system

Abstract

The system according to the embodiment aims to comprehensively manage the health condition of pets, recommend appropriate food, and schedule veterinary appointments. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a recommendation unit, and a reservation unit. The collection unit collects health data of the pet. The analysis unit analyzes the data collected by the collection unit and predicts the pet's future health condition. The recommendation unit recommends an appropriate food based on the prediction results obtained by the analysis unit. The reservation unit makes a veterinary appointment as needed based on the food recommended by the recommendation unit.

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 not being able to comprehensively manage pet health, recommend appropriate food, or schedule veterinary appointments.

[0005] The system according to the embodiment aims to comprehensively manage the health condition of pets, recommend appropriate food, and schedule veterinary appointments. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a recommendation unit, and a reservation unit. The collection unit collects health data of the pet. The analysis unit analyzes the data collected by the collection unit and predicts the future health condition of the pet. The recommendation unit recommends an appropriate food based on the prediction result obtained by the analysis unit. The reservation unit makes a veterinary appointment as needed based on the food recommended by the recommendation unit. [Effects of the Invention]

[0007] The system according to the embodiment can comprehensively manage the health condition of a pet, recommend appropriate food, and schedule an appointment with a veterinarian. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) A pet care support system according to an embodiment of the present invention monitors a pet's health, predicts its future health, recommends an optimal pet food, and schedules a veterinary appointment as needed. This pet care support system collects pet health data and uses AI to analyze the data to predict its future health. Furthermore, it recommends an optimal pet food based on the pet's health and schedules a veterinary appointment as needed. For example, data such as a pet's weight, food intake, exercise, and sleep duration are collected. This data is collected in real time using sensors and smart devices. The collected data is then analyzed by AI. The AI ​​analyzes the pet's health data and predicts its future health. For example, if a pet's weight is increasing, it predicts that the pet is at risk of becoming obese in the future. Furthermore, the AI ​​recommends an optimal pet food based on the pet's health. For example, if a pet is prone to obesity, it recommends a low-calorie pet food. Furthermore, if a pet has a specific allergy, it recommends a pet food that addresses that allergy. Finally, it schedules a veterinary appointment as needed. For example, if a pet's health condition worsens, the AI ​​automatically schedules a veterinary appointment. In this way, pet health management is efficiently performed, reducing the burden on pet owners. For example, by monitoring a pet's health in real time and predicting its future health condition, early action can be taken. It can also maintain the pet's health by recommending the most suitable food. Furthermore, by making an appointment with a veterinarian as needed, the pet can receive appropriate medical care promptly. In this way, the pet care support system can efficiently manage the health of pets and reduce the burden on owners.

[0029] A pet care support system according to an embodiment includes a collection unit, an analysis unit, a recommendation unit, and a reservation unit. The collection unit collects health data of the pet. The pet's health data includes, but is not limited to, weight, food intake, exercise, and sleep duration. For example, the collection unit measures the pet's weight using a sensor and collects the data. The collection unit can also record the pet's food intake using a smart device. The collection unit can also use an acceleration sensor attached to the pet's collar to measure the amount of exercise. For example, the collection unit periodically measures the pet's weight and collects the data. The collection unit can also record the pet's food intake daily and collect the data. The collection unit can also measure the pet's amount of exercise in real time and collect the data. The analysis unit analyzes the data collected by the collection unit and predicts the pet's future health condition. For example, the analysis unit analyzes the data using a machine learning algorithm to predict the future health condition. For example, the analysis unit analyzes the pet's weight data and predicts the pet's risk of becoming obese in the future. The analysis unit can also analyze data on the amount of food eaten by the pet and predict imbalances in nutritional balance. Furthermore, the analysis unit can also analyze data on the amount of exercise the pet performs and predict the risk of insufficient exercise. For example, the analysis unit can predict the risk of future obesity based on data on the pet's weight. Furthermore, the analysis unit can predict imbalances in nutritional balance based on data on the amount of food eaten by the pet. Furthermore, the analysis unit can predict the risk of insufficient exercise based on data on the amount of exercise the pet performs. The recommendation unit recommends an optimal food based on the prediction results obtained by the analysis unit. For example, the recommendation unit can recommend a low-calorie food based on the pet's health condition. For example, if the pet is prone to obesity, the recommendation unit can recommend a low-calorie food. Furthermore, if the pet has a specific allergy, the recommendation unit can recommend a food that addresses that allergy. Furthermore, the recommendation unit can recommend an appropriate food based on the pet's age and activity level. For example, if the pet is prone to obesity, the recommendation unit can recommend a low-calorie food. Furthermore, if the pet has a specific allergy, the recommendation unit can recommend a food that addresses that allergy.Furthermore, the recommendation unit can recommend an appropriate food depending on the age and activity level of the pet. The reservation unit makes a veterinary appointment as needed based on the food recommended by the recommendation unit. The reservation unit automatically makes a veterinary appointment, for example, when the pet's health condition deteriorates. For example, the reservation unit makes a veterinary appointment when the pet's weight increases rapidly. The reservation unit can also make a veterinary appointment when the pet's food intake decreases. The reservation unit can also make a veterinary appointment when the pet's exercise level decreases. For example, the reservation unit makes a veterinary appointment when the pet's weight increases rapidly. The reservation unit can also make a veterinary appointment when the pet's food intake decreases. The reservation unit can also make a veterinary appointment when the pet's exercise level decreases. In this way, the pet care support system according to the embodiment can efficiently manage the health of pets and reduce the burden on owners.

[0030] The collection unit can collect data on a pet's weight, amount of food eaten, amount of exercise, and amount of sleep. The collection unit, for example, uses a scale to measure the pet's weight. For example, the collection unit periodically measures the pet's weight and collects the data. The collection unit can also use a smart feeder to record the pet's amount of food eaten. For example, the collection unit records the pet's amount of food eaten daily and collects the data. The collection unit can also use an acceleration sensor to measure the pet's amount of exercise. For example, the collection unit measures the pet's amount of exercise in real time and collects the data. The collection unit can also use a smart bed to record the pet's sleeping hours. For example, the collection unit records the pet's sleeping hours daily and collects the data. This allows the collection unit to closely monitor the pet's health. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the pet's weight data into a generation AI to analyze weight fluctuations.

[0031] The analysis unit can analyze the collected data and predict the pet's future health condition. The analysis unit can analyze the data using, for example, a machine learning algorithm to predict the future health condition. For example, the analysis unit can analyze the pet's weight data and predict the future risk of obesity. The analysis unit can also analyze the pet's food intake data and predict nutritional imbalance. The analysis unit can also analyze the pet's exercise data and predict the risk of insufficient exercise. For example, the analysis unit can predict the future risk of obesity based on the pet's weight data. The analysis unit can also predict nutritional imbalance based on the pet's food intake data. The analysis unit can also predict the risk of insufficient exercise based on the pet's exercise data. This allows the pet's future health risks to be predicted and measures to be taken early. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the pet's health data into a generation AI to predict the future health condition.

