System

Abstract

An object of the system according to the embodiment is to understand an action or a request of a pet and automatically cope with the pet based on the action or the request.SOLUTION: A system according to an embodiment includes an action understanding unit, an IoT control unit, and an ordering unit. The action understanding unit understands actions and requests of the pet. The IoT control unit controls the IoT home appliance based on the behavior and the request of the pet understood by the behavior understanding unit. The ordering part detects the shortage of the feed for the pet and automatically orders the feed.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 technologies have had the problem of making it difficult to properly understand a pet's behavior and needs and automatically respond based on them.

[0005] The system according to the embodiment aims to understand the behavior and needs of pets and automatically respond accordingly. [Means for solving the problem]

[0006] The system according to the embodiment includes a behavior understanding unit, an IoT control unit, and an ordering unit. The behavior understanding unit understands the behavior and requests of pets. The IoT control unit controls IoT home appliances based on the behavior and requests of pets understood by the behavior understanding unit. The ordering unit detects a shortage of pet food and automatically orders food. [Effects of the Invention]

[0007] The system according to the embodiment is able to understand the behavior and needs of pets and automatically respond accordingly. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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 pet self-AI system according to an embodiment of the present invention is a system that understands pet behaviors and requests through natural language processing and supports pet life in cooperation with IoT home appliances. As a result, the pet self-AI system can understand pet behaviors and requests, control IoT home appliances, and automatically order food when it is low, thereby supporting the comfortable life of pets.

[0029] The pet self-AI system according to the embodiment includes a behavior understanding unit, an IoT control unit, and an ordering unit. The behavior understanding unit understands the behavior and needs of a pet. For example, the behavior understanding unit uses sensors and cameras to analyze the pet's movements and facial expressions to determine the pet's state. The behavior understanding unit can also use voice recognition technology to analyze the pet's cries and understand the pet's needs. For example, if the pet appears sleepy, the behavior understanding unit analyzes the pet's behavior and determines that the pet is sleepy. The IoT control unit controls IoT home appliances based on the pet's behavior and needs understood by the behavior understanding unit. For example, if the IoT control unit determines that the pet is sleepy, it sends a signal to a smart light to turn off the lights. If the IoT control unit determines that the pet is hungry, it can also send a signal to a feeder to provide food. For example, after the pet finishes eating, the IoT control unit checks the remaining amount of food in the feeder, and if there is insufficient food, it automatically places an order with an online store. The ordering unit detects a shortage of pet food and automatically orders more food. For example, the ordering unit automatically places an order with an online store if there is a shortage of food based on the remaining amount of food in the feeder. As a result, the pet self-AI system according to the embodiment can understand the behavior and needs of pets, control IoT home appliances, and automatically order food when there is a shortage, thereby supporting the comfortable life of pets.

[0030] The behavior understanding unit can analyze a pet's body temperature and heart rate to monitor its health. For example, the behavior understanding unit measures the pet's body temperature with a sensor and inputs the data into the generation AI. The generation AI analyzes body temperature fluctuations and monitors the pet's health. For example, a high body temperature may indicate a fever and notify the owner. The behavior understanding unit also measures the pet's heart rate with a sensor and inputs the data into the generation AI. The generation AI analyzes heart rate fluctuations and monitors the pet's health. For example, an abnormally high heart rate may indicate stress and notify the owner. The behavior understanding unit also combines body temperature and heart rate data to comprehensively monitor the pet's health. For example, it can obtain a detailed understanding of the pet's health based on fluctuations in body temperature and heart rate. This makes it possible to manage a pet's health by analyzing its body temperature and heart rate and monitoring its health.

[0031] The behavior understanding unit collects pet behavior data over a long period of time and learns and predicts behavioral patterns. For example, the behavior understanding unit collects pet behavior data using sensors or cameras and inputs the data into the generation AI. The generation AI analyzes the long-term data and learns the pet's behavioral patterns. For example, it understands the pet's habit of eating at specific times. The behavior understanding unit also collects pet behavior data and the generation AI learns the behavioral patterns, allowing it to predict pet behavior. For example, it predicts that the pet will ask for a walk at a specific time. The behavior understanding unit also develops an algorithm for the generation AI to learn behavioral patterns based on the pet behavior data and predict the pet's behavior. For example, it analyzes the pet's behavior data and learns the behavioral patterns to predict the pet's behavior. In this way, by collecting pet behavior data over a long period of time and learning and predicting the behavioral patterns, the pet's behavior can be understood more accurately.

[0032] The behavior understanding unit can also analyze the behavior and tone of voice of the pet owner, taking into account the relationship between the pet and owner. For example, the behavior understanding unit collects the owner's behavior data and inputs it into the generation AI. The generation AI compares the owner's behavior with the pet's behavior and analyzes the relationship. For example, it learns the pattern of a pet becoming excited when the owner returns home. The behavior understanding unit also records the tone of the owner's voice and inputs the audio data into the generation AI. The generation AI performs audio analysis to estimate the owner's emotions. For example, it can determine from the tone of voice that the owner is relaxed. The behavior understanding unit also combines the owner's behavior data and tone of voice data, allowing the generation AI to comprehensively analyze the relationship between the pet and owner. For example, it can grasp the relationship between the pet and owner in detail based on the owner's behavior and tone of voice. This allows the system to analyze the pet owner's behavior and tone of voice and take the relationship between the pet and owner into consideration, enabling more appropriate responses.

