System

Abstract

An object of a system according to an embodiment is to visualize a health condition of a pet and provide an appropriate health management advice to an owner.SOLUTION: A system includes a collection unit, an analysis unit, a display unit, and a provision unit. The collection unit is configured to collect health data of a companion animal. The analysis unit analyzes the data collected by the collection unit and evaluates the health condition of the pet. The display unit visually displays the health condition evaluated by the analysis unit. The providing part provides the health care advice to the owner based on the evaluation result displayed by the display part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to grasp a pet's health condition at a glance and provide appropriate health management.

[0005] The system according to the embodiment aims to visualize the health condition of a pet and provide appropriate health management advice to the owner. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a display unit, and a provision unit. The collection unit collects health data of the pet. The analysis unit analyzes the data collected by the collection unit and evaluates the health condition of the pet. The display unit visually displays the health condition evaluated by the analysis unit. The provision unit provides health management advice to the owner based on the evaluation results displayed by the display unit. [Effects of the Invention]

[0007] The system according to the embodiment can visualize the health condition of a pet and provide appropriate health care advice to the owner. [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 health checkup system according to an embodiment of the present invention collects health data of a pet, analyzes it using AI, evaluates the pet's health status, visually displays the data, and provides health management advice to the pet owner. The pet health checkup system collects health data of a pet, analyzes it using AI, and evaluates the pet's health status. The evaluation results are displayed in a visual format, such as a graph or chart, and provided to the pet owner. For example, the pet health checkup system collects data such as the pet's weight, food intake, exercise level, and body temperature. The pet health checkup system then analyzes the collected data using AI to evaluate the pet's health status. For example, the pet health checkup system analyzes changes in weight, food intake, and increases or decreases in exercise level to comprehensively evaluate the pet's health status. The pet health checkup system then displays the evaluation results in a visual format, such as a graph or chart. For example, weight fluctuations may be displayed as a line graph, and changes in food intake may be displayed as a bar graph. The pet health checkup system then provides appropriate health management advice to the pet owner based on the evaluation results. For example, if the pet is gaining weight, the system may advise the pet to reduce its food intake, or if the pet is not getting enough exercise, the system may advise the pet to increase its exercise. This allows the pet health checkup system to visualize the health condition of the pet and provide the owner with appropriate health management advice. This allows the pet health checkup system to visualize the health condition of the pet and provide the owner with appropriate health management advice. For example, the pet's health condition can be grasped at a glance, allowing for appropriate health management. Furthermore, the owner can resolve concerns about pet health management.

[0029] The pet health checkup system according to the embodiment includes a collection unit, an analysis unit, a display unit, and a provision 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 amount, and body temperature. The collection unit collects data entered by the owner, for example. The collection unit can also collect data automatically using a sensor. For example, the collection unit collects data using a weighing scale, an activity monitor, a temperature sensor, or the like. The analysis unit analyzes the data collected by the collection unit to evaluate the pet's health condition. The analysis unit analyzes, for example, changes in weight, changes in food intake, and increases or decreases in exercise amount, and comprehensively evaluates the pet's health condition. The analysis unit can analyze the data using statistical analysis or machine learning algorithms. The display unit visually displays the health condition evaluated by the analysis unit. For example, the display unit displays the evaluation results in a visual format such as a graph or chart. For example, weight fluctuations may be displayed as a line graph, and changes in food intake may be displayed as a bar graph. The providing unit provides health management advice to the owner based on the evaluation results displayed by the display unit. The providing unit provides specific health management advice, such as adjusting the amount of food or exercise, based on the evaluation results. For example, if the pet is gaining weight, the providing unit may advise reducing the amount of food, or if the pet is not getting enough exercise, the providing unit may advise increasing the amount of exercise. In this way, the pet health checkup system according to the embodiment can collect, analyze, and display health data of the pet and provide appropriate health management advice to the owner.

[0030] The collection unit can collect data entered by the owner. Data entered by the owner may be, for example, text input or multiple choice format, but is not limited to these examples. For example, the collection unit allows the owner to enter data such as the pet's weight, amount of food eaten, amount of exercise, and body temperature into the app. The collection unit can also save the data entered by the owner and provide it to the analysis unit. In this way, by collecting the data entered by the owner, accurate health data of the pet can be obtained.

[0031] The collection unit can automatically collect data using sensors. The collection unit collects data using, for example, a weighing scale, an activity meter, a temperature sensor, etc. For example, the collection unit measures the pet's weight using a weighing scale and collects data. The collection unit can also measure the pet's exercise amount using an activity meter and collect data. The collection unit can also measure the pet's body temperature using a temperature sensor and collect data. In this way, by using sensors, data collection can be automated, reducing the owner's workload.

[0032] The analysis unit can analyze changes in weight, changes in food intake, and increases or decreases in the amount of exercise, and can comprehensively evaluate the health condition of the pet. The analysis unit, for example, analyzes changes in weight. For example, the analysis unit analyzes changes in weight based on weight data measured periodically. The analysis unit can also analyze changes in food intake. For example, the analysis unit analyzes changes in food intake based on food intake data input by the owner. The analysis unit can also analyze increases or decreases in the amount of exercise. For example, the analysis unit analyzes increases or decreases in the amount of exercise based on data from an activity monitor. This allows for a comprehensive evaluation of the pet's health condition, enabling more accurate health management.

[0033] The display unit can display the evaluation results in a visual format such as a graph or chart. The display unit, for example, displays the evaluation results as a line graph. For example, the display unit displays changes in weight as a line graph. The display unit can also display the evaluation results as a bar graph. For example, the display unit displays changes in food intake as a bar graph. The display unit can also display the evaluation results as a pie chart. For example, the display unit displays the percentage of exercise volume as a pie chart. By visually displaying the evaluation results, the owner can grasp the health condition of their pet at a glance.

[0034] The providing unit can provide specific health management advice, such as adjusting the amount of food or exercise, based on the evaluation results. For example, if the pet is gaining weight, the providing unit advises reducing the amount of food. For example, if the pet is gaining weight, the providing unit advises reducing the amount of food. The providing unit can also advise increasing the amount of exercise if the pet is not getting enough exercise. For example, if the pet is not getting enough exercise, the providing unit advises increasing the amount of exercise. The providing unit can also advise changing the type of food. For example, the providing unit advises changing the type of food depending on the pet's health condition. In this way, by providing specific health management advice, the owner can manage their health appropriately.