[0032] The recommendation unit can recommend an appropriate food based on the pet's health condition. The recommendation unit, for example, recommends a low-calorie food based on the pet's health condition. For example, if the pet is prone to obesity, the recommendation unit can recommend a low-calorie food. Furthermore, if the pet has a specific allergy, the recommendation unit can recommend a food that addresses that allergy. Furthermore, the recommendation unit can recommend an appropriate food based on the pet's age and activity level. For example, if the pet is prone to obesity, the recommendation unit can recommend a low-calorie food. Furthermore, if the pet has a specific allergy, the recommendation unit can recommend a food that addresses that allergy. Furthermore, the recommendation unit can recommend an appropriate food based on the pet's age and activity level. This allows the pet to be provided with the optimal food for maintaining its health. Some or all of the above-described processing in the recommendation unit may be performed, for example, using AI, or may be performed without AI. For example, the recommendation unit can input the pet's health data into a generation AI to recommend an appropriate food.

[0033] The recommendation unit can recommend food based on the pet's allergy information. The recommendation unit, for example, recommends food based on the pet's allergy information. For example, if the pet has a specific allergy, the recommendation unit recommends food that is compatible with that allergy. The recommendation unit can also recommend food that is compatible with allergies based on the pet's allergy information. Furthermore, the recommendation unit can also recommend food that is compatible with allergies based on the pet's allergy information. This makes it possible to provide food that is compatible with pet allergies. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input the pet's allergy information into a generation AI and recommend food that is compatible with allergies.

[0034] The reservation unit can make a veterinary appointment if the pet's health condition deteriorates. The reservation unit can make a veterinary appointment if, for example, the pet's weight increases suddenly. For example, the reservation unit can make a veterinary appointment if the pet's weight increases suddenly. The reservation unit can also make a veterinary appointment if the pet's food intake decreases. Furthermore, the reservation unit can also make a veterinary appointment if the pet's exercise level decreases. For example, the reservation unit can make a veterinary appointment if the pet's weight increases suddenly. The reservation unit can also make a veterinary appointment if the pet's food intake decreases. Furthermore, the reservation unit can also make a veterinary appointment if the pet's exercise level decreases. This allows a veterinary appointment to be made quickly if the pet's health condition deteriorates. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the pet's health data into a generation AI, detect a deterioration in the pet's health condition, and make a veterinary appointment.

[0035] The collection unit can analyze the pet's past health data and select an optimal data collection method. The collection unit, for example, analyzes the pet's past weight fluctuation data and adjusts the frequency of weight measurement. For example, the collection unit can analyze the pet's past weight fluctuation data and adjust the frequency of weight measurement. The collection unit can also analyze the pet's past food intake data and optimize the method for recording the food intake. The collection unit can also analyze the pet's past exercise amount data and improve the method for recording the exercise amount. For example, the collection unit can analyze the pet's past food intake data and optimize the method for recording the food intake. The collection unit can also analyze the pet's past exercise amount data and improve the method for recording the exercise amount. This improves the efficiency of data collection by selecting an optimal data collection method based on the past health data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the pet's past health data into a generation AI and select an optimal data collection method.

[0036] When collecting data, the collection unit can perform filtering based on the pet's current activity status and environment. For example, when the pet is exercising, the collection unit prioritizes collecting exercise data. For example, when the pet is exercising, the collection unit prioritizes collecting exercise data. Furthermore, when the pet is resting, the collection unit can also prioritize collecting rest data. Furthermore, when the pet is eating, the collection unit can also prioritize collecting meal data. For example, when the pet is resting, the collection unit prioritizes collecting rest data. Furthermore, when the pet is eating, the collection unit can also prioritize collecting meal data. This allows for more accurate data collection by filtering data according to the pet's current activity status and environment. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the pet's activity status data into a generation AI to filter the data.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the pet. For example, when the pet is in a park, the collection unit prioritizes collecting exercise data. For example, when the pet is in a park, the collection unit prioritizes collecting exercise data. The collection unit can also prioritize collecting rest data when the pet is at home. The collection unit can also prioritize collecting health checkup data when the pet is at a veterinary clinic. For example, when the pet is at home, the collection unit prioritizes collecting rest data. The collection unit can also prioritize collecting health checkup data when the pet is at a veterinary clinic. This prioritizes collecting highly relevant data based on the geographical location information of the pet, thereby improving data accuracy. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the geographical location information of the pet into a generation AI and prioritize collecting highly relevant data.

[0038] The collection unit can analyze the social media activities of the pet during data collection and collect related data. For example, the collection unit can analyze the details of the pet's diet from the pet's social media posts and collect diet data. For example, the collection unit can analyze the details of the pet's diet from the pet's social media posts and collect diet data. The collection unit can also analyze the frequency of exercise from the pet's social media posts and collect exercise data. The collection unit can also analyze the health condition from the pet's social media posts and collect health data. For example, the collection unit can analyze the frequency of exercise from the pet's social media posts and collect exercise data. The collection unit can also analyze the health condition from the pet's social media posts and collect health data. This allows for efficient collection of related data by analyzing the pet's social media activities. Some or all of the above-described processing by the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the pet's social media data into a generation AI to collect related data.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the pet's health data. The analysis unit, for example, analyzes important health data (weight, food intake, etc.) in detail. For example, the analysis unit analyzes important health data (weight, food intake, etc.) in detail. The analysis unit can also perform a standard analysis of general health data (amount of exercise, sleep time, etc.). The analysis unit can also perform a simplified analysis of auxiliary health data (social media activity, etc.). For example, the analysis unit analyzes general health data (amount of exercise, sleep time, etc.) in a standard manner. The analysis unit can also perform a simplified analysis of auxiliary health data (social media activity, etc.). In this way, by adjusting the level of detail of the analysis based on the importance of the health data, important data can be analyzed in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the pet's health data into a generation AI and adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the pet's health category. For example, the analysis unit applies a weight fluctuation analysis algorithm to data related to the pet's weight management. For example, the analysis unit applies a weight fluctuation analysis algorithm to data related to the pet's weight management. The analysis unit can also apply a food amount analysis algorithm to data related to the pet's dietary management. The analysis unit can also apply an exercise amount analysis algorithm to data related to the pet's exercise management. For example, the analysis unit applies a food amount analysis algorithm to data related to the pet's dietary management. The analysis unit can also apply an exercise amount analysis algorithm to data related to the pet's exercise management. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the health category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the pet's health data into a generation AI and apply different analysis algorithms depending on the health category.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the time of submission of the pet's health data. The analysis unit, for example, prioritizes analysis of the most recent health data. For example, the analysis unit prioritizes analysis of the most recent health data. The analysis unit can also perform analysis while referring to past health data. The analysis unit can also auxiliary analyze data that was submitted earlier. For example, the analysis unit performs analysis while referring to past health data. The analysis unit can also auxiliary analyze data that was submitted earlier. In this way, by adjusting the order of analysis based on the time of submission of the health data, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the pet's health data into the generation AI and adjust the order of analysis based on the time of submission.

[0042] The analysis unit can improve the accuracy of the analysis by referring to literature related to pets during the analysis. The analysis unit, for example, performs the analysis by referring to the latest research papers on pet health conditions. For example, the analysis unit performs the analysis by referring to the latest research papers on pet health conditions. The analysis unit can also perform the analysis by referring to literature on specific pet diseases. The analysis unit can also perform the analysis by referring to guidelines on pet health care. For example, the analysis unit performs the analysis by referring to literature on specific pet diseases. The analysis unit can also perform the analysis by referring to guidelines on pet health care. By doing so, the accuracy of the analysis is improved by referring to related literature. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input pet health data into the generation AI and improve the accuracy of the analysis by referring to related literature.

