system

Abstract

The system according to this embodiment aims to analyze pet behavior and health data and provide appropriate advice. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, a dialogue unit, and a collaboration unit. The collection unit collects pet behavior and health data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides advice based on the data analyzed by the analysis unit. The dialogue unit responds to pet care consultations via chatbot or voice dialogue. The collaboration unit collaborates with the pet's regular veterinarian.

Description

Technical Field

[0006] , , ,

[0005] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the health management of pets and the collection and analysis of behavioral data are not sufficiently carried out, and it is difficult for the owner to grasp the health status of the pet.

[0005] The system according to the embodiment aims to analyze the behavior and health data of pets and provide appropriate advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, a dialogue unit, and a collaboration unit. The data collection unit collects pet behavior and health data. The analysis unit analyzes the data collected by the data collection unit. The data provision unit provides advice based on the data analyzed by the analysis unit. The dialogue unit responds to pet care consultations via chatbot or voice dialogue. The collaboration unit collaborates with the pet's veterinarian. [Effects of the Invention]

[0007] The system according to this embodiment can analyze pet behavior and health data and provide appropriate advice. [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 controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 three or more matters are connected and expressed with "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-powered pet health management concierge according to an embodiment of the present invention is a system designed to support the happy lives of pet owners and their pets. This system automatically collects pet behavior and health data 24 hours a day, and the AI ​​analyzes this data. Next, it provides advice and alert notifications supervised by veterinarians to support pet health management. It also responds to pet care consultations through chatbots and voice dialogues, alleviating the anxieties of pet owners. Furthermore, effective health management becomes possible through collaboration with the owner's regular veterinarian. This system makes it easier for pet owners to manage their pets' health even with busy lifestyles and can reduce behavioral problems caused by a lack of communication with their pets. For example, sensors and cameras are used to automatically collect pet behavior and health data 24 hours a day. This allows for real-time acquisition of data such as the pet's movements, eating habits, and excretion. For example, it collects information such as how often the pet eats and how long it sleeps. Next, the AI ​​analyzes the collected data. The AI ​​analyzes the pet's behavior patterns and health status and issues alerts if there are any abnormalities. For example, it notifies the owner if the pet eats less than usual or exhibits abnormal behavior. This allows for early detection of abnormalities and appropriate action. Furthermore, it provides advice supervised by veterinarians. Based on data analyzed by AI, it provides pet owners with advice supervised by veterinarians. For example, it provides advice on pet diet and exercise levels to support health management. It also collaborates with the owner's regular veterinarian as needed, allowing for professional diagnosis and treatment. In addition, it provides consultation on pet care through chatbots and voice dialogues. Pet owners can ask questions about their pet's health and behavior via chatbots and voice dialogues, and the AI ​​provides appropriate answers. This allows pet owners to consult about their pet's health management anytime, anywhere. This system makes it easier for pet owners to manage their pet's health even with a busy lifestyle. For example, even when a working couple is busy with work, the AI ​​monitors their pet's health and notifies them of any abnormalities, allowing them to own a pet with peace of mind.Furthermore, it can reduce behavioral problems resulting from a lack of communication with pets. For example, if a pet is stressed, the AI ​​can detect the signs and advise the owner on appropriate responses. In this way, the AI ​​pet health management concierge is a system designed to support a happy life for both owners and their pets, and is an extremely useful tool for busy pet owners. Thus, the AI ​​pet health management concierge can support a happy life for both owners and their pets.

[0029] The pet health management system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, a dialogue unit, and a collaboration unit. The data collection unit collects pet behavior and health data. The data collection unit collects pet behavior and health data using, for example, sensors or cameras. The data collection unit can acquire data such as pet movement, eating, and excretion in real time. The data collection unit can collect information such as how often a pet eats and how long it sleeps. Some or all of the processing described above in the data collection unit may be performed using AI or not. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze, for example, the pet's behavior patterns and health status and issue an alert if there is an abnormality. The analysis unit can notify the owner if, for example, the pet eats less than usual or exhibits abnormal behavior. The analysis unit can analyze the pet's behavior patterns and detect abnormalities. Some or all of the processing described above in the analysis unit may be performed using AI or not. The provision unit provides advice based on data analyzed by the analysis unit. The provision unit provides, for example, advice supervised by a veterinarian to pet owners. The provision unit can provide, for example, advice on pet diet and exercise levels to support health management. The provision unit can provide, for example, appropriate advice based on the pet's health condition. Some or all of the above processing in the provision unit may be performed using AI or not. The dialogue unit responds to pet care consultations via chatbot or voice dialogue. The dialogue unit allows, for example, pet owners to ask questions about their pet's health and behavior via chatbot or voice dialogue, and the AI ​​provides appropriate answers. The dialogue unit allows, for example, pet owners to consult about their pet's health management anytime, anywhere. Some or all of the above processing in the dialogue unit may be performed using AI or not. The collaboration unit collaborates with the pet's regular veterinarian. The collaboration unit collaborates with the pet's regular veterinarian as needed, allowing for professional diagnosis and treatment.The collaboration unit can, for example, collaborate with the pet's veterinarian based on the pet's health condition. Some or all of the processing described above in the collaboration unit may be performed using AI or not. As a result, the pet health management system according to this embodiment can collect, analyze, provide advice on pet care, and collaborate with veterinarians on pet behavior and health data.

[0030] The data collection unit collects pet behavior and health data. For example, it uses sensors and cameras to collect pet behavior and health data. Specifically, it can use accelerometers and gyroscopes attached to the pet's collar or harness to record the pet's movements and activity levels in detail. This allows for understanding how much time the pet spends exercising and what kind of movements it is making. It can also use cameras to monitor the pet's eating and elimination habits, recording the frequency and amount of food, and the number and condition of elimination. Furthermore, it can use body temperature sensors and heart rate sensors to monitor the pet's body temperature and heart rate in real time, allowing for an understanding of its health status. This data is centrally managed by the data collection unit and transmitted to a cloud server. Some or all of the above processing in the data collection unit may be performed using AI, or not. When AI is used, the collected data can be analyzed in real time to immediately detect abnormal patterns and behaviors. For example, if a pet is less active than usual or its food intake decreases, the AI ​​can detect the abnormality and notify the owner. This allows the collection unit to constantly monitor the pet's health and detect abnormalities early.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the pet's behavior patterns and health status and issues an alert if an abnormality is detected. Specifically, it uses AI to analyze the collected data and analyze the pet's behavior patterns and health status in detail. For example, based on data such as the pet's activity level, feeding frequency, and number of times it urinates or defecates, it can learn normal behavior patterns and detect abnormal behavior or health conditions. The AI ​​can compare with past data and notify the owner if there is an abnormality in the pet's behavior or health status. For example, it can issue an alert to the owner if the pet eats less than usual or exhibits abnormal behavior. The analysis unit can also analyze the pet's behavior patterns and detect abnormalities. For example, it can detect an abnormality and notify the owner if the pet sleeps for a longer period than usual or if its activity level suddenly decreases. Some or all of the above processing in the analysis unit may be performed using AI or not. When using AI, the collected data can be analyzed in real time and abnormal patterns or behaviors can be detected immediately. This allows the analysis unit to constantly monitor the pet's health and detect abnormalities early.

[0032] The service provider provides advice based on data analyzed by the analysis unit. For example, the service provider can provide pet owners with advice supervised by a veterinarian. Specifically, it can provide advice on pet diet and exercise levels to support health management. For example, regarding pet diet, it can suggest appropriate food amounts and nutritional balance according to the pet's age, weight, and activity level. Regarding exercise, it can suggest appropriate exercise levels and methods according to the pet's breed and age. The service provider can provide appropriate advice based on the pet's health condition. For example, if a pet is gaining weight, it can suggest adjusting food intake or increasing exercise levels to support health management. Some or all of the above processing in the service provider may be performed using AI or not. When using AI, it can automatically generate advice tailored to the pet's health condition based on data analyzed by the analysis unit and provide it to the pet owner. This allows the service provider to support pet health management and provide information to enable pet owners to take appropriate action.

[0033] The dialogue unit handles pet care consultations via chatbot or voice interaction. For example, pet owners can ask questions about their pet's health and behavior using the chatbot or voice interaction, and the AI ​​provides appropriate answers. Specifically, when a pet owner inputs questions about their pet's health and behavior using a smartphone or tablet, the AI ​​can provide appropriate answers based on past data and expertise. For example, in response to questions about a pet's food intake and exercise, the AI ​​can provide advice tailored to the pet's health condition. When using voice interaction, the AI ​​analyzes the question using speech recognition technology and provides an appropriate answer via voice. This allows pet owners to consult about their pet's health management anytime, anywhere. Some or all of the above-described processes in the dialogue unit may be performed using AI or not. When using AI, it can provide quick and appropriate answers to pet owners' questions based on past data and expertise. This allows the dialogue unit to support pet owners in resolving their doubts and anxieties about their pet's health management and taking appropriate action.