[0033] The behavior understanding unit compares a pet's behavioral data with other pets and can detect abnormal behavior early. The behavior understanding unit, for example, collects behavioral data from multiple pets and inputs it into the generation AI. The generation AI compares the behavioral patterns of each pet and detects abnormal behavior. For example, if a specific pet behaves differently from other pets, it will determine this as abnormal. The behavior understanding unit also collects pet behavioral data and develops an algorithm for the generation AI to detect abnormal behavior. For example, it detects abnormal behavior based on deviations in behavioral patterns. The behavior understanding unit also compares a pet's behavioral data with other pets and the generation AI detects abnormal behavior early. For example, it detects abnormal behavior early based on a definition of abnormal behavior. In this way, by comparing a pet's behavioral data with other pets and detecting abnormal behavior early, it is possible to detect health problems in pets early.

[0034] The IoT control unit can automatically adjust the environment of the entire home according to the behavior and requests of your pet. For example, the IoT control unit's generating AI automatically adjusts the temperature in the home based on your pet's behavior data. For example, if your pet feels hot, it will automatically adjust the air conditioner to maintain a comfortable temperature. The IoT control unit's generating AI also automatically adjusts the humidity in the home based on your pet's behavior data. For example, if your pet feels dry, it will automatically adjust the humidifier to maintain a comfortable humidity level. The IoT control unit's generating AI also automatically adjusts the lighting in the home based on your pet's behavior data. For example, if your pet feels sleepy, it will automatically adjust the lighting to maintain a comfortable brightness. This allows the entire home environment to be automatically adjusted according to your pet's behavior and requests, providing a comfortable living environment for your pet.

[0035] The IoT control unit can play music and videos that match the pet's preferences based on the pet's behavioral data. For example, the IoT control unit analyzes the pet's behavioral data, and the generation AI plays music that matches the pet's preferences. For example, when the pet is relaxed, it selects music that has a relaxing effect. The IoT control unit also analyzes the pet's behavioral data, and the generation AI plays videos that match the pet's preferences. For example, when the pet is excited, it selects videos that have a relaxing effect. The IoT control unit also develops an algorithm that enables the generation AI to play music and videos that match the pet's preferences based on the pet's behavioral data. For example, it analyzes the pet's behavioral data and selects music and videos that match the preferences. In this way, playing music and videos that match the pet's preferences based on the pet's behavioral data can reduce the pet's stress and help it relax.

[0036] The IoT control unit controls the smart door according to the pet's behavior and requests, allowing the pet to enter and exit freely. For example, the IoT control unit uses a generating AI to control the smart door based on the pet's behavior data. For example, if the pet wants to go outside, the door will automatically open. The IoT control unit also develops an algorithm for the generating AI to control the smart door based on the pet's behavior data. For example, it analyzes the pet's behavior data and controls the door opening and closing. The IoT control unit also uses a generating AI to control the smart door based on the pet's behavior data, allowing the pet to enter and exit freely. For example, if the pet wants to go back inside, the door will automatically open. This allows the smart door to be controlled according to the pet's behavior and requests, allowing the pet to enter and exit freely.

[0037] The IoT control unit can manage the amount of exercise a pet receives and control automatic toys to encourage appropriate exercise. For example, the IoT control unit collects exercise data from the pet, and the generation AI controls the automatic toy. For example, if the pet is not getting enough exercise, the automatic toy is activated to encourage exercise. The IoT control unit also collects the pet's exercise data, and the generation AI develops an algorithm to control the automatic toy. For example, the IoT control unit analyzes the pet's exercise data and manages the amount of exercise. The IoT control unit also controls the automatic toy based on the pet's exercise data, and encourages appropriate exercise. For example, if the pet is not getting enough exercise, the automatic toy is activated to encourage exercise. In this way, the pet's health is maintained by managing the amount of exercise a pet receives and controlling the automatic toy to encourage appropriate exercise.

[0038] The ordering unit can analyze the pet's dietary history and suggest the type and amount of food that is optimal for the pet's health condition. For example, the ordering unit collects the pet's dietary history, and the generation AI analyzes that data. For example, if the pet prefers a particular food, the order unit suggests the type and amount of that food. The ordering unit also analyzes the pet's dietary history and develops an algorithm for the generation AI to suggest the type and amount of food that is optimal for the pet's health condition. For example, the order unit analyzes the pet's dietary history and suggests the type and amount of food based on the pet's health condition. The order unit also uses the pet's dietary history to suggest the type and amount of food that is optimal for the pet's health condition. For example, if the pet needs a particular nutrient, the order unit suggests food that contains that nutrient. In this way, the pet's health is maintained by analyzing the pet's dietary history and suggesting the type and amount of food that is optimal for the pet's health condition.

[0039] The order unit can monitor the pet's weight and activity level and automatically adjust the amount of food. For example, the order unit may periodically measure the pet's weight and input that data into the generation AI. The generation AI may analyze fluctuations in weight and automatically adjust the amount of food. For example, if the pet has gained too much weight, it may reduce the amount of food. The order unit may also measure the pet's activity level with a sensor and input that data into the generation AI. The generation AI may analyze fluctuations in activity level and automatically adjust the amount of food. For example, if the pet is not very active, it may reduce the amount of food. The order unit may also combine the weight and activity level data and have the generation AI automatically adjust the amount of food. For example, it may fine-tune the amount of food based on fluctuations in weight and activity level. In this way, the pet's health can be maintained by monitoring the pet's weight and activity level and automatically adjusting the amount of food.