[0035] The collection unit can analyze the pet's past health data and select an appropriate data collection method. The collection unit, for example, analyzes the pet's past weight fluctuations and adjusts the frequency of weight measurement. For example, the collection unit analyzes the pet's past weight fluctuations and adjusts the frequency of weight measurement. The collection unit can also analyze the pet's past changes in food intake and optimize the method of collecting food data. For example, the collection unit analyzes the pet's past changes in food intake and optimizes the method of collecting food data. The collection unit can also analyze the pet's past exercise amount and select a method of collecting exercise data. For example, the collection unit analyzes the pet's past exercise amount and selects a method of collecting exercise data. In this way, the optimal data collection method can be selected by analyzing the past health data.

[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. The collection unit can also prioritize collecting body temperature data when the pet is resting. For example, when the pet is resting, the collection unit prioritizes collecting body temperature data. The collection unit can also prioritize collecting food amount data when the pet is eating. For example, when the pet is eating, the collection unit prioritizes collecting food amount data. This allows for more accurate data collection by filtering data based on the pet's activity status and environment.

[0037] When collecting data, the collection unit can select the optimal collection means depending on the owner's input method. For example, if the owner uses voice input, the collection unit collects data using voice recognition technology. For example, if the owner uses voice input, the collection unit collects data using voice recognition technology. Furthermore, if the owner uses text input, the collection unit can also collect data using text analysis technology. For example, if the owner uses text input, the collection unit collects data using text analysis technology. Furthermore, if the owner uses image input, the collection unit can also collect data using image recognition technology. For example, if the owner uses image input, the collection unit collects data using image recognition technology. This improves the efficiency of data collection by selecting the optimal collection means depending on the owner's input method.

[0038] 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, if the pet is in a park, the collection unit prioritizes collecting exercise data. For example, if the pet is in a park, the collection unit prioritizes collecting exercise data. The collection unit can also prioritize collecting rest data if the pet is at home. For example, if the pet is at home, the collection unit prioritizes collecting rest data. The collection unit can also prioritize collecting health data if the pet is at a veterinary clinic. For example, if the pet is at a veterinary clinic, the collection unit prioritizes collecting health data. In this way, highly relevant data can be collected by taking into account the geographical location information of the pet.

[0039] When collecting data, the collection unit can analyze the owner's social media activities and collect related data. For example, when the owner posts a photo of their pet on social media, the collection unit collects health data from the photo. For example, when the owner posts a photo of their pet on social media, the collection unit collects health data from the photo. The collection unit can also collect activity data when the owner shares their pet's activities on social media. For example, when the owner shares their pet's activities on social media, the collection unit collects the activity data. The collection unit can also collect diet data when the owner posts their pet's diet on social media. For example, when the owner posts their pet's diet on social media, the collection unit collects the diet data. This makes it possible to efficiently collect related data by analyzing the owner's social media activities.

[0040] The collection unit can customize the collection method by reflecting the owner's past feedback when collecting data. The collection unit, for example, adjusts the frequency of data collection based on feedback provided by the owner in the past. For example, the collection unit adjusts the frequency of data collection based on feedback provided by the owner in the past. The collection unit can also adjust the type of data to be collected based on feedback provided by the owner in the past. For example, the collection unit adjusts the type of data to be collected based on feedback provided by the owner in the past. The collection unit can also optimize the collection method based on feedback provided by the owner in the past. For example, the collection unit optimizes the collection method based on feedback provided by the owner in the past. In this way, the collection method can be optimized by reflecting the owner's past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the pet's health data. For example, if weight data is important, the analysis unit performs a detailed weight analysis. For example, if weight data is important, the analysis unit performs a detailed weight analysis. Furthermore, if food intake data is important, the analysis unit can also perform a detailed food intake analysis. For example, if food intake data is important, the analysis unit performs a detailed food intake analysis. Furthermore, if exercise amount data is important, the analysis unit can also perform a detailed exercise amount analysis. For example, if exercise amount data is important, the analysis unit performs a detailed exercise amount analysis. In this way, by adjusting the level of detail of the analysis based on the importance of the pet's health data, important data can be analyzed in more detail.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the type and age of the pet. For example, in the case of a dog, the analysis unit applies an analysis algorithm specifically for dogs. For example, in the case of a dog, the analysis unit applies an analysis algorithm specifically for dogs. Furthermore, in the case of a cat, the analysis unit can also apply an analysis algorithm specifically for cats. For example, in the case of a cat, the analysis unit applies an analysis algorithm specifically for cats. Furthermore, in the case of a young pet, the analysis unit can also apply an analysis algorithm according to the pet's growth. For example, in the case of a young pet, the analysis unit applies an analysis algorithm according to the pet's growth. In this way, by applying an analysis algorithm according to the type and age of the pet, more accurate analysis results can be obtained.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the owner's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on feedback provided by the owner in the past. For example, the analysis unit adjusts the analysis algorithm based on feedback provided by the owner in the past. The analysis unit can also improve the accuracy of the analysis based on the owner's past analysis results. For example, the analysis unit improves the accuracy of the analysis based on the owner's past analysis results. The analysis unit can also optimize the analysis method based on the owner's past analysis results. For example, the analysis unit optimizes the analysis method based on the owner's past analysis results. In this way, the accuracy of the analysis is improved by referring to the owner's past analysis results.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the latest data. For example, the analysis unit prioritizes analyzing the latest data. The analysis unit can also analyze the latest data by referring to past data. For example, the analysis unit analyzes the latest data by referring to past data. The analysis unit can also determine the analysis priority based on the time when the data was collected. For example, the analysis unit determines the analysis priority based on the time when the data was collected. In this way, by determining the analysis priority based on the time when the data was collected, the latest data can be analyzed with priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of data. The analysis unit adjusts the order of analysis based on, for example, the relevance between weight data and food intake data. For example, the analysis unit adjusts the order of analysis based on the relevance between weight data and food intake data. The analysis unit can also adjust the order of analysis based on the relevance between exercise amount data and body temperature data. For example, the analysis unit adjusts the order of analysis based on the relevance between exercise amount data and body temperature data. The analysis unit can also adjust the order of analysis based on the relevance of data. For example, the analysis unit adjusts the order of analysis based on the relevance of data. In this way, by adjusting the order of analysis based on the relevance of data, highly relevant data can be analyzed preferentially.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the owner's level of expertise. For example, if the owner has technical expertise, the analysis unit uses detailed technical terms. For example, if the owner has technical expertise, the analysis unit uses detailed technical terms. The analysis unit can also provide analysis results in simple language if the owner does not have technical expertise. For example, if the owner does not have technical expertise, the analysis unit provides analysis results in simple language. The analysis unit can also adjust the use of technical terms in the analysis according to the owner's level of expertise. For example, the analysis unit adjusts the use of technical terms in the analysis according to the owner's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the owner's level of expertise, it is possible to provide analysis results that are easy for the owner to understand.