[0034] The liaison unit coordinates with the pet's primary veterinarian. For example, the liaison unit can coordinate with the primary veterinarian as needed to provide specialized diagnosis and treatment. Specifically, if an abnormality is detected in the pet's health, the liaison unit automatically notifies the primary veterinarian and schedules a diagnosis and treatment. For example, if the pet's body temperature is abnormally high or its food intake decreases sharply, the liaison unit notifies the primary veterinarian so that diagnosis and treatment can be provided early. The liaison unit can also coordinate with the primary veterinarian based on the pet's health condition. For example, it can manage the schedule for regular health checks and vaccinations, supporting coordination with the primary veterinarian. Some or all of the above processes in the liaison unit may be performed using AI or not. When using AI, it can automatically coordinate with the primary veterinarian based on the pet's health condition, supporting a quick and appropriate response. This allows the liaison unit to support pet health management and provide information to help pet owners take appropriate action.

[0035] The data collection unit can collect pet behavior and health data using sensors or cameras. For example, the data collection unit can acquire data such as the pet's movements, eating habits, and excretion in real time using sensors or cameras. For example, the data collection unit can collect information such as how often the pet eats and how long it sleeps. For example, the data collection unit can detect the pet's movements and measure its activity level using an accelerometer. For example, the data collection unit can measure the pet's body temperature using a temperature sensor. For example, the data collection unit can record the pet's behavior using a wearable camera. This allows for the accurate collection of pet behavior and health data using sensors and cameras. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input data acquired by sensors or cameras into a generating AI and have the generating AI perform data analysis.

[0036] The analysis unit can analyze the collected data and issue alerts if an anomaly is detected. For example, the analysis unit can analyze a pet's behavior patterns and health status and issue alerts if an anomaly is detected. For example, the analysis unit can notify the owner if the pet eats less than usual or exhibits abnormal behavior. For example, the analysis unit can analyze a pet's behavior patterns and detect anomalies. For example, the analysis unit can analyze a pet's health data and detect anomalies. For example, the analysis unit can analyze data such as a pet's body temperature and heart rate and detect anomalies. This allows for early detection of anomalies and the issuance of alerts, enabling a rapid response. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform anomaly detection.

[0037] The service provider can provide advice supervised by a veterinarian. For example, the service provider can provide veterinarian-supervised advice to pet owners. For example, the service provider can provide advice on pet diet and exercise levels to support health management. For example, the service provider can provide appropriate advice based on the pet's health condition. For example, the service provider can provide appropriate advice based on the pet's behavioral patterns. For example, the service provider can provide appropriate advice based on the pet's health data. This enables highly reliable health management by providing veterinarian-supervised advice. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input data analyzed by the analysis unit into a generation AI and have the generation AI generate advice.

[0038] The dialogue unit can handle pet care consultations through chatbots or voice dialogues. For example, the dialogue unit can allow pet owners to ask questions about their pet's health or behavior via chatbot or voice dialogue, and the AI ​​will provide appropriate answers. For example, the dialogue unit can allow pet owners to consult about their pet's health management anytime, anywhere. For example, the dialogue unit can allow pet owners to ask questions about their pet's health condition, and the AI ​​will provide appropriate answers. For example, the dialogue unit can allow pet owners to ask questions about their pet's behavior, and the AI ​​will provide appropriate answers. For example, the dialogue unit can allow pet owners to ask questions about their pet's diet, and the AI ​​will provide appropriate answers. This allows pet owners to alleviate their anxieties by handling pet care consultations through chatbots or voice dialogues. Some or all of the above processing in the dialogue unit may be performed using AI, or it may not be performed using AI. For example, the dialogue unit can input the pet owner's question into a generating AI, and have the generating AI produce an appropriate answer.

[0039] The collaboration unit can collaborate with the pet's primary veterinarian. For example, the collaboration unit can collaborate with the pet's primary veterinarian as needed to receive specialized diagnosis and treatment. For example, the collaboration unit can collaborate with the pet's primary veterinarian based on the pet's health condition. For example, the collaboration unit can collaborate with the pet's primary veterinarian based on the pet's behavioral patterns. For example, the collaboration unit can collaborate with the pet's primary veterinarian based on the pet's health data. This allows for specialized diagnosis and treatment through collaboration with the pet's primary veterinarian. Some or all of the above-described processes in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input pet health data into a generating AI and have the generating AI select the collaboration method.

[0040] The data collection unit can estimate the pet's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the pet is stressed, the data collection unit can increase the frequency of data collection and collect detailed behavioral data. For example, if the pet is relaxed, the data collection unit can decrease the frequency of data collection and collect only the minimum necessary data. For example, if the pet is excited, the data collection unit can prioritize the collection of specific behavioral data. This allows for more appropriate data collection by adjusting the frequency of data collection according to the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the pet's emotion data into the generative AI and have the generative AI adjust the frequency of data collection.

[0041] The data collection unit can analyze past behavioral data of pets and select the optimal data collection method. For example, the data collection unit can select the types of data to collect based on behaviors that pets have frequently performed in the past. For example, the data collection unit can analyze past behavioral patterns of pets and determine the optimal timing for data collection. For example, the data collection unit can prioritize the collection of specific health indicators based on past health data of pets. This allows for the selection of the optimal data collection method by analyzing past behavioral data of pets. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past behavioral data of pets into a generating AI and have the generating AI select the optimal data collection method.

[0042] The data collection unit can filter data based on the pet's current health status and activity level during data collection. For example, if the pet is healthy, the data collection unit will perform normal data collection. If the pet is unwell, for example, the data collection unit can prioritize the collection of detailed health data. If the pet is highly active, for example, the data collection unit can focus on collecting exercise data. This allows for efficient collection of necessary data by filtering the data based on the pet's current health status and activity level. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the pet's health status and activity level into a generating AI and have the generating AI perform the filtering.

[0043] The data collection unit can estimate the pet's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the pet is stressed, the data collection unit will prioritize collecting stress-related data. For example, if the pet is relaxed, the data collection unit can collect normal behavioral data. For example, if the pet is excited, the data collection unit can prioritize collecting data that is causing the excitement. This allows for the priority collection of important data by determining the priority of data to collect based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input pet emotion data into a generative AI and have the generative AI determine the priority of the data.

[0044] The data collection unit can prioritize the collection of highly relevant data based on the pet's living environment information during data collection. For example, if the pet spends a lot of time indoors, the data collection unit can collect indoor environment data. For example, if the pet spends a lot of time outdoors, the data collection unit can collect external environment data. For example, if the pet's living environment changes, the data collection unit can collect data to help the pet adapt to the new environment. This allows for more appropriate data collection by prioritizing the collection of highly relevant data based on the pet's living environment information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the pet's living environment information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0045] The data collection unit can analyze the lifestyle patterns of pet owners and collect relevant data during data collection. For example, the data collection unit can collect pet behavior data during times when the owner is away at work. For example, the data collection unit can collect pet behavior data during times when the owner is at home. For example, the data collection unit can collect pet health data in accordance with the owner's lifestyle patterns. This allows for the efficient collection of relevant data by analyzing the lifestyle patterns of pet owners. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the owner's lifestyle pattern data into a generating AI and have the generating AI perform the collection of relevant data.

[0046] The analysis unit can estimate the pet's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the pet is stressed, the analysis unit will focus on analyzing stress-related data. For example, if the pet is relaxed, the analysis unit can analyze normal behavioral data. For example, if the pet is excited, the analysis unit can focus on analyzing data that causes the excitement. By adjusting the analysis algorithm based on the pet's emotions, a more accurate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input pet emotion data into a generative AI and have the generative AI adjust the analysis algorithm.

[0047] The analysis unit can improve the accuracy of anomaly detection based on the pet's behavior patterns during analysis. For example, the analysis unit can learn the pet's normal behavior patterns and detect abnormal behavior. For example, the analysis unit can detect early signs of abnormal behavior based on the pet's past behavior data. For example, the analysis unit can analyze the pet's behavior patterns in real time and immediately detect abnormal behavior. This allows for early detection of abnormal behavior by improving the accuracy of anomaly detection based on the pet's behavior patterns. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input pet behavior pattern data into a generating AI and have the generating AI perform the task of improving the accuracy of anomaly detection.

