system

Abstract

The system according to this embodiment aims to analyze the behavior and sounds of animals, understand their condition, and provide appropriate advice and countermeasures. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects the animal's behavior and sounds it makes. The analysis unit analyzes the data collected by the collection unit and determines the animal's condition. The provision unit provides advice and treatment methods regarding the animal based on the information determined by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to accurately grasp the state of an animal from its behavior and sound and provide an appropriate countermeasure.

[0005] The system according to the embodiment aims to analyze the behavior and sound of an animal, grasp its state, and provide appropriate advice and countermeasures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects the animal's behavior and sounds it makes. The analysis unit analyzes the data collected by the collection unit and determines the animal's condition. The provision unit provides advice and treatment methods for the animal based on the information determined by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the behavior and sounds of animals, understand their condition, and provide appropriate advice and countermeasures. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​assistant that supports communication with animals according to an embodiment of the present invention is a system that analyzes the behavior and sounds of animals and communicates their meaning and state to the user. This system collects the behavior and sounds of animals, analyzes them, and determines the state of the animal. For example, it can determine whether a pet is stressed and provide the user with advice and coping methods. It also has a function to provide instructions and feedback for training and discipline. This allows the user to understand the state of their pet more accurately and take appropriate action. For example, there is a collection unit for collecting animal behavior and sounds. This collection unit collects animal behavior and sounds using sensors and microphones. For example, it collects sounds such as a dog barking, a cat meowing, and animal movements. Next, there is an analysis unit that analyzes the collected data. This analysis unit analyzes the collected data using AI and determines the state of the animal. For example, it analyzes the pattern of a dog's barking sounds and determines whether the dog is stressed. Based on the analyzed information, the provision unit provides advice and coping methods regarding the animal. For example, if a dog is stressed, it provides the user with coping methods to reduce stress. It also provides information on training and discipline. For example, it provides information on dog training methods and tips. Furthermore, the information provider has the ability to estimate the user's emotions and change the information provided based on those estimates. For example, if the user is feeling stressed, it will provide advice on how to relax. It also has the ability to estimate the emotions of animals and determine advice and countermeasures based on those estimates. For example, if a cat is feeling anxious, it will provide countermeasures to reduce its anxiety. The data collection unit also has the ability to filter data based on the animal's current health condition and environment when collecting animal behavior and sounds. For example, if an animal is sick, it will collect data taking that condition into consideration. This allows for the collection of more accurate data and improves the accuracy of the analysis results. As a result, the AI ​​assistant that supports communication with animals allows users to understand the animal's condition more accurately and take appropriate action.

[0029] The AI ​​assistant that supports communication with animals according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects the animal's behavior and sounds it makes. For example, the data collection unit observes the animal's behavior and collects actions such as walking, eating, and playing. The data collection unit also collects sounds the animal makes. For example, the data collection unit can collect cries, barks, growls, etc. The data collection unit collects the animal's behavior and sounds using sensors and microphones. For example, the data collection unit uses a motion sensor to detect the animal's movement. The data collection unit can also use a high-sensitivity microphone to collect sounds from the animal. The analysis unit analyzes the data collected by the data collection unit and determines the animal's condition. For example, the analysis unit analyzes the sounds the animal makes using a voice analysis algorithm. The analysis unit can also analyze the animal's behavior using behavior pattern analysis. The analysis unit uses AI to analyze the collected data and determines the animal's health condition, stress level, emotional state, etc. For example, the analysis unit analyzes the patterns of animal vocalizations to determine whether the animal is experiencing stress. The analysis unit can also analyze the animal's behavioral patterns to determine its health status. The provision unit provides advice and solutions regarding the animal based on the information determined by the analysis unit. The provision unit provides, for example, suggestions for changing the animal's diet, recommending exercise, and providing medical solutions. The provision unit can also provide information on training and discipline for the animal. For example, the provision unit provides basic commands and behavior modification techniques. The provision unit provides the user with advice and solutions tailored to the animal's condition. For example, if the animal is experiencing stress, the provision unit provides solutions to reduce stress. The provision unit can also provide medical solutions if the animal has health problems. This allows the AI ​​assistant supporting communication with animals according to the embodiment to allow the user to more accurately understand the animal's condition and take appropriate action. Some or all of the processing described above in the provision unit may be performed using AI, for example, or without AI.For example, the service provider can use an AI model that takes information determined by the analysis unit as input and outputs advice and solutions to provide advice and solutions.

[0030] The data collection unit collects animal behavior and sounds they emit. For example, it observes animal behavior and collects data on activities such as walking, eating, and playing. Specifically, the data collection unit uses motion sensors to detect animal movement. Motion sensors can detect changes in the animal's body movement and position with high precision and collect data in real time. For example, it can record in detail the movement of the animal's legs when it is walking or the movement of its head while it is eating. The data collection unit also uses high-sensitivity microphones to collect sounds emitted by the animals. High-sensitivity microphones can clearly collect sounds such as animal cries, barks, and growls, and can record in detail changes in sound frequency and volume. As a result, the data collection unit can collect animal behavior and sounds from multiple angles and provide basic data for accurately understanding the animal's condition. Furthermore, the data collection unit centrally manages this data and makes it accessible to the analysis unit and the data provision unit. For example, the collected data is stored on a cloud server, and the analysis unit can access the data in real time and perform analysis. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit to determine the animal's condition. For example, the analysis unit analyzes the sounds emitted by the animal using a voice analysis algorithm. Specifically, an AI-based voice analysis algorithm analyzes the frequency, volume, and pattern of the animal's vocalizations to determine the animal's emotional state and stress level. For example, if an animal vocalizes frequently at a high volume, it can be determined that it is likely experiencing stress. The analysis unit can also analyze the animal's behavior using behavioral pattern analysis. An AI-based behavioral pattern analysis algorithm analyzes data on the animal's movements to determine its health condition and activity level. For example, if an animal is less active than usual, it can be determined that there may be a health problem. Furthermore, the analysis unit can utilize historical data and statistical information to evaluate the animal's long-term health condition and behavioral patterns. For example, based on historical data, it can predict fluctuations in animal behavior during specific seasons or time periods and formulate future countermeasures. This allows the analysis unit to quickly and accurately analyze the collected data and understand the animal's condition in real time. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The service provider provides advice and solutions regarding animals based on information determined by the analysis unit. For example, the service provider can offer suggestions such as dietary changes, exercise recommendations, and medical interventions. Specifically, it proposes appropriate dietary plans and exercise programs based on the animal's health status and stress levels determined by the analysis unit. For instance, if an animal is stressed, it can suggest a diet plan that includes specific ingredients to reduce stress. The service provider can also provide information on animal training and discipline. For example, it can offer basic commands and behavior modification techniques, and suggest specific methods to improve the animal's behavior. Furthermore, the service provider provides users with advice and solutions tailored to the animal's condition. For example, if an animal has health problems, it can offer medical solutions and, if necessary, recommend a veterinary consultation. The service provider can utilize an AI-powered interface to present this advice and solutions to users in an easily understandable way. For example, the service provider can use an AI model that takes information determined by the analysis unit as input and outputs advice and solutions to provide users with appropriate information. This allows the service provider to help users more accurately understand their animal's condition and take appropriate action. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of advice and solutions. This allows the service provider to quickly and reliably provide information to users and fulfill its role as an AI assistant that supports communication with animals.