[0047] The display unit can adjust the level of detail of the display based on the importance of the pet's health data when displaying the data. For example, if weight data is important, the display unit displays a detailed weight graph. For example, if weight data is important, the display unit displays a detailed weight graph. The display unit can also display a detailed food amount graph if food amount data is important. For example, if food amount data is important, the display unit displays a detailed food amount graph. The display unit can also display a detailed exercise amount graph if exercise amount data is important. For example, if exercise amount data is important, the display unit displays a detailed exercise amount graph. In this way, by adjusting the level of detail of the display based on the importance of the pet's health data, important data can be displayed in more detail.

[0048] The display unit can apply different display formats depending on the type and age of the pet when displaying. For example, in the case of a dog, the display unit applies a display format dedicated to dogs. For example, in the case of a dog, the display unit applies a display format dedicated to dogs. Furthermore, in the case of a cat, the display unit can also apply a display format dedicated to cats. For example, in the case of a cat, the display unit applies a display format dedicated to cats. Furthermore, in the case of a young pet, the display unit can also apply a display format according to the pet's growth. For example, in the case of a young pet, the display unit applies a display format according to the pet's growth. In this way, by applying a display format according to the type and age of the pet, more appropriate display results can be obtained.

[0049] The display unit can improve the accuracy of the display when displaying by referring to the owner's past display results. The display unit, for example, adjusts the display method based on feedback provided by the owner in the past. For example, the display unit adjusts the display method based on feedback provided by the owner in the past. The display unit can also improve the accuracy of the display based on the owner's past display results. For example, the display unit improves the accuracy of the display based on the owner's past display results. The display unit can also optimize the display method based on the owner's past display results. For example, the display unit optimizes the display method based on the owner's past display results. In this way, the accuracy of the display is improved by referring to the owner's past display results.

[0050] The display unit can determine the display priority based on the time when the data was collected when displaying the data. The display unit, for example, prioritizes displaying the latest data. For example, the display unit prioritizes displaying the latest data. The display unit can also display the latest data by referring to past data. For example, the display unit displays the latest data by referring to past data. The display unit can also determine the display priority based on the time when the data was collected. For example, the display unit determines the display priority based on the time when the data was collected. In this way, by determining the display priority based on the time when the data was collected, the latest data can be displayed preferentially.

[0051] The display unit can adjust the display order based on the relevance of data when displaying the data. The display unit adjusts the display order based on, for example, the relevance between weight data and food amount data. For example, the display unit adjusts the display order based on the relevance between weight data and food amount data. The display unit can also adjust the display order based on the relevance between exercise amount data and body temperature data. For example, the display unit adjusts the display order based on the relevance between exercise amount data and body temperature data. The display unit can also adjust the display order based on the relevance of data. For example, the display unit adjusts the display order based on the relevance of data. In this way, by adjusting the display order based on the relevance of data, highly relevant data can be displayed preferentially.

[0052] The display unit can adjust the use of technical terms in the display depending on the owner's level of expertise when displaying. For example, if the owner has technical knowledge, the display unit uses detailed technical terms. For example, if the owner has technical knowledge, the display unit uses detailed technical terms. The display unit can also provide display results in simple language if the owner does not have technical knowledge. For example, if the owner does not have technical knowledge, the display unit provides display results in simple language. The display unit can also adjust the use of technical terms in the display depending on the owner's level of expertise. For example, the display unit adjusts the use of technical terms in the display depending on the owner's level of expertise. In this way, by adjusting the use of technical terms in the display depending on the owner's level of expertise, it is possible to provide display results that are easy for the owner to understand.

[0053] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the pet's health data. For example, if weight data is important, the providing unit provides detailed weight management advice. For example, if weight data is important, the providing unit provides detailed weight management advice. The providing unit can also provide detailed diet management advice if food amount data is important. For example, if food amount data is important, the providing unit provides detailed diet management advice. The providing unit can also provide detailed exercise management advice if exercise amount data is important. For example, if exercise amount data is important, the providing unit provides detailed exercise management advice. In this way, by adjusting the level of detail of the advice based on the importance of the pet's health data, advice for important data can be given in more detail.

[0054] When providing advice, the providing unit can apply different advice algorithms depending on the type and age of the pet. For example, in the case of a dog, the providing unit applies an advice algorithm specifically for dogs. For example, in the case of a dog, the providing unit applies an advice algorithm specifically for dogs. Furthermore, in the case of a cat, the providing unit can also apply an advice algorithm specifically for cats. For example, in the case of a cat, the providing unit applies an advice algorithm specifically for cats. Furthermore, in the case of a young pet, the providing unit can also apply an advice algorithm according to the pet's growth. For example, in the case of a young pet, the providing unit applies an advice algorithm according to the pet's growth. In this way, more appropriate advice can be obtained by applying an advice algorithm according to the type and age of the pet.

[0055] When providing advice, the providing unit can improve the accuracy of the advice by referring to past advice results from the owner. The providing unit, for example, adjusts the advice algorithm based on feedback provided by the owner in the past. For example, the providing unit adjusts the advice algorithm based on feedback provided by the owner in the past. The providing unit can also improve the accuracy of the advice based on the past advice results from the owner. For example, the providing unit improves the accuracy of the advice based on the past advice results from the owner. The providing unit can also optimize the advice method based on the past advice results from the owner. For example, the providing unit optimizes the advice method based on the past advice results from the owner. In this way, the accuracy of the advice is improved by referring to the past advice results from the owner.

[0056] When providing advice, the providing unit can determine the priority of advice based on the time when data was collected. The providing unit, for example, provides advice based on the latest data. For example, the providing unit provides advice based on the latest data. The providing unit can also provide advice based on the latest data by referring to past data. For example, the providing unit provides advice based on the latest data by referring to past data. The providing unit can also determine the priority of advice based on the time when data was collected. For example, the providing unit determines the priority of advice based on the time when data was collected. In this way, by determining the priority of advice based on the time when data was collected, advice based on the latest data is provided.

