System

Abstract

An object of a system according to an embodiment is to provide a quick and appropriate coping method for a change in a health condition of a pet.SOLUTION: A system includes an information collection unit, an analysis unit, a determination unit, a proposal unit, and a notification unit. The information collection unit collects data on a health condition of a pet. The analysis unit analyzes the data collected by the information collection unit. The determination unit determines the severity based on the data analyzed by the analysis unit. The proposal unit proposes a handling method based on the severity determined by the determination unit. The notification part notifies the owner of the coping method proposed by the proposal part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to quickly determine appropriate measures to take in the event of changes in a pet's health or accidental ingestion of a substance.

[0005] The system according to the embodiment aims to provide a quick and appropriate response to changes in the health condition of a pet. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, a determination unit, a suggestion unit, and a notification unit. The information collection unit collects data related to the pet's health condition. The analysis unit analyzes the data collected by the information collection unit. The determination unit determines the severity based on the data analyzed by the analysis unit. The suggestion unit suggests a solution based on the severity determined by the determination unit. The notification unit notifies the owner of the solution suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a quick and appropriate response to changes in the health condition of a pet. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The pet health management system according to an embodiment of the present invention collects data on the health condition of pets, analyzes it using a generative AI, determines the severity of the condition, proposes countermeasures, and notifies the owner. This allows the pet health management system to properly manage the health condition of pets without the owner having to frequently visit a veterinary clinic.

[0029] A pet health management system according to an embodiment includes an information collection unit, an analysis unit, a determination unit, a suggestion unit, and a notification unit. The information collection unit collects data related to the pet's health condition. For example, the information collection unit collects data on the pet's appetite, excretion, changes in behavior, etc. The information collection unit can also collect biological data on the pet, such as body temperature and heart rate. The information collection unit can also collect environmental data on the pet (temperature, humidity, noise, etc.). The analysis unit analyzes the data collected by the information collection unit. For example, the analysis unit performs statistical analysis of the data to evaluate the pet's health condition. The analysis unit can also analyze the data using a machine learning algorithm. The analysis unit can also detect abnormalities by comparing the data with the pet's past health data. The determination unit determines the severity of the condition based on the data analyzed by the analysis unit. For example, if the pet accidentally ingests something, the determination unit analyzes information such as the type and amount of the substance, the pet's weight, and age, and determines the severity of the condition. The determination unit can also analyze the pet's genetic information to consider genetic risks. Furthermore, the determination unit can refer to the pet's past medical history and consider the effectiveness of treatment. The suggestion unit proposes a countermeasure based on the severity determined by the determination unit. For example, the suggestion unit may suggest a home treatment for a mild problem, and recommend a visit to a veterinarian for a severe problem. The suggestion unit can also provide a customized countermeasure based on the pet's individual health condition and personality. Furthermore, the suggestion unit can learn the effectiveness of past countermeasures and propose the optimal countermeasure. The notification unit notifies the owner of the countermeasure proposed by the suggestion unit. For example, the notification unit may notify the owner via a smartphone app. The notification unit may also notify the owner via email or SMS. The notification unit may also send a notification to the owner's smartwatch. As a result, the pet health management system according to the embodiment allows the owner to appropriately manage the health of the pet without frequent visits to the veterinarian. For example, even if the pet accidentally ingests something, the AI ​​can suggest an appropriate countermeasure, allowing the owner to respond with peace of mind. Furthermore, by continuously monitoring the pet's health condition, abnormalities can be detected early and appropriate measures can be taken.

[0030] The information collection unit can monitor pet behavior in real time and immediately notify the owner when abnormal behavior is detected. For example, the information collection unit uses AI to monitor pet behavior in real time and immediately notify the owner when abnormal behavior is detected. For example, if a pet behaves differently than normal, the AI ​​analyzes the movement and, if it determines that the behavior is abnormal, sends a notification to the owner's smartphone. This allows for the immediate detection of abnormal pet behavior and the notification of the owner, enabling a rapid response.

[0031] When monitoring a pet's health, the analysis unit can detect abnormalities by comparing it with the pet's past health data. For example, when AI monitors a pet's health, the analysis unit can detect abnormalities by comparing it with the pet's past health data. For example, it can compare changes in a pet's weight or appetite with past data and notify the owner if any abnormalities are found. This allows for early detection of abnormalities by comparing it with the pet's past health data.

