[0079] The instruction generation unit can generate instructions for the animal in different languages ​​or dialects to accommodate international pet owners. For example, the instruction generation unit can develop a system that generates instructions for the animal in different languages ​​or dialects to accommodate international pet owners. For example, instructions can be generated in English or French. The instruction generation unit can also generate instructions in Spanish or German. The instruction generation unit can also generate instructions in a Kansai dialect or a New York accent. This allows instructions for the animal to be generated in different languages ​​or dialects to accommodate international pet owners.

[0080] The instruction generation unit can generate instructions for the animal in a voice that imitates the owner's voice, making the animal feel more familiar. For example, the instruction generation unit develops a system that generates instructions for the animal in a voice that imitates the owner's voice. For example, the owner's voice is recorded and instructions are generated using that voice. The instruction generation unit can also imitate the owner's voice using voice synthesis technology and generate instructions using that voice. The instruction generation unit can also imitate the owner's voice using recording and playback technology and generate instructions using that voice. In this way, instructions for the animal can be generated in a voice that imitates the owner's voice, making the animal feel more familiar.

[0081] The instruction generation unit can use the emotion estimation function to generate audio in a tone or rhythm that is most relaxing for the animal when generating instructions for the animal. The instruction generation unit, for example, uses the emotion estimation function to build a system that generates audio in a tone or rhythm that is most relaxing for the animal. For example, instructions are generated in a tone that is relaxing for a dog. The instruction generation unit can also generate instructions in a tone that is relaxing for a cat. The instruction generation unit can also generate instructions in a tone that is relaxing for a bird. This makes it possible to generate audio in a tone or rhythm that is most relaxing for the animal.

[0082] The audio output unit can generate audio that takes into account the auditory characteristics of animals in order to generate audio that is easy for animals to understand. The audio output unit, for example, constructs an audio generation system that takes into account the auditory characteristics of animals and generates audio that is easy for animals to understand. For example, audio is generated in a frequency range that matches the auditory characteristics of dogs. The audio output unit can also generate audio at a volume that matches the auditory characteristics of cats. The audio output unit can also generate audio with a tone that matches the auditory characteristics of birds. This makes it possible to generate audio that takes into account the auditory characteristics of animals in order to generate audio that is easy for animals to understand.

[0083] The audio output unit can learn past reaction data of animals and generate optimal audio patterns for each individual animal in order to generate audio that is easy for animals to understand. The audio output unit, for example, collects past reaction data of animals and builds a system that generates optimal audio patterns for each individual animal. For example, the audio output unit generates optimal audio based on audio patterns to which dogs have responded in the past. The audio output unit can also generate optimal audio based on audio patterns to which cats have responded in the past. The audio output unit can also generate optimal audio based on audio patterns to which birds have responded in the past. In this way, it is possible to learn past reaction data of animals and generate optimal audio patterns for each individual animal in order to generate audio that is easy for animals to understand.

[0084] The audio output unit uses the emotion estimation function to consider the current emotional state of the animal and select an audio tone that corresponds to the emotion when generating audio that is easy for animals to understand. The audio output unit, for example, uses the emotion estimation function to analyze the current emotional state of the animal in real time and build a system that selects an audio tone that corresponds to the emotion. For example, if a dog is relaxed, the audio output unit generates an audio in a relaxing tone. The audio output unit can also generate an audio in a calming tone if a cat is excited. The audio output unit can also generate an audio in a relaxing tone if a bird is relaxed. This makes it possible to consider the current emotional state of the animal and select an audio tone that corresponds to the emotion when generating audio that is easy for animals to understand.

[0085] The audio output unit can customize sounds that are easy for animals to understand to accommodate different animal species. The audio output unit will develop a system that collects and analyzes the calls and behavioral data of not only dogs and cats, but also birds and fish. For example, it analyzes the chirps of birds and the swimming patterns of fish to determine emotions. The audio output unit can also collect and analyze behavioral data of reptiles. The audio output unit can also collect and analyze the calls and behavioral data of mammals. This makes it possible to customize sounds that are easy for animals to understand to accommodate different animal species.