[0057] The providing unit can adjust the order of advice based on the relevance of data when providing advice. The providing unit adjusts the order of advice based on, for example, the relevance between weight data and food amount data. For example, the providing unit adjusts the order of advice based on the relevance between weight data and food amount data. The providing unit can also adjust the order of advice based on the relevance between exercise amount data and body temperature data. For example, the providing unit adjusts the order of advice based on the relevance between exercise amount data and body temperature data. The providing unit can also adjust the order of advice based on the relevance of data. For example, the providing unit adjusts the order of advice based on the relevance of data. In this way, by adjusting the order of advice based on the relevance of data, advice based on highly relevant data is preferentially provided.

[0058] When providing advice, the providing unit can adjust the use of technical terms in the advice according to the owner's level of expertise. For example, if the owner has technical knowledge, the providing unit uses detailed technical terms. For example, if the owner has technical knowledge, the providing unit uses detailed technical terms. Furthermore, if the owner does not have technical knowledge, the providing unit can provide advice in simple language. For example, if the owner does not have technical knowledge, the providing unit provides advice in simple language. Furthermore, the providing unit can adjust the use of technical terms in the advice according to the owner's level of expertise. For example, the providing unit adjusts the use of technical terms in the advice according to the owner's level of expertise. In this way, by adjusting the use of technical terms in the advice according to the owner's level of expertise, advice that is easy for the owner to understand is provided.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When collecting pet health data, the collection unit can learn the pet's behavioral patterns and automatically adjust the optimal data collection timing. For example, the collection unit collects food intake data during the pet's usual mealtimes and exercise amount data during the pet's exercise time. The collection unit can also obtain more accurate health data by collecting body temperature data while the pet is resting. Furthermore, the collection unit can adjust the frequency of data collection based on the pet's behavioral patterns, reducing the burden on the owner. This makes it possible to collect data based on the pet's behavioral patterns, allowing for more accurate health management.

[0061] When analyzing a pet's health data, the analysis unit can compare it with the pet's past health data to detect abnormalities. For example, the analysis unit can detect an abnormality if the pet's weight suddenly increases or decreases, and notify the owner. The analysis unit can also detect an abnormality if the pet's food intake suddenly changes. Furthermore, the analysis unit can detect an abnormality if the pet's exercise volume increases or decreases significantly compared to normal, and provide the owner with appropriate advice. This allows for early detection of abnormalities in the pet's health condition and the implementation of appropriate measures.

[0062] The display unit can provide a customizable display format according to the owner's preferences when displaying the pet's health data. For example, the display unit can allow the owner to select their preferred colors and fonts. The display unit can also provide a function for the owner to highlight specific data. Furthermore, the display unit can provide a more user-friendly interface by allowing the owner to freely change the display order of the data. This allows the owner to check the health data according to their preferences, leading to more effective health management.

[0063] The providing unit can propose a specific action plan to the owner based on the pet's health data. For example, if the pet is gaining weight, the providing unit can propose a specific meal menu and exercise plan. The providing unit can also provide a specific exercise schedule if the pet is not getting enough exercise. Furthermore, the providing unit can also propose a schedule for regular health checks depending on the pet's health condition. This allows the owner to manage the health of their pet based on a specific action plan.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects health data of the pet. The pet's health data includes, for example, weight, amount of food eaten, amount of exercise, and body temperature. The collection unit not only collects data entered by the owner, but can also collect data automatically using sensors. For example, data is collected using a weighing scale, activity monitor, temperature sensor, etc. Step 2: The analysis unit analyzes the data collected by the collection unit and evaluates the pet's health condition. The analysis unit analyzes fluctuations in weight, changes in food intake, increases or decreases in exercise, etc., and makes a comprehensive assessment of the pet's health condition. The analysis unit can analyze the data using statistical analysis and machine learning algorithms. Step 3: The display unit visually displays the health status evaluated by the analysis unit. The display unit displays the evaluation results in a visual format such as a graph or chart. For example, the display unit may display weight fluctuations as a line graph, or food intake changes as a bar graph. Step 4: The providing unit provides health management advice to the owner based on the evaluation results displayed by the display unit. The providing unit provides specific health management advice, such as adjusting the amount of food or exercise, based on the evaluation results. For example, if the pet is gaining weight, the providing unit may advise the owner to reduce the amount of food, or if the pet is not getting enough exercise, the providing unit may advise the owner to exercise more.

[0066] (Example 2) A pet health checkup system according to an embodiment of the present invention collects health data of a pet, analyzes it using AI, evaluates the pet's health status, visually displays the data, and provides health management advice to the pet owner. The pet health checkup system collects health data of a pet, analyzes it using AI, and evaluates the pet's health status. The evaluation results are displayed in a visual format, such as a graph or chart, and provided to the pet owner. For example, the pet health checkup system collects data such as the pet's weight, food intake, exercise level, and body temperature. The pet health checkup system then analyzes the collected data using AI to evaluate the pet's health status. For example, the pet health checkup system analyzes changes in weight, food intake, and increases or decreases in exercise level to comprehensively evaluate the pet's health status. The pet health checkup system then displays the evaluation results in a visual format, such as a graph or chart. For example, weight fluctuations may be displayed as a line graph, and changes in food intake may be displayed as a bar graph. The pet health checkup system then provides appropriate health management advice to the pet owner based on the evaluation results. For example, if the pet is gaining weight, the system may advise the pet to reduce its food intake, or if the pet is not getting enough exercise, the system may advise the pet to increase its exercise. This allows the pet health checkup system to visualize the health condition of the pet and provide the owner with appropriate health management advice. This allows the pet health checkup system to visualize the health condition of the pet and provide the owner with appropriate health management advice. For example, the pet's health condition can be grasped at a glance, allowing for appropriate health management. Furthermore, the owner can resolve concerns about pet health management.

[0067] The pet health checkup system according to the embodiment includes a collection unit, an analysis unit, a display unit, and a provision 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 amount, and body temperature. The collection unit collects data entered by the owner, for example. The collection unit can also collect data automatically using a sensor. For example, the collection unit collects data using a weighing scale, an activity monitor, a temperature sensor, or the like. The analysis unit analyzes the data collected by the collection unit to evaluate the pet's health condition. The analysis unit analyzes, for example, changes in weight, changes in food intake, and increases or decreases in exercise amount, and comprehensively evaluates the pet's health condition. The analysis unit can analyze the data using statistical analysis or machine learning algorithms. The display unit visually displays the health condition evaluated by the analysis unit. For example, the display unit displays the evaluation results in a visual format such as a graph or chart. For example, weight fluctuations may be displayed as a line graph, and changes in food intake may be displayed as a bar graph. The providing unit provides health management advice to the owner based on the evaluation results displayed by the display unit. The providing unit provides specific health management advice, such as adjusting the amount of food or exercise, based on the evaluation results. For example, if the pet is gaining weight, the providing unit may advise reducing the amount of food, or if the pet is not getting enough exercise, the providing unit may advise increasing the amount of exercise. In this way, the pet health checkup system according to the embodiment can collect, analyze, and display health data of the pet and provide appropriate health management advice to the owner.