[0043] When making a recommendation, the recommendation unit can adjust the level of detail of the recommendation based on the importance of the pet's health data. The recommendation unit, for example, makes detailed food recommendations based on important health data (weight, food intake, etc.). For example, the recommendation unit makes detailed food recommendations based on important health data (weight, food intake, etc.). The recommendation unit can also make standard food recommendations based on general health data (amount of exercise, sleep time, etc.). The recommendation unit can also make simple food recommendations based on auxiliary health data (social media activity, etc.). For example, the recommendation unit makes standard food recommendations based on general health data (amount of exercise, sleep time, etc.). The recommendation unit can also make simple food recommendations based on auxiliary health data (social media activity, etc.). In this way, adjusting the level of detail of the recommendation based on the importance of the health data enables detailed recommendations based on important data. Some or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI or without AI. For example, the recommendation unit can input the pet's health data into a generation AI and adjust the level of detail of the recommendation.

[0044] When making a recommendation, the recommendation unit can apply different recommendation algorithms depending on the pet's health category. For example, the recommendation unit applies a weight management food recommendation algorithm to data related to the pet's weight management. For example, the recommendation unit applies a weight management food recommendation algorithm to data related to the pet's weight management. The recommendation unit can also apply a diet management food recommendation algorithm to data related to the pet's diet management. The recommendation unit can also apply an exercise management food recommendation algorithm to data related to the pet's exercise management. For example, the recommendation unit applies a diet management food recommendation algorithm to data related to the pet's diet management. The recommendation unit can also apply an exercise management food recommendation algorithm to data related to the pet's exercise management. This improves the accuracy of recommendations by applying an appropriate recommendation algorithm depending on the health category. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the pet's health data into a generation AI and apply different recommendation algorithms depending on the health category.

[0045] When making a recommendation, the recommendation unit can adjust the order of recommendations based on the time of submission of the pet's health data. For example, the recommendation unit prioritizes recommending foods based on the most recent health data. For example, the recommendation unit prioritizes recommending foods based on the most recent health data. The recommendation unit can also recommend foods while referring to past health data. Furthermore, the recommendation unit can also recommend foods supplementarily for data submitted earlier. For example, the recommendation unit recommends foods while referring to past health data. The recommendation unit can also recommend foods supplementarily for data submitted earlier. In this way, adjusting the order of recommendations based on the time of submission of the health data enables recommendations based on the most recent data. Some or all of the above-described processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input the pet's health data into a generation AI and adjust the order of recommendations based on the time of submission.

[0046] The recommendation unit can improve the accuracy of the recommendation by referring to literature related to the pet when making a recommendation. For example, the recommendation unit can recommend a food by referring to the latest research papers on the health condition of the pet. For example, the recommendation unit can recommend a food by referring to the latest research papers on the health condition of the pet. The recommendation unit can also recommend a food by referring to literature on a specific pet disease. Furthermore, the recommendation unit can recommend a food by referring to guidelines on pet health management. For example, the recommendation unit can recommend a food by referring to literature on a specific pet disease. The recommendation unit can also recommend a food by referring to guidelines on pet health management. In this way, the accuracy of the recommendation is improved by referring to related literature. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input pet health data into the generation AI and improve the accuracy of the recommendation by referring to related literature.

[0047] The reservation unit can determine the priority of reservations based on the importance of the pet's health data when making a reservation. The reservation unit, for example, prioritizes reservations based on important health data (weight, amount of food, etc.). For example, the reservation unit prioritizes reservations based on important health data (weight, amount of food, etc.). The reservation unit can also make standard reservations based on general health data (amount of exercise, amount of sleep, etc.). The reservation unit can also make simple reservations based on auxiliary health data (social media activity, etc.). For example, the reservation unit can make standard reservations based on general health data (amount of exercise, amount of sleep, etc.). The reservation unit can also make simple reservations based on auxiliary health data (social media activity, etc.). In this way, by prioritizing reservations based on the importance of the health data, important reservations can be prioritized. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the pet's health data into a generation AI and determine the priority of reservations based on the importance.

[0048] The reservation unit can apply different reservation algorithms depending on the pet's health category when making a reservation. For example, the reservation unit applies a weight management reservation algorithm to data related to the pet's weight management. For example, the reservation unit applies a weight management reservation algorithm to data related to the pet's weight management. The reservation unit can also apply a dietary management reservation algorithm to data related to the pet's dietary management. The reservation unit can also apply an exercise management reservation algorithm to data related to the pet's exercise management. For example, the reservation unit applies a dietary management reservation algorithm to data related to the pet's dietary management. The reservation unit can also apply an exercise management reservation algorithm to data related to the pet's exercise management. This improves reservation accuracy by applying an appropriate reservation algorithm depending on the health category. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the pet's health data into a generation AI and apply different reservation algorithms depending on the health category.

[0049] At the time of reservation, the reservation unit can determine the priority of reservations based on the time of submission of the pet's health data. The reservation unit, for example, prioritizes reservations based on the most recent health data. For example, the reservation unit prioritizes reservations based on the most recent health data. The reservation unit can also make reservations while referring to past health data. Furthermore, the reservation unit can also make reservations supplementarily for data submitted earlier. For example, the reservation unit makes reservations while referring to past health data. Furthermore, the reservation unit can also make reservations supplementarily for data submitted earlier. In this way, by determining the priority of reservations based on the time of submission of health data, reservations can be made based on the most recent data. Some or all of the above-described processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the pet's health data into a generation AI and determine the priority of reservations based on the time of submission.

[0050] The reservation unit can improve the accuracy of reservations by referring to pet-related literature when making a reservation. The reservation unit, for example, makes a reservation by referring to the latest research papers on pet health conditions. For example, the reservation unit makes a reservation by referring to the latest research papers on pet health conditions. The reservation unit can also make a reservation by referring to literature on a specific pet disease. The reservation unit can also make a reservation by referring to guidelines on pet health management. For example, the reservation unit makes a reservation by referring to literature on a specific pet disease. The reservation unit can also make a reservation by referring to guidelines on pet health management. By doing so, the accuracy of reservations is improved by referring to related literature. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input pet health data into a generation AI and improve the accuracy of reservations by referring to related literature.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The pet care support system may further include a behavior prediction unit that learns the behavioral patterns of the pet and predicts the pet's behavior. The behavior prediction unit analyzes the pet's past behavioral data and predicts future behavior. For example, if the pet tends to exercise during a specific time of day, exercise data can be collected preferentially during that time. Also, if the pet tends to rest in a specific place, detailed resting data at that place can be collected. Furthermore, if the pet has a specific eating pattern, eating data can be collected based on that pattern. This can improve the efficiency of data collection based on the pet's behavioral patterns.

[0053] The collection unit can collect environmental data (such as temperature, humidity, and noise level) in addition to the pet's health data. For example, the collection unit can measure the temperature of the pet's living environment and collect the data. The collection unit can also measure the humidity of the pet's living environment and collect the data. Furthermore, the collection unit can measure the noise level of the pet's living environment and collect the data. This allows for detailed monitoring of environmental factors that affect the pet's health condition.