[0040] The ordering unit can automatically order new food samples tailored to the pet's preferences based on the pet's eating history. For example, the ordering unit analyzes the pet's eating history, and the generation AI automatically orders new food samples tailored to the pet's preferences. For example, if the pet likes a particular flavor, it orders new food with that flavor. The ordering unit also analyzes the pet's eating history, and develops an algorithm for the generation AI to order new food samples. For example, it analyzes the pet's eating history and selects food samples tailored to the pet's preferences. The ordering unit also has the generation AI automatically order new food samples based on the pet's eating history. For example, if the pet likes a particular nutrient, it orders new food containing that nutrient. In this way, by automatically ordering new food samples tailored to the pet's preferences based on the pet's eating history, the variety of the pet's diet is increased and satisfaction is improved.

[0041] The ordering unit can monitor the pet's health condition and automatically order any necessary supplements. For example, the ordering unit collects the pet's health data, and the generation AI analyzes that data. For example, if the pet's coat is in poor condition, the order unit orders supplements to improve that coat. The ordering unit also analyzes the pet's health data and develops an algorithm for the generation AI to order supplements. For example, the order unit analyzes the pet's health data and selects the necessary supplements. The order unit also uses the generation AI to automatically order supplements based on the pet's health data. For example, if the pet needs a specific nutrient, the order unit orders supplements containing that nutrient. In this way, the pet's health condition is monitored and the necessary supplements are automatically ordered, thereby maintaining the pet's health.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The behavior understanding unit can learn the pet's preferences and habits based on the pet's behavioral data, and provide an environment in which the pet can be most comfortable. For example, if the pet often rests in a particular place, a comfortable cushion can be placed in that place. The behavior understanding unit can also provide toys that the pet prefers based on the pet's behavioral data. For example, if the pet often plays with a particular type of toy, that toy can be automatically provided. The behavior understanding unit can also adjust the pet's preferred meal times based on the pet's behavioral data. For example, if the pet prefers to eat at a particular time, food can be provided at that time. In this way, an environment in which the pet can be most comfortable can be provided based on the pet's behavioral data.

[0044] The behavior understanding unit can evaluate the sociability of the pet based on the behavioral data of the pet and make suggestions to promote interaction with other pets and people. For example, if the pet likes to play with other pets, the behavior understanding unit can suggest events to promote interaction between the pets. The behavior understanding unit can also make suggestions to increase the time the pet spends with a specific person based on the behavioral data of the pet. For example, if the pet likes to spend time with a specific family member, the behavior understanding unit can suggest increasing that time. The behavior understanding unit can also support the pet in adapting to a new environment based on the behavioral data of the pet. For example, when the pet moves to a new place, the behavior understanding unit can support the pet in getting used to the place. In this way, the sociability of the pet can be evaluated based on the behavioral data of the pet and make suggestions to promote interaction with other pets and people.

[0045] The behavior understanding unit can predict the health condition of a pet based on the pet's behavioral data and make suggestions for health management. For example, if a pet frequently performs a certain behavior, it can evaluate the possibility that this behavior may affect its health and suggest countermeasures. The behavior understanding unit can also evaluate the balance of a pet's diet and exercise based on the pet's behavioral data and make suggestions for living a healthy lifestyle. For example, if a pet is not getting enough exercise, it can make suggestions for increasing exercise. The behavior understanding unit can also support weight management of a pet based on the pet's behavioral data. For example, if a pet is gaining weight, it can suggest adjusting the amount of food it eats. In this way, it is possible to predict the health condition of a pet based on the pet's behavioral data and make suggestions for health management.

[0046] The behavior understanding unit can learn the pet's preferences and habits based on the pet's behavioral data, and provide an environment in which the pet can be most comfortable. For example, if the pet often rests in a particular place, a comfortable cushion can be placed in that place. The behavior understanding unit can also provide toys that the pet prefers based on the pet's behavioral data. For example, if the pet often plays with a particular type of toy, that toy can be automatically provided. The behavior understanding unit can also adjust the pet's preferred meal times based on the pet's behavioral data. For example, if the pet prefers to eat at a particular time, food can be provided at that time. In this way, an environment in which the pet can be most comfortable can be provided based on the pet's behavioral data.

[0047] The behavior understanding unit can evaluate the sociability of the pet based on the behavioral data of the pet and make suggestions to promote interaction with other pets and people. For example, if the pet likes to play with other pets, the behavior understanding unit can suggest events to promote interaction between the pets. The behavior understanding unit can also make suggestions to increase the time the pet spends with a specific person based on the behavioral data of the pet. For example, if the pet likes to spend time with a specific family member, the behavior understanding unit can suggest increasing that time. The behavior understanding unit can also support the pet in adapting to a new environment based on the behavioral data of the pet. For example, when the pet moves to a new place, the behavior understanding unit can support the pet in getting used to the place. In this way, the sociability of the pet can be evaluated based on the behavioral data of the pet and make suggestions to promote interaction with other pets and people.