[0033] The service provider can provide information on animal training or discipline based on the information determined by the analysis unit. For example, the service provider can provide methods for teaching animals basic commands. For example, the service provider can provide methods for teaching dogs commands such as "sit" and "stay." The service provider can also provide animal behavior modification techniques. For example, the service provider can provide methods for correcting excessive barking in dogs. The service provider can also provide tips and advice on animal training. For example, the service provider can provide tips for successfully training dogs. By providing information on animal training and discipline, users can implement appropriate training methods. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide information using an AI model that takes information determined by the analysis unit as input and outputs information on training and discipline.

[0034] The service provider can detect stress and discomfort in animals, identify their causes, and provide users with coping strategies to reduce stress. For example, the service provider can analyze changes in an animal's behavior and sounds to identify the cause of stress or discomfort. For instance, if a dog barks frequently, the service provider can identify the cause and provide coping strategies to reduce stress. The service provider can also monitor the animal's health and provide solutions if abnormalities are detected. For example, if a cat has a poor appetite, the service provider can identify the cause and provide appropriate solutions. The service provider can also provide advice on adjusting the animal's environment. For example, if a dog is stressed, the service provider can provide advice on adjusting the environment. This improves the animal's health by detecting stress and discomfort and providing appropriate solutions. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide coping strategies using an AI model that takes information determined by the analysis unit as input, identifies the cause of stress or discomfort, and outputs solutions.

[0035] The analysis unit can analyze animal behavior and sound patterns based on collected data and determine their meaning and state. For example, the analysis unit can analyze an animal's behavior patterns to determine its health and emotional state. For example, it can analyze a dog's behavior patterns to determine if the dog is stressed. The analysis unit can also analyze sound patterns emitted by animals and determine their meaning and state. For example, it can analyze a cat's meow patterns to determine if the cat is anxious. The analysis unit can also use algorithms to analyze animal behavior and sound patterns. For example, it can analyze temporal changes and frequency to determine the animal's state. This allows for a more accurate determination of the animal's state by analyzing its behavior and sound patterns. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use collected data as input and an AI model to analyze animal behavior and sound patterns to determine the animal's state.

[0036] The analysis unit can estimate an animal's emotions and determine advice or actions based on the estimated emotions. For example, the analysis unit can analyze an animal's behavior or sounds to estimate its emotions. For example, it can analyze a dog's behavior to estimate whether the dog is happy. The analysis unit can also analyze an animal's vocalizations to estimate its emotions. For example, it can analyze a cat's meow to estimate whether the cat is anxious. The analysis unit can also use algorithms to estimate animal emotions. For example, it can perform behavioral and vocal analyses to estimate an animal's emotions. This allows the analysis unit to estimate the animal's emotions and provide appropriate advice or actions to improve the animal's condition. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use collected data as input and an AI model to estimate animal emotions to determine advice or actions for the animal.

[0037] The data collection unit can filter the animal's behavior and sounds based on the animal's current health status and environment. For example, if the animal is sick, the data collection unit will take that condition into account when collecting data. For instance, the data collection unit will collect specific behaviors or sounds when the animal is ill. The data collection unit can also collect data considering the animal's environment. For example, if the animal is experiencing stress in a particular environment, the data collection unit will collect data in that environment. This allows for the collection of more accurate data by filtering the data based on the animal's health status and environment. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect data using an AI model that takes data on the animal's health status and environment as input and performs filtering.

[0038] The data collection unit can analyze the animal's past behavioral history and select the optimal data collection method. For example, the data collection unit can analyze the animal's past behavioral history and identify behavioral patterns in specific time periods or environments. For example, if the animal was active during a specific time period in the past, the data collection unit can concentrate data collection during that time period. The data collection unit can also prioritize data collection in specific environments if the animal exhibited specific behaviors in those environments in the past. For example, the data collection unit can analyze the animal's past behavioral patterns and create the most efficient data collection schedule. This enables efficient data collection by analyzing the animal's past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can determine the data collection method using an AI model that takes the animal's past behavioral history as input and selects the optimal data collection method.

[0039] The data collection unit can filter the collected animal behavior and sounds based on the animal's current activity level and environment. For example, if the animal is resting, the data collection unit will pause collection and resume collection when the animal resumes activity. For example, if the data collection unit is stressed in a particular environment, it will avoid collecting data in that environment. The data collection unit can also collect only data related to a specific activity if the animal is performing that activity. For example, if the data collection unit is playing, it will collect play-related data. This allows for the collection of more accurate data by filtering the data based on the animal's activity level and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can collect data using an AI model that takes data on the animal's activity level and environment as input and performs filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data based on the animal's geographical location when collecting animal behavior and sounds. For example, if an animal exhibits a specific behavior in a specific location, the data collection unit will prioritize the collection of data from that location. For example, if an animal is playing in a park, the data collection unit will collect behavioral data from that location. The data collection unit can also prioritize the collection of data along the animal's travel route if the animal is on the move. For example, if an animal emits a specific sound in a specific area, the data collection unit will prioritize the collection of sound data from that area. For example, if an animal makes a vocalization in a specific area, the data collection unit will collect that sound data. This allows for the priority collection of highly relevant data by considering the animal's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect data using an AI model that takes the animal's geographical location as input and prioritizes the collection of highly relevant data.

[0041] The data collection unit can analyze the animals' social media activity and collect relevant data when collecting animal behavior and sounds. For example, if an animal exhibits a specific behavior on social media, the data collection unit can collect data related to that behavior. For example, if an animal posts a video of itself playing on social media, the data collection unit can collect data related to that behavior. The data collection unit can also collect data related to a specific sound if the animal makes that sound on social media. For example, if an animal posts a sound on social media, the data collection unit can collect that sound data. The data collection unit can also analyze the animals' social media activity and collect the most relevant data. For example, the data collection unit analyzes the animals' social media activity and prioritizes collecting the most relevant data. This allows for the collection of highly relevant data by analyzing the animals' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect data using an AI model that takes the animals' social media activity as input and collects relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the animal's behavior and sounds during the analysis. For example, the analysis unit performs a detailed analysis if the behavior or sounds are related to the animal's health. The analysis unit can also perform a rapid analysis if the behavior or sounds are related to the animal's stress state. Furthermore, the analysis unit can provide specific feedback if the behavior or sounds are related to the animal's training. This allows for a more detailed analysis of important data by adjusting the level of detail based on the importance of the animal's behavior and sounds. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform the analysis using an AI model that takes the importance of animal behavior and sounds as input and adjusts the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the type of animal and individual differences during analysis. For example, when analyzing the behavior and sounds of dogs and cats, the analysis unit uses algorithms appropriate for each. For instance, the analysis unit uses different algorithms for analyzing dog behavior and cat behavior. Furthermore, even for animals of the same species, the analysis unit can adjust the analysis algorithm according to individual differences. For example, the analysis unit uses an algorithm specialized for a particular dog breed. The analysis unit can also develop and apply analysis algorithms specialized for specific animal species. For example, the analysis unit develops an algorithm specialized for a specific animal species and applies it to that species. This allows for more accurate analysis results by applying analysis algorithms according to the type of animal and individual differences. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the type of animal and individual differences as input and selects an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the timing of animal behavior and sound collection. For example, the analysis unit may prioritize the analysis of data recently collected by the animal. For example, the analysis unit may prioritize the analysis of data of high importance based on the timing of animal behavior and sound collection. The analysis unit can also prioritize the analysis of data of high urgency based on the timing of animal behavior and sound collection. For example, the analysis unit may prioritize the analysis of data of high urgency based on the timing of animal behavior and sound collection. This allows for the prioritization of more important data by determining the priority of analysis based on the timing of animal behavior and sound collection. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the timing of animal behavior and sound collection as input and determines the priority of analysis.