[0054] The recommendation unit can recommend an exercise program based on the health condition of the pet. For example, if the pet is not getting enough exercise, the recommendation unit can recommend an appropriate exercise program. If the pet is overweight, the recommendation unit can also recommend an exercise program for weight management. Furthermore, if the pet is elderly, the recommendation unit can also recommend an exercise program that is gentle on the pet's joints. This makes it possible to provide an exercise program that is appropriate for the pet's health condition.

[0055] The reservation unit can send reminders for regular health checks based on the pet's health data. For example, the reservation unit can send reminders for regular weight measurements based on the pet's weight data. The reservation unit can also send reminders for regular food intake checks based on the pet's diet data. Furthermore, the reservation unit can also send reminders for regular exercise checks based on the pet's exercise data. This allows for continuous support for pet health management.

[0056] The analysis unit can assess the health risks of a pet based on the pet's health data. For example, the analysis unit can analyze the pet's weight data and assess the risk of obesity. The analysis unit can also analyze the pet's dietary data and assess the risk of nutritional imbalance. Furthermore, the analysis unit can analyze the pet's exercise data and assess the risk of lack of exercise. This allows for a detailed assessment of the pet's health risks and allows for early countermeasures to be taken.

[0057] In addition to the pet's health data, the collection unit can collect the pet's social interaction data. For example, the collection unit can record how often the pet plays with other pets. The collection unit can also record how much time the pet spends with its owner. The collection unit can also record how often the pet interacts with other people. This allows for detailed monitoring of the pet's social interaction status.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit collects the pet's health data. The pet's health data includes weight, amount of food eaten, amount of exercise, and sleep time. The collection unit measures the pet's weight using a sensor and collects the data. The collection unit can also record the pet's food intake using a smart device and measure the amount of exercise using an acceleration sensor attached to the pet's collar. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts the pet's future health condition. The analysis unit analyzes the data using a machine learning algorithm to predict the pet's future health condition. For example, the analysis unit analyzes the pet's weight data to predict the pet's risk of becoming obese in the future, analyzes food intake data to predict nutritional imbalances, and analyzes exercise volume data to predict the pet's risk of lack of exercise. Step 3: The recommendation unit recommends the optimal food based on the prediction results obtained by the analysis unit. For example, if your pet is prone to obesity, it will recommend a low-calorie food, or if your pet has a specific allergy, it will recommend a food that addresses that allergy. It can also recommend food that is appropriate for your pet's age and activity level. Step 4: The reservation unit schedules veterinary appointments as needed based on the food recommendations made by the recommendation unit, for example, if the pet's health condition deteriorates, if the pet experiences a sudden weight gain, if the pet's food intake decreases, or if the pet's exercise level decreases.

[0060] (Example 2) A pet care support system according to an embodiment of the present invention monitors a pet's health, predicts its future health, recommends an optimal pet food, and schedules a veterinary appointment as needed. This pet care support system collects pet health data and uses AI to analyze the data to predict its future health. Furthermore, it recommends an optimal pet food based on the pet's health and schedules a veterinary appointment as needed. For example, data such as a pet's weight, food intake, exercise, and sleep duration are collected. This data is collected in real time using sensors and smart devices. The collected data is then analyzed by AI. The AI ​​analyzes the pet's health data and predicts its future health. For example, if a pet's weight is increasing, it predicts that the pet is at risk of becoming obese in the future. Furthermore, the AI ​​recommends an optimal pet food based on the pet's health. For example, if a pet is prone to obesity, it recommends a low-calorie pet food. Furthermore, if a pet has a specific allergy, it recommends a pet food that addresses that allergy. Finally, it schedules a veterinary appointment as needed. For example, if a pet's health condition worsens, the AI ​​automatically schedules a veterinary appointment. In this way, pet health management is efficiently performed, reducing the burden on pet owners. For example, by monitoring a pet's health in real time and predicting its future health condition, early action can be taken. It can also maintain the pet's health by recommending the most suitable food. Furthermore, by making an appointment with a veterinarian as needed, the pet can receive appropriate medical care promptly. In this way, the pet care support system can efficiently manage the health of pets and reduce the burden on owners.

[0061] A pet care support system according to an embodiment includes a collection unit, an analysis unit, a recommendation unit, and a reservation unit. The collection unit collects health data of the pet. The pet's health data includes, but is not limited to, weight, food intake, exercise, and sleep duration. For example, the collection unit measures the pet's weight using a sensor and collects the data. The collection unit can also record the pet's food intake using a smart device. The collection unit can also use an acceleration sensor attached to the pet's collar to measure the amount of exercise. For example, the collection unit periodically measures the pet's weight and collects the data. The collection unit can also record the pet's food intake daily and collect the data. The collection unit can also measure the pet's amount of exercise in real time and collect the data. The analysis unit analyzes the data collected by the collection unit and predicts the pet's future health condition. For example, the analysis unit analyzes the data using a machine learning algorithm to predict the future health condition. For example, the analysis unit analyzes the pet's weight data and predicts the pet's risk of becoming obese in the future. The analysis unit can also analyze data on the amount of food eaten by the pet and predict imbalances in nutritional balance. Furthermore, the analysis unit can also analyze data on the amount of exercise the pet performs and predict the risk of insufficient exercise. For example, the analysis unit can predict the risk of future obesity based on data on the pet's weight. Furthermore, the analysis unit can predict imbalances in nutritional balance based on data on the amount of food eaten by the pet. Furthermore, the analysis unit can predict the risk of insufficient exercise based on data on the amount of exercise the pet performs. The recommendation unit recommends an optimal food based on the prediction results obtained by the analysis unit. For example, the recommendation unit can recommend a low-calorie food based on the pet's health condition. For example, if the pet is prone to obesity, the recommendation unit can recommend a low-calorie food. Furthermore, if the pet has a specific allergy, the recommendation unit can recommend a food that addresses that allergy. Furthermore, the recommendation unit can recommend an appropriate food based on the pet's age and activity level. For example, if the pet is prone to obesity, the recommendation unit can recommend a low-calorie food. Furthermore, if the pet has a specific allergy, the recommendation unit can recommend a food that addresses that allergy.Furthermore, the recommendation unit can recommend an appropriate food depending on the age and activity level of the pet. The reservation unit makes a veterinary appointment as needed based on the food recommended by the recommendation unit. The reservation unit automatically makes a veterinary appointment, for example, when the pet's health condition deteriorates. For example, the reservation unit makes a veterinary appointment when the pet's weight increases rapidly. The reservation unit can also make a veterinary appointment when the pet's food intake decreases. The reservation unit can also make a veterinary appointment when the pet's exercise level decreases. For example, the reservation unit makes a veterinary appointment when the pet's weight increases rapidly. The reservation unit can also make a veterinary appointment when the pet's food intake decreases. The reservation unit can also make a veterinary appointment when the pet's exercise level decreases. In this way, the pet care support system according to the embodiment can efficiently manage the health of pets and reduce the burden on owners.

[0062] The collection unit can collect data on a pet's weight, amount of food eaten, amount of exercise, and amount of sleep. The collection unit, for example, uses a scale to measure the pet's weight. For example, the collection unit periodically measures the pet's weight and collects the data. The collection unit can also use a smart feeder to record the pet's amount of food eaten. For example, the collection unit records the pet's amount of food eaten daily and collects the data. The collection unit can also use an acceleration sensor to measure the pet's amount of exercise. For example, the collection unit measures the pet's amount of exercise in real time and collects the data. The collection unit can also use a smart bed to record the pet's sleeping hours. For example, the collection unit records the pet's sleeping hours daily and collects the data. This allows the collection unit to closely monitor the pet's health. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the pet's weight data into a generation AI to analyze weight fluctuations.