[0048] The behavior understanding unit can predict the health condition of a pet based on the pet's behavioral data and make suggestions for health management. For example, if a pet frequently performs a certain behavior, it can evaluate the possibility that this behavior may affect its health and suggest countermeasures. The behavior understanding unit can also evaluate the balance of a pet's diet and exercise based on the pet's behavioral data and make suggestions for living a healthy lifestyle. For example, if a pet is not getting enough exercise, it can make suggestions for increasing exercise. The behavior understanding unit can also support weight management of a pet based on the pet's behavioral data. For example, if a pet is gaining weight, it can suggest adjusting the amount of food it eats. In this way, it is possible to predict the health condition of a pet based on the pet's behavioral data and make suggestions for health management.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The behavior understanding unit understands the pet's behavior and requests. For example, the behavior understanding unit uses sensors and cameras to analyze the pet's movements and facial expressions to determine the pet's state. It can also use voice recognition technology to analyze the pet's cries and understand its requests. For example, if the pet appears sleepy, it analyzes its behavior and determines that the pet is sleepy. Step 2: The IoT control unit controls IoT home appliances based on the pet's behavior and requests understood by the behavior understanding unit. For example, if it determines that the pet is sleepy, it sends a signal to the smart light to turn off the lights. Also, if it determines that the pet is hungry, it can send a signal to the feeder to provide food. Furthermore, after the pet has finished eating, it checks the amount of food remaining in the feeder and automatically places an order from the online store if there is not enough food. Step 3: The ordering unit detects when pet food is running low and automatically orders more. For example, if there is a shortage of food, it automatically places an order with the online store based on the remaining amount of food in the feeder.

[0051] (Example 2) The pet self-AI system according to an embodiment of the present invention is a system that understands pet behaviors and requests through natural language processing and supports pet life in cooperation with IoT home appliances. As a result, the pet self-AI system can understand pet behaviors and requests, control IoT home appliances, and automatically order food when it is low, thereby supporting the comfortable life of pets.

[0052] The pet self-AI system according to the embodiment includes a behavior understanding unit, an IoT control unit, and an ordering unit. The behavior understanding unit understands the behavior and needs of a pet. For example, the behavior understanding unit uses sensors and cameras to analyze the pet's movements and facial expressions to determine the pet's state. The behavior understanding unit can also use voice recognition technology to analyze the pet's cries and understand the pet's needs. For example, if the pet appears sleepy, the behavior understanding unit analyzes the pet's behavior and determines that the pet is sleepy. The IoT control unit controls IoT home appliances based on the pet's behavior and needs understood by the behavior understanding unit. For example, if the IoT control unit determines that the pet is sleepy, it sends a signal to a smart light to turn off the lights. If the IoT control unit determines that the pet is hungry, it can also send a signal to a feeder to provide food. For example, after the pet finishes eating, the IoT control unit checks the remaining amount of food in the feeder, and if there is insufficient food, it automatically places an order with an online store. The ordering unit detects a shortage of pet food and automatically orders more food. For example, the ordering unit automatically places an order with an online store if there is a shortage of food based on the remaining amount of food in the feeder. As a result, the pet self-AI system according to the embodiment can understand the behavior and needs of pets, control IoT home appliances, and automatically order food when there is a shortage, thereby supporting the comfortable life of pets.

[0053] The behavior understanding unit can analyze a pet's body temperature and heart rate to monitor its health. For example, the behavior understanding unit measures the pet's body temperature with a sensor and inputs the data into the generation AI. The generation AI analyzes body temperature fluctuations and monitors the pet's health. For example, a high body temperature may indicate a fever and notify the owner. The behavior understanding unit also measures the pet's heart rate with a sensor and inputs the data into the generation AI. The generation AI analyzes heart rate fluctuations and monitors the pet's health. For example, an abnormally high heart rate may indicate stress and notify the owner. The behavior understanding unit also combines body temperature and heart rate data to comprehensively monitor the pet's health. For example, it can obtain a detailed understanding of the pet's health based on fluctuations in body temperature and heart rate. This makes it possible to manage a pet's health by analyzing its body temperature and heart rate and monitoring its health.

[0054] The behavior understanding unit collects pet behavior data over a long period of time and learns and predicts behavioral patterns. For example, the behavior understanding unit collects pet behavior data using sensors or cameras and inputs the data into the generation AI. The generation AI analyzes the long-term data and learns the pet's behavioral patterns. For example, it understands the pet's habit of eating at specific times. The behavior understanding unit also collects pet behavior data and the generation AI learns the behavioral patterns, allowing it to predict pet behavior. For example, it predicts that the pet will ask for a walk at a specific time. The behavior understanding unit also develops an algorithm for the generation AI to learn behavioral patterns based on the pet behavior data and predict the pet's behavior. For example, it analyzes the pet's behavior data and learns the behavioral patterns to predict the pet's behavior. In this way, by collecting pet behavior data over a long period of time and learning and predicting the behavioral patterns, the pet's behavior can be understood more accurately.