[0032] When monitoring the health condition of a pet, the analysis unit can simultaneously monitor the health condition of the owner and analyze the correlation between the health conditions of the pet and the owner. For example, when the AI ​​monitors the health condition of a pet, the analysis unit can simultaneously monitor the health condition of the owner and analyze the correlation between the health conditions of the pet and the owner. For example, the analysis unit can monitor the stress level and amount of exercise of the owner and analyze the correlation with the pet's health condition. This allows for more appropriate health management by analyzing the correlation between the health conditions of the pet and the owner.

[0033] When monitoring a pet's health, the analysis unit can simultaneously monitor the pet's environment and analyze the impact of environmental factors on health. For example, when the AI ​​monitors a pet's health, the analysis unit can simultaneously monitor the pet's environment (temperature, humidity, noise, etc.) and analyze the impact of environmental factors on health. For example, it can analyze the impact that changes in room temperature and humidity have on the pet's health. This allows for a more accurate understanding of the pet's health by taking into account the pet's environmental factors.

[0034] The determination unit can analyze the pet's genetic information and take genetic risks into account when determining the severity of the pet's health condition. For example, when the AI ​​determines the severity of the pet's health condition, the determination unit analyzes the pet's genetic information and takes genetic risks into account. For example, if there is a specific gene mutation, that risk is reflected in the severity determination. In this way, by taking the pet's genetic information into account, a more accurate determination of severity is possible.

[0035] The determination unit can refer to the pet's past treatment history and take into account the effectiveness of treatment when determining the severity of the pet's health condition. For example, when the AI ​​determines the severity of the pet's health condition, the determination unit can refer to the pet's past treatment history and take into account the effectiveness of treatment. For example, it can analyze whether past treatments were effective and reflect this in the severity determination. In this way, by taking the pet's past treatment history into account, a more accurate determination of the severity is possible.

[0036] When determining the severity of a pet's health condition, the determination unit can compare it with data from other pets and calculate a statistical severity. For example, when the AI ​​determines the severity of a pet's health condition, the determination unit compares it with data from other pets and calculates a statistical severity. For example, the severity is evaluated based on data from other pets with the same symptoms. This makes it possible to make a statistically valid determination of severity by comparing it with data from other pets.

[0037] When determining the severity of a pet's health condition, the determination unit can take into account the pet's lifestyle habits and analyze the impact of the lifestyle habits on the severity. For example, when the AI ​​determines the severity of a pet's health condition, the determination unit can take into account the pet's lifestyle habits (diet, exercise, etc.) and analyze the impact of the lifestyle habits on the severity. For example, the determination unit can analyze the pet's diet and amount of exercise and reflect this in the severity determination. By taking the pet's lifestyle habits into account, a more accurate determination of the severity is possible.

[0038] The suggestion unit can provide customized remedies based on the pet's individual health condition and personality. For example, when the AI ​​suggests remedies, the suggestion unit provides customized remedies based on the pet's individual health condition and personality. For example, it suggests optimal treatments and care methods for pets with a specific illness. This allows for more appropriate care by providing remedies based on the pet's individual health condition and personality.

[0039] The suggestion unit can learn the effectiveness of past countermeasures and suggest the most appropriate countermeasure. For example, when the AI ​​suggests a countermeasure, the suggestion unit learns the effectiveness of past countermeasures and suggests the most appropriate countermeasure. For example, it suggests the most appropriate countermeasure based on treatments and care methods that have been effective in the past. In this way, by learning the effectiveness of past countermeasures, it can suggest more effective countermeasures.

[0040] The suggestion unit can refer to data on other pets and suggest statistically effective countermeasures. For example, when the AI ​​suggests a countermeasure, the suggestion unit refers to data on other pets and suggests statistically effective countermeasures. For example, it can suggest a treatment that has been effective on other pets with the same symptoms. In this way, by referring to data on other pets, it is possible to suggest statistically effective countermeasures.

[0041] The suggestion unit can take into account the pet's living environment and propose a solution that is appropriate for the environment. For example, when the AI ​​proposes a solution, the suggestion unit considers the pet's living environment (such as the residence and the owner's lifestyle) and proposes a solution that is appropriate for the environment. For example, it proposes an appropriate exercise method for a pet living in an urban area. In this way, by taking the pet's living environment into consideration, it is possible to propose a more appropriate solution.

[0042] The notification unit can notify the owner through a smartphone app. The notification unit notifies the owner through, for example, a smartphone app. For example, information about the pet's health condition and how to deal with it can be notified through the app. This allows for quick information transmission by notifying the owner through the smartphone app.

[0043] The analysis unit can periodically check the health condition of the pet and notify the owner if an abnormality is found. For example, the analysis unit periodically checks the health condition of the pet and notifies the owner if an abnormality is found. For example, the analysis unit can check the health condition of the pet at a specific time every day or every week and notify the owner if an abnormality is found. In this way, by periodically checking the health condition of the pet and notifying the owner if an abnormality is found, early action can be taken.