[0048] The analysis unit can perform analysis based on the pet's health history. For example, the analysis unit can analyze the pet's current health status based on the pet's past health data. For example, the analysis unit can detect signs of abnormalities by considering the pet's health history. For example, the analysis unit can predict future health risks based on the pet's health history. This makes it possible to perform a more accurate analysis of the pet's health status by performing analysis based on the pet's health history. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the pet's health history data into a generating AI and have the generating AI perform the analysis.

[0049] The analysis unit can estimate the pet's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the pet is stressed, the analysis unit can highlight stress-related data. For example, if the pet is relaxed, the analysis unit can display normal behavioral data. For example, if the pet is excited, the analysis unit can highlight data that is causing the excitement. By adjusting the display method of the analysis results based on the pet's emotions, a more easily understandable display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the pet's emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0050] The analysis unit can improve the accuracy of its analysis based on the pet's living environment data. For example, the analysis unit can analyze the pet's health status based on the pet's living environment data. For example, if the pet's living environment changes, the analysis unit can analyze data to help the pet adapt to the new environment. For example, the analysis unit can identify the cause of abnormal behavior by referring to the pet's living environment data. By improving the accuracy of the analysis based on the pet's living environment data, a more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the pet's living environment data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0051] The analysis unit can optimize its analysis algorithm by incorporating feedback from pet owners during analysis. For example, the analysis unit can adjust the analysis algorithm based on owner feedback. For example, the analysis unit can improve the accuracy of anomaly detection by incorporating owner feedback. For example, the analysis unit can optimize the display method of the analysis results based on owner feedback. This makes it possible to perform more accurate analysis by optimizing the analysis algorithm in accordance with pet owner feedback. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input owner feedback data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0052] The service provider can estimate the pet's emotions and adjust the content of the advice based on the estimated emotions. For example, if the pet is stressed, the service provider can provide advice to reduce stress. For example, if the pet is relaxed, the service provider can provide regular health management advice. For example, if the pet is excited, the service provider can provide advice to calm the excitement. By adjusting the content of the advice based on the pet's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input pet emotion data into the generative AI and have the generative AI adjust the content of the advice.

[0053] The service provider can adjust the level of detail of the advice based on the pet's health condition when providing advice. For example, if the pet is healthy, the service provider will provide general health management advice. For example, if the pet is unwell, the service provider can provide detailed health management advice. The service provider can adjust the content of the advice according to the pet's health condition. By adjusting the level of detail of the advice based on the pet's health condition, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input pet health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0054] The service provider can provide different advice depending on the pet's behavioral patterns. For example, the service provider can provide advice on the appropriate amount of exercise based on the pet's behavioral patterns. For example, the service provider can provide advice on the pet's diet depending on the pet's behavioral patterns. For example, the service provider can provide advice on stress reduction, taking into account the pet's behavioral patterns. By providing different advice depending on the pet's behavioral patterns, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input pet behavioral pattern data into a generating AI and have the generating AI adjust the content of the advice.

[0055] The service provider can estimate the pet's emotions and prioritize advice based on the estimated emotions. For example, if the pet is stressed, the service provider will prioritize stress reduction advice. For example, if the pet is relaxed, the service provider can provide regular health management advice. For example, if the pet is excited, the service provider can prioritize advice to calm the excitement. This allows for the priority of important advice by prioritizing it based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input pet emotion data into a generative AI and have the generative AI determine the priority of advice.

[0056] The service provider can provide optimal advice based on the pet owner's lifestyle when offering advice. For example, if the owner is busy, the service provider can provide easy-to-implement advice. For example, if the owner spends a lot of time at home, the service provider can provide detailed health management advice. For example, the service provider can adjust the content of the advice to suit the owner's lifestyle. By providing optimal advice based on the pet owner's lifestyle, the service provider can offer advice that is easy for the owner to implement. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the owner's lifestyle data into a generating AI and have the generating AI adjust the content of the advice.

[0057] The service provider can optimize the advice content by reflecting feedback from pet owners when providing advice. For example, the service provider can adjust the advice content based on the owner's feedback. For example, the service provider can improve the effectiveness of the advice by reflecting feedback from the owner. For example, the service provider can determine the priority of the advice based on feedback from the owner. This allows for the provision of more effective advice by optimizing the advice content by reflecting feedback from pet owners. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input owner feedback data into a generating AI and have the generating AI perform the optimization of the advice content.

[0058] The dialogue unit can estimate the pet's emotions and adjust the content of the dialogue based on the estimated emotions. For example, if the pet is stressed, the dialogue unit can engage in dialogue to reduce stress. For example, if the pet is relaxed, the dialogue unit can engage in dialogue about normal health management. For example, if the pet is excited, the dialogue unit can engage in dialogue to calm the excitement. By adjusting the content of the dialogue based on the pet's emotions, more appropriate dialogue becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input pet emotion data into the generative AI and have the generative AI adjust the content of the dialogue.

[0059] The dialogue unit can provide the best answer by referring to the pet owner's past consultation history during the conversation. For example, the dialogue unit can provide the best answer to a similar problem based on the owner's past consultation history. For example, the dialogue unit can refer to the content of past consultations by the owner and provide relevant advice. For example, the dialogue unit can analyze the owner's past consultation history and provide the most effective answer. This allows for the provision of more appropriate answers by referring to the pet owner's past consultation history. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the owner's past consultation history data into a generating AI and have the generating AI perform the generation of the best answer.

[0060] The dialogue unit can select a conversation topic based on the pet's health condition during the conversation. For example, if the pet is healthy, the dialogue unit will engage in conversation about general health management. If the pet is unwell, the dialogue unit can engage in conversation about improving its condition. The dialogue unit can select an appropriate conversation topic according to the pet's health condition. This allows for more appropriate conversations by selecting a conversation topic based on the pet's health condition. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input pet health data into a generating AI and have the generating AI select the conversation topic.

[0061] The dialogue unit can estimate the pet's emotions and determine the priority of the dialogue based on the estimated emotions. For example, if the pet is stressed, the dialogue unit will prioritize dialogue to reduce stress. If the pet is relaxed, the dialogue unit can engage in dialogue about normal health management. If the pet is excited, the dialogue unit can prioritize dialogue to calm the excitement. In this way, important dialogues can be prioritized by determining the priority of the dialogue based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input pet emotion data into the generative AI and have the generative AI determine the priority of the dialogue.

[0062] The dialogue unit can provide optimal dialogue content based on the pet owner's living environment during a conversation. For example, if the owner is busy, the dialogue unit can provide concise and easy-to-follow dialogue content. For example, if the owner spends a lot of time at home, the dialogue unit can provide detailed dialogue content regarding health management. The dialogue unit can adjust the dialogue content to suit the owner's living environment. By providing optimal dialogue content based on the pet owner's living environment, it can provide dialogue that is easy for the owner to follow. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the owner's living environment data into a generating AI and have the generating AI adjust the dialogue content.

[0063] The dialogue unit can optimize its dialogue algorithm by reflecting feedback from the pet owner during the dialogue. For example, the dialogue unit can adjust the dialogue algorithm based on the owner's feedback. For example, the dialogue unit can improve the effectiveness of the dialogue by reflecting the owner's feedback. For example, the dialogue unit can determine the priority of the dialogue content based on the owner's feedback. This makes it possible to have more effective dialogues by optimizing the dialogue algorithm by reflecting feedback from the pet owner. Some or all of the above processes in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input owner feedback data into a generating AI and have the generating AI perform the optimization of the dialogue algorithm.

[0064] The collaboration unit can estimate the pet's emotions and adjust the collaboration method based on the estimated emotions. For example, if the pet is stressed, the collaboration unit can provide a collaboration method to reduce stress. For example, if the pet is relaxed, the collaboration unit can provide a collaboration method related to normal health management. For example, if the pet is excited, the collaboration unit can provide a collaboration method to calm the excitement. By adjusting the collaboration method based on the pet's emotions, more appropriate collaboration becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using AI or not using AI. For example, the collaboration unit can input pet emotion data into a generative AI and have the generative AI perform the adjustment of the collaboration method.

[0065] The integration unit can select the optimal integration method based on the pet's health data during integration. For example, the integration unit selects the optimal integration method based on the pet's health data. For example, the integration unit can adjust the integration method according to the pet's health condition. For example, the integration unit can refer to the pet's health data and change the integration method if there is an abnormality. This makes more effective integration possible by selecting the optimal integration method based on the pet's health data. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input the pet's health data into a generating AI and have the generating AI select the optimal integration method.