[0055] The behavior understanding unit can infer emotions from a pet's cries and facial expressions and respond appropriately based on those emotions. For example, the behavior understanding unit records the pet's cries and inputs the audio data into the generation AI. The generation AI performs audio analysis to infer the pet's emotions. For example, it determines that the pet is feeling anxious based on the tone and pattern of the cries. The behavior understanding unit also captures the pet's facial expressions with a camera and inputs the image data into the generation AI. The generation AI performs image analysis to infer the pet's emotions. For example, it determines that the pet is happy based on changes in facial expression. The behavior understanding unit also combines the cries and facial expression data, allowing the generation AI to comprehensively infer the pet's emotions. For example, it grasps the pet's emotions in detail based on changes in cries and facial expression. This makes it possible to infer emotions from the pet's cries and facial expressions and respond appropriately based on those emotions, thereby responding according to the pet's emotions.

[0056] The behavior understanding unit can also analyze the behavior and tone of voice of the pet owner, taking into account the relationship between the pet and owner. For example, the behavior understanding unit collects the owner's behavior data and inputs it into the generation AI. The generation AI compares the owner's behavior with the pet's behavior and analyzes the relationship. For example, it learns the pattern of a pet becoming excited when the owner returns home. The behavior understanding unit also records the tone of the owner's voice and inputs the audio data into the generation AI. The generation AI performs audio analysis to estimate the owner's emotions. For example, it can determine from the tone of voice that the owner is relaxed. The behavior understanding unit also combines the owner's behavior data and tone of voice data, allowing the generation AI to comprehensively analyze the relationship between the pet and owner. For example, it can grasp the relationship between the pet and owner in detail based on the owner's behavior and tone of voice. This allows the system to analyze the pet owner's behavior and tone of voice and take the relationship between the pet and owner into consideration, enabling more appropriate responses.

[0057] The behavior understanding unit compares a pet's behavioral data with other pets and can detect abnormal behavior early. The behavior understanding unit, for example, collects behavioral data from multiple pets and inputs it into the generation AI. The generation AI compares the behavioral patterns of each pet and detects abnormal behavior. For example, if a specific pet behaves differently from other pets, it will determine this as abnormal. The behavior understanding unit also collects pet behavioral data and develops an algorithm for the generation AI to detect abnormal behavior. For example, it detects abnormal behavior based on deviations in behavioral patterns. The behavior understanding unit also compares a pet's behavioral data with other pets and the generation AI detects abnormal behavior early. For example, it detects abnormal behavior early based on a definition of abnormal behavior. In this way, by comparing a pet's behavioral data with other pets and detecting abnormal behavior early, it is possible to detect health problems in pets early.

[0058] The behavior understanding unit analyzes the owner's emotional response to the pet's behavior and can suggest how the owner should respond to the pet. For example, to analyze the owner's emotional response, the behavior understanding unit collects the owner's facial expressions and tone of voice and inputs them into the generation AI. The generation AI infers the owner's emotions and suggests appropriate responses to the pet. For example, if the owner is feeling stressed, it suggests ways to help the pet relax. The behavior understanding unit also analyzes the owner's emotional response and the generation AI suggests to the owner how to respond to the pet. For example, it suggests specific actions the owner should take toward the pet. The behavior understanding unit also analyzes the owner's emotional response and develops an algorithm for the generation AI to suggest to the owner how to respond to the pet. For example, it analyzes the owner's emotions and suggests appropriate responses to the pet. This allows the generation AI to analyze the owner's emotional response to the pet's behavior and suggest how the owner should respond to the pet, thereby maintaining a better relationship between owner and pet.

[0059] The IoT control unit can automatically adjust the environment of the entire home according to the behavior and requests of your pet. For example, the IoT control unit's generating AI automatically adjusts the temperature in the home based on your pet's behavior data. For example, if your pet feels hot, it will automatically adjust the air conditioner to maintain a comfortable temperature. The IoT control unit's generating AI also automatically adjusts the humidity in the home based on your pet's behavior data. For example, if your pet feels dry, it will automatically adjust the humidifier to maintain a comfortable humidity level. The IoT control unit's generating AI also automatically adjusts the lighting in the home based on your pet's behavior data. For example, if your pet feels sleepy, it will automatically adjust the lighting to maintain a comfortable brightness. This allows the entire home environment to be automatically adjusted according to your pet's behavior and requests, providing a comfortable living environment for your pet.

[0060] The IoT control unit can play music and videos that match the pet's preferences based on the pet's behavioral data. For example, the IoT control unit analyzes the pet's behavioral data, and the generation AI plays music that matches the pet's preferences. For example, when the pet is relaxed, it selects music that has a relaxing effect. The IoT control unit also analyzes the pet's behavioral data, and the generation AI plays videos that match the pet's preferences. For example, when the pet is excited, it selects videos that have a relaxing effect. The IoT control unit also develops an algorithm that enables the generation AI to play music and videos that match the pet's preferences based on the pet's behavioral data. For example, it analyzes the pet's behavioral data and selects music and videos that match the preferences. In this way, playing music and videos that match the pet's preferences based on the pet's behavioral data can reduce the pet's stress and help it relax.

[0061] The IoT control unit can emit a relaxing scent from the diffuser according to the pet's emotional state. For example, the IoT control unit analyzes the pet's emotional state, and the generation AI emits a relaxing scent from the diffuser. For example, if the pet is feeling stressed, it selects a lavender scent. The IoT control unit also analyzes the pet's emotional state and develops an algorithm for the generation AI to select a relaxing scent. For example, it analyzes the pet's emotional state and selects a relaxing scent. The IoT control unit also analyzes the pet's emotional state and selects a relaxing scent. The IoT control unit also analyzes the pet's emotional state, and the generation AI emits a relaxing scent from the diffuser according to the pet's emotional state. For example, if the pet is feeling anxious, it selects a chamomile scent. In this way, by emitting a relaxing scent according to the pet's emotional state, the pet's stress can be reduced and it can be relaxed.