[0063] The analysis unit can analyze the collected data and predict the pet's future health condition. The analysis unit can analyze the data using, for example, a machine learning algorithm to predict the future health condition. For example, the analysis unit can analyze the pet's weight data and predict the future risk of obesity. The analysis unit can also analyze the pet's food intake data and predict nutritional imbalance. The analysis unit can also analyze the pet's exercise data and predict the risk of insufficient exercise. For example, the analysis unit can predict the future risk of obesity based on the pet's weight data. The analysis unit can also predict nutritional imbalance based on the pet's food intake data. The analysis unit can also predict the risk of insufficient exercise based on the pet's exercise data. This allows the pet's future health risks to be predicted and measures to be taken early. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the pet's health data into a generation AI to predict the future health condition.

[0064] The recommendation unit can recommend an appropriate food based on the pet's health condition. The recommendation unit, for example, recommends a low-calorie food based on the pet's health condition. For example, if the pet is prone to obesity, the recommendation unit can recommend a low-calorie food. Furthermore, if the pet has a specific allergy, the recommendation unit can recommend a food that addresses that allergy. Furthermore, the recommendation unit can recommend an appropriate food based on the pet's age and activity level. For example, if the pet is prone to obesity, the recommendation unit can recommend a low-calorie food. Furthermore, if the pet has a specific allergy, the recommendation unit can recommend a food that addresses that allergy. Furthermore, the recommendation unit can recommend an appropriate food based on the pet's age and activity level. This allows the pet to be provided with the optimal food for maintaining its health. Some or all of the above-described processing in the recommendation unit may be performed, for example, using AI, or may be performed without AI. For example, the recommendation unit can input the pet's health data into a generation AI to recommend an appropriate food.

[0065] The recommendation unit can recommend food based on the pet's allergy information. The recommendation unit, for example, recommends food based on the pet's allergy information. For example, if the pet has a specific allergy, the recommendation unit recommends food that is compatible with that allergy. The recommendation unit can also recommend food that is compatible with allergies based on the pet's allergy information. Furthermore, the recommendation unit can also recommend food that is compatible with allergies based on the pet's allergy information. This makes it possible to provide food that is compatible with pet allergies. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input the pet's allergy information into a generation AI and recommend food that is compatible with allergies.

[0066] The reservation unit can make a veterinary appointment if the pet's health condition deteriorates. The reservation unit can make a veterinary appointment if, for example, the pet's weight increases suddenly. For example, the reservation unit can make a veterinary appointment if the pet's weight increases suddenly. The reservation unit can also make a veterinary appointment if the pet's food intake decreases. Furthermore, the reservation unit can also make a veterinary appointment if the pet's exercise level decreases. For example, the reservation unit can make a veterinary appointment if the pet's weight increases suddenly. The reservation unit can also make a veterinary appointment if the pet's food intake decreases. Furthermore, the reservation unit can also make a veterinary appointment if the pet's exercise level decreases. This allows a veterinary appointment to be made quickly if the pet's health condition deteriorates. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the pet's health data into a generation AI, detect a deterioration in the pet's health condition, and make a veterinary appointment.

[0067] The collection unit can estimate the pet's emotions and adjust the frequency of data collection based on the estimated pet's emotions. For example, if the pet is stressed, the collection unit reduces the frequency of data collection to reduce the burden on the pet. For example, if the pet is stressed, the collection unit reduces the frequency of data collection to reduce the burden on the pet. The collection unit can also increase the frequency of data collection to collect detailed health data if the pet is relaxed. Furthermore, if the pet is excited, the collection unit can temporarily reduce the frequency of data collection and wait until the pet's behavior stabilizes. For example, if the pet is relaxed, the collection unit increases the frequency of data collection to collect detailed health data. Furthermore, if the pet is excited, the collection unit can temporarily reduce the frequency of data collection and wait until the pet's behavior stabilizes. In this way, the burden on the pet can be reduced by adjusting the frequency of data collection according to the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input emotional data of a pet into the generation AI, estimate the emotion, and adjust the frequency of data collection.

[0068] The collection unit can analyze the pet's past health data and select an optimal data collection method. The collection unit, for example, analyzes the pet's past weight fluctuation data and adjusts the frequency of weight measurement. For example, the collection unit can analyze the pet's past weight fluctuation data and adjust the frequency of weight measurement. The collection unit can also analyze the pet's past food intake data and optimize the method for recording the food intake. The collection unit can also analyze the pet's past exercise amount data and improve the method for recording the exercise amount. For example, the collection unit can analyze the pet's past food intake data and optimize the method for recording the food intake. The collection unit can also analyze the pet's past exercise amount data and improve the method for recording the exercise amount. This improves the efficiency of data collection by selecting an optimal data collection method based on the past health data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the pet's past health data into a generation AI and select an optimal data collection method.

[0069] When collecting data, the collection unit can perform filtering based on the pet's current activity status and environment. For example, when the pet is exercising, the collection unit prioritizes collecting exercise data. For example, when the pet is exercising, the collection unit prioritizes collecting exercise data. Furthermore, when the pet is resting, the collection unit can also prioritize collecting rest data. Furthermore, when the pet is eating, the collection unit can also prioritize collecting meal data. For example, when the pet is resting, the collection unit prioritizes collecting rest data. Furthermore, when the pet is eating, the collection unit can also prioritize collecting meal data. This allows for more accurate data collection by filtering data according to the pet's current activity status and environment. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the pet's activity status data into a generation AI to filter the data.

[0070] The collection unit can estimate the pet's emotions and determine the priority of data to be collected based on the estimated pet's emotions. For example, if the pet is stressed, the collection unit prioritizes collecting stress-related data. For example, if the pet is stressed, the collection unit prioritizes collecting stress-related data. Furthermore, if the pet is relaxed, the collection unit can also collect general health data in a balanced manner. Furthermore, if the pet is excited, the collection unit can also prioritize collecting behavioral data. For example, if the pet is relaxed, the collection unit collects general health data in a balanced manner. Furthermore, if the pet is excited, the collection unit can also prioritize collecting behavioral data. In this way, by determining the priority of data to be collected according to the pet's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input emotional data of a pet into the generation AI and determine the priority of the data to be collected.

[0071] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the pet. For example, when the pet is in a park, the collection unit prioritizes collecting exercise data. For example, when the pet is in a park, the collection unit prioritizes collecting exercise data. The collection unit can also prioritize collecting rest data when the pet is at home. The collection unit can also prioritize collecting health checkup data when the pet is at a veterinary clinic. For example, when the pet is at home, the collection unit prioritizes collecting rest data. The collection unit can also prioritize collecting health checkup data when the pet is at a veterinary clinic. This prioritizes collecting highly relevant data based on the geographical location information of the pet, thereby improving data accuracy. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the geographical location information of the pet into a generation AI and prioritize collecting highly relevant data.