[0044] The analysis unit can continuously monitor the health condition of the pet and provide feedback as needed. For example, the analysis unit can continuously monitor the health condition of the pet and provide feedback as needed. For example, a sensor can be used to monitor the pet's health condition in real time and notify the owner if there is an abnormality. This allows the pet's health condition to be continuously monitored and feedback can be provided as needed, enabling appropriate health management.

[0045] The analysis unit can collect and analyze data such as changes in the pet's appetite, excretion, and behavior when monitoring the pet's health condition. For example, the analysis unit collects and analyzes data such as changes in the pet's appetite, excretion, and behavior when monitoring the pet's health condition. For example, the analysis unit collects and analyzes data such as the pet's food intake, frequency of excretion, and changes in behavior patterns. In this way, by collecting and analyzing data such as changes in the pet's appetite, excretion, and behavior, it is possible to grasp the health condition more accurately.

[0046] When a pet accidentally ingests something, the determination unit can analyze information such as the type and amount of the substance, the pet's weight, and age, and determine the severity. For example, when a pet accidentally ingests something, the determination unit analyzes information such as the type and amount of the substance, the pet's weight, and age, and determines the severity. For example, when a pet accidentally ingests a specific substance, the severity is determined taking into account the toxicity and amount of the substance, and the pet's weight and age. As a result, when a pet accidentally ingests something, information such as the type and amount of the substance, the pet's weight, and age can be analyzed to determine the severity, enabling appropriate action to be taken.

[0047] The suggestion unit can suggest home remedies for minor problems and recommend a visit to a veterinarian for major problems. For example, the suggestion unit can suggest home remedies for minor problems and recommend a visit to a veterinarian for major problems. For example, the suggestion unit can suggest home care methods for minor dermatitis and recommend a visit to a veterinarian for major symptoms of poisoning. This allows for appropriate responses by suggesting home remedies for minor problems and recommending a visit to a veterinarian for major problems.

[0048] The suggestion unit can provide preventive advice to the owner based on the information about the pet's health condition. The suggestion unit, for example, provides preventive advice to the owner based on the information about the pet's health condition. For example, the suggestion unit suggests regular health checks and lifestyle improvements. This makes it easier to maintain the pet's health by providing preventive advice based on the information about the pet's health condition.

[0049] The suggestion unit can provide dietary and exercise advice to the owner based on information about the pet's health condition. The suggestion unit, for example, provides dietary and exercise advice to the owner based on information about the pet's health condition. For example, the suggestion unit suggests appropriate amounts and types of food, and frequency and types of exercise. This makes it easier to maintain the pet's health by providing dietary and exercise advice based on information about the pet's health condition.

[0050] The suggestion unit can provide stress management advice to the owner based on the information about the pet's health condition. The suggestion unit provides stress management advice to the owner based on, for example, the information about the pet's health condition. For example, the suggestion unit suggests relaxation methods or environmental improvements. In this way, providing stress management advice based on the information about the pet's health condition makes it easier to maintain the mental health of the pet.

[0051] The suggestion unit can provide the owner with advice on improving the pet's behavior based on the information on the pet's health condition. The suggestion unit provides the owner with advice on improving the pet's behavior based on, for example, the information on the pet's health condition. For example, the suggestion unit suggests training methods and behavior modification techniques. This makes it easier to properly manage the pet's behavior by providing advice on improving the behavior based on the information on the pet's health condition.

[0052] The suggestion unit can provide the owner with advice for maintaining the health of the pet based on the information about the pet's health condition. The suggestion unit, for example, provides the owner with advice for maintaining the health of the pet based on the information about the pet's health condition. For example, the suggestion unit suggests regular health checks and appropriate diet and exercise. In this way, providing health maintenance advice based on the information about the pet's health condition makes it easier to maintain the health of the pet.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The pet health management system can further include a unit for monitoring the social behavior of pets. For example, it can monitor how pets interact with other animals and humans to detect signs of social stress or anxiety. It can analyze how often pets play with other animals and how much time they spend in contact with their owners to assess their social health. This allows the system to understand the social health of pets and suggest appropriate measures if necessary.

[0055] The pet health management system can also be equipped with a unit that monitors the pet's sleep patterns. For example, it can analyze the pet's sleep duration and quality and notify the owner if there are any abnormalities. It can detect changes in the pet's sleep pattern, such as frequent nighttime awakenings or excessive daytime sleepiness. This allows the system to understand the pet's sleep status and suggest appropriate measures if necessary.