[0045] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on animals during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest research papers on animal behavior and sounds. It can also improve the accuracy of its analysis by referring to past research data on animal behavior and sounds. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to expert opinions on animal behavior and sounds. In this way, the accuracy of the analysis can be improved by referring to relevant literature on animals. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take relevant literature on animals as input and perform the analysis using an AI model that improves the accuracy of the analysis.

[0046] The information provider can adjust the level of detail of the information provided based on the importance of the animal's condition at the time of provision. For example, when providing information about an animal's health, the provider can include detailed explanations. It can also provide quick solutions when providing information about an animal's stress level. Furthermore, when providing information about an animal's training, the provider can provide specific feedback. This allows for the provision of more important information in detail by adjusting the level of detail based on the importance of the animal's condition. Some or all of the above processing in the information provider may be performed using AI, for example, or not. For example, the information provider can provide information using an AI model that takes the importance of the animal's condition as input and adjusts the level of detail of the information.

[0047] The service provider can apply different advice and treatment methods depending on the type of animal and individual differences when providing information. For example, when providing training methods for dogs and cats, the service provider will provide appropriate advice for each. For example, the service provider will use different training methods for dogs and cats. Furthermore, even for animals of the same species, the service provider can adjust advice and treatment methods according to individual differences. For example, the service provider will provide advice specific to a particular dog breed. The service provider can also develop and provide advice and treatment methods specific to a particular animal species. For example, the service provider will develop advice and treatment methods specific to a particular animal species and apply them to that species. This allows for the provision of more appropriate information by applying advice and treatment methods according to the type of animal and individual differences. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can provide information using an AI model that takes the type of animal and individual differences as input and selects appropriate advice and treatment methods.

[0048] The information provider can adjust the order of information provided based on the timing of animal behavior and sound collection. For example, the provider can prioritize providing the most recent information based on recently collected animal data. For example, the provider can prioritize providing information of high importance based on the timing of animal behavior and sound collection. The provider can also prioritize providing information of high urgency based on the timing of animal behavior and sound collection. For example, the provider can prioritize providing information of high urgency based on the timing of animal behavior and sound collection. By adjusting the order of information based on the timing of animal behavior and sound collection, more important information can be prioritized. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can provide information using an AI model that takes the timing of animal behavior and sound collection as input and adjusts the order of information.

[0049] The information provider can improve the accuracy of the information it provides by referring to relevant literature on animals at the time of provision. For example, the information provider can improve the accuracy of the information it provides by referring to the latest research papers on animal behavior and sounds. For example, the information provider can improve the accuracy of the information it provides by referring to the latest research papers on animal behavior and sounds. The information provider can also improve the accuracy of the information it provides by referring to past research data on animal behavior and sounds. For example, the information provider can improve the accuracy of the information it provides by referring to past research data on animal behavior and sounds. The information provider can also improve the accuracy of the information it provides by referring to expert opinions on animal behavior and sounds. For example, the information provider can improve the accuracy of the information it provides by referring to expert opinions on animal behavior and sounds. In this way, the accuracy of the information provided can be improved by referring to relevant literature on animals. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can provide information using an AI model that takes relevant literature on animals as input and improves the accuracy of the information.

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

[0051] The analysis unit can consider the animal's past behavioral history when analyzing its behavior and sound patterns. For example, the analysis unit can identify the time periods and environments in which the animal exhibited specific behaviors in the past and analyze its current behavior based on that information. The analysis unit can also consider the animal's past health condition and stress levels. For example, if the animal has suffered from an illness in the past, the analysis unit will consider its effects when analyzing its current behavior. Furthermore, the analysis unit can also consider the animal's past training history. For example, if the animal has received specific training in the past, the analysis unit will consider the effects of that training when analyzing its current behavior. By considering the animal's past behavioral history, the analysis unit can provide more accurate results.

[0052] The system can monitor the health status and stress levels of animals based on the analysis of their behavior and sounds. For example, it can analyze changes in animal behavior patterns and sounds to detect fluctuations in health status and stress levels. Furthermore, the system can provide users with appropriate advice and coping strategies based on the animal's health status and stress levels. For instance, if an animal is experiencing stress, it can provide coping strategies to reduce that stress. In addition, the system can monitor fluctuations in the animal's health status and stress levels over the long term and provide users with regular reports. This allows for the early detection of problems and appropriate interventions by continuously monitoring the animal's health status and stress levels.

[0053] The service provider can customize animal training plans based on the analysis of animal behavior and sounds. For example, it can create an optimal training plan for each individual animal based on the analysis of the animal's behavior patterns and sounds. The service provider can also monitor the progress of the animal's training and adjust the training plan as needed. For instance, if an animal responds well to a particular training exercise, it can intensify that exercise. Furthermore, the service provider can provide users with training tips and advice, such as effective methods for animals to learn specific behaviors. This allows for more effective training by customizing the animal's training plan.

[0054] The service provider can predict the health status and stress levels of animals based on the analysis of their behavior and sounds, and provide preventative advice to users. For example, the service provider can analyze changes in an animal's behavior patterns and sounds to predict the possibility of future deterioration in health status and stress levels. Furthermore, based on the prediction results, the service provider can provide preventative advice and coping strategies to users. For instance, if an animal is likely to experience stress in the future, it can provide coping strategies to prevent stress. In addition, the service provider can monitor the predicted health status and stress levels of animals over the long term and provide regular reports to users. This enables the prediction of animal health status and stress levels, allowing for preventative measures.

[0055] The analysis unit can predict animal behavior patterns based on the analysis results of animal behavior and sounds, and provide the user with the prediction results. For example, the analysis unit can analyze an animal's past behavior patterns and predict future behavior. Furthermore, the analysis unit can provide the user with appropriate advice and countermeasures based on the prediction results. For example, if an animal is likely to exhibit a specific behavior in the future, it can provide countermeasures for that behavior. In addition, the analysis unit can monitor the predicted results of animal behavior patterns over the long term and provide the user with regular reports. This enables the prediction of animal behavior patterns and appropriate countermeasures.