[0066] The collaboration unit can optimize its collaboration algorithm by reflecting feedback from pet owners during collaboration. For example, the collaboration unit can adjust the collaboration algorithm based on owner feedback. For example, the collaboration unit can improve the effectiveness of collaboration by reflecting owner feedback. For example, the collaboration unit can determine the priority of collaboration content based on owner feedback. This makes more effective collaboration possible by optimizing the collaboration algorithm by reflecting feedback from pet owners. Some or all of the above processes in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input owner feedback data into a generating AI and have the generating AI perform the optimization of the collaboration algorithm.

[0067] The collaboration unit can estimate the pet's emotions and determine the priority of collaborations based on the estimated emotions. For example, if the pet is stressed, the collaboration unit will prioritize stress reduction collaborations. For example, if the pet is relaxed, the collaboration unit can perform collaborations related to normal health management. For example, if the pet is excited, the collaboration unit can prioritize collaborations to calm the excitement. In this way, by determining the priority of collaborations based on the pet's emotions, important collaborations can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using AI or not using AI. For example, the collaboration unit can input pet emotion data into a generative AI and have the generative AI perform the determination of collaboration priorities.

[0068] The integration unit can provide the optimal integration method based on the pet owner's living environment during integration. For example, if the owner is busy, the integration unit can provide a simple and easy-to-implement integration method. For example, if the owner spends a lot of time at home, the integration unit can provide a detailed health management integration method. For example, the integration unit can adjust the integration method to suit the owner's living environment. By providing the optimal integration method based on the pet owner's living environment, it can provide integration that is easy for the owner to implement. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input the owner's living environment data into a generating AI and have the generating AI perform the adjustment of the integration method.

[0069] The integration unit can optimize the integration process by reflecting feedback from pet owners during integration. For example, the integration unit can adjust the integration process based on owner feedback. For example, the integration unit can improve the effectiveness of the integration by reflecting owner feedback. For example, the integration unit can determine the priority of the integration process based on owner feedback. This makes more effective integration possible by optimizing the integration process by reflecting feedback from pet owners. Some or all of the above processes in the integration unit may be performed using AI or not. For example, the integration unit can input owner feedback data into a generating AI and have the generating AI perform the optimization of the integration process.

[0070] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0071] Pet health management systems can also incorporate predictive functions based on pet behavior data. These predictive functions, for example, analyze past behavioral data to predict future behavioral patterns. For instance, if a pet tends to be more active during certain times of the day, the predictive function can adjust the data collection frequency accordingly. Furthermore, if a pet is prone to illness during certain seasons, the predictive function can provide health management advice tailored to those seasons. In this way, incorporating predictive functions based on pet behavior data enables more effective health management.

[0072] The analysis unit can be equipped with a function to predict abnormal behavior based on the pet's behavioral data. This function can, for example, analyze the pet's past behavioral data to detect early signs of abnormal behavior. It can also predict the possibility of abnormal behavior if the pet exhibits a specific behavioral pattern. Furthermore, if the pet tends to exhibit abnormal behavior in a particular environment, it can predict abnormal behavior in that environment. By incorporating a function to predict abnormal behavior based on the pet's behavioral data, early detection of abnormal behavior becomes possible.

[0073] The service provider can be equipped with a customized advice function based on pet behavior data. This customized advice function can, for example, provide individualized health management advice based on the pet's behavior patterns. It can also provide optimal advice based on the pet's diet and exercise level. Furthermore, it can provide specific health management advice based on the pet's health condition. By incorporating a customized advice function based on pet behavior data, more personalized health management becomes possible.

[0074] The analysis unit can be equipped with a function for early detection of abnormal behavior based on pet behavioral data. This function can, for example, analyze the pet's behavioral patterns in real time and immediately detect abnormal behavior. It can also, for example, detect early signs of abnormal behavior based on the pet's past behavioral data. Furthermore, it can predict abnormal behavior based on the pet's health condition. By incorporating this early detection function for abnormal behavior based on pet behavioral data, a rapid response becomes possible.

[0075] The following briefly describes the processing flow for example form 1.

[0076] Step 1: The collection unit collects pet behavior and health data. The collection unit collects pet behavior and health data using, for example, sensors or cameras. The collection unit can acquire data such as pet movement, eating, and excretion in real time. The collection unit can collect information such as how often the pet eats and how long it sleeps. Some or all of the above processing in the collection unit may be performed using AI or not. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes, for example, the pet's behavior patterns and health status, and issues an alert if there is an abnormality. The analysis unit can notify the owner, for example, if the pet eats less than usual or exhibits abnormal behavior. The analysis unit can analyze the pet's behavior patterns and detect abnormalities. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without using AI. Step 3: The service provider provides advice based on the data analyzed by the analysis unit. For example, the service provider provides pet owners with advice supervised by a veterinarian. For example, the service provider can provide advice on pet diet and exercise levels to support health management. For example, the service provider can provide appropriate advice based on the pet's health condition. Some or all of the above processing in the service provider may be performed using AI or not. Step 4: The dialogue unit handles pet care consultations via chatbot or voice interaction. For example, pet owners can ask questions about their pet's health and behavior via chatbot or voice interaction, and the AI ​​will provide appropriate answers. For example, pet owners can consult about their pet's health management anytime, anywhere. Some or all of the above processing in the dialogue unit may be performed using AI or not. Step 5: The liaison unit collaborates with the pet's primary veterinarian. The liaison unit can collaborate with the pet's primary veterinarian as needed to obtain specialized diagnoses and treatments. The liaison unit can collaborate with the pet's primary veterinarian based on the pet's health condition, for example. Some or all of the above processes in the liaison unit may be performed using AI or not.

[0077] (Example of form 2) The AI-powered pet health management concierge according to an embodiment of the present invention is a system designed to support the happy lives of pet owners and their pets. This system automatically collects pet behavior and health data 24 hours a day, and the AI ​​analyzes this data. Next, it provides advice and alert notifications supervised by veterinarians to support pet health management. It also responds to pet care consultations through chatbots and voice dialogues, alleviating the anxieties of pet owners. Furthermore, effective health management becomes possible through collaboration with the owner's regular veterinarian. This system makes it easier for pet owners to manage their pets' health even with busy lifestyles and can reduce behavioral problems caused by a lack of communication with their pets. For example, sensors and cameras are used to automatically collect pet behavior and health data 24 hours a day. This allows for real-time acquisition of data such as the pet's movements, eating habits, and excretion. For example, it collects information such as how often the pet eats and how long it sleeps. Next, the AI ​​analyzes the collected data. The AI ​​analyzes the pet's behavior patterns and health status and issues alerts if there are any abnormalities. For example, it notifies the owner if the pet eats less than usual or exhibits abnormal behavior. This allows for early detection of abnormalities and appropriate action. Furthermore, it provides advice supervised by veterinarians. Based on data analyzed by AI, it provides pet owners with advice supervised by veterinarians. For example, it provides advice on pet diet and exercise levels to support health management. It also collaborates with the owner's regular veterinarian as needed, allowing for professional diagnosis and treatment. In addition, it provides consultation on pet care through chatbots and voice dialogues. Pet owners can ask questions about their pet's health and behavior via chatbots and voice dialogues, and the AI ​​provides appropriate answers. This allows pet owners to consult about their pet's health management anytime, anywhere. This system makes it easier for pet owners to manage their pet's health even with a busy lifestyle. For example, even when a working couple is busy with work, the AI ​​monitors their pet's health and notifies them of any abnormalities, allowing them to own a pet with peace of mind.Furthermore, it can reduce behavioral problems resulting from a lack of communication with pets. For example, if a pet is stressed, the AI ​​can detect the signs and advise the owner on appropriate responses. In this way, the AI ​​pet health management concierge is a system designed to support a happy life for both owners and their pets, and is an extremely useful tool for busy pet owners. Thus, the AI ​​pet health management concierge can support a happy life for both owners and their pets.

[0078] The pet health management system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, a dialogue unit, and a collaboration unit. The data collection unit collects pet behavior and health data. The data collection unit collects pet behavior and health data using, for example, sensors or cameras. The data collection unit can acquire data such as pet movement, eating, and excretion in real time. The data collection unit can collect information such as how often a pet eats and how long it sleeps. Some or all of the processing described above in the data collection unit may be performed using AI or not. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze, for example, the pet's behavior patterns and health status and issue an alert if there is an abnormality. The analysis unit can notify the owner if, for example, the pet eats less than usual or exhibits abnormal behavior. The analysis unit can analyze the pet's behavior patterns and detect abnormalities. Some or all of the processing described above in the analysis unit may be performed using AI or not. The provision unit provides advice based on data analyzed by the analysis unit. The provision unit provides, for example, advice supervised by a veterinarian to pet owners. The provision unit can provide, for example, advice on pet diet and exercise levels to support health management. The provision unit can provide, for example, appropriate advice based on the pet's health condition. Some or all of the above processing in the provision unit may be performed using AI or not. The dialogue unit responds to pet care consultations via chatbot or voice dialogue. The dialogue unit allows, for example, pet owners to ask questions about their pet's health and behavior via chatbot or voice dialogue, and the AI ​​provides appropriate answers. The dialogue unit allows, for example, pet owners to consult about their pet's health management anytime, anywhere. Some or all of the above processing in the dialogue unit may be performed using AI or not. The collaboration unit collaborates with the pet's regular veterinarian. The collaboration unit collaborates with the pet's regular veterinarian as needed, allowing for professional diagnosis and treatment.The collaboration unit can, for example, collaborate with the pet's veterinarian based on the pet's health condition. Some or all of the processing described above in the collaboration unit may be performed using AI or not. As a result, the pet health management system according to this embodiment can collect, analyze, provide advice on pet care, and collaborate with veterinarians on pet behavior and health data.