[0056] The pet health management system can also be equipped with a unit that records the details of a pet's diet. For example, it can record what a pet eats and how much it eats, and analyze the nutritional balance. If a pet has allergies to certain ingredients, the diet can be adjusted based on that information. This allows for a detailed understanding of a pet's diet, which can be useful in maintaining its health.

[0057] The pet health management system can further include a unit that monitors the amount of exercise a pet receives. For example, it can record how much exercise a pet receives and detect whether the pet is getting enough or too much exercise. It can also propose an exercise plan to help the pet maintain an appropriate amount of exercise. This allows the pet's amount of exercise to be properly managed, helping to maintain its health.

[0058] The pet health management system may further include a unit for managing the pet's weight. For example, the system may periodically measure the pet's weight and record any weight gain or loss. If there is a sudden change in weight, the owner may be notified and an appropriate course of action may be suggested. This allows the pet's weight to be properly managed, helping to maintain its health.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The information collection unit collects data on the pet's health, such as the pet's appetite, excretion, behavioral changes, body temperature, heart rate, and other biological data, as well as the pet's environmental data (temperature, humidity, noise, etc.). Step 2: The analysis unit analyzes the data collected by the information collection unit. For example, it performs statistical analysis of the data to evaluate the pet's health status. It also analyzes the data using machine learning algorithms and compares it with the pet's past health data to detect abnormalities. Step 3: The assessment unit determines the severity of the condition based on the data analyzed by the analysis unit. For example, if a pet accidentally ingests something, the assessment unit analyzes information such as the type and amount of the substance, as well as the pet's weight and age, to determine the severity of the condition. The assessment unit also considers genetic risks and treatment effects by referencing the pet's genetic information and past treatment history. Step 4: The suggestion unit suggests solutions based on the severity determined by the evaluation unit. For example, it suggests home solutions for mild problems and recommends a visit to a veterinarian for severe problems. It also provides customized solutions based on the pet's individual health condition and personality, learning from the effectiveness of past solutions to suggest the optimal solution. Step 5: The notification unit notifies the owner of the solution proposed by the suggestion unit, for example, via a smartphone app, email, SMS, or smartwatch.

[0061] (Example 2) The pet health management system according to an embodiment of the present invention collects data on the health condition of pets, analyzes it using a generative AI, determines the severity of the condition, proposes countermeasures, and notifies the owner. This allows the pet health management system to properly manage the health condition of pets without the owner having to frequently visit a veterinary clinic.

[0062] A pet health management system according to an embodiment includes an information collection unit, an analysis unit, a determination unit, a suggestion unit, and a notification unit. The information collection unit collects data related to the pet's health condition. For example, the information collection unit collects data on the pet's appetite, excretion, changes in behavior, etc. The information collection unit can also collect biological data on the pet, such as body temperature and heart rate. The information collection unit can also collect environmental data on the pet (temperature, humidity, noise, etc.). The analysis unit analyzes the data collected by the information collection unit. For example, the analysis unit performs statistical analysis of the data to evaluate the pet's health condition. The analysis unit can also analyze the data using a machine learning algorithm. The analysis unit can also detect abnormalities by comparing the data with the pet's past health data. The determination unit determines the severity of the condition based on the data analyzed by the analysis unit. For example, if the pet accidentally ingests something, the determination unit analyzes information such as the type and amount of the substance, the pet's weight, and age, and determines the severity of the condition. The determination unit can also analyze the pet's genetic information to consider genetic risks. Furthermore, the determination unit can refer to the pet's past medical history and consider the effectiveness of treatment. The suggestion unit proposes a countermeasure based on the severity determined by the determination unit. For example, the suggestion unit may suggest a home treatment for a mild problem, and recommend a visit to a veterinarian for a severe problem. The suggestion unit can also provide a customized countermeasure based on the pet's individual health condition and personality. Furthermore, the suggestion unit can learn the effectiveness of past countermeasures and propose the optimal countermeasure. The notification unit notifies the owner of the countermeasure proposed by the suggestion unit. For example, the notification unit may notify the owner via a smartphone app. The notification unit may also notify the owner via email or SMS. The notification unit may also send a notification to the owner's smartwatch. As a result, the pet health management system according to the embodiment allows the owner to appropriately manage the health of the pet without frequent visits to the veterinarian. For example, even if the pet accidentally ingests something, the AI ​​can suggest an appropriate countermeasure, allowing the owner to respond with peace of mind. Furthermore, by continuously monitoring the pet's health condition, abnormalities can be detected early and appropriate measures can be taken.