[0056] The service provider can predict animal behavior patterns based on the analysis of animal behavior and sounds, and provide the user with the prediction results. For example, the service provider can analyze an animal's past behavior patterns and predict future behavior. Furthermore, the service provider can provide users with appropriate advice and countermeasures based on the prediction results. For instance, if an animal is likely to exhibit a specific behavior in the future, it can provide countermeasures for that behavior. In addition, the service provider can monitor the predicted animal behavior patterns over the long term and provide users with regular reports. This enables the prediction of animal behavior patterns and appropriate countermeasures.

[0057] The service provider can predict animal behavior patterns based on the analysis of animal behavior and sounds, and provide the user with the prediction results. For example, the service provider can analyze an animal's past behavior patterns and predict future behavior. Furthermore, the service provider can provide users with appropriate advice and countermeasures based on the prediction results. For instance, if an animal is likely to exhibit a specific behavior in the future, it can provide countermeasures for that behavior. In addition, the service provider can monitor the predicted animal behavior patterns over the long term and provide users with regular reports. This enables the prediction of animal behavior patterns and appropriate countermeasures.

[0058] The service provider can predict animal behavior patterns based on the analysis of animal behavior and sounds, and provide the user with the prediction results. For example, the service provider can analyze an animal's past behavior patterns and predict future behavior. Furthermore, the service provider can provide users with appropriate advice and countermeasures based on the prediction results. For instance, if an animal is likely to exhibit a specific behavior in the future, it can provide countermeasures for that behavior. In addition, the service provider can monitor the predicted animal behavior patterns over the long term and provide users with regular reports. This enables the prediction of animal behavior patterns and appropriate countermeasures.

[0059] The service provider can predict animal behavior patterns based on the analysis of animal behavior and sounds, and provide the user with the prediction results. For example, the service provider can analyze an animal's past behavior patterns and predict future behavior. Furthermore, the service provider can provide users with appropriate advice and countermeasures based on the prediction results. For instance, if an animal is likely to exhibit a specific behavior in the future, it can provide countermeasures for that behavior. In addition, the service provider can monitor the predicted animal behavior patterns over the long term and provide users with regular reports. This enables the prediction of animal behavior patterns and appropriate countermeasures.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The collection unit collects animal behavior and sounds it makes. For example, the collection unit observes animal behavior and collects actions such as walking, eating, and playing. The collection unit also collects sounds made by the animal. For example, the collection unit can collect sounds such as cries, barks, and growls. The collection unit collects animal behavior and sounds using sensors and microphones. For example, the collection unit uses motion sensors to detect animal movement. The collection unit can also use high-sensitivity microphones to collect animal sounds. Step 2: The analysis unit analyzes the data collected by the collection unit to determine the animal's condition. The analysis unit can, for example, analyze the sounds the animal makes using a voice analysis algorithm. The analysis unit can also analyze the animal's behavior using behavioral pattern analysis. The analysis unit uses AI to analyze the collected data and determine the animal's health status, stress level, emotional state, etc. For example, the analysis unit can analyze the patterns of the animal's vocalizations to determine whether the animal is stressed. The analysis unit can also analyze the animal's behavioral patterns to determine the animal's health status. Step 3: The service provider provides advice and solutions regarding the animal based on the information determined by the analysis unit. The service provider provides, for example, suggestions for changing the animal's diet, recommending exercise, and providing medical solutions. The service provider can also provide information on training and discipline for the animal. For example, the service provider can provide basic commands and behavior modification techniques. The service provider provides the user with advice and solutions tailored to the animal's condition. For example, if the animal is stressed, the service provider can provide solutions to reduce stress. The service provider can also provide medical solutions if the animal has health problems. This allows the AI ​​assistant supporting communication with animals according to the embodiment to enable the user to more accurately understand the animal's condition and take appropriate action. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide advice and solutions using an AI model that takes the information determined by the analysis unit as input and outputs advice and solutions.

[0062] (Example of form 2) The AI ​​assistant that supports communication with animals according to an embodiment of the present invention is a system that analyzes the behavior and sounds of animals and communicates their meaning and state to the user. This system collects the behavior and sounds of animals, analyzes them, and determines the state of the animal. For example, it can determine whether a pet is stressed and provide the user with advice and coping methods. It also has a function to provide instructions and feedback for training and discipline. This allows the user to understand the state of their pet more accurately and take appropriate action. For example, there is a collection unit for collecting animal behavior and sounds. This collection unit collects animal behavior and sounds using sensors and microphones. For example, it collects sounds such as a dog barking, a cat meowing, and animal movements. Next, there is an analysis unit that analyzes the collected data. This analysis unit analyzes the collected data using AI and determines the state of the animal. For example, it analyzes the pattern of a dog's barking sounds and determines whether the dog is stressed. Based on the analyzed information, the provision unit provides advice and coping methods regarding the animal. For example, if a dog is stressed, it provides the user with coping methods to reduce stress. It also provides information on training and discipline. For example, it provides information on dog training methods and tips. Furthermore, the information provider has the ability to estimate the user's emotions and change the information provided based on those estimates. For example, if the user is feeling stressed, it will provide advice on how to relax. It also has the ability to estimate the emotions of animals and determine advice and countermeasures based on those estimates. For example, if a cat is feeling anxious, it will provide countermeasures to reduce its anxiety. The data collection unit also has the ability to filter data based on the animal's current health condition and environment when collecting animal behavior and sounds. For example, if an animal is sick, it will collect data taking that condition into consideration. This allows for the collection of more accurate data and improves the accuracy of the analysis results. As a result, the AI ​​assistant that supports communication with animals allows users to understand the animal's condition more accurately and take appropriate action.

[0063] The AI ​​assistant that supports communication with animals according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects the animal's behavior and sounds it makes. For example, the data collection unit observes the animal's behavior and collects actions such as walking, eating, and playing. The data collection unit also collects sounds the animal makes. For example, the data collection unit can collect cries, barks, growls, etc. The data collection unit collects the animal's behavior and sounds using sensors and microphones. For example, the data collection unit uses a motion sensor to detect the animal's movement. The data collection unit can also use a high-sensitivity microphone to collect sounds from the animal. The analysis unit analyzes the data collected by the data collection unit and determines the animal's condition. For example, the analysis unit analyzes the sounds the animal makes using a voice analysis algorithm. The analysis unit can also analyze the animal's behavior using behavior pattern analysis. The analysis unit uses AI to analyze the collected data and determines the animal's health condition, stress level, emotional state, etc. For example, the analysis unit analyzes the patterns of animal vocalizations to determine whether the animal is experiencing stress. The analysis unit can also analyze the animal's behavioral patterns to determine its health status. The provision unit provides advice and solutions regarding the animal based on the information determined by the analysis unit. The provision unit provides, for example, suggestions for changing the animal's diet, recommending exercise, and providing medical solutions. The provision unit can also provide information on training and discipline for the animal. For example, the provision unit provides basic commands and behavior modification techniques. The provision unit provides the user with advice and solutions tailored to the animal's condition. For example, if the animal is experiencing stress, the provision unit provides solutions to reduce stress. The provision unit can also provide medical solutions if the animal has health problems. This allows the AI ​​assistant supporting communication with animals according to the embodiment to allow the user to more accurately understand the animal's condition and take appropriate action. Some or all of the processing described above in the provision unit may be performed using AI, for example, or without AI.For example, the service provider can use an AI model that takes information determined by the analysis unit as input and outputs advice and solutions to provide advice and solutions.