[0079] The data collection unit collects pet behavior and health data. For example, it uses sensors and cameras to collect pet behavior and health data. Specifically, it can use accelerometers and gyroscopes attached to the pet's collar or harness to record the pet's movements and activity levels in detail. This allows for understanding how much time the pet spends exercising and what kind of movements it is making. It can also use cameras to monitor the pet's eating and elimination habits, recording the frequency and amount of food, and the number and condition of elimination. Furthermore, it can use body temperature sensors and heart rate sensors to monitor the pet's body temperature and heart rate in real time, allowing for an understanding of its health status. This data is centrally managed by the data collection unit and transmitted to a cloud server. Some or all of the above processing in the data collection unit may be performed using AI, or not. When AI is used, the collected data can be analyzed in real time to immediately detect abnormal patterns and behaviors. For example, if a pet is less active than usual or its food intake decreases, the AI ​​can detect the abnormality and notify the owner. This allows the collection unit to constantly monitor the pet's health and detect abnormalities early.

[0080] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the pet's behavior patterns and health status and issues an alert if an abnormality is detected. Specifically, it uses AI to analyze the collected data and analyze the pet's behavior patterns and health status in detail. For example, based on data such as the pet's activity level, feeding frequency, and number of times it urinates or defecates, it can learn normal behavior patterns and detect abnormal behavior or health conditions. The AI ​​can compare with past data and notify the owner if there is an abnormality in the pet's behavior or health status. For example, it can issue an alert to the owner if the pet eats less than usual or exhibits abnormal behavior. The analysis unit can also analyze the pet's behavior patterns and detect abnormalities. For example, it can detect an abnormality and notify the owner if the pet sleeps for a longer period than usual or if its activity level suddenly decreases. Some or all of the above processing in the analysis unit may be performed using AI or not. When using AI, the collected data can be analyzed in real time and abnormal patterns or behaviors can be detected immediately. This allows the analysis unit to constantly monitor the pet's health and detect abnormalities early.

[0081] The service provider provides advice based on data analyzed by the analysis unit. For example, the service provider can provide pet owners with advice supervised by a veterinarian. Specifically, it can provide advice on pet diet and exercise levels to support health management. For example, regarding pet diet, it can suggest appropriate food amounts and nutritional balance according to the pet's age, weight, and activity level. Regarding exercise, it can suggest appropriate exercise levels and methods according to the pet's breed and age. The service provider can provide appropriate advice based on the pet's health condition. For example, if a pet is gaining weight, it can suggest adjusting food intake or increasing exercise levels to support health management. Some or all of the above processing in the service provider may be performed using AI or not. When using AI, it can automatically generate advice tailored to the pet's health condition based on data analyzed by the analysis unit and provide it to the pet owner. This allows the service provider to support pet health management and provide information to enable pet owners to take appropriate action.

[0082] The dialogue unit handles pet care consultations via chatbot or voice interaction. For example, pet owners can ask questions about their pet's health and behavior using the chatbot or voice interaction, and the AI ​​provides appropriate answers. Specifically, when a pet owner inputs questions about their pet's health and behavior using a smartphone or tablet, the AI ​​can provide appropriate answers based on past data and expertise. For example, in response to questions about a pet's food intake and exercise, the AI ​​can provide advice tailored to the pet's health condition. When using voice interaction, the AI ​​analyzes the question using speech recognition technology and provides an appropriate answer via voice. This allows pet owners to consult about their pet's health management anytime, anywhere. Some or all of the above-described processes in the dialogue unit may be performed using AI or not. When using AI, it can provide quick and appropriate answers to pet owners' questions based on past data and expertise. This allows the dialogue unit to support pet owners in resolving their doubts and anxieties about their pet's health management and taking appropriate action.

[0083] The liaison unit coordinates with the pet's primary veterinarian. For example, the liaison unit can coordinate with the primary veterinarian as needed to provide specialized diagnosis and treatment. Specifically, if an abnormality is detected in the pet's health, the liaison unit automatically notifies the primary veterinarian and schedules a diagnosis and treatment. For example, if the pet's body temperature is abnormally high or its food intake decreases sharply, the liaison unit notifies the primary veterinarian so that diagnosis and treatment can be provided early. The liaison unit can also coordinate with the primary veterinarian based on the pet's health condition. For example, it can manage the schedule for regular health checks and vaccinations, supporting coordination with the primary veterinarian. Some or all of the above processes in the liaison unit may be performed using AI or not. When using AI, it can automatically coordinate with the primary veterinarian based on the pet's health condition, supporting a quick and appropriate response. This allows the liaison unit to support pet health management and provide information to help pet owners take appropriate action.

[0084] The data collection unit can collect pet behavior and health data using sensors or cameras. For example, the data collection unit can acquire data such as the pet's movements, eating habits, and excretion in real time using sensors or cameras. For example, the data collection unit can collect information such as how often the pet eats and how long it sleeps. For example, the data collection unit can detect the pet's movements and measure its activity level using an accelerometer. For example, the data collection unit can measure the pet's body temperature using a temperature sensor. For example, the data collection unit can record the pet's behavior using a wearable camera. This allows for the accurate collection of pet behavior and health data using sensors and cameras. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input data acquired by sensors or cameras into a generating AI and have the generating AI perform data analysis.

[0085] The analysis unit can analyze the collected data and issue alerts if an anomaly is detected. For example, the analysis unit can analyze a pet's behavior patterns and health status and issue alerts if an anomaly is detected. For example, the analysis unit can notify the owner if the pet eats less than usual or exhibits abnormal behavior. For example, the analysis unit can analyze a pet's behavior patterns and detect anomalies. For example, the analysis unit can analyze a pet's health data and detect anomalies. For example, the analysis unit can analyze data such as a pet's body temperature and heart rate and detect anomalies. This allows for early detection of anomalies and the issuance of alerts, enabling a rapid response. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform anomaly detection.

[0086] The service provider can provide advice supervised by a veterinarian. For example, the service provider can provide veterinarian-supervised advice to pet owners. For example, the service provider can provide advice on pet diet and exercise levels to support health management. For example, the service provider can provide appropriate advice based on the pet's health condition. For example, the service provider can provide appropriate advice based on the pet's behavioral patterns. For example, the service provider can provide appropriate advice based on the pet's health data. This enables highly reliable health management by providing veterinarian-supervised advice. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input data analyzed by the analysis unit into a generation AI and have the generation AI generate advice.

[0087] The dialogue unit can handle pet care consultations through chatbots or voice dialogues. For example, the dialogue unit can allow pet owners to ask questions about their pet's health or behavior via chatbot or voice dialogue, and the AI ​​will provide appropriate answers. For example, the dialogue unit can allow pet owners to consult about their pet's health management anytime, anywhere. For example, the dialogue unit can allow pet owners to ask questions about their pet's health condition, and the AI ​​will provide appropriate answers. For example, the dialogue unit can allow pet owners to ask questions about their pet's behavior, and the AI ​​will provide appropriate answers. For example, the dialogue unit can allow pet owners to ask questions about their pet's diet, and the AI ​​will provide appropriate answers. This allows pet owners to alleviate their anxieties by handling pet care consultations through chatbots or voice dialogues. Some or all of the above processing in the dialogue unit may be performed using AI, or it may not be performed using AI. For example, the dialogue unit can input the pet owner's question into a generating AI, and have the generating AI produce an appropriate answer.

[0088] The collaboration unit can collaborate with the pet's primary veterinarian. For example, the collaboration unit can collaborate with the pet's primary veterinarian as needed to receive specialized diagnosis and treatment. For example, the collaboration unit can collaborate with the pet's primary veterinarian based on the pet's health condition. For example, the collaboration unit can collaborate with the pet's primary veterinarian based on the pet's behavioral patterns. For example, the collaboration unit can collaborate with the pet's primary veterinarian based on the pet's health data. This allows for specialized diagnosis and treatment through collaboration with the pet's primary veterinarian. Some or all of the above-described processes in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input pet health data into a generating AI and have the generating AI select the collaboration method.