[0063] The information collection unit can monitor pet behavior in real time and immediately notify the owner when abnormal behavior is detected. For example, the information collection unit uses AI to monitor pet behavior in real time and immediately notify the owner when abnormal behavior is detected. For example, if a pet behaves differently than normal, the AI ​​analyzes the movement and, if it determines that the behavior is abnormal, sends a notification to the owner's smartphone. This allows for the immediate detection of abnormal pet behavior and the notification of the owner, enabling a rapid response.

[0064] When monitoring a pet's health, the analysis unit can detect abnormalities by comparing it with the pet's past health data. For example, when AI monitors a pet's health, the analysis unit can detect abnormalities by comparing it with the pet's past health data. For example, it can compare changes in a pet's weight or appetite with past data and notify the owner if any abnormalities are found. This allows for early detection of abnormalities by comparing it with the pet's past health data.

[0065] The analysis unit can use the emotion estimation function to estimate emotions from the pet's behavior and facial expressions and detect signs of stress or anxiety. For example, the analysis unit can use the emotion estimation function to estimate emotions from the pet's behavior and facial expressions and detect signs of stress or anxiety. For example, if the pet barks frequently or appears restless, the AI ​​analyzes that behavior and detects signs of stress or anxiety. This makes it possible to estimate the pet's emotions and detect signs of stress or anxiety early on.

[0066] When monitoring the health condition of a pet, the analysis unit can simultaneously monitor the health condition of the owner and analyze the correlation between the health conditions of the pet and the owner. For example, when the AI ​​monitors the health condition of a pet, the analysis unit can simultaneously monitor the health condition of the owner and analyze the correlation between the health conditions of the pet and the owner. For example, the analysis unit can monitor the stress level and amount of exercise of the owner and analyze the correlation with the pet's health condition. This allows for more appropriate health management by analyzing the correlation between the health conditions of the pet and the owner.

[0067] When monitoring a pet's health, the analysis unit can simultaneously monitor the pet's environment and analyze the impact of environmental factors on health. For example, when the AI ​​monitors a pet's health, the analysis unit can simultaneously monitor the pet's environment (temperature, humidity, noise, etc.) and analyze the impact of environmental factors on health. For example, it can analyze the impact that changes in room temperature and humidity have on the pet's health. This allows for a more accurate understanding of the pet's health by taking into account the pet's environmental factors.

[0068] The analysis unit can use the emotion estimation function to analyze the owner's emotional response to the pet's health condition and provide advice to reduce the owner's stress. For example, the analysis unit can use the emotion estimation function to analyze the owner's emotional response to the pet's health condition and provide advice to reduce the owner's stress. For example, when the pet's health condition deteriorates, the analysis unit can analyze the owner's stress level and suggest relaxation methods. In this way, by analyzing the owner's emotional response and providing advice to reduce stress, the mental burden on the owner can be reduced.

[0069] The determination unit can analyze the pet's genetic information and take genetic risks into account when determining the severity of the pet's health condition. For example, when the AI ​​determines the severity of the pet's health condition, the determination unit analyzes the pet's genetic information and takes genetic risks into account. For example, if there is a specific gene mutation, that risk is reflected in the severity determination. In this way, by taking the pet's genetic information into account, a more accurate determination of severity is possible.

[0070] The determination unit can refer to the pet's past treatment history and take into account the effectiveness of treatment when determining the severity of the pet's health condition. For example, when the AI ​​determines the severity of the pet's health condition, the determination unit can refer to the pet's past treatment history and take into account the effectiveness of treatment. For example, it can analyze whether past treatments were effective and reflect this in the severity determination. In this way, by taking the pet's past treatment history into account, a more accurate determination of the severity is possible.

[0071] The determination unit can use the emotion estimation function to estimate the emotion from the behavior and facial expression of the pet and consider the impact of emotional stress on the severity. For example, the determination unit uses the emotion estimation function to estimate the emotion from the behavior and facial expression of the pet and consider the impact of emotional stress on the severity. For example, if the pet is feeling stressed, the impact is reflected in the severity determination. In this way, by considering the emotional stress of the pet, a more accurate severity determination is possible.

[0072] When determining the severity of a pet's health condition, the determination unit can compare it with data from other pets and calculate a statistical severity. For example, when the AI ​​determines the severity of a pet's health condition, the determination unit compares it with data from other pets and calculates a statistical severity. For example, the severity is evaluated based on data from other pets with the same symptoms. This makes it possible to make a statistically valid determination of severity by comparing it with data from other pets.