[0068] The collection unit can collect data entered by the owner. Data entered by the owner may be, for example, text input or multiple choice format, but is not limited to these examples. For example, the collection unit allows the owner to enter data such as the pet's weight, amount of food eaten, amount of exercise, and body temperature into the app. The collection unit can also save the data entered by the owner and provide it to the analysis unit. In this way, by collecting the data entered by the owner, accurate health data of the pet can be obtained.

[0069] The collection unit can automatically collect data using sensors. The collection unit collects data using, for example, a weighing scale, an activity meter, a temperature sensor, etc. For example, the collection unit measures the pet's weight using a weighing scale and collects data. The collection unit can also measure the pet's exercise amount using an activity meter and collect data. The collection unit can also measure the pet's body temperature using a temperature sensor and collect data. In this way, by using sensors, data collection can be automated, reducing the owner's workload.

[0070] The analysis unit can analyze changes in weight, changes in food intake, and increases or decreases in the amount of exercise, and can comprehensively evaluate the health condition of the pet. The analysis unit, for example, analyzes changes in weight. For example, the analysis unit analyzes changes in weight based on weight data measured periodically. The analysis unit can also analyze changes in food intake. For example, the analysis unit analyzes changes in food intake based on food intake data input by the owner. The analysis unit can also analyze increases or decreases in the amount of exercise. For example, the analysis unit analyzes increases or decreases in the amount of exercise based on data from an activity monitor. This allows for a comprehensive evaluation of the pet's health condition, enabling more accurate health management.

[0071] The display unit can display the evaluation results in a visual format such as a graph or chart. The display unit, for example, displays the evaluation results as a line graph. For example, the display unit displays changes in weight as a line graph. The display unit can also display the evaluation results as a bar graph. For example, the display unit displays changes in food intake as a bar graph. The display unit can also display the evaluation results as a pie chart. For example, the display unit displays the percentage of exercise volume as a pie chart. By visually displaying the evaluation results, the owner can grasp the health condition of their pet at a glance.

[0072] The providing unit can provide specific health management advice, such as adjusting the amount of food or exercise, based on the evaluation results. For example, if the pet is gaining weight, the providing unit advises reducing the amount of food. For example, if the pet is gaining weight, the providing unit advises reducing the amount of food. The providing unit can also advise increasing the amount of exercise if the pet is not getting enough exercise. For example, if the pet is not getting enough exercise, the providing unit advises increasing the amount of exercise. The providing unit can also advise changing the type of food. For example, the providing unit advises changing the type of food depending on the pet's health condition. In this way, by providing specific health management advice, the owner can manage their health appropriately.

[0073] The collection unit can estimate the owner's emotions and adjust the timing of data collection based on the estimated owner's emotions. For example, if the owner is feeling stressed, the collection unit reduces the frequency of data collection, thereby reducing the burden on the owner. For example, if the owner is feeling stressed, the collection unit reduces the frequency of data collection, thereby reducing the burden on the owner. The collection unit can also collect detailed data and obtain more information if the owner is relaxed. For example, if the owner is relaxed, the collection unit collects detailed data and obtains more information. The collection unit can also automate data collection if the owner is busy, thereby reducing the effort required. For example, if the owner is busy, the collection unit automates data collection, thereby reducing the effort required. This allows the burden on the owner to be reduced by adjusting the timing of data collection according to the owner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0074] The collection unit can analyze the pet's past health data and select an appropriate data collection method. The collection unit, for example, analyzes the pet's past weight fluctuations and adjusts the frequency of weight measurement. For example, the collection unit analyzes the pet's past weight fluctuations and adjusts the frequency of weight measurement. The collection unit can also analyze the pet's past changes in food intake and optimize the method of collecting food data. For example, the collection unit analyzes the pet's past changes in food intake and optimizes the method of collecting food data. The collection unit can also analyze the pet's past exercise amount and select a method of collecting exercise data. For example, the collection unit analyzes the pet's past exercise amount and selects a method of collecting exercise data. In this way, the optimal data collection method can be selected by analyzing the past health data.

[0075] 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. The collection unit can also prioritize collecting body temperature data when the pet is resting. For example, when the pet is resting, the collection unit prioritizes collecting body temperature data. The collection unit can also prioritize collecting food amount data when the pet is eating. For example, when the pet is eating, the collection unit prioritizes collecting food amount data. This allows for more accurate data collection by filtering data based on the pet's activity status and environment.

[0076] When collecting data, the collection unit can select the optimal collection means depending on the owner's input method. For example, if the owner uses voice input, the collection unit collects data using voice recognition technology. For example, if the owner uses voice input, the collection unit collects data using voice recognition technology. Furthermore, if the owner uses text input, the collection unit can also collect data using text analysis technology. For example, if the owner uses text input, the collection unit collects data using text analysis technology. Furthermore, if the owner uses image input, the collection unit can also collect data using image recognition technology. For example, if the owner uses image input, the collection unit collects data using image recognition technology. This improves the efficiency of data collection by selecting the optimal collection means depending on the owner's input method.

[0077] The collection unit can estimate the owner's emotions and determine the priority of data to be collected based on the estimated owner's emotions. For example, when the owner is stressed, the collection unit prioritizes collecting only important data. For example, when the owner is stressed, the collection unit prioritizes collecting only important data. The collection unit can also prioritize collecting detailed data when the owner is relaxed. For example, when the owner is relaxed, the collection unit prioritizes collecting detailed data. The collection unit can also prioritize collecting the most necessary data when the owner is busy. For example, when the owner is busy, the collection unit prioritizes collecting the most necessary data. In this way, by determining the priority of data according to the owner's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] 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, if the pet is in a park, the collection unit prioritizes collecting exercise data. For example, if the pet is in a park, the collection unit prioritizes collecting exercise data. The collection unit can also prioritize collecting rest data if the pet is at home. For example, if the pet is at home, the collection unit prioritizes collecting rest data. The collection unit can also prioritize collecting health data if the pet is at a veterinary clinic. For example, if the pet is at a veterinary clinic, the collection unit prioritizes collecting health data. In this way, highly relevant data can be collected by taking into account the geographical location information of the pet.