[0064] The data collection unit collects animal behavior and sounds they emit. For example, it observes animal behavior and collects data on activities such as walking, eating, and playing. Specifically, the data collection unit uses motion sensors to detect animal movement. Motion sensors can detect changes in the animal's body movement and position with high precision and collect data in real time. For example, it can record in detail the movement of the animal's legs when it is walking or the movement of its head while it is eating. The data collection unit also uses high-sensitivity microphones to collect sounds emitted by the animals. High-sensitivity microphones can clearly collect sounds such as animal cries, barks, and growls, and can record in detail changes in sound frequency and volume. As a result, the data collection unit can collect animal behavior and sounds from multiple angles and provide basic data for accurately understanding the animal's condition. Furthermore, the data collection unit centrally manages this data and makes it accessible to the analysis unit and the data provision unit. For example, the collected data is stored on a cloud server, and the analysis unit can access the data in real time and perform analysis. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0065] The analysis unit analyzes the data collected by the collection unit to determine the animal's condition. For example, the analysis unit analyzes the sounds emitted by the animal using a voice analysis algorithm. Specifically, an AI-based voice analysis algorithm analyzes the frequency, volume, and pattern of the animal's vocalizations to determine the animal's emotional state and stress level. For example, if an animal vocalizes frequently at a high volume, it can be determined that it is likely experiencing stress. The analysis unit can also analyze the animal's behavior using behavioral pattern analysis. An AI-based behavioral pattern analysis algorithm analyzes data on the animal's movements to determine its health condition and activity level. For example, if an animal is less active than usual, it can be determined that there may be a health problem. Furthermore, the analysis unit can utilize historical data and statistical information to evaluate the animal's long-term health condition and behavioral patterns. For example, based on historical data, it can predict fluctuations in animal behavior during specific seasons or time periods and formulate future countermeasures. This allows the analysis unit to quickly and accurately analyze the collected data and understand the animal's condition in real time. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0066] The service provider provides advice and solutions regarding animals based on information determined by the analysis unit. For example, the service provider can offer suggestions such as dietary changes, exercise recommendations, and medical interventions. Specifically, it proposes appropriate dietary plans and exercise programs based on the animal's health status and stress levels determined by the analysis unit. For instance, if an animal is stressed, it can suggest a diet plan that includes specific ingredients to reduce stress. The service provider can also provide information on animal training and discipline. For example, it can offer basic commands and behavior modification techniques, and suggest specific methods to improve the animal's behavior. Furthermore, the service provider provides users with advice and solutions tailored to the animal's condition. For example, if an animal has health problems, it can offer medical solutions and, if necessary, recommend a veterinary consultation. The service provider can utilize an AI-powered interface to present this advice and solutions to users in an easily understandable way. For example, the service provider can use an AI model that takes information determined by the analysis unit as input and outputs advice and solutions to provide users with appropriate information. This allows the service provider to help users more accurately understand their animal's condition and take appropriate action. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of advice and solutions. This allows the service provider to quickly and reliably provide information to users and fulfill its role as an AI assistant that supports communication with animals.

[0067] The service provider can provide information on animal training or discipline based on the information determined by the analysis unit. For example, the service provider can provide methods for teaching animals basic commands. For example, the service provider can provide methods for teaching dogs commands such as "sit" and "stay." The service provider can also provide animal behavior modification techniques. For example, the service provider can provide methods for correcting excessive barking in dogs. The service provider can also provide tips and advice on animal training. For example, the service provider can provide tips for successfully training dogs. By providing information on animal training and discipline, users can implement appropriate training methods. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide information using an AI model that takes information determined by the analysis unit as input and outputs information on training and discipline.

[0068] The service provider can detect stress and discomfort in animals, identify their causes, and provide users with coping strategies to reduce stress. For example, the service provider can analyze changes in an animal's behavior and sounds to identify the cause of stress or discomfort. For instance, if a dog barks frequently, the service provider can identify the cause and provide coping strategies to reduce stress. The service provider can also monitor the animal's health and provide solutions if abnormalities are detected. For example, if a cat has a poor appetite, the service provider can identify the cause and provide appropriate solutions. The service provider can also provide advice on adjusting the animal's environment. For example, if a dog is stressed, the service provider can provide advice on adjusting the environment. This improves the animal's health by detecting stress and discomfort and providing appropriate solutions. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide coping strategies using an AI model that takes information determined by the analysis unit as input, identifies the cause of stress or discomfort, and outputs solutions.

[0069] The service provider can estimate the user's emotions and modify the information provided to the user based on the estimated emotions. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on changes in facial expressions and modify the information provided. The service provider can also record the user's voice and estimate their emotions using voice analysis technology. For example, the service provider can analyze the tone and speed of the voice, calculate an emotion score, and modify the information provided. The service provider can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on fluctuations in heart rate and modify the information provided. By modifying the information provided according to the user's emotions, more appropriate advice and solutions can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provision unit may be performed using AI, for example, or without AI. For example, the service provision unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0070] The analysis unit can analyze animal behavior and sound patterns based on collected data and determine their meaning and state. For example, the analysis unit can analyze an animal's behavior patterns to determine its health and emotional state. For example, it can analyze a dog's behavior patterns to determine if the dog is stressed. The analysis unit can also analyze sound patterns emitted by animals and determine their meaning and state. For example, it can analyze a cat's meow patterns to determine if the cat is anxious. The analysis unit can also use algorithms to analyze animal behavior and sound patterns. For example, it can analyze temporal changes and frequency to determine the animal's state. This allows for a more accurate determination of the animal's state by analyzing its behavior and sound patterns. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use collected data as input and an AI model to analyze animal behavior and sound patterns to determine the animal's state.

[0071] The analysis unit can estimate an animal's emotions and determine advice or actions based on the estimated emotions. For example, the analysis unit can analyze an animal's behavior or sounds to estimate its emotions. For example, it can analyze a dog's behavior to estimate whether the dog is happy. The analysis unit can also analyze an animal's vocalizations to estimate its emotions. For example, it can analyze a cat's meow to estimate whether the cat is anxious. The analysis unit can also use algorithms to estimate animal emotions. For example, it can perform behavioral and vocal analyses to estimate an animal's emotions. This allows the analysis unit to estimate the animal's emotions and provide appropriate advice or actions to improve the animal's condition. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use collected data as input and an AI model to estimate animal emotions to determine advice or actions for the animal.

[0072] The data collection unit can filter the animal's behavior and sounds based on the animal's current health status and environment. For example, if the animal is sick, the data collection unit will take that condition into account when collecting data. For instance, the data collection unit will collect specific behaviors or sounds when the animal is ill. The data collection unit can also collect data considering the animal's environment. For example, if the animal is experiencing stress in a particular environment, the data collection unit will collect data in that environment. This allows for the collection of more accurate data by filtering the data based on the animal's health status and environment. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect data using an AI model that takes data on the animal's health status and environment as input and performs filtering.