[0073] When determining the severity of a pet's health condition, the determination unit can take into account the pet's lifestyle habits and analyze the impact of the lifestyle habits on the severity. For example, when the AI ​​determines the severity of a pet's health condition, the determination unit can take into account the pet's lifestyle habits (diet, exercise, etc.) and analyze the impact of the lifestyle habits on the severity. For example, the determination unit can analyze the pet's diet and amount of exercise and reflect this in the severity determination. By taking the pet's lifestyle habits into account, a more accurate determination of the severity is possible.

[0074] The determination unit can use the emotion estimation function to analyze the owner's emotional response to the pet's health condition and consider the impact of the owner's emotions on the pet's health. For example, the determination unit can use the emotion estimation function to analyze the owner's emotional response to the pet's health condition and consider the impact of the owner's emotions on the pet's health. For example, if the owner is feeling stressed, that impact is reflected in the severity determination. This allows for a more accurate severity determination by considering the impact of the owner's emotions on the pet's health.

[0075] The suggestion unit can provide customized remedies based on the pet's individual health condition and personality. For example, when the AI ​​suggests remedies, the suggestion unit provides customized remedies based on the pet's individual health condition and personality. For example, it suggests optimal treatments and care methods for pets with a specific illness. This allows for more appropriate care by providing remedies based on the pet's individual health condition and personality.

[0076] The suggestion unit can learn the effectiveness of past countermeasures and suggest the most appropriate countermeasure. For example, when the AI ​​suggests a countermeasure, the suggestion unit learns the effectiveness of past countermeasures and suggests the most appropriate countermeasure. For example, it suggests the most appropriate countermeasure based on treatments and care methods that have been effective in the past. In this way, by learning the effectiveness of past countermeasures, it can suggest more effective countermeasures.

[0077] The suggestion unit can use the emotion estimation function to estimate the emotion from the behavior and facial expression of the pet and suggest a coping method for reducing emotional stress. For example, the suggestion unit uses the emotion estimation function to estimate the emotion from the behavior and facial expression of the pet and suggest a coping method for reducing emotional stress. For example, if the pet is feeling stressed, the suggestion unit suggests a coping method for reducing the stress. In this way, by suggesting a coping method for reducing the emotional stress of the pet, the mental health of the pet can be maintained.

[0078] The suggestion unit can refer to data on other pets and suggest statistically effective countermeasures. For example, when the AI ​​suggests a countermeasure, the suggestion unit refers to data on other pets and suggests statistically effective countermeasures. For example, it can suggest a treatment that has been effective on other pets with the same symptoms. In this way, by referring to data on other pets, it is possible to suggest statistically effective countermeasures.

[0079] The suggestion unit can take into account the pet's living environment and propose a solution that is appropriate for the environment. For example, when the AI ​​proposes a solution, the suggestion unit considers the pet's living environment (such as the residence and the owner's lifestyle) and proposes a solution that is appropriate for the environment. For example, it proposes an appropriate exercise method for a pet living in an urban area. In this way, by taking the pet's living environment into consideration, it is possible to propose a more appropriate solution.

[0080] The suggestion unit can use the emotion estimation function to analyze the owner's emotional response to the pet's health condition and suggest a countermeasure that takes the owner's emotions into consideration. For example, the suggestion unit can use the emotion estimation function to analyze the owner's emotional response to the pet's health condition and suggest a countermeasure that takes the owner's emotions into consideration. For example, if the owner is feeling stressed, the suggestion unit can suggest a countermeasure to reduce the stress. In this way, by suggesting a countermeasure that takes the owner's emotions into consideration, the mental burden on the owner can be reduced.

[0081] The notification unit can notify the owner through a smartphone app. The notification unit notifies the owner through, for example, a smartphone app. For example, information about the pet's health condition and how to deal with it can be notified through the app. This allows for quick information transmission by notifying the owner through the smartphone app.

[0082] The analysis unit can periodically check the health condition of the pet and notify the owner if an abnormality is found. For example, the analysis unit periodically checks the health condition of the pet and notifies the owner if an abnormality is found. For example, the analysis unit can check the health condition of the pet at a specific time every day or every week and notify the owner if an abnormality is found. In this way, by periodically checking the health condition of the pet and notifying the owner if an abnormality is found, early action can be taken.

[0083] The analysis unit can continuously monitor the health condition of the pet and provide feedback as needed. For example, the analysis unit can continuously monitor the health condition of the pet and provide feedback as needed. For example, a sensor can be used to monitor the pet's health condition in real time and notify the owner if there is an abnormality. This allows the pet's health condition to be continuously monitored and feedback can be provided as needed, enabling appropriate health management.