[0079] When collecting data, the collection unit can analyze the owner's social media activities and collect related data. For example, when the owner posts a photo of their pet on social media, the collection unit collects health data from the photo. For example, when the owner posts a photo of their pet on social media, the collection unit collects health data from the photo. The collection unit can also collect activity data when the owner shares their pet's activities on social media. For example, when the owner shares their pet's activities on social media, the collection unit collects the activity data. The collection unit can also collect diet data when the owner posts their pet's diet on social media. For example, when the owner posts their pet's diet on social media, the collection unit collects the diet data. This makes it possible to efficiently collect related data by analyzing the owner's social media activities.

[0080] The collection unit can customize the collection method by reflecting the owner's past feedback when collecting data. The collection unit, for example, adjusts the frequency of data collection based on feedback provided by the owner in the past. For example, the collection unit adjusts the frequency of data collection based on feedback provided by the owner in the past. The collection unit can also adjust the type of data to be collected based on feedback provided by the owner in the past. For example, the collection unit adjusts the type of data to be collected based on feedback provided by the owner in the past. The collection unit can also optimize the collection method based on feedback provided by the owner in the past. For example, the collection unit optimizes the collection method based on feedback provided by the owner in the past. In this way, the collection method can be optimized by reflecting the owner's past feedback.

[0081] The analysis unit can estimate the owner's emotions and adjust the way the analysis is presented based on the estimated owner's emotions. For example, if the owner is feeling stressed, the analysis unit provides a simple analysis result. For example, if the owner is feeling stressed, the analysis unit provides a simple analysis result. The analysis unit can also provide a detailed analysis result if the owner is relaxed. For example, if the owner is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also provide a summary analysis result if the owner is busy. For example, if the owner is busy, the analysis unit provides a summary analysis result. In this way, by adjusting the way the analysis is presented according to the owner's emotions, it is possible to provide an analysis result that is easy for the owner to understand. Emotion estimation is realized using an emotion estimation function, 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.

[0082] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the pet's health data. For example, if weight data is important, the analysis unit performs a detailed weight analysis. For example, if weight data is important, the analysis unit performs a detailed weight analysis. Furthermore, if food intake data is important, the analysis unit can also perform a detailed food intake analysis. For example, if food intake data is important, the analysis unit performs a detailed food intake analysis. Furthermore, if exercise amount data is important, the analysis unit can also perform a detailed exercise amount analysis. For example, if exercise amount data is important, the analysis unit performs a detailed exercise amount analysis. In this way, by adjusting the level of detail of the analysis based on the importance of the pet's health data, important data can be analyzed in more detail.

[0083] During analysis, the analysis unit can apply different analysis algorithms depending on the type and age of the pet. For example, in the case of a dog, the analysis unit applies an analysis algorithm specifically for dogs. For example, in the case of a dog, the analysis unit applies an analysis algorithm specifically for dogs. Furthermore, in the case of a cat, the analysis unit can also apply an analysis algorithm specifically for cats. For example, in the case of a cat, the analysis unit applies an analysis algorithm specifically for cats. Furthermore, in the case of a young pet, the analysis unit can also apply an analysis algorithm according to the pet's growth. For example, in the case of a young pet, the analysis unit applies an analysis algorithm according to the pet's growth. In this way, by applying an analysis algorithm according to the type and age of the pet, more accurate analysis results can be obtained.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the owner's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on feedback provided by the owner in the past. For example, the analysis unit adjusts the analysis algorithm based on feedback provided by the owner in the past. The analysis unit can also improve the accuracy of the analysis based on the owner's past analysis results. For example, the analysis unit improves the accuracy of the analysis based on the owner's past analysis results. The analysis unit can also optimize the analysis method based on the owner's past analysis results. For example, the analysis unit optimizes the analysis method based on the owner's past analysis results. In this way, the accuracy of the analysis is improved by referring to the owner's past analysis results.

[0085] The analysis unit can estimate the owner's emotions and adjust the length of the analysis based on the estimated owner's emotions. For example, if the owner is feeling stressed, the analysis unit provides a short analysis result. For example, if the owner is feeling stressed, the analysis unit provides a short analysis result. The analysis unit can also provide a detailed analysis result if the owner is relaxed. For example, if the owner is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also provide a concise analysis result if the owner is busy. For example, if the owner is busy, the analysis unit provides a concise analysis result. In this way, by adjusting the length of the analysis according to the owner's emotions, it is possible to provide an analysis result of an appropriate length for the owner. Emotion estimation is realized using an emotion estimation function, 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.

[0086] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the latest data. For example, the analysis unit prioritizes analyzing the latest data. The analysis unit can also analyze the latest data by referring to past data. For example, the analysis unit analyzes the latest data by referring to past data. The analysis unit can also determine the analysis priority based on the time when the data was collected. For example, the analysis unit determines the analysis priority based on the time when the data was collected. In this way, by determining the analysis priority based on the time when the data was collected, the latest data can be analyzed with priority.

[0087] During analysis, the analysis unit can adjust the order of analysis based on the relevance of data. The analysis unit adjusts the order of analysis based on, for example, the relevance between weight data and food intake data. For example, the analysis unit adjusts the order of analysis based on the relevance between weight data and food intake data. The analysis unit can also adjust the order of analysis based on the relevance between exercise amount data and body temperature data. For example, the analysis unit adjusts the order of analysis based on the relevance between exercise amount data and body temperature data. The analysis unit can also adjust the order of analysis based on the relevance of data. For example, the analysis unit adjusts the order of analysis based on the relevance of data. In this way, by adjusting the order of analysis based on the relevance of data, highly relevant data can be analyzed preferentially.

[0088] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the owner's level of expertise. For example, if the owner has technical expertise, the analysis unit uses detailed technical terms. For example, if the owner has technical expertise, the analysis unit uses detailed technical terms. The analysis unit can also provide analysis results in simple language if the owner does not have technical expertise. For example, if the owner does not have technical expertise, the analysis unit provides analysis results in simple language. The analysis unit can also adjust the use of technical terms in the analysis according to the owner's level of expertise. For example, the analysis unit adjusts the use of technical terms in the analysis according to the owner's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the owner's level of expertise, it is possible to provide analysis results that are easy for the owner to understand.