[0089] The data collection unit can estimate the pet's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the pet is stressed, the data collection unit can increase the frequency of data collection and collect detailed behavioral data. For example, if the pet is relaxed, the data collection unit can decrease the frequency of data collection and collect only the minimum necessary data. For example, if the pet is excited, the data collection unit can prioritize the collection of specific behavioral data. This allows for more appropriate data collection by adjusting the frequency of data collection according to the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the pet's emotion data into the generative AI and have the generative AI adjust the frequency of data collection.

[0090] The data collection unit can analyze past behavioral data of pets and select the optimal data collection method. For example, the data collection unit can select the types of data to collect based on behaviors that pets have frequently performed in the past. For example, the data collection unit can analyze past behavioral patterns of pets and determine the optimal timing for data collection. For example, the data collection unit can prioritize the collection of specific health indicators based on past health data of pets. This allows for the selection of the optimal data collection method by analyzing past behavioral data of pets. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past behavioral data of pets into a generating AI and have the generating AI select the optimal data collection method.

[0091] The data collection unit can filter data based on the pet's current health status and activity level during data collection. For example, if the pet is healthy, the data collection unit will perform normal data collection. If the pet is unwell, for example, the data collection unit can prioritize the collection of detailed health data. If the pet is highly active, for example, the data collection unit can focus on collecting exercise data. This allows for efficient collection of necessary data by filtering the data based on the pet's current health status and activity level. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the pet's health status and activity level into a generating AI and have the generating AI perform the filtering.

[0092] The data collection unit can estimate the pet's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the pet is stressed, the data collection unit will prioritize collecting stress-related data. For example, if the pet is relaxed, the data collection unit can collect normal behavioral data. For example, if the pet is excited, the data collection unit can prioritize collecting data that is causing the excitement. This allows for the priority collection of important data by determining the priority of data to collect based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input pet emotion data into a generative AI and have the generative AI determine the priority of the data.

[0093] The data collection unit can prioritize the collection of highly relevant data based on the pet's living environment information during data collection. For example, if the pet spends a lot of time indoors, the data collection unit can collect indoor environment data. For example, if the pet spends a lot of time outdoors, the data collection unit can collect external environment data. For example, if the pet's living environment changes, the data collection unit can collect data to help the pet adapt to the new environment. This allows for more appropriate data collection by prioritizing the collection of highly relevant data based on the pet's living environment information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the pet's living environment information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0094] The data collection unit can analyze the lifestyle patterns of pet owners and collect relevant data during data collection. For example, the data collection unit can collect pet behavior data during times when the owner is away at work. For example, the data collection unit can collect pet behavior data during times when the owner is at home. For example, the data collection unit can collect pet health data in accordance with the owner's lifestyle patterns. This allows for the efficient collection of relevant data by analyzing the lifestyle patterns of pet owners. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the owner's lifestyle pattern data into a generating AI and have the generating AI perform the collection of relevant data.

[0095] The analysis unit can estimate the pet's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the pet is stressed, the analysis unit will focus on analyzing stress-related data. For example, if the pet is relaxed, the analysis unit can analyze normal behavioral data. For example, if the pet is excited, the analysis unit can focus on analyzing data that causes the excitement. By adjusting the analysis algorithm based on the pet's emotions, a more accurate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input pet emotion data into a generative AI and have the generative AI adjust the analysis algorithm.

[0096] The analysis unit can improve the accuracy of anomaly detection based on the pet's behavior patterns during analysis. For example, the analysis unit can learn the pet's normal behavior patterns and detect abnormal behavior. For example, the analysis unit can detect early signs of abnormal behavior based on the pet's past behavior data. For example, the analysis unit can analyze the pet's behavior patterns in real time and immediately detect abnormal behavior. This allows for early detection of abnormal behavior by improving the accuracy of anomaly detection based on the pet's behavior patterns. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input pet behavior pattern data into a generating AI and have the generating AI perform the task of improving the accuracy of anomaly detection.

[0097] The analysis unit can perform analysis based on the pet's health history. For example, the analysis unit can analyze the pet's current health status based on the pet's past health data. For example, the analysis unit can detect signs of abnormalities by considering the pet's health history. For example, the analysis unit can predict future health risks based on the pet's health history. This makes it possible to perform a more accurate analysis of the pet's health status by performing analysis based on the pet's health history. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the pet's health history data into a generating AI and have the generating AI perform the analysis.

[0098] The analysis unit can estimate the pet's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the pet is stressed, the analysis unit can highlight stress-related data. For example, if the pet is relaxed, the analysis unit can display normal behavioral data. For example, if the pet is excited, the analysis unit can highlight data that is causing the excitement. By adjusting the display method of the analysis results based on the pet's emotions, a more easily understandable display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the pet's emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0099] The analysis unit can improve the accuracy of its analysis based on the pet's living environment data. For example, the analysis unit can analyze the pet's health status based on the pet's living environment data. For example, if the pet's living environment changes, the analysis unit can analyze data to help the pet adapt to the new environment. For example, the analysis unit can identify the cause of abnormal behavior by referring to the pet's living environment data. By improving the accuracy of the analysis based on the pet's living environment data, a more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the pet's living environment data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0100] The analysis unit can optimize its analysis algorithm by incorporating feedback from pet owners during analysis. For example, the analysis unit can adjust the analysis algorithm based on owner feedback. For example, the analysis unit can improve the accuracy of anomaly detection by incorporating owner feedback. For example, the analysis unit can optimize the display method of the analysis results based on owner feedback. This makes it possible to perform more accurate analysis by optimizing the analysis algorithm in accordance with pet owner feedback. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input owner feedback data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0101] The service provider can estimate the pet's emotions and adjust the content of the advice based on the estimated emotions. For example, if the pet is stressed, the service provider can provide advice to reduce stress. For example, if the pet is relaxed, the service provider can provide regular health management advice. For example, if the pet is excited, the service provider can provide advice to calm the excitement. By adjusting the content of the advice based on the pet's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input pet emotion data into the generative AI and have the generative AI adjust the content of the advice.

[0102] The service provider can adjust the level of detail of the advice based on the pet's health condition when providing advice. For example, if the pet is healthy, the service provider will provide general health management advice. For example, if the pet is unwell, the service provider can provide detailed health management advice. The service provider can adjust the content of the advice according to the pet's health condition. By adjusting the level of detail of the advice based on the pet's health condition, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input pet health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0103] The service provider can provide different advice depending on the pet's behavioral patterns. For example, the service provider can provide advice on the appropriate amount of exercise based on the pet's behavioral patterns. For example, the service provider can provide advice on the pet's diet depending on the pet's behavioral patterns. For example, the service provider can provide advice on stress reduction, taking into account the pet's behavioral patterns. By providing different advice depending on the pet's behavioral patterns, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input pet behavioral pattern data into a generating AI and have the generating AI adjust the content of the advice.

[0104] The service provider can estimate the pet's emotions and prioritize advice based on the estimated emotions. For example, if the pet is stressed, the service provider will prioritize stress reduction advice. For example, if the pet is relaxed, the service provider can provide regular health management advice. For example, if the pet is excited, the service provider can prioritize advice to calm the excitement. This allows for the priority of important advice by prioritizing it based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input pet emotion data into a generative AI and have the generative AI determine the priority of advice.

[0105] The service provider can provide optimal advice based on the pet owner's lifestyle when offering advice. For example, if the owner is busy, the service provider can provide easy-to-implement advice. For example, if the owner spends a lot of time at home, the service provider can provide detailed health management advice. For example, the service provider can adjust the content of the advice to suit the owner's lifestyle. By providing optimal advice based on the pet owner's lifestyle, the service provider can offer advice that is easy for the owner to implement. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the owner's lifestyle data into a generating AI and have the generating AI adjust the content of the advice.

[0106] The service provider can optimize the advice content by reflecting feedback from pet owners when providing advice. For example, the service provider can adjust the advice content based on the owner's feedback. For example, the service provider can improve the effectiveness of the advice by reflecting feedback from the owner. For example, the service provider can determine the priority of the advice based on feedback from the owner. This allows for the provision of more effective advice by optimizing the advice content by reflecting feedback from pet owners. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input owner feedback data into a generating AI and have the generating AI perform the optimization of the advice content.