[0084] The analysis unit can collect and analyze data such as changes in the pet's appetite, excretion, and behavior when monitoring the pet's health condition. For example, the analysis unit collects and analyzes data such as changes in the pet's appetite, excretion, and behavior when monitoring the pet's health condition. For example, the analysis unit collects and analyzes data such as the pet's food intake, frequency of excretion, and changes in behavior patterns. In this way, by collecting and analyzing data such as changes in the pet's appetite, excretion, and behavior, it is possible to grasp the health condition more accurately.

[0085] When a pet accidentally ingests something, the determination unit can analyze information such as the type and amount of the substance, the pet's weight, and age, and determine the severity. For example, when a pet accidentally ingests something, the determination unit analyzes information such as the type and amount of the substance, the pet's weight, and age, and determines the severity. For example, when a pet accidentally ingests a specific substance, the severity is determined taking into account the toxicity and amount of the substance, and the pet's weight and age. As a result, when a pet accidentally ingests something, information such as the type and amount of the substance, the pet's weight, and age can be analyzed to determine the severity, enabling appropriate action to be taken.

[0086] The suggestion unit can suggest home remedies for minor problems and recommend a visit to a veterinarian for major problems. For example, the suggestion unit can suggest home remedies for minor problems and recommend a visit to a veterinarian for major problems. For example, the suggestion unit can suggest home care methods for minor dermatitis and recommend a visit to a veterinarian for major symptoms of poisoning. This allows for appropriate responses by suggesting home remedies for minor problems and recommending a visit to a veterinarian for major problems.

[0087] The suggestion unit can provide preventive advice to the owner based on the information about the pet's health condition. The suggestion unit, for example, provides preventive advice to the owner based on the information about the pet's health condition. For example, the suggestion unit suggests regular health checks and lifestyle improvements. This makes it easier to maintain the pet's health by providing preventive advice based on the information about the pet's health condition.

[0088] The suggestion unit can provide dietary and exercise advice to the owner based on information about the pet's health condition. The suggestion unit, for example, provides dietary and exercise advice to the owner based on information about the pet's health condition. For example, the suggestion unit suggests appropriate amounts and types of food, and frequency and types of exercise. This makes it easier to maintain the pet's health by providing dietary and exercise advice based on information about the pet's health condition.

[0089] The suggestion unit can provide stress management advice to the owner based on the information about the pet's health condition. The suggestion unit provides stress management advice to the owner based on, for example, the information about the pet's health condition. For example, the suggestion unit suggests relaxation methods or environmental improvements. In this way, providing stress management advice based on the information about the pet's health condition makes it easier to maintain the mental health of the pet.

[0090] The suggestion unit can provide the owner with advice on improving the pet's behavior based on the information on the pet's health condition. The suggestion unit provides the owner with advice on improving the pet's behavior based on, for example, the information on the pet's health condition. For example, the suggestion unit suggests training methods and behavior modification techniques. This makes it easier to properly manage the pet's behavior by providing advice on improving the behavior based on the information on the pet's health condition.

[0091] The suggestion unit can provide the owner with advice for maintaining the health of the pet based on the information about the pet's health condition. The suggestion unit, for example, provides the owner with advice for maintaining the health of the pet based on the information about the pet's health condition. For example, the suggestion unit suggests regular health checks and appropriate diet and exercise. In this way, providing health maintenance advice based on the information about the pet's health condition makes it easier to maintain the health of the pet.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The pet health management system can further include a unit for monitoring the social behavior of pets. For example, it can monitor how pets interact with other animals and humans to detect signs of social stress or anxiety. It can analyze how often pets play with other animals and how much time they spend in contact with their owners to assess their social health. This allows the system to understand the social health of pets and suggest appropriate measures if necessary.

[0094] The pet health management system can also be equipped with a unit that monitors the pet's sleep patterns. For example, it can analyze the pet's sleep duration and quality and notify the owner if there are any abnormalities. It can detect changes in the pet's sleep pattern, such as frequent nighttime awakenings or excessive daytime sleepiness. This allows the system to understand the pet's sleep status and suggest appropriate measures if necessary.

[0095] The pet health management system can also be equipped with a unit that records the details of a pet's diet. For example, it can record what a pet eats and how much it eats, and analyze the nutritional balance. If a pet has allergies to certain ingredients, the diet can be adjusted based on that information. This allows for a detailed understanding of a pet's diet, which can be useful in maintaining its health.

[0096] The pet health management system can further include a unit that monitors the amount of exercise a pet receives. For example, it can record how much exercise a pet receives and detect whether the pet is getting enough or too much exercise. It can also propose an exercise plan to help the pet maintain an appropriate amount of exercise. This allows the pet's amount of exercise to be properly managed, helping to maintain its health.