[0073] The data collection unit can estimate the user's emotions and adjust the timing of collecting animal behavior and sounds based on the estimated emotions. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on changes in facial expressions and adjust the collection timing. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the data collection unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the collection timing. Furthermore, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on fluctuations in heart rate and adjust the collection timing. By adjusting the collection timing according to the user's emotions, the burden on the user is reduced, and efficient data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit can input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0074] The data collection unit can analyze the animal's past behavioral history and select the optimal data collection method. For example, the data collection unit can analyze the animal's past behavioral history and identify behavioral patterns in specific time periods or environments. For example, if the animal was active during a specific time period in the past, the data collection unit can concentrate data collection during that time period. The data collection unit can also prioritize data collection in specific environments if the animal exhibited specific behaviors in those environments in the past. For example, the data collection unit can analyze the animal's past behavioral patterns and create the most efficient data collection schedule. This enables efficient data collection by analyzing the animal's past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can determine the data collection method using an AI model that takes the animal's past behavioral history as input and selects the optimal data collection method.

[0075] The data collection unit can filter the collected animal behavior and sounds based on the animal's current activity level and environment. For example, if the animal is resting, the data collection unit will pause collection and resume collection when the animal resumes activity. For example, if the data collection unit is stressed in a particular environment, it will avoid collecting data in that environment. The data collection unit can also collect only data related to a specific activity if the animal is performing that activity. For example, if the data collection unit is playing, it will collect play-related data. This allows for the collection of more accurate data by filtering the data based on the animal's activity level and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can collect data using an AI model that takes data on the animal's activity level and environment as input and performs filtering.

[0076] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, the data collection unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on changes in facial expressions and determine the priority of data to collect. The data collection unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the data collection unit can analyze the tone and speed of the voice, calculate an emotion score, and determine the priority of data to collect. The data collection unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on fluctuations in heart rate and determine the priority of data to collect. This allows for the priority of data collection based on the user's emotions, enabling the collection of more important data. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0077] The data collection unit can prioritize the collection of highly relevant data based on the animal's geographical location when collecting animal behavior and sounds. For example, if an animal exhibits a specific behavior in a specific location, the data collection unit will prioritize the collection of data from that location. For example, if an animal is playing in a park, the data collection unit will collect behavioral data from that location. The data collection unit can also prioritize the collection of data along the animal's travel route if the animal is on the move. For example, if an animal emits a specific sound in a specific area, the data collection unit will prioritize the collection of sound data from that area. For example, if an animal makes a vocalization in a specific area, the data collection unit will collect that sound data. This allows for the priority collection of highly relevant data by considering the animal's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect data using an AI model that takes the animal's geographical location as input and prioritizes the collection of highly relevant data.

[0078] The data collection unit can analyze the animals' social media activity and collect relevant data when collecting animal behavior and sounds. For example, if an animal exhibits a specific behavior on social media, the data collection unit can collect data related to that behavior. For example, if an animal posts a video of itself playing on social media, the data collection unit can collect data related to that behavior. The data collection unit can also collect data related to a specific sound if the animal makes that sound on social media. For example, if an animal posts a sound on social media, the data collection unit can collect that sound data. The data collection unit can also analyze the animals' social media activity and collect the most relevant data. For example, the data collection unit analyzes the animals' social media activity and prioritizes collecting the most relevant data. This allows for the collection of highly relevant data by analyzing the animals' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect data using an AI model that takes the animals' social media activity as input and collects relevant data.

[0079] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions and adjust the presentation of the analysis. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the presentation of the analysis. The analysis unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate and adjust the presentation of the analysis. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the animal's behavior and sounds during the analysis. For example, the analysis unit performs a detailed analysis if the behavior or sounds are related to the animal's health. The analysis unit can also perform a rapid analysis if the behavior or sounds are related to the animal's stress state. Furthermore, the analysis unit can provide specific feedback if the behavior or sounds are related to the animal's training. This allows for a more detailed analysis of important data by adjusting the level of detail based on the importance of the animal's behavior and sounds. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform the analysis using an AI model that takes the importance of animal behavior and sounds as input and adjusts the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the type of animal and individual differences during analysis. For example, when analyzing the behavior and sounds of dogs and cats, the analysis unit uses algorithms appropriate for each. For instance, the analysis unit uses different algorithms for analyzing dog behavior and cat behavior. Furthermore, even for animals of the same species, the analysis unit can adjust the analysis algorithm according to individual differences. For example, the analysis unit uses an algorithm specialized for a particular dog breed. The analysis unit can also develop and apply analysis algorithms specialized for specific animal species. For example, the analysis unit develops an algorithm specialized for a specific animal species and applies it to that species. This allows for more accurate analysis results by applying analysis algorithms according to the type of animal and individual differences. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the type of animal and individual differences as input and selects an appropriate analysis algorithm.

[0082] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions and adjust the display method of the analysis results. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the display method of the analysis results. The analysis unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate and adjust the display method of the analysis results. By adjusting the display method of the analysis results according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0083] The analysis unit can determine the priority of analysis based on the timing of animal behavior and sound collection. For example, the analysis unit may prioritize the analysis of data recently collected by the animal. For example, the analysis unit may prioritize the analysis of data of high importance based on the timing of animal behavior and sound collection. The analysis unit can also prioritize the analysis of data of high urgency based on the timing of animal behavior and sound collection. For example, the analysis unit may prioritize the analysis of data of high urgency based on the timing of animal behavior and sound collection. This allows for the prioritization of more important data by determining the priority of analysis based on the timing of animal behavior and sound collection. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the timing of animal behavior and sound collection as input and determines the priority of analysis.

[0084] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on animals during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest research papers on animal behavior and sounds. It can also improve the accuracy of its analysis by referring to past research data on animal behavior and sounds. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to expert opinions on animal behavior and sounds. In this way, the accuracy of the analysis can be improved by referring to relevant literature on animals. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take relevant literature on animals as input and perform the analysis using an AI model that improves the accuracy of the analysis.

[0085] The service provider can estimate the user's emotions and adjust the way advice and coping strategies are presented based on those estimated emotions. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on changes in facial expressions and adjust the way advice and coping strategies are presented. The service provider can also record the user's voice and estimate their emotions using voice analysis technology. For example, the service provider can analyze the tone and speed of the voice, calculate an emotion score, and adjust the way advice and coping strategies are presented. Furthermore, the service provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on fluctuations in heart rate and adjust the way advice and coping strategies are presented. This allows the service provider to provide more appropriate information by adjusting the way advice and coping strategies are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the service provider may be performed using AI, or not using AI. For example, the service provider can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0086] The information provider can adjust the level of detail of the information provided based on the importance of the animal's condition at the time of provision. For example, when providing information about an animal's health, the provider can include detailed explanations. It can also provide quick solutions when providing information about an animal's stress level. Furthermore, when providing information about an animal's training, the provider can provide specific feedback. This allows for the provision of more important information in detail by adjusting the level of detail based on the importance of the animal's condition. Some or all of the above processing in the information provider may be performed using AI, for example, or not. For example, the information provider can provide information using an AI model that takes the importance of the animal's condition as input and adjusts the level of detail of the information.