[0107] The dialogue unit can estimate the pet's emotions and adjust the content of the dialogue based on the estimated emotions. For example, if the pet is stressed, the dialogue unit can engage in dialogue to reduce stress. For example, if the pet is relaxed, the dialogue unit can engage in dialogue about normal health management. For example, if the pet is excited, the dialogue unit can engage in dialogue to calm the excitement. By adjusting the content of the dialogue based on the pet's emotions, more appropriate dialogue becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input pet emotion data into the generative AI and have the generative AI adjust the content of the dialogue.

[0108] The dialogue unit can provide the best answer by referring to the pet owner's past consultation history during the conversation. For example, the dialogue unit can provide the best answer to a similar problem based on the owner's past consultation history. For example, the dialogue unit can refer to the content of past consultations by the owner and provide relevant advice. For example, the dialogue unit can analyze the owner's past consultation history and provide the most effective answer. This allows for the provision of more appropriate answers by referring to the pet owner's past consultation history. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the owner's past consultation history data into a generating AI and have the generating AI perform the generation of the best answer.

[0109] The dialogue unit can select a conversation topic based on the pet's health condition during the conversation. For example, if the pet is healthy, the dialogue unit will engage in conversation about general health management. If the pet is unwell, the dialogue unit can engage in conversation about improving its condition. The dialogue unit can select an appropriate conversation topic according to the pet's health condition. This allows for more appropriate conversations by selecting a conversation topic based on the pet's health condition. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input pet health data into a generating AI and have the generating AI select the conversation topic.

[0110] The dialogue unit can estimate the pet's emotions and determine the priority of the dialogue based on the estimated emotions. For example, if the pet is stressed, the dialogue unit will prioritize dialogue to reduce stress. If the pet is relaxed, the dialogue unit can engage in dialogue about normal health management. If the pet is excited, the dialogue unit can prioritize dialogue to calm the excitement. In this way, important dialogues can be prioritized by determining the priority of the dialogue based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input pet emotion data into the generative AI and have the generative AI determine the priority of the dialogue.

[0111] The dialogue unit can provide optimal dialogue content based on the pet owner's living environment during a conversation. For example, if the owner is busy, the dialogue unit can provide concise and easy-to-follow dialogue content. For example, if the owner spends a lot of time at home, the dialogue unit can provide detailed dialogue content regarding health management. The dialogue unit can adjust the dialogue content to suit the owner's living environment. By providing optimal dialogue content based on the pet owner's living environment, it can provide dialogue that is easy for the owner to follow. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the owner's living environment data into a generating AI and have the generating AI adjust the dialogue content.

[0112] The dialogue unit can optimize its dialogue algorithm by reflecting feedback from the pet owner during the dialogue. For example, the dialogue unit can adjust the dialogue algorithm based on the owner's feedback. For example, the dialogue unit can improve the effectiveness of the dialogue by reflecting the owner's feedback. For example, the dialogue unit can determine the priority of the dialogue content based on the owner's feedback. This makes it possible to have more effective dialogues by optimizing the dialogue algorithm by reflecting feedback from the pet owner. Some or all of the above processes in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input owner feedback data into a generating AI and have the generating AI perform the optimization of the dialogue algorithm.

[0113] The collaboration unit can estimate the pet's emotions and adjust the collaboration method based on the estimated emotions. For example, if the pet is stressed, the collaboration unit can provide a collaboration method to reduce stress. For example, if the pet is relaxed, the collaboration unit can provide a collaboration method related to normal health management. For example, if the pet is excited, the collaboration unit can provide a collaboration method to calm the excitement. By adjusting the collaboration method based on the pet's emotions, more appropriate collaboration becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using AI or not using AI. For example, the collaboration unit can input pet emotion data into a generative AI and have the generative AI perform the adjustment of the collaboration method.

[0114] The integration unit can select the optimal integration method based on the pet's health data during integration. For example, the integration unit selects the optimal integration method based on the pet's health data. For example, the integration unit can adjust the integration method according to the pet's health condition. For example, the integration unit can refer to the pet's health data and change the integration method if there is an abnormality. This makes more effective integration possible by selecting the optimal integration method based on the pet's health data. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input the pet's health data into a generating AI and have the generating AI select the optimal integration method.

[0115] The collaboration unit can optimize its collaboration algorithm by reflecting feedback from pet owners during collaboration. For example, the collaboration unit can adjust the collaboration algorithm based on owner feedback. For example, the collaboration unit can improve the effectiveness of collaboration by reflecting owner feedback. For example, the collaboration unit can determine the priority of collaboration content based on owner feedback. This makes more effective collaboration possible by optimizing the collaboration algorithm by reflecting feedback from pet owners. Some or all of the above processes in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input owner feedback data into a generating AI and have the generating AI perform the optimization of the collaboration algorithm.

[0116] The collaboration unit can estimate the pet's emotions and determine the priority of collaborations based on the estimated emotions. For example, if the pet is stressed, the collaboration unit will prioritize stress reduction collaborations. For example, if the pet is relaxed, the collaboration unit can perform collaborations related to normal health management. For example, if the pet is excited, the collaboration unit can prioritize collaborations to calm the excitement. In this way, by determining the priority of collaborations based on the pet's emotions, important collaborations can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using AI or not using AI. For example, the collaboration unit can input pet emotion data into a generative AI and have the generative AI perform the determination of collaboration priorities.

[0117] The integration unit can provide the optimal integration method based on the pet owner's living environment during integration. For example, if the owner is busy, the integration unit can provide a simple and easy-to-implement integration method. For example, if the owner spends a lot of time at home, the integration unit can provide a detailed health management integration method. For example, the integration unit can adjust the integration method to suit the owner's living environment. By providing the optimal integration method based on the pet owner's living environment, it can provide integration that is easy for the owner to implement. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input the owner's living environment data into a generating AI and have the generating AI perform the adjustment of the integration method.

[0118] The integration unit can optimize the integration process by reflecting feedback from pet owners during integration. For example, the integration unit can adjust the integration process based on owner feedback. For example, the integration unit can improve the effectiveness of the integration by reflecting owner feedback. For example, the integration unit can determine the priority of the integration process based on owner feedback. This makes more effective integration possible by optimizing the integration process by reflecting feedback from pet owners. Some or all of the above processes in the integration unit may be performed using AI or not. For example, the integration unit can input owner feedback data into a generating AI and have the generating AI perform the optimization of the integration process.

[0119] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0120] Pet health management systems can also incorporate predictive functions based on pet behavior data. These predictive functions, for example, analyze past behavioral data to predict future behavioral patterns. For instance, if a pet tends to be more active during certain times of the day, the predictive function can adjust the data collection frequency accordingly. Furthermore, if a pet is prone to illness during certain seasons, the predictive function can provide health management advice tailored to those seasons. In this way, incorporating predictive functions based on pet behavior data enables more effective health management.

[0121] The data collection unit can be equipped with a real-time feedback function based on pet behavior data. This real-time feedback function can, for example, immediately notify the owner if the pet exhibits abnormal behavior. It can also, for example, provide the owner with advice on stress reduction if the pet is experiencing stress. Furthermore, it can encourage the owner to take early action if the pet has an abnormal health condition. Thus, by incorporating a real-time feedback function based on pet behavior data, rapid response becomes possible.

[0122] The analysis unit can be equipped with a function to predict abnormal behavior based on the pet's behavioral data. This function can, for example, analyze the pet's past behavioral data to detect early signs of abnormal behavior. It can also predict the possibility of abnormal behavior if the pet exhibits a specific behavioral pattern. Furthermore, if the pet tends to exhibit abnormal behavior in a particular environment, it can predict abnormal behavior in that environment. By incorporating a function to predict abnormal behavior based on the pet's behavioral data, early detection of abnormal behavior becomes possible.

[0123] The service provider can be equipped with a customized advice function based on pet behavior data. This customized advice function can, for example, provide individualized health management advice based on the pet's behavior patterns. It can also provide optimal advice based on the pet's diet and exercise level. Furthermore, it can provide specific health management advice based on the pet's health condition. By incorporating a customized advice function based on pet behavior data, more personalized health management becomes possible.

[0124] The dialogue unit can be equipped with a function to personalize dialogue content based on the pet's behavioral data. This dialogue personalization function can, for example, provide individual dialogue content based on the pet's behavioral patterns. It can also, for example, provide appropriate dialogue content according to the pet's health condition. Furthermore, it can, for example, provide dialogue content to reduce stress based on the pet's emotional state. By incorporating a dialogue personalization function based on the pet's behavioral data, more appropriate dialogue becomes possible.