[0072] The collection unit can analyze the social media activities of the pet during data collection and collect related data. For example, the collection unit can analyze the details of the pet's diet from the pet's social media posts and collect diet data. For example, the collection unit can analyze the details of the pet's diet from the pet's social media posts and collect diet data. The collection unit can also analyze the frequency of exercise from the pet's social media posts and collect exercise data. The collection unit can also analyze the health condition from the pet's social media posts and collect health data. For example, the collection unit can analyze the frequency of exercise from the pet's social media posts and collect exercise data. The collection unit can also analyze the health condition from the pet's social media posts and collect health data. This allows for efficient collection of related data by analyzing the pet's social media activities. Some or all of the above-described processing by the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the pet's social media data into a generation AI to collect related data.

[0073] The analysis unit can estimate the pet's emotions and adjust the analysis algorithm based on the estimated pet emotions. For example, if the pet is stressed, the analysis unit strengthens the analysis of stress-related data. For example, if the pet is stressed, the analysis unit strengthens the analysis of stress-related data. The analysis unit can also analyze overall health data if the pet is relaxed. The analysis unit can also strengthen the analysis of behavioral data if the pet is excited. For example, if the pet is relaxed, the analysis unit analyzes overall health data. The analysis unit can also strengthen the analysis of behavioral data if the pet is excited. This improves the accuracy of the analysis by adjusting the analysis algorithm according to the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the pet's emotional data into the generation AI and adjust the analysis algorithm.

[0074] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the pet's health data. The analysis unit, for example, analyzes important health data (weight, food intake, etc.) in detail. For example, the analysis unit analyzes important health data (weight, food intake, etc.) in detail. The analysis unit can also perform a standard analysis of general health data (amount of exercise, sleep time, etc.). The analysis unit can also perform a simplified analysis of auxiliary health data (social media activity, etc.). For example, the analysis unit analyzes general health data (amount of exercise, sleep time, etc.) in a standard manner. The analysis unit can also perform a simplified analysis of auxiliary health data (social media activity, etc.). In this way, by adjusting the level of detail of the analysis based on the importance of the health data, important data can be analyzed in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the pet's health data into a generation AI and adjust the level of detail of the analysis.

[0075] During analysis, the analysis unit can apply different analysis algorithms depending on the pet's health category. For example, the analysis unit applies a weight fluctuation analysis algorithm to data related to the pet's weight management. For example, the analysis unit applies a weight fluctuation analysis algorithm to data related to the pet's weight management. The analysis unit can also apply a food amount analysis algorithm to data related to the pet's dietary management. The analysis unit can also apply an exercise amount analysis algorithm to data related to the pet's exercise management. For example, the analysis unit applies a food amount analysis algorithm to data related to the pet's dietary management. The analysis unit can also apply an exercise amount analysis algorithm to data related to the pet's exercise management. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the health category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the pet's health data into a generation AI and apply different analysis algorithms depending on the health category.

[0076] The analysis unit can estimate the pet's emotions and determine the priority of analysis based on the estimated pet's emotions. For example, if the pet is stressed, the analysis unit prioritizes the analysis of stress-related data. For example, if the pet is stressed, the analysis unit prioritizes the analysis of stress-related data. Furthermore, if the pet is relaxed, the analysis unit can also perform a balanced analysis of overall health data. Furthermore, if the pet is excited, the analysis unit can prioritize the analysis of behavioral data. For example, if the pet is relaxed, the analysis unit can perform a balanced analysis of overall health data. Furthermore, if the pet is excited, the analysis unit can prioritize the analysis of behavioral data. In this way, by determining the analysis priority according to the pet's emotions, important data can be analyzed preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or without AI. For example, the analysis unit can input the pet's emotional data into the generation AI and determine the priority of the analysis.

[0077] During analysis, the analysis unit can adjust the order of analysis based on the time of submission of the pet's health data. The analysis unit, for example, prioritizes analysis of the most recent health data. For example, the analysis unit prioritizes analysis of the most recent health data. The analysis unit can also perform analysis while referring to past health data. The analysis unit can also auxiliary analyze data that was submitted earlier. For example, the analysis unit performs analysis while referring to past health data. The analysis unit can also auxiliary analyze data that was submitted earlier. In this way, by adjusting the order of analysis based on the time of submission of the health data, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the pet's health data into the generation AI and adjust the order of analysis based on the time of submission.

[0078] The analysis unit can improve the accuracy of the analysis by referring to literature related to pets during the analysis. The analysis unit, for example, performs the analysis by referring to the latest research papers on pet health conditions. For example, the analysis unit performs the analysis by referring to the latest research papers on pet health conditions. The analysis unit can also perform the analysis by referring to literature on specific pet diseases. The analysis unit can also perform the analysis by referring to guidelines on pet health care. For example, the analysis unit performs the analysis by referring to literature on specific pet diseases. The analysis unit can also perform the analysis by referring to guidelines on pet health care. By doing so, the accuracy of the analysis is improved by referring to related literature. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input pet health data into the generation AI and improve the accuracy of the analysis by referring to related literature.

[0079] The recommendation unit can estimate the pet's emotions and adjust the food recommendation method based on the estimated pet's emotions. For example, if the pet is stressed, the recommendation unit recommends food with a stress-reducing effect. For example, if the pet is stressed, the recommendation unit can recommend food with a stress-reducing effect. Furthermore, if the pet is relaxed, the recommendation unit can recommend a balanced food. Furthermore, if the pet is excited, the recommendation unit can recommend food suitable for replenishing energy. For example, if the pet is relaxed, the recommendation unit can recommend a balanced food. Furthermore, if the pet is excited, the recommendation unit can recommend food suitable for replenishing energy. In this way, by adjusting the food recommendation method according to the pet's emotions, it is possible to provide food suitable for the pet. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or without AI. For example, the recommendation unit can input emotional data about the pet into the generation AI to adjust how it recommends food.

[0080] When making a recommendation, the recommendation unit can adjust the level of detail of the recommendation based on the importance of the pet's health data. The recommendation unit, for example, makes detailed food recommendations based on important health data (weight, food intake, etc.). For example, the recommendation unit makes detailed food recommendations based on important health data (weight, food intake, etc.). The recommendation unit can also make standard food recommendations based on general health data (amount of exercise, sleep time, etc.). The recommendation unit can also make simple food recommendations based on auxiliary health data (social media activity, etc.). For example, the recommendation unit makes standard food recommendations based on general health data (amount of exercise, sleep time, etc.). The recommendation unit can also make simple food recommendations based on auxiliary health data (social media activity, etc.). In this way, adjusting the level of detail of the recommendation based on the importance of the health data enables detailed recommendations based on important data. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the pet's health data into a generation AI and adjust the level of detail of the recommendation.

[0081] When making a recommendation, the recommendation unit can apply different recommendation algorithms depending on the pet's health category. For example, the recommendation unit applies a weight management food recommendation algorithm to data related to the pet's weight management. For example, the recommendation unit applies a weight management food recommendation algorithm to data related to the pet's weight management. The recommendation unit can also apply a diet management food recommendation algorithm to data related to the pet's diet management. The recommendation unit can also apply an exercise management food recommendation algorithm to data related to the pet's exercise management. For example, the recommendation unit applies a diet management food recommendation algorithm to data related to the pet's diet management. The recommendation unit can also apply an exercise management food recommendation algorithm to data related to the pet's exercise management. This improves the accuracy of recommendations by applying an appropriate recommendation algorithm depending on the health category. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the pet's health data into a generation AI and apply different recommendation algorithms depending on the health category.