[0062] The IoT control unit controls the smart door according to the pet's behavior and requests, allowing the pet to enter and exit freely. For example, the IoT control unit uses a generating AI to control the smart door based on the pet's behavior data. For example, if the pet wants to go outside, the door will automatically open. The IoT control unit also develops an algorithm for the generating AI to control the smart door based on the pet's behavior data. For example, it analyzes the pet's behavior data and controls the door opening and closing. The IoT control unit also uses a generating AI to control the smart door based on the pet's behavior data, allowing the pet to enter and exit freely. For example, if the pet wants to go back inside, the door will automatically open. This allows the smart door to be controlled according to the pet's behavior and requests, allowing the pet to enter and exit freely.

[0063] The IoT control unit can manage the amount of exercise a pet receives and control automatic toys to encourage appropriate exercise. For example, the IoT control unit collects exercise data from the pet, and the generation AI controls the automatic toy. For example, if the pet is not getting enough exercise, the automatic toy is activated to encourage exercise. The IoT control unit also collects the pet's exercise data, and the generation AI develops an algorithm to control the automatic toy. For example, the IoT control unit analyzes the pet's exercise data and manages the amount of exercise. The IoT control unit also controls the automatic toy based on the pet's exercise data, and encourages appropriate exercise. For example, if the pet is not getting enough exercise, the automatic toy is activated to encourage exercise. In this way, the pet's health is maintained by managing the amount of exercise a pet receives and controlling the automatic toy to encourage appropriate exercise.

[0064] The IoT control unit can reassure the pet by playing the owner's voice from the smart speaker depending on the pet's emotional state. For example, the IoT control unit analyzes the pet's emotional state, and the generation AI plays the owner's voice from the smart speaker. For example, if the pet is feeling anxious, the owner's voice will reassure the pet. The IoT control unit also develops an algorithm to analyze the pet's emotional state and the generation AI plays the owner's voice. For example, the IoT control unit analyzes the pet's emotional state and plays the owner's voice. The IoT control unit also develops an algorithm to analyze the pet's emotional state and the generation AI plays the owner's voice from the smart speaker based on the pet's emotional state. For example, if the pet is feeling lonely, the owner's voice will reassure the pet. This allows the pet to be reassured by playing the owner's voice from the smart speaker depending on the pet's emotional state.

[0065] The ordering unit can analyze the pet's dietary history and suggest the type and amount of food that is optimal for the pet's health condition. For example, the ordering unit collects the pet's dietary history, and the generation AI analyzes that data. For example, if the pet prefers a particular food, the order unit suggests the type and amount of that food. The ordering unit also analyzes the pet's dietary history and develops an algorithm for the generation AI to suggest the type and amount of food that is optimal for the pet's health condition. For example, the order unit analyzes the pet's dietary history and suggests the type and amount of food based on the pet's health condition. The order unit also uses the pet's dietary history to suggest the type and amount of food that is optimal for the pet's health condition. For example, if the pet needs a particular nutrient, the order unit suggests food that contains that nutrient. In this way, the pet's health is maintained by analyzing the pet's dietary history and suggesting the type and amount of food that is optimal for the pet's health condition.

[0066] The order unit can monitor the pet's weight and activity level and automatically adjust the amount of food. For example, the order unit may periodically measure the pet's weight and input that data into the generation AI. The generation AI may analyze fluctuations in weight and automatically adjust the amount of food. For example, if the pet has gained too much weight, it may reduce the amount of food. The order unit may also measure the pet's activity level with a sensor and input that data into the generation AI. The generation AI may analyze fluctuations in activity level and automatically adjust the amount of food. For example, if the pet is not very active, it may reduce the amount of food. The order unit may also combine the weight and activity level data and have the generation AI automatically adjust the amount of food. For example, it may fine-tune the amount of food based on fluctuations in weight and activity level. In this way, the pet's health can be maintained by monitoring the pet's weight and activity level and automatically adjusting the amount of food.

[0067] The ordering unit can analyze how a pet feels about a particular food and select food based on that emotion. The ordering unit, for example, analyzes the pet's emotional state, and the generation AI infers its emotion toward a particular food. For example, if a pet is happy when eating a particular food, that food is preferentially selected. The ordering unit also analyzes the pet's emotional state and develops an algorithm for the generation AI to select food. For example, the order unit analyzes the pet's emotional state and selects food based on the emotion. The ordering unit also allows the generation AI to select food based on the pet's emotional state. For example, if a pet dislikes a particular food, that food is avoided. In this way, the pet's satisfaction is improved by analyzing how a pet feels about a particular food and selecting food based on that emotion.