[0087] The service provider can apply different advice and treatment methods depending on the type of animal and individual differences when providing information. For example, when providing training methods for dogs and cats, the service provider will provide appropriate advice for each. For example, the service provider will use different training methods for dogs and cats. Furthermore, even for animals of the same species, the service provider can adjust advice and treatment methods according to individual differences. For example, the service provider will provide advice specific to a particular dog breed. The service provider can also develop and provide advice and treatment methods specific to a particular animal species. For example, the service provider will develop advice and treatment methods specific to a particular animal species and apply them to that species. This allows for the provision of more appropriate information by applying advice and treatment methods according to the type of animal and individual differences. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can provide information using an AI model that takes the type of animal and individual differences as input and selects appropriate advice and treatment methods.

[0088] The service provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on changes in facial expressions and determine the priority of the information to be provided. The service provider can also record the user's voice and estimate their emotions using voice analysis technology. For example, the service provider can analyze the tone and speed of the voice, calculate an emotion score, and determine the priority of the information to be provided. The service provider can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on fluctuations in heart rate and determine the priority of the information to be provided. This allows the service provider to prioritize information according to the user's emotions, thereby providing more important information preferentially. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provision unit may be performed using AI, for example, or without AI. For example, the service provision unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0089] The information provider can adjust the order of information provided based on the timing of animal behavior and sound collection. For example, the provider can prioritize providing the most recent information based on recently collected animal data. For example, the provider can prioritize providing information of high importance based on the timing of animal behavior and sound collection. The provider can also prioritize providing information of high urgency based on the timing of animal behavior and sound collection. For example, the provider can prioritize providing information of high urgency based on the timing of animal behavior and sound collection. By adjusting the order of information based on the timing of animal behavior and sound collection, more important information can be prioritized. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can provide information using an AI model that takes the timing of animal behavior and sound collection as input and adjusts the order of information.

[0090] The information provider can improve the accuracy of the information it provides by referring to relevant literature on animals at the time of provision. For example, the information provider can improve the accuracy of the information it provides by referring to the latest research papers on animal behavior and sounds. For example, the information provider can improve the accuracy of the information it provides by referring to the latest research papers on animal behavior and sounds. The information provider can also improve the accuracy of the information it provides by referring to past research data on animal behavior and sounds. For example, the information provider can improve the accuracy of the information it provides by referring to past research data on animal behavior and sounds. The information provider can also improve the accuracy of the information it provides by referring to expert opinions on animal behavior and sounds. For example, the information provider can improve the accuracy of the information it provides by referring to expert opinions on animal behavior and sounds. In this way, the accuracy of the information provided can be improved by referring to relevant literature on animals. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can provide information using an AI model that takes relevant literature on animals as input and improves the accuracy of the information.

[0091] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0092] The analysis unit can consider the animal's past behavioral history when analyzing its behavior and sound patterns. For example, the analysis unit can identify the time periods and environments in which the animal exhibited specific behaviors in the past and analyze its current behavior based on that information. The analysis unit can also consider the animal's past health condition and stress levels. For example, if the animal has suffered from an illness in the past, the analysis unit will consider its effects when analyzing its current behavior. Furthermore, the analysis unit can also consider the animal's past training history. For example, if the animal has received specific training in the past, the analysis unit will consider the effects of that training when analyzing its current behavior. By considering the animal's past behavioral history, the analysis unit can provide more accurate results.

[0093] The system can monitor the health status and stress levels of animals based on the analysis of their behavior and sounds. For example, it can analyze changes in animal behavior patterns and sounds to detect fluctuations in health status and stress levels. Furthermore, the system can provide users with appropriate advice and coping strategies based on the animal's health status and stress levels. For instance, if an animal is experiencing stress, it can provide coping strategies to reduce that stress. In addition, the system can monitor fluctuations in the animal's health status and stress levels over the long term and provide users with regular reports. This allows for the early detection of problems and appropriate interventions by continuously monitoring the animal's health status and stress levels.

[0094] The service provider can customize animal training plans based on the analysis of animal behavior and sounds. For example, it can create an optimal training plan for each individual animal based on the analysis of the animal's behavior patterns and sounds. The service provider can also monitor the progress of the animal's training and adjust the training plan as needed. For instance, if an animal responds well to a particular training exercise, it can intensify that exercise. Furthermore, the service provider can provide users with training tips and advice, such as effective methods for animals to learn specific behaviors. This allows for more effective training by customizing the animal's training plan.

[0095] The service provider can estimate an animal's emotional state based on the analysis of its behavior and sounds, and provide emotion-based advice to the user. For example, the service provider can analyze changes in an animal's behavior patterns and sounds to estimate whether the animal is happy, sad, or stressed. Furthermore, the service provider can provide appropriate advice and coping strategies to the user based on the animal's emotional state. For instance, if the animal is stressed, it can provide coping strategies to reduce stress. In addition, the service provider can monitor the animal's emotional state over the long term and provide regular reports to the user. This allows for early detection of problems and appropriate intervention through continuous monitoring of the animal's emotional state.

[0096] The service provider can predict the health status and stress levels of animals based on the analysis of their behavior and sounds, and provide preventative advice to users. For example, the service provider can analyze changes in an animal's behavior patterns and sounds to predict the possibility of future deterioration in health status and stress levels. Furthermore, based on the prediction results, the service provider can provide preventative advice and coping strategies to users. For instance, if an animal is likely to experience stress in the future, it can provide coping strategies to prevent stress. In addition, the service provider can monitor the predicted health status and stress levels of animals over the long term and provide regular reports to users. This enables the prediction of animal health status and stress levels, allowing for preventative measures.

[0097] The analysis unit can predict animal behavior patterns based on the analysis results of animal behavior and sounds, and provide the user with the prediction results. For example, the analysis unit can analyze an animal's past behavior patterns and predict future behavior. Furthermore, the analysis unit can provide the user with appropriate advice and countermeasures based on the prediction results. For example, if an animal is likely to exhibit a specific behavior in the future, it can provide countermeasures for that behavior. In addition, the analysis unit can monitor the predicted results of animal behavior patterns over the long term and provide the user with regular reports. This enables the prediction of animal behavior patterns and appropriate countermeasures.

[0098] The service provider can predict animal behavior patterns based on the analysis of animal behavior and sounds, and provide the user with the prediction results. For example, the service provider can analyze an animal's past behavior patterns and predict future behavior. Furthermore, the service provider can provide users with appropriate advice and countermeasures based on the prediction results. For instance, if an animal is likely to exhibit a specific behavior in the future, it can provide countermeasures for that behavior. In addition, the service provider can monitor the predicted animal behavior patterns over the long term and provide users with regular reports. This enables the prediction of animal behavior patterns and appropriate countermeasures.