[0086] The audio output unit generates audio that is easy for animals to understand by imitating the owner's voice, making the animal feel more familiar with it. For example, a system is developed in which the audio output unit generates instructions for the animal by imitating the owner's voice. For example, the owner's voice is recorded and instructions are generated using that voice. The audio output unit can also imitate the owner's voice using voice synthesis technology and generate instructions using that voice. The audio output unit can also imitate the owner's voice using recording and playback technology and generate instructions using that voice. In this way, instructions for the animal can be generated in a voice that imitates the owner's voice, making the animal feel more familiar with it.

[0087] The audio output unit can use the emotion estimation function to generate audio that is easy for animals to understand, using a tone or rhythm that is most relaxing for animals. The audio output unit, for example, uses the emotion estimation function to build a system that generates audio in a tone or rhythm that is most relaxing for animals. For example, instructions are generated in a tone that is relaxing for dogs. The audio output unit can also generate instructions in a tone that is relaxing for cats. The audio output unit can also generate instructions in a tone that is relaxing for birds. This makes it possible to generate audio in a tone or rhythm that is most relaxing for animals.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The animal emotion detection system can also monitor an animal's eating patterns and determine its emotions based on the timing and amount of food it eats. For example, if a dog eats its meal quickly, it may be feeling stressed. If a cat leaves its food uneaten, it may be a sign of poor health or stress. If a bird refuses to eat, it may be showing anxiety about a change in its environment. This makes it possible to monitor an animal's eating patterns and use them to determine its emotions.

[0090] The animal emotion detection system can also monitor an animal's sleep patterns and determine its emotions based on the quality and quantity of sleep. For example, if a dog wakes up frequently during the night, it may be feeling anxious or stressed. If a cat sleeps for a long time during the day, it may be that it is excessively active at night. If a bird sleeps during the day, it may be suggesting that it is not adapting well to changes in its environment. This makes it possible to monitor an animal's sleep patterns and use them to determine its emotions.

[0091] The animal emotion detection system can also monitor the amount of exercise an animal does and determine its emotions based on the frequency and intensity of its exercise. For example, if a dog exercises excessively, it may be experiencing stress or anxiety. If a cat avoids exercise, it may be a sign of poor health or stress. If a bird avoids flying, it may be indicating anxiety about changes in its environment. This makes it possible to monitor an animal's exercise volume and use it to determine its emotions.

[0092] The animal emotion determination system can also monitor the social interactions of animals and determine their emotions based on their relationships with other animals and humans. For example, it can analyze the behavior of a dog when playing with other dogs to determine its emotions. It can also analyze the behavior of a cat when playing with its owner to determine its emotions. It can also analyze the behavior of a bird when singing with other birds to determine its emotions. This makes it possible to monitor the social interactions of animals and use it to determine their emotions.

[0093] The animal emotion determination system can also monitor the health data of animals and determine their emotions based on their health condition. For example, it can analyze changes in a dog's weight and body temperature to determine its emotions. It can also analyze changes in a cat's coat condition and appetite to determine its emotions. It can also analyze changes in a bird's feather condition and appetite to determine its emotions. This makes it possible to monitor an animal's health data and use it to determine its emotions.

[0094] The emotion determination unit can use the emotion estimation function to suggest music that is most relaxing for animals based on the emotions of the animals. For example, classical music that is relaxing for dogs can be automatically played. The emotion determination unit can also automatically play environmental sounds that are relaxing for cats. The emotion determination unit can also automatically play natural sounds that are relaxing for birds. In this way, it is possible to suggest music that is most relaxing for animals.

[0095] The emotion determination unit can use the emotion estimation function to suggest a game that the animal would enjoy most based on the emotion of the animal. For example, it can suggest a game that is suitable for a dog that is excited. The emotion determination unit can also suggest a toy that is suitable for a cat that is relaxed. The emotion determination unit can also suggest a game that is suitable for a bird that is excited. In this way, it is possible to suggest a game that the animal would enjoy most.