[0097] The pet health management system may further include a unit for managing the pet's weight. For example, the system may periodically measure the pet's weight and record any weight gain or loss. If there is a sudden change in weight, the owner may be notified and an appropriate course of action may be suggested. This allows the pet's weight to be properly managed, helping to maintain its health.

[0098] The analysis unit uses the emotion estimation function to estimate emotions from the pet's behavior and facial expressions, and can detect whether the pet is feeling lonely. For example, if the pet is alone for a long time, the analysis unit analyzes the behavior and facial expressions to detect loneliness. This allows the system to understand the pet's emotions and suggest ways to alleviate loneliness.

[0099] The analysis unit uses the emotion estimation function to estimate emotions from the pet's behavior and facial expressions, and can detect whether the pet is excited. For example, if the pet is overly excited, the analysis unit analyzes the behavior and facial expressions to identify the cause of the excitement. This allows the system to understand the pet's emotions and suggest ways to reduce the excitement.

[0100] The analysis unit uses the emotion estimation function to estimate emotions from the pet's behavior and facial expressions, and can detect whether the pet feels safe. For example, if the pet is relaxed, the analysis unit analyzes its behavior and facial expressions to confirm the sense of security. This allows the system to understand the pet's emotions and suggest ways to maintain a sense of security.

[0101] The analysis unit uses the emotion estimation function to estimate emotions from the pet's behavior and facial expressions, and can detect whether the pet is feeling fear. For example, if the pet is feeling fear in a particular situation, the analysis unit analyzes the behavior and facial expressions to identify the cause of the fear. This allows the system to understand the pet's emotions and suggest ways to alleviate the fear.

[0102] The analysis unit uses the emotion estimation function to estimate emotions from the pet's behavior and facial expressions, and can detect whether the pet is happy. For example, it analyzes the behavior and facial expressions of the pet when playing or interacting with its owner to confirm the emotion of joy. This allows the system to understand the pet's emotions and suggest ways to increase its joy.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The information collection unit collects data on the pet's health, such as the pet's appetite, excretion, behavioral changes, body temperature, heart rate, and other biological data, as well as the pet's environmental data (temperature, humidity, noise, etc.). Step 2: The analysis unit analyzes the data collected by the information collection unit. For example, it performs statistical analysis of the data to evaluate the pet's health status. It also analyzes the data using machine learning algorithms and compares it with the pet's past health data to detect abnormalities. Step 3: The assessment unit determines the severity of the condition based on the data analyzed by the analysis unit. For example, if a pet accidentally ingests something, the assessment unit analyzes information such as the type and amount of the substance, as well as the pet's weight and age, to determine the severity of the condition. The assessment unit also considers genetic risks and treatment effects by referencing the pet's genetic information and past treatment history. Step 4: The suggestion unit suggests solutions based on the severity determined by the evaluation unit. For example, it suggests home solutions for mild problems and recommends a visit to a veterinarian for severe problems. It also provides customized solutions based on the pet's individual health condition and personality, learning from the effectiveness of past solutions to suggest the optimal solution. Step 5: The notification unit notifies the owner of the solution proposed by the suggestion unit, for example, via a smartphone app, email, SMS, or smartwatch.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

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

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

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

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0149] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] 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.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information collection department that collects data on the health status of pets; an analysis unit that analyzes the data collected by the information collection unit; a determination unit that determines the severity based on the data analyzed by the analysis unit; a suggestion unit that suggests a countermeasure based on the severity determined by the determination unit; a notification unit that notifies the owner of the solution proposed by the suggestion unit. A system characterized by:

2. The analysis unit Infer emotions from the pet's behavior and facial expressions to detect signs of stress or anxiety 2. The system of claim 1.

3. The analysis unit When monitoring the health condition of the pet, the health condition of the owner is also monitored at the same time, and the correlation between the health conditions of the pet and the owner is analyzed.

2. The system of claim 1.

4. The determination unit Analyzing genetic information of the pet and taking genetic risks into account when determining the severity of the health condition of the pet.

2. The system of claim 1.

5. The proposal unit Providing a customized treatment based on the individual health condition and personality of the pet.

2. The system of claim 1.

6. The notification unit The owner is notified via a smartphone app.

2. The system of claim 1.

7. The determination unit Estimate emotions from the behavior and facial expressions of the pet, and consider the impact of emotional stress on the severity of the condition.

2. The system of claim 1.

8. The proposal unit Analyzing the owner's emotional response to the health condition of the pet and proposing the coping method taking the owner's emotions into consideration 2. The system of claim 1.

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