[0125] The integration unit can be equipped with a function to optimize the integration method based on pet behavior data. This integration method optimization function can, for example, select the optimal integration method based on the pet's health condition. It can also, for example, adjust the integration method according to the pet's behavior patterns. Furthermore, it can optimize the integration method based on the pet's emotional state. By incorporating this integration method optimization function based on pet behavior data, more effective integration becomes possible.

[0126] The data collection unit can be equipped with functions to improve the efficiency of data collection based on pet behavior data. These functions can, for example, adjust the frequency of data collection based on the pet's behavior patterns. They can also, for example, select the type of data to collect based on the pet's health condition. Furthermore, they can determine the priority of data collection based on the pet's emotional state. By incorporating these data collection efficiency functions based on pet behavior data, more efficient data collection becomes possible.

[0127] The analysis unit can be equipped with a function for early detection of abnormal behavior based on pet behavioral data. This function can, for example, analyze the pet's behavioral patterns in real time and immediately detect abnormal behavior. It can also, for example, detect early signs of abnormal behavior based on the pet's past behavioral data. Furthermore, it can predict abnormal behavior based on the pet's health condition. By incorporating this early detection function for abnormal behavior based on pet behavioral data, a rapid response becomes possible.

[0128] The service provider can be equipped with a personalized advice function based on pet behavior data. This personalized advice function can, for example, provide individualized health management advice based on the pet's behavior patterns. It can also, for example, provide optimal advice according to the pet's health condition. Furthermore, it can, for example, provide stress reduction advice based on the pet's emotional state. By incorporating a personalized advice function based on pet behavior data, more appropriate advice can be provided.

[0129] The dialogue unit can be equipped with a function to optimize dialogue content based on the pet's behavioral data. This function can, for example, provide optimal dialogue based on the pet's behavioral patterns. It can also provide appropriate dialogue based on the pet's health condition. Furthermore, it can provide dialogue to reduce stress based on the pet's emotional state. By incorporating a dialogue optimization function based on pet behavioral data, more appropriate dialogue becomes possible.

[0130] The following briefly describes the processing flow for example form 2.

[0131] Step 1: The collection unit collects pet behavior and health data. The collection unit collects pet behavior and health data using, for example, sensors or cameras. The collection unit can acquire data such as pet movement, eating, and excretion in real time. The collection unit can collect information such as how often the pet eats and how long it sleeps. Some or all of the above processing in the collection unit may be performed using AI or not. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes, for example, the pet's behavior patterns and health status, and issues an alert if there is an abnormality. The analysis unit can notify the owner, for example, if the pet eats less than usual or exhibits abnormal behavior. The analysis unit can analyze the pet's behavior patterns and detect abnormalities. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without using AI. Step 3: The service provider provides advice based on the data analyzed by the analysis unit. For example, the service provider provides pet owners with advice supervised by a veterinarian. For example, the service provider can provide advice on pet diet and exercise levels to support health management. For example, the service provider can provide appropriate advice based on the pet's health condition. Some or all of the above processing in the service provider may be performed using AI or not. Step 4: The dialogue unit handles pet care consultations via chatbot or voice interaction. For example, pet owners can ask questions about their pet's health and behavior via chatbot or voice interaction, and the AI ​​will provide appropriate answers. For example, pet owners can consult about their pet's health management anytime, anywhere. Some or all of the above processing in the dialogue unit may be performed using AI or not. Step 5: The liaison unit collaborates with the pet's primary veterinarian. The liaison unit can collaborate with the pet's primary veterinarian as needed to obtain specialized diagnoses and treatments. The liaison unit can collaborate with the pet's primary veterinarian based on the pet's health condition, for example. Some or all of the above processes in the liaison unit may be performed using AI or not.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, dialogue unit, and collaboration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects pet behavior and health data using the camera 42 and sensors of the smart device 14, and the control unit 46A acquires the data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected data and issue an alert if there is an abnormality. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to provide pet owners with advice supervised by a veterinarian. The dialogue unit is implemented in the specific processing unit 46A of the smart device 14, for example, to respond to pet care consultations via chatbot or voice dialogue. The collaboration unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to collaborate with the owner's regular veterinarian. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0136] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, dialogue unit, and collaboration unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects pet behavior and health data using the camera 42 and sensors of the smart glasses 214, and the control unit 46A acquires the data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data and issues an alert if there is an abnormality. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and provides veterinarian-supervised advice to pet owners. The dialogue unit is implemented in the specific processing unit 46A of the smart glasses 214, for example, and responds to pet care consultations via chatbot or voice dialogue. The collaboration unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and can collaborate with the owner's regular veterinarian. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0152] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, dialogue unit, and collaboration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects pet behavior and health data using the camera 42 and sensors of the headset terminal 314, and the control unit 46A acquires the data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected data and issue an alert if there is an abnormality. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to provide pet owners with advice supervised by a veterinarian. The dialogue unit is implemented in the specific processing unit 46A of the headset terminal 314, for example, to respond to pet care consultations via chatbot or voice dialogue. The collaboration unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to collaborate with the pet owner's regular veterinarian. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0168] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, dialogue unit, and collaboration unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects pet behavior and health data using the camera 42 and sensors of the robot 414, and the control unit 46A acquires the data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected data and issue an alert if there is an abnormality. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to provide pet owners with advice supervised by a veterinarian. The dialogue unit is implemented in the specific processing unit 46A of the robot 414, for example, to respond to pet care consultations via chatbot or voice dialogue. The collaboration unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to collaborate with the pet's regular veterinarian. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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."

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] (Note 1) A collection unit that collects pet behavior and health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A provisioning unit that provides advice based on the data analyzed by the aforementioned analysis unit, A dialogue unit that handles pet care consultations via chatbot or voice interaction, It includes a liaison department that coordinates with the patient's primary veterinarian. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect pet behavior and health data using sensors or cameras. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed, and an alert is issued if an anomaly is detected. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We provide advice supervised by veterinarians. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned dialogue unit, We provide pet care consultations via chatbot or voice interaction. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned linkage unit is, We will collaborate with your regular veterinarian. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the pet's emotions and adjust the frequency of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past pet behavior data to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the pet's current health status and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate the pet's emotions and prioritize the data to collect based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data based on information about the pet's living environment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, we analyze the lifestyle patterns of pet owners and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the pet's emotions and adjusts the analysis algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, improve the accuracy of anomaly detection based on the pet's behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The analysis will be based on the pet's health history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the pet'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 17) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved based on data about the pet's living environment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by incorporating feedback from pet owners. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the pet's emotions and adjusts the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing advice, we adjust the level of detail based on the pet's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing advice, we offer different advice depending on the pet's behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the pet's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing advice, we offer the most suitable advice based on the pet owner's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing advice, we optimize the advice by incorporating feedback from pet owners. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned dialogue unit, It estimates the pet's emotions and adjusts the content of the conversation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned dialogue unit, During the conversation, we refer to the pet owner's past consultation history to provide the most appropriate answer. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned dialogue unit, During the conversation, select topics based on the pet's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned dialogue unit, It estimates the pet's emotions and determines the priority of interactions based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned dialogue unit, During the conversation, we provide the most appropriate dialogue based on the pet owner's living environment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned dialogue unit, During conversations, the dialogue algorithm is optimized by incorporating feedback from pet owners. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned linkage unit is, It estimates the pet's emotions and adjusts the method of interaction based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned linkage unit is, During the integration process, the optimal integration method will be selected based on the pet's health data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned linkage unit is, During integration, the integration algorithm is optimized by incorporating feedback from pet owners. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned linkage unit is, It estimates the pet's emotions and determines the priority of collaboration based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned linkage unit is, When connecting, we provide the optimal connection method based on the pet owner's living environment. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned linkage unit is, During integration, we optimize the integration process by incorporating feedback from pet owners. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0204] 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 pet behavior and health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A provisioning unit that provides advice based on the data analyzed by the aforementioned analysis unit, A dialogue unit that handles pet care consultations via chatbot or voice interaction, It includes a liaison department that coordinates with the patient's primary veterinarian. A system characterized by the following features.

2. The aforementioned collection unit is Collect pet behavior and health data using sensors or cameras. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed, and an alert is issued if an anomaly is detected. The system according to feature 1.

4. The aforementioned supply unit is, We provide advice supervised by veterinarians. The system according to feature 1.

5. The aforementioned dialogue unit, We provide pet care consultations via chatbot or voice interaction. The system according to feature 1.

6. The aforementioned linkage unit is, We will collaborate with your regular veterinarian. The system according to feature 1.

7. The aforementioned collection unit is We estimate the pet's emotions and adjust the frequency of data collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past pet behavior data to select the optimal data collection method. The system according to feature 1.

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