[0068] The ordering unit can automatically order new food samples tailored to the pet's preferences based on the pet's eating history. For example, the ordering unit analyzes the pet's eating history, and the generation AI automatically orders new food samples tailored to the pet's preferences. For example, if the pet likes a particular flavor, it orders new food with that flavor. The ordering unit also analyzes the pet's eating history, and develops an algorithm for the generation AI to order new food samples. For example, it analyzes the pet's eating history and selects food samples tailored to the pet's preferences. The ordering unit also has the generation AI automatically order new food samples based on the pet's eating history. For example, if the pet likes a particular nutrient, it orders new food containing that nutrient. In this way, by automatically ordering new food samples tailored to the pet's preferences based on the pet's eating history, the variety of the pet's diet is increased and satisfaction is improved.

[0069] The ordering unit can monitor the pet's health condition and automatically order any necessary supplements. For example, the ordering unit collects the pet's health data, and the generation AI analyzes that data. For example, if the pet's coat is in poor condition, the order unit orders supplements to improve that coat. The ordering unit also analyzes the pet's health data and develops an algorithm for the generation AI to order supplements. For example, the order unit analyzes the pet's health data and selects the necessary supplements. The order unit also uses the generation AI to automatically order supplements based on the pet's health data. For example, if the pet needs a specific nutrient, the order unit orders supplements containing that nutrient. In this way, the pet's health condition is monitored and the necessary supplements are automatically ordered, thereby maintaining the pet's health.

[0070] The ordering unit can analyze the emotional response of a pet when it eats food and suggest a food combination that will please the pet the most. The ordering unit, for example, analyzes the emotional state of a pet and the generation AI estimates the emotional response when it eats food. For example, if a pet is happy when eating a particular food, it will preferentially suggest that food. The ordering unit also analyzes the emotional state of a pet and develops an algorithm for the generation AI to suggest food combinations. For example, it analyzes the emotional state of a pet and suggests food combinations based on the emotion. The ordering unit also allows the generation AI to suggest food combinations based on the emotional state of a pet. For example, if a pet dislikes a particular food, it will avoid that food. In this way, the emotional response of a pet when it eats food is analyzed and the food combination that the pet will most enjoy is suggested, thereby improving pet satisfaction.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The behavior understanding unit can learn the pet's preferences and habits based on the pet's behavioral data, and provide an environment in which the pet can be most comfortable. For example, if the pet often rests in a particular place, a comfortable cushion can be placed in that place. The behavior understanding unit can also provide toys that the pet prefers based on the pet's behavioral data. For example, if the pet often plays with a particular type of toy, that toy can be automatically provided. The behavior understanding unit can also adjust the pet's preferred meal times based on the pet's behavioral data. For example, if the pet prefers to eat at a particular time, food can be provided at that time. In this way, an environment in which the pet can be most comfortable can be provided based on the pet's behavioral data.

[0073] The behavior understanding unit can monitor the stress level of a pet based on the pet's behavior data and suggest measures to reduce stress. For example, if a pet feels stressed in a particular situation, it can suggest ways to avoid that situation. The behavior understanding unit can also provide an environment in which the pet can relax based on the pet's behavior data. For example, if a pet feels relaxed when listening to particular music, it can play that music. The behavior understanding unit can also identify the causes of stress in a pet based on the pet's behavior data and suggest measures to remove those causes. For example, if a pet feels stressed in a particular place, it can suggest ways to avoid that place. In this way, it is possible to monitor the stress level of a pet based on the pet's behavior data and suggest measures to reduce stress.

[0074] The behavior understanding unit can evaluate the sociability of the pet based on the behavioral data of the pet and make suggestions to promote interaction with other pets and people. For example, if the pet likes to play with other pets, the behavior understanding unit can suggest events to promote interaction between the pets. The behavior understanding unit can also make suggestions to increase the time the pet spends with a specific person based on the behavioral data of the pet. For example, if the pet likes to spend time with a specific family member, the behavior understanding unit can suggest increasing that time. The behavior understanding unit can also support the pet in adapting to a new environment based on the behavioral data of the pet. For example, when the pet moves to a new place, the behavior understanding unit can support the pet in getting used to the place. In this way, the sociability of the pet can be evaluated based on the behavioral data of the pet and make suggestions to promote interaction with other pets and people.

[0075] The behavior understanding unit can predict the health condition of a pet based on the pet's behavioral data and make suggestions for health management. For example, if a pet frequently performs a certain behavior, it can evaluate the possibility that this behavior may affect its health and suggest countermeasures. The behavior understanding unit can also evaluate the balance of a pet's diet and exercise based on the pet's behavioral data and make suggestions for living a healthy lifestyle. For example, if a pet is not getting enough exercise, it can make suggestions for increasing exercise. The behavior understanding unit can also support weight management of a pet based on the pet's behavioral data. For example, if a pet is gaining weight, it can suggest adjusting the amount of food it eats. In this way, it is possible to predict the health condition of a pet based on the pet's behavioral data and make suggestions for health management.

[0076] The behavior understanding unit can estimate the emotional state of the pet based on the pet's behavior data and predict the pet's behavior based on that emotion. For example, if the pet is feeling anxious, it predicts what kind of behavior the anxiety will lead to. The behavior understanding unit can also develop an algorithm for estimating the emotional state of the pet based on the pet's behavior data and predicting the pet's behavior based on that emotion. For example, if the pet is happy, it predicts what kind of behavior that joy will lead to. The behavior understanding unit can also estimate the emotional state of the pet based on the pet's behavior data and predict the pet's behavior based on that emotion, thereby more accurately understanding the pet's behavior. This makes it possible to estimate the emotional state of the pet based on the pet's behavior data and predict the pet's behavior based on that emotion.