[0089] The display unit can estimate the owner's emotions and adjust the display method based on the estimated owner's emotions. For example, if the owner is feeling stressed, the display unit provides a simple display method. For example, if the owner is feeling stressed, the display unit provides a simple display method. The display unit can also provide a detailed display method if the owner is relaxed. For example, if the owner is relaxed, the display unit provides a detailed display method. The display unit can also provide a display method that focuses on the main points if the owner is busy. For example, if the owner is busy, the display unit provides a display method that focuses on the main points. In this way, by adjusting the display method according to the owner's emotions, it is possible to provide a display result that is easy for the owner to view. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] The display unit can adjust the level of detail of the display based on the importance of the pet's health data when displaying the data. For example, if weight data is important, the display unit displays a detailed weight graph. For example, if weight data is important, the display unit displays a detailed weight graph. The display unit can also display a detailed food amount graph if food amount data is important. For example, if food amount data is important, the display unit displays a detailed food amount graph. The display unit can also display a detailed exercise amount graph if exercise amount data is important. For example, if exercise amount data is important, the display unit displays a detailed exercise amount graph. In this way, by adjusting the level of detail of the display based on the importance of the pet's health data, important data can be displayed in more detail.

[0091] The display unit can apply different display formats depending on the type and age of the pet when displaying. For example, in the case of a dog, the display unit applies a display format dedicated to dogs. For example, in the case of a dog, the display unit applies a display format dedicated to dogs. Furthermore, in the case of a cat, the display unit can also apply a display format dedicated to cats. For example, in the case of a cat, the display unit applies a display format dedicated to cats. Furthermore, in the case of a young pet, the display unit can also apply a display format according to the pet's growth. For example, in the case of a young pet, the display unit applies a display format according to the pet's growth. In this way, by applying a display format according to the type and age of the pet, more appropriate display results can be obtained.

[0092] The display unit can improve the accuracy of the display when displaying by referring to the owner's past display results. The display unit, for example, adjusts the display method based on feedback provided by the owner in the past. For example, the display unit adjusts the display method based on feedback provided by the owner in the past. The display unit can also improve the accuracy of the display based on the owner's past display results. For example, the display unit improves the accuracy of the display based on the owner's past display results. The display unit can also optimize the display method based on the owner's past display results. For example, the display unit optimizes the display method based on the owner's past display results. In this way, the accuracy of the display is improved by referring to the owner's past display results.

[0093] The display unit can estimate the owner's emotions and adjust the length of the display based on the estimated owner's emotions. For example, if the owner is feeling stressed, the display unit provides a short display result. For example, if the owner is feeling stressed, the display unit provides a short display result. The display unit can also provide a detailed display result if the owner is relaxed. For example, if the owner is relaxed, the display unit provides a detailed display result. The display unit can also provide a display result that focuses on the main points if the owner is busy. For example, if the owner is busy, the display unit provides a display result that focuses on the main points. In this way, by adjusting the length of the display according to the owner's emotions, it is possible to provide a display result of an appropriate length for the owner. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0094] The display unit can determine the display priority based on the time when the data was collected when displaying the data. The display unit, for example, prioritizes displaying the latest data. For example, the display unit prioritizes displaying the latest data. The display unit can also display the latest data by referring to past data. For example, the display unit displays the latest data by referring to past data. The display unit can also determine the display priority based on the time when the data was collected. For example, the display unit determines the display priority based on the time when the data was collected. In this way, by determining the display priority based on the time when the data was collected, the latest data can be displayed preferentially.

[0095] The display unit can adjust the display order based on the relevance of data when displaying the data. The display unit adjusts the display order based on, for example, the relevance between weight data and food amount data. For example, the display unit adjusts the display order based on the relevance between weight data and food amount data. The display unit can also adjust the display order based on the relevance between exercise amount data and body temperature data. For example, the display unit adjusts the display order based on the relevance between exercise amount data and body temperature data. The display unit can also adjust the display order based on the relevance of data. For example, the display unit adjusts the display order based on the relevance of data. In this way, by adjusting the display order based on the relevance of data, highly relevant data can be displayed preferentially.

[0096] The display unit can adjust the use of technical terms in the display depending on the owner's level of expertise when displaying. For example, if the owner has technical knowledge, the display unit uses detailed technical terms. For example, if the owner has technical knowledge, the display unit uses detailed technical terms. The display unit can also provide display results in simple language if the owner does not have technical knowledge. For example, if the owner does not have technical knowledge, the display unit provides display results in simple language. The display unit can also adjust the use of technical terms in the display depending on the owner's level of expertise. For example, the display unit adjusts the use of technical terms in the display depending on the owner's level of expertise. In this way, by adjusting the use of technical terms in the display depending on the owner's level of expertise, it is possible to provide display results that are easy for the owner to understand.

[0097] The providing unit can estimate the owner's emotions and adjust the way in which advice is expressed based on the estimated owner's emotions. For example, if the owner is feeling stressed, the providing unit provides simple and easy-to-understand advice. For example, if the owner is feeling stressed, the providing unit provides simple and easy-to-understand advice. The providing unit can also provide detailed advice if the owner is relaxed. For example, if the owner is relaxed, the providing unit provides detailed advice. The providing unit can also provide advice that focuses on the main points if the owner is busy. For example, if the owner is busy, the providing unit provides advice that focuses on the main points. In this way, by adjusting the way in which advice is expressed according to the owner's emotions, it is possible to provide advice that is easy for the owner to understand. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the pet's health data. For example, if weight data is important, the providing unit provides detailed weight management advice. For example, if weight data is important, the providing unit provides detailed weight management advice. The providing unit can also provide detailed diet management advice if food amount data is important. For example, if food amount data is important, the providing unit provides detailed diet management advice. The providing unit can also provide detailed exercise management advice if exercise amount data is important. For example, if exercise amount data is important, the providing unit provides detailed exercise management advice. In this way, by adjusting the level of detail of the advice based on the importance of the pet's health data, advice for important data can be given in more detail.

[0099] When providing advice, the providing unit can apply different advice algorithms depending on the type and age of the pet. For example, in the case of a dog, the providing unit applies an advice algorithm specifically for dogs. For example, in the case of a dog, the providing unit applies an advice algorithm specifically for dogs. Furthermore, in the case of a cat, the providing unit can also apply an advice algorithm specifically for cats. For example, in the case of a cat, the providing unit applies an advice algorithm specifically for cats. Furthermore, in the case of a young pet, the providing unit can also apply an advice algorithm according to the pet's growth. For example, in the case of a young pet, the providing unit applies an advice algorithm according to the pet's growth. In this way, more appropriate advice can be obtained by applying an advice algorithm according to the type and age of the pet.