[0099] The service provider can predict animal behavior patterns based on the analysis of animal behavior and sounds, and provide the user with the prediction results. For example, the service provider can analyze an animal's past behavior patterns and predict future behavior. Furthermore, the service provider can provide users with appropriate advice and countermeasures based on the prediction results. For instance, if an animal is likely to exhibit a specific behavior in the future, it can provide countermeasures for that behavior. In addition, the service provider can monitor the predicted animal behavior patterns over the long term and provide users with regular reports. This enables the prediction of animal behavior patterns and appropriate countermeasures.

[0100] The service provider can predict animal behavior patterns based on the analysis of animal behavior and sounds, and provide the user with the prediction results. For example, the service provider can analyze an animal's past behavior patterns and predict future behavior. Furthermore, the service provider can provide users with appropriate advice and countermeasures based on the prediction results. For instance, if an animal is likely to exhibit a specific behavior in the future, it can provide countermeasures for that behavior. In addition, the service provider can monitor the predicted animal behavior patterns over the long term and provide users with regular reports. This enables the prediction of animal behavior patterns and appropriate countermeasures.

[0101] The service provider can predict animal behavior patterns based on the analysis of animal behavior and sounds, and provide the user with the prediction results. For example, the service provider can analyze an animal's past behavior patterns and predict future behavior. Furthermore, the service provider can provide users with appropriate advice and countermeasures based on the prediction results. For instance, if an animal is likely to exhibit a specific behavior in the future, it can provide countermeasures for that behavior. In addition, the service provider can monitor the predicted animal behavior patterns over the long term and provide users with regular reports. This enables the prediction of animal behavior patterns and appropriate countermeasures.

[0102] The following briefly describes the processing flow for example form 2.

[0103] Step 1: The collection unit collects animal behavior and sounds it makes. For example, the collection unit observes animal behavior and collects actions such as walking, eating, and playing. The collection unit also collects sounds made by the animal. For example, the collection unit can collect sounds such as cries, barks, and growls. The collection unit collects animal behavior and sounds using sensors and microphones. For example, the collection unit uses motion sensors to detect animal movement. The collection unit can also use high-sensitivity microphones to collect animal sounds. Step 2: The analysis unit analyzes the data collected by the collection unit to determine the animal's condition. The analysis unit can, for example, analyze the sounds the animal makes using a voice analysis algorithm. The analysis unit can also analyze the animal's behavior using behavioral pattern analysis. The analysis unit uses AI to analyze the collected data and determine the animal's health status, stress level, emotional state, etc. For example, the analysis unit can analyze the patterns of the animal's vocalizations to determine whether the animal is stressed. The analysis unit can also analyze the animal's behavioral patterns to determine the animal's health status. Step 3: The service provider provides advice and solutions regarding the animal based on the information determined by the analysis unit. The service provider provides, for example, suggestions for changing the animal's diet, recommending exercise, and providing medical solutions. The service provider can also provide information on training and discipline for the animal. For example, the service provider can provide basic commands and behavior modification techniques. The service provider provides the user with advice and solutions tailored to the animal's condition. For example, if the animal is stressed, the service provider can provide solutions to reduce stress. The service provider can also provide medical solutions if the animal has health problems. This allows the AI ​​assistant supporting communication with animals according to the embodiment to enable the user to more accurately understand the animal's condition and take appropriate action. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide advice and solutions using an AI model that takes the information determined by the analysis unit as input and outputs advice and solutions.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0106] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] For example, the collection unit can collect animal behavior and sounds using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the collected data using AI to determine the animal's condition. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which provides advice and countermeasures regarding the animal based on the analysis results. The provision unit may also be implemented by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0108] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0109] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0114] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0115] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0116] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0117] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0118] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] For example, the data collection unit can collect animal behavior and sounds using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the collected data using AI to determine the animal's condition. The information provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which provides advice and countermeasures regarding the animal based on the analysis results. The information provision unit may also be implemented by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0124] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0125] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0127] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0128] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0130] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0131] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0132] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0133] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0134] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] For example, the collection unit can collect animal behavior and sounds using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the collected data using AI to determine the animal's condition. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, which provides advice and countermeasures regarding the animal based on the analysis results. The provision unit may also be implemented by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0140] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0141] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0144] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0147] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0148] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] For example, the collection unit can collect animal behavior and sounds using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the collected data using AI to determine the animal's condition. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which provides advice and countermeasures regarding the animal based on the analysis results. The provision unit may also be implemented by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0159] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0160] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0161] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0165] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0166] 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.

[0167] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0168] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0169] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0170] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0172] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0173] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0174] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0175] (Note 1) A collection unit that collects animal behavior and sounds made by animals, An analysis unit analyzes the data collected by the aforementioned collection unit and determines the condition of the animal, The system includes a provisioning unit that provides advice and treatment methods regarding animals based on the information determined by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Based on the information determined by the analysis unit, the system provides information regarding animal training or discipline. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, It detects stress and discomfort in animals, identifies the causes, and provides users with coping strategies to reduce stress. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, The system estimates the user's emotions and modifies the information provided to the user based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Based on the collected data, the behavior and sound patterns of animals are analyzed to determine their meaning and state. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, The system estimates the animal's emotions and determines advice or actions regarding the animal based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting animal behavior and sounds, filtering is performed based on the animal's current health status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of animal behavior and sound collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the animals' past behavioral history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting animal behavior and sounds, filtering is performed based on the animal's current activity status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting animal behavior and sounds, the system prioritizes the collection of highly relevant data based on the animals' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting animal behavior and sounds, analyze the animals' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of animal behavior and sounds. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the animal species and individual differences. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on the animal's behavior and the timing of sound collection. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, we refer to relevant animal literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice and solutions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, adjust the level of detail based on the importance of the animal's condition. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing care, different advice and treatment methods will be applied depending on the type of animal and individual differences. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the information, adjust the order of the information based on the animal's behavior and the timing of sound collection. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing information, we will improve the accuracy of the information provided by referring to relevant animal literature. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects animal behavior and sounds made by animals, An analysis unit analyzes the data collected by the aforementioned collection unit and determines the condition of the animal, The system includes a provisioning unit that provides advice and treatment methods regarding animals based on the information determined by the analysis unit. A system characterized by the following features.

2. The aforementioned supply unit is, Based on the information determined by the analysis unit, the system provides information regarding animal training or discipline. The system according to feature 1.

3. The aforementioned supply unit is, It detects stress and discomfort in animals, identifies the causes, and provides users with coping strategies to reduce stress. The system according to feature 1.

4. The aforementioned supply unit is, The system estimates the user's emotions and modifies the information provided to the user based on those estimated emotions. The system according to feature 1.

5. The aforementioned analysis unit, Based on the collected data, the behavior and sound patterns of animals are analyzed to determine their meaning and state. The system according to feature 1.

6. The aforementioned analysis unit, The system estimates the animal's emotions and determines advice or actions regarding the animal based on the estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is When collecting animal behavior and sounds, filtering is performed based on the animal's current health status and environment. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of animal behavior and sound collection based on those estimated emotions. The system according to feature 1.

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