[0082] The recommendation unit can estimate the pet's emotions and determine the priority of recommendations based on the estimated pet's emotions. For example, if the pet is stressed, the recommendation unit can prioritize recommending foods with a stress-reducing effect. For example, if the pet is stressed, the recommendation unit can prioritize recommending foods with a stress-reducing effect. Furthermore, if the pet is relaxed, the recommendation unit can prioritize recommending balanced foods. Furthermore, if the pet is excited, the recommendation unit can prioritize recommending foods suitable for energy replenishment. For example, if the pet is relaxed, the recommendation unit can prioritize recommending balanced foods. Furthermore, if the pet is excited, the recommendation unit can prioritize recommending foods suitable for energy replenishment. In this way, by determining the priority of recommendations according to the pet's emotions, important recommendations can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit may input pet emotion data into the generation AI and determine the priority of recommendations.

[0083] When making a recommendation, the recommendation unit can adjust the order of recommendations based on the time of submission of the pet's health data. For example, the recommendation unit prioritizes recommending foods based on the most recent health data. For example, the recommendation unit prioritizes recommending foods based on the most recent health data. The recommendation unit can also recommend foods while referring to past health data. Furthermore, the recommendation unit can also recommend foods supplementarily for data submitted earlier. For example, the recommendation unit recommends foods while referring to past health data. The recommendation unit can also recommend foods supplementarily for data submitted earlier. In this way, adjusting the order of recommendations based on the time of submission of the health data enables recommendations based on the most recent data. Some or all of the above-described processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input the pet's health data into a generation AI and adjust the order of recommendations based on the time of submission.

[0084] The recommendation unit can improve the accuracy of the recommendation by referring to literature related to the pet when making a recommendation. For example, the recommendation unit can recommend a food by referring to the latest research papers on the health condition of the pet. For example, the recommendation unit can recommend a food by referring to the latest research papers on the health condition of the pet. The recommendation unit can also recommend a food by referring to literature on a specific pet disease. Furthermore, the recommendation unit can recommend a food by referring to guidelines on pet health management. For example, the recommendation unit can recommend a food by referring to literature on a specific pet disease. The recommendation unit can also recommend a food by referring to guidelines on pet health management. In this way, the accuracy of the recommendation is improved by referring to related literature. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input pet health data into the generation AI and improve the accuracy of the recommendation by referring to related literature.

[0085] The reservation unit can estimate the pet's emotion and adjust the timing of the reservation based on the estimated pet's emotion. For example, if the pet is stressed, the reservation unit makes a reservation at a timing when the stress will be reduced. For example, if the pet is stressed, the reservation unit makes a reservation at a timing when the stress will be reduced. Furthermore, if the pet is relaxed, the reservation unit can make a reservation at a timing when the relaxed state will continue. Furthermore, if the pet is excited, the reservation unit can make a reservation at a timing when the excitement will subside. For example, if the pet is relaxed, the reservation unit can make a reservation at a timing when the relaxed state will continue. Furthermore, if the pet is excited, the reservation unit can make a reservation at a timing when the excitement will subside. In this way, by adjusting the timing of the reservation according to the pet's emotion, the reservation can be made at a timing that is optimal for the pet. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or without AI. For example, the reservation department can input the pet's emotional data into the generation AI and adjust the timing of the reservation.

[0086] The reservation unit can determine the priority of reservations based on the importance of the pet's health data when making a reservation. The reservation unit, for example, prioritizes reservations based on important health data (weight, amount of food, etc.). For example, the reservation unit prioritizes reservations based on important health data (weight, amount of food, etc.). The reservation unit can also make standard reservations based on general health data (amount of exercise, amount of sleep, etc.). The reservation unit can also make simple reservations based on auxiliary health data (social media activity, etc.). For example, the reservation unit can make standard reservations based on general health data (amount of exercise, amount of sleep, etc.). The reservation unit can also make simple reservations based on auxiliary health data (social media activity, etc.). In this way, by prioritizing reservations based on the importance of the health data, important reservations can be prioritized. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the pet's health data into a generation AI and determine the priority of reservations based on the importance.

[0087] The reservation unit can apply different reservation algorithms depending on the pet's health category when making a reservation. For example, the reservation unit applies a weight management reservation algorithm to data related to the pet's weight management. For example, the reservation unit applies a weight management reservation algorithm to data related to the pet's weight management. The reservation unit can also apply a dietary management reservation algorithm to data related to the pet's dietary management. The reservation unit can also apply an exercise management reservation algorithm to data related to the pet's exercise management. For example, the reservation unit applies a dietary management reservation algorithm to data related to the pet's dietary management. The reservation unit can also apply an exercise management reservation algorithm to data related to the pet's exercise management. This improves reservation accuracy by applying an appropriate reservation algorithm depending on the health category. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the pet's health data into a generation AI and apply different reservation algorithms depending on the health category.

[0088] The reservation unit can estimate the pet's emotions and adjust the order of reservations based on the estimated pet's emotions. For example, if the pet is stressed, the reservation unit makes reservations after the stress is reduced. For example, if the pet is stressed, the reservation unit makes reservations after the stress is reduced. Furthermore, if the pet is relaxed, the reservation unit can make reservations in an order that maintains the relaxed state. Furthermore, if the pet is excited, the reservation unit can make reservations in an order that maintains the excited state. For example, if the pet is relaxed, the reservation unit can make reservations in an order that maintains the relaxed state. Furthermore, if the pet is excited, the reservation unit can make reservations in an order that maintains the excited state. In this way, by adjusting the order of reservations according to the pet's emotions, reservations can be made in an order that is optimal for the pet. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or without AI. For example, the reservation department can input pet emotional data into the generation AI and adjust the order of reservations.

[0089] At the time of reservation, the reservation unit can determine the priority of reservations based on the time of submission of the pet's health data. The reservation unit, for example, prioritizes reservations based on the most recent health data. For example, the reservation unit prioritizes reservations based on the most recent health data. The reservation unit can also make reservations while referring to past health data. Furthermore, the reservation unit can also make reservations supplementarily for data submitted earlier. For example, the reservation unit makes reservations while referring to past health data. Furthermore, the reservation unit can also make reservations supplementarily for data submitted earlier. In this way, by determining the priority of reservations based on the time of submission of health data, reservations can be made based on the most recent data. Some or all of the above-described processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the pet's health data into a generation AI and determine the priority of reservations based on the time of submission.

[0090] The reservation unit can improve the accuracy of reservations by referring to pet-related literature when making a reservation. The reservation unit, for example, makes a reservation by referring to the latest research papers on pet health conditions. For example, the reservation unit makes a reservation by referring to the latest research papers on pet health conditions. The reservation unit can also make a reservation by referring to literature on a specific pet disease. The reservation unit can also make a reservation by referring to guidelines on pet health management. For example, the reservation unit makes a reservation by referring to literature on a specific pet disease. The reservation unit can also make a reservation by referring to guidelines on pet health management. By doing so, the accuracy of reservations is improved by referring to related literature. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input pet health data into a generation AI and improve the accuracy of reservations by referring to related literature. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, recommendation unit, and reservation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects health data of the pet using the sensors and camera 42 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to predict the future health condition. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends an optimal food based on the analysis results. The reservation unit is realized by the specific processing unit 290 of the data processing device 12 and makes a veterinary appointment as necessary. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, recommendation unit, and reservation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects pet health data using the sensors and camera 42 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to predict the future health condition. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends an optimal food based on the analysis results. The reservation unit is realized by the specific processing unit 290 of the data processing device 12 and makes a veterinary appointment as necessary. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, recommendation unit, and reservation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects health data of the pet using the sensors and camera 42 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to predict the future health condition. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends an optimal food based on the analysis results. The reservation unit is realized by the specific processing unit 290 of the data processing device 12 and makes a veterinary appointment as necessary. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, recommendation unit, and reservation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects health data of the pet using the sensors and camera 42 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to predict the future health condition. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends an optimal food based on the analysis results. The reservation unit is realized by the specific processing unit 290 of the data processing device 12 and makes a veterinary appointment as necessary.