[0077] The behavior understanding unit can learn the pet's preferences and habits based on the pet's behavioral data, and provide an environment in which the pet can be most comfortable. For example, if the pet often rests in a particular place, a comfortable cushion can be placed in that place. The behavior understanding unit can also provide toys that the pet prefers based on the pet's behavioral data. For example, if the pet often plays with a particular type of toy, that toy can be automatically provided. The behavior understanding unit can also adjust the pet's preferred meal times based on the pet's behavioral data. For example, if the pet prefers to eat at a particular time, food can be provided at that time. In this way, an environment in which the pet can be most comfortable can be provided based on the pet's behavioral data.

[0078] The behavior understanding unit can monitor the stress level of a pet based on the pet's behavior data and suggest measures to reduce stress. For example, if a pet feels stressed in a particular situation, it can suggest ways to avoid that situation. The behavior understanding unit can also provide an environment in which the pet can relax based on the pet's behavior data. For example, if a pet feels relaxed when listening to particular music, it can play that music. The behavior understanding unit can also identify the causes of stress in a pet based on the pet's behavior data and suggest measures to remove those causes. For example, if a pet feels stressed in a particular place, it can suggest ways to avoid that place. In this way, it is possible to monitor the stress level of a pet based on the pet's behavior data and suggest measures to reduce stress.

[0079] The behavior understanding unit can evaluate the sociability of the pet based on the behavioral data of the pet and make suggestions to promote interaction with other pets and people. For example, if the pet likes to play with other pets, the behavior understanding unit can suggest events to promote interaction between the pets. The behavior understanding unit can also make suggestions to increase the time the pet spends with a specific person based on the behavioral data of the pet. For example, if the pet likes to spend time with a specific family member, the behavior understanding unit can suggest increasing that time. The behavior understanding unit can also support the pet in adapting to a new environment based on the behavioral data of the pet. For example, when the pet moves to a new place, the behavior understanding unit can support the pet in getting used to the place. In this way, the sociability of the pet can be evaluated based on the behavioral data of the pet and make suggestions to promote interaction with other pets and people.

[0080] The behavior understanding unit can predict the health condition of a pet based on the pet's behavioral data and make suggestions for health management. For example, if a pet frequently performs a certain behavior, it can evaluate the possibility that this behavior may affect its health and suggest countermeasures. The behavior understanding unit can also evaluate the balance of a pet's diet and exercise based on the pet's behavioral data and make suggestions for living a healthy lifestyle. For example, if a pet is not getting enough exercise, it can make suggestions for increasing exercise. The behavior understanding unit can also support weight management of a pet based on the pet's behavioral data. For example, if a pet is gaining weight, it can suggest adjusting the amount of food it eats. In this way, it is possible to predict the health condition of a pet based on the pet's behavioral data and make suggestions for health management.

[0081] The behavior understanding unit can estimate the emotional state of the pet based on the pet's behavior data and predict the pet's behavior based on that emotion. For example, if the pet is feeling anxious, it predicts what kind of behavior the anxiety will lead to. The behavior understanding unit can also develop an algorithm for estimating the emotional state of the pet based on the pet's behavior data and predicting the pet's behavior based on that emotion. For example, if the pet is happy, it predicts what kind of behavior that joy will lead to. The behavior understanding unit can also estimate the emotional state of the pet based on the pet's behavior data and predict the pet's behavior based on that emotion, thereby more accurately understanding the pet's behavior. This makes it possible to estimate the emotional state of the pet based on the pet's behavior data and predict the pet's behavior based on that emotion.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The behavior understanding unit understands the pet's behavior and requests. For example, the behavior understanding unit uses sensors and cameras to analyze the pet's movements and facial expressions to determine the pet's state. It can also use voice recognition technology to analyze the pet's cries and understand its requests. For example, if the pet appears sleepy, it analyzes its behavior and determines that the pet is sleepy. Step 2: The IoT control unit controls IoT home appliances based on the pet's behavior and requests understood by the behavior understanding unit. For example, if it determines that the pet is sleepy, it sends a signal to the smart light to turn off the lights. Also, if it determines that the pet is hungry, it can send a signal to the feeder to provide food. Furthermore, after the pet has finished eating, it checks the amount of food remaining in the feeder and automatically places an order from the online store if there is not enough food. Step 3: The ordering unit detects when pet food is running low and automatically orders more. For example, if there is a shortage of food, it automatically places an order with the online store based on the remaining amount of food in the feeder.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 behavioral understanding section that understands pets' behaviors and requests, an IoT control unit that controls IoT home appliances based on the behavior and requests of the pet understood by the behavior understanding unit; an ordering unit that detects a shortage of food for the pet and automatically orders food. A system characterized by:

2. The behavior understanding unit Inferring emotions from the pet's cries and facial expressions, and responding appropriately based on those emotions 2. The system of claim 1.

3. The IoT control unit Automatically adjust the environment of the entire house according to the pet's behavior and needs 2. The system of claim 1.

4. The ordering unit Analyzing the pet's dietary history and proposing the type and amount of food that is optimal for the pet's health condition 2. The system of claim 1.

5. The behavior understanding unit Analyzing the owner's emotional response to the pet's behavior and suggesting how the owner should respond to the pet 2. The system of claim 1.

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