[0096] The emotion determination unit can use the emotion estimation function to suggest environmental settings that will help animals to relax most based on the emotions of the animals. For example, the emotion determination unit can automatically adjust lighting to help dogs relax. The emotion determination unit can also automatically adjust temperature to help cats relax. The emotion determination unit can also automatically adjust humidity to help birds relax. In this way, it is possible to suggest environmental settings that will help animals to relax most.

[0097] The emotion determination unit can use the emotion estimation function to evaluate the stress level of an animal based on the animal's emotions and make suggestions for stress reduction. For example, the emotion determination unit can analyze the frequency and intensity of a dog's barking to determine its stress level. The emotion determination unit can also analyze the frequency and intensity of a cat's purring to determine its stress level. The emotion determination unit can also analyze the frequency and intensity of a bird's chirping to determine its stress level. This makes it possible to evaluate the stress level of an animal and make suggestions for stress reduction.

[0098] The emotion determination unit uses the emotion estimation function to monitor the health condition of an animal based on the animal's emotion, and can issue an alert if an abnormality is detected. For example, it can analyze changes in a dog's barking to detect an abnormality in health. The emotion determination unit can also analyze changes in a cat's purring to detect an abnormality in health. The emotion determination unit can also analyze changes in a bird's singing to detect an abnormality in health. This makes it possible to monitor the health condition of an animal, and to issue an alert if an abnormality is detected.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The sound analyzer analyzes animal sounds or behaviors, such as a dog's bark or tail wagging, a cat's purring, or a bird's chirp. Step 2: The emotion determination unit determines the animal's emotion based on the data analyzed by the sound analysis unit. For example, it determines whether a dog is happy or alert, a cat is relaxed or nervous, and a bird is excited or calm. Step 3: The instruction generator generates instructions for the animal based on the emotion determined by the emotion determiner, such as "sit," "wait," "calm down," "be good," "let's play," or "here's a treat." Step 4: The voice output unit outputs the instruction generated by the instruction generation unit in a voice that is easy for animals to understand. For example, when instructing a dog to "sit," a voice is generated in a tone and rhythm that is easy for dogs to understand, when instructing a cat to "be a good boy," a voice is generated in a tone and rhythm that is easy for cats to understand, and when instructing a bird to "let's play," a voice is generated in a tone and rhythm that is easy for birds to understand.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] 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.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] 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.

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

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0146] 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.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0167] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a call analysis unit that analyzes the animal's call or behavior; an emotion determination unit that determines the emotion of the animal based on the data analyzed by the cry analysis unit; an instruction generation unit that generates an instruction for the animal based on the emotion determined by the emotion determination unit; a voice output unit that outputs the instruction generated by the instruction generation unit in a voice that is easy for animals to understand; A system characterized by:

2. The bird cry analysis unit Physiological data such as the animal's body temperature or heart rate are also collected at the same time to determine its overall emotions.

2. The system of claim 1.

3. The bird cry analysis unit Applies to different types of animals and is compatible with a wide range of animal species 2. The system of claim 1.

4. The emotion determination unit Learn from the animal's past behavioral history or vocalization data, and reflect each individual's emotional patterns in the model 2. The system of claim 1.

5. The instruction generation unit Learns from the animal's past response data and generates optimal instructions for each individual 2. The system of claim 1.

6. The audio output unit To generate sounds that are easy for animals to understand, we take into account the auditory characteristics of animals.

2. The system of claim 1.

7. The emotion determination unit Emotion estimation function assesses the stress level of animals based on emotions inferred from their sounds or behaviors, and provides suggestions for stress reduction.

2. The system of claim 1.

8. The emotion determination unit Using emotion estimation function, the system suggests environmental settings that will help the animal to feel most relaxed based on the results of the animal's emotion determination.

2. The system of claim 1.

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