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

[0092] The pet care support system may further include a behavior prediction unit that learns the behavioral patterns of the pet and predicts the pet's behavior. The behavior prediction unit analyzes the pet's past behavioral data and predicts future behavior. For example, if the pet tends to exercise during a specific time of day, exercise data can be collected preferentially during that time. Also, if the pet tends to rest in a specific place, detailed resting data at that place can be collected. Furthermore, if the pet has a specific eating pattern, eating data can be collected based on that pattern. This can improve the efficiency of data collection based on the pet's behavioral patterns.

[0093] The collection unit can collect environmental data (such as temperature, humidity, and noise level) in addition to the pet's health data. For example, the collection unit can measure the temperature of the pet's living environment and collect the data. The collection unit can also measure the humidity of the pet's living environment and collect the data. Furthermore, the collection unit can measure the noise level of the pet's living environment and collect the data. This allows for detailed monitoring of environmental factors that affect the pet's health condition.

[0094] The analysis unit can estimate the stress level of a pet based on the pet's health data. For example, the analysis unit can analyze the pet's heart rate data to estimate the stress level. The analysis unit can also analyze the pet's behavior data to estimate the stress level. Furthermore, the analysis unit can analyze the pet's dietary data to estimate the stress level. This allows the pet's stress level to be monitored in detail and appropriate measures to be taken.

[0095] The recommendation unit can recommend an exercise program based on the health condition of the pet. For example, if the pet is not getting enough exercise, the recommendation unit can recommend an appropriate exercise program. If the pet is overweight, the recommendation unit can also recommend an exercise program for weight management. Furthermore, if the pet is elderly, the recommendation unit can also recommend an exercise program that is gentle on the pet's joints. This makes it possible to provide an exercise program that is appropriate for the pet's health condition.

[0096] The recommendation unit can estimate the pet's emotions and recommend music or fragrances that have a relaxing effect based on the estimated pet's emotions. For example, if the pet is feeling stressed, the recommendation unit can recommend music that has a relaxing effect. Also, if the pet is feeling anxious, the recommendation unit can recommend fragrances that have a relaxing effect. Furthermore, if the pet is excited, the recommendation unit can recommend music that has a calming effect. This makes it possible to provide a relaxation method that suits the pet's emotions.

[0097] The reservation unit can send reminders for regular health checks based on the pet's health data. For example, the reservation unit can send reminders for regular weight measurements based on the pet's weight data. The reservation unit can also send reminders for regular food intake checks based on the pet's diet data. Furthermore, the reservation unit can also send reminders for regular exercise checks based on the pet's exercise data. This allows for continuous support for pet health management.

[0098] The collection unit can estimate the pet's emotions and suggest games and toys that the pet likes based on the estimated pet's emotions. For example, if the pet is bored, the collection unit can suggest toys that will interest the pet. Also, if the pet is stressed, the collection unit can suggest games that will help relieve stress. Furthermore, if the pet is relaxed, the collection unit can suggest games that have a relaxing effect. In this way, games and toys can be provided that correspond to the pet's emotions.

[0099] The analysis unit can assess the health risks of a pet based on the pet's health data. For example, the analysis unit can analyze the pet's weight data and assess the risk of obesity. The analysis unit can also analyze the pet's dietary data and assess the risk of nutritional imbalance. Furthermore, the analysis unit can analyze the pet's exercise data and assess the risk of lack of exercise. This allows for a detailed assessment of the pet's health risks and allows for early countermeasures to be taken.

[0100] In addition to the pet's health data, the collection unit can collect the pet's social interaction data. For example, the collection unit can record how often the pet plays with other pets. The collection unit can also record how much time the pet spends with its owner. The collection unit can also record how often the pet interacts with other people. This allows for detailed monitoring of the pet's social interaction status.

[0101] The analysis unit can estimate the pet's emotions and create a stress management plan for the pet based on the estimated emotions. For example, if the pet is feeling stressed, the analysis unit can create an exercise plan to reduce stress. Also, if the pet is feeling anxious, the analysis unit can create a diet plan to reduce anxiety. Furthermore, if the pet is excited, the analysis unit can create a relaxation plan to reduce excitement. This makes it possible to provide a stress management plan that corresponds to the pet's emotions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit collects the pet's health data. The pet's health data includes weight, amount of food eaten, amount of exercise, and sleep time. The collection unit measures the pet's weight using a sensor and collects the data. The collection unit can also record the pet's food intake using a smart device and measure the amount of exercise using an acceleration sensor attached to the pet's collar. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts the pet's future health condition. The analysis unit analyzes the data using a machine learning algorithm to predict the pet's future health condition. For example, the analysis unit analyzes the pet's weight data to predict the pet's risk of becoming obese in the future, analyzes food intake data to predict nutritional imbalances, and analyzes exercise volume data to predict the pet's risk of lack of exercise. Step 3: The recommendation unit recommends the optimal food based on the prediction results obtained by the analysis unit. For example, if your pet is prone to obesity, it will recommend a low-calorie food, or if your pet has a specific allergy, it will recommend a food that addresses that allergy. It can also recommend food that is appropriate for your pet's age and activity level. Step 4: The reservation unit schedules veterinary appointments as needed based on the food recommendations made by the recommendation unit, for example, if the pet's health condition deteriorates, if the pet experiences a sudden weight gain, if the pet's food intake decreases, or if the pet's exercise level decreases.

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

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[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 a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0142] The data processing device 12 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] 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.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0154] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

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

Claims

1. a collection unit that collects pet health data; an analysis unit that analyzes the data collected by the collection unit and predicts the future health condition of the pet; a recommendation unit that recommends an appropriate hood based on the prediction result obtained by the analysis unit; a reservation unit that makes an appointment with a veterinarian as needed based on the food recommended by the recommendation unit. A system characterized by:

2. The collecting unit Collect data on your pet's weight, food intake, exercise, and sleep time 2. The system of claim 1.

3. The analysis unit Analyze the collected data to predict your pet's future health condition 2. The system of claim 1.

4. The recommendation unit Recommend appropriate food based on your pet's health 2. The system of claim 1.

5. The recommendation unit Recommend food based on your pet's allergies 2. The system of claim 1.

6. The reservation unit Make a vet appointment if your pet's health is deteriorating 2. The system of claim 1.

7. The collecting unit Inferring pet emotions and adjusting data collection frequency based on the estimated pet emotions 2. The system of claim 1.

8. The collecting unit Analyze your pet's past health data and select the best data collection method 2. The system of claim 1.

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