[0100] When providing advice, the providing unit can improve the accuracy of the advice by referring to past advice results from the owner. The providing unit, for example, adjusts the advice algorithm based on feedback provided by the owner in the past. For example, the providing unit adjusts the advice algorithm based on feedback provided by the owner in the past. The providing unit can also improve the accuracy of the advice based on the past advice results from the owner. For example, the providing unit improves the accuracy of the advice based on the past advice results from the owner. The providing unit can also optimize the advice method based on the past advice results from the owner. For example, the providing unit optimizes the advice method based on the past advice results from the owner. In this way, the accuracy of the advice is improved by referring to the past advice results from the owner.

[0101] The providing unit can estimate the owner's emotions and adjust the length of the advice based on the estimated owner's emotions. For example, if the owner is feeling stressed, the providing unit provides short advice. For example, if the owner is feeling stressed, the providing unit provides short advice. The providing unit can also provide detailed advice if the owner is relaxed. For example, if the owner is relaxed, the providing unit provides detailed advice. The providing unit can also provide advice that focuses on the main points if the owner is busy. For example, if the owner is busy, the providing unit provides advice that focuses on the main points. In this way, by adjusting the length of the advice according to the owner's emotions, it is possible to provide advice of an appropriate length for the owner. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] When providing advice, the providing unit can determine the priority of advice based on the time when data was collected. The providing unit, for example, provides advice based on the latest data. For example, the providing unit provides advice based on the latest data. The providing unit can also provide advice based on the latest data by referring to past data. For example, the providing unit provides advice based on the latest data by referring to past data. The providing unit can also determine the priority of advice based on the time when data was collected. For example, the providing unit determines the priority of advice based on the time when data was collected. In this way, by determining the priority of advice based on the time when data was collected, advice based on the latest data is provided.

[0103] The providing unit can adjust the order of advice based on the relevance of data when providing advice. The providing unit adjusts the order of advice based on, for example, the relevance between weight data and food amount data. For example, the providing unit adjusts the order of advice based on the relevance between weight data and food amount data. The providing unit can also adjust the order of advice based on the relevance between exercise amount data and body temperature data. For example, the providing unit adjusts the order of advice based on the relevance between exercise amount data and body temperature data. The providing unit can also adjust the order of advice based on the relevance of data. For example, the providing unit adjusts the order of advice based on the relevance of data. In this way, by adjusting the order of advice based on the relevance of data, advice based on highly relevant data is preferentially provided.

[0104] When providing advice, the providing unit can adjust the use of technical terms in the advice according to the owner's level of expertise. For example, if the owner has technical knowledge, the providing unit uses detailed technical terms. For example, if the owner has technical knowledge, the providing unit uses detailed technical terms. Furthermore, if the owner does not have technical knowledge, the providing unit can provide advice in simple language. For example, if the owner does not have technical knowledge, the providing unit provides advice in simple language. Furthermore, the providing unit can adjust the use of technical terms in the advice according to the owner's level of expertise. For example, the providing unit adjusts the use of technical terms in the advice according to the owner's level of expertise. In this way, by adjusting the use of technical terms in the advice according to the owner's level of expertise, advice that is easy for the owner to understand is provided. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, display unit, and provision 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 data through sensors in the smart device 14 or input from the owner, and the collected data is analyzed by the specific processing unit 290 in the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 in the data processing device 12, and analyzes the collected data to evaluate the health condition of the pet. The display unit visually displays the evaluation results on, for example, the display 40A of the smart device 14. The provision unit provides advice generated by the specific processing unit 290 in the data processing device 12 to the owner via the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, display unit, and provision 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 data through sensors in the smart glasses 214 or input from the owner, and the data is analyzed by the specific processing unit 290 in the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 in the data processing device 12, and analyzes the collected data to evaluate the pet's health condition. The display unit visually displays the evaluation results on, for example, the display of the smart glasses 214. The provision unit provides advice generated by the specific processing unit 290 in the data processing device 12 to the owner through the speaker 240 of the smart glasses 214, for example. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, display unit, and provision unit described above 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 data through sensors in the headset-type terminal 314 or input by the owner, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to evaluate the health condition of the pet. The display unit visually displays the evaluation results on, for example, the display 343 of the headset-type terminal 314. The provision unit provides advice generated by the specific processing unit 290 of the data processing device 12 to the owner via the speaker 240 of the headset-type terminal 314, for example. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, display unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data through sensors in the robot 414 or input from the owner, and the data is analyzed by the specific processing unit 290 in the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 in the data processing device 12, and analyzes the collected data to evaluate the health condition of the pet. The display unit visually displays the evaluation results on the display of the robot 414, for example. The provision unit provides advice generated by the specific processing unit 290 in the data processing device 12 to the owner through the speaker 240 of the robot 414, for example.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] When collecting pet health data, the collection unit can learn the pet's behavioral patterns and automatically adjust the optimal data collection timing. For example, the collection unit collects food intake data during the pet's usual mealtimes and exercise amount data during the pet's exercise time. The collection unit can also obtain more accurate health data by collecting body temperature data while the pet is resting. Furthermore, the collection unit can adjust the frequency of data collection based on the pet's behavioral patterns, reducing the burden on the owner. This makes it possible to collect data based on the pet's behavioral patterns, allowing for more accurate health management.

[0107] When analyzing a pet's health data, the analysis unit can compare it with the pet's past health data to detect abnormalities. For example, the analysis unit can detect an abnormality if the pet's weight suddenly increases or decreases, and notify the owner. The analysis unit can also detect an abnormality if the pet's food intake suddenly changes. Furthermore, the analysis unit can detect an abnormality if the pet's exercise volume increases or decreases significantly compared to normal, and provide the owner with appropriate advice. This allows for early detection of abnormalities in the pet's health condition and the implementation of appropriate measures.

[0108] The display unit can provide a customizable display format according to the owner's preferences when displaying the pet's health data. For example, the display unit can allow the owner to select their preferred colors and fonts. The display unit can also provide a function for the owner to highlight specific data. Furthermore, the display unit can provide a more user-friendly interface by allowing the owner to freely change the display order of the data. This allows the owner to check the health data according to their preferences, leading to more effective health management.

[0109] The providing unit can propose a specific action plan to the owner based on the pet's health data. For example, if the pet is gaining weight, the providing unit can propose a specific meal menu and exercise plan. The providing unit can also provide a specific exercise schedule if the pet is not getting enough exercise. Furthermore, the providing unit can also propose a schedule for regular health checks depending on the pet's health condition. This allows the owner to manage the health of their pet based on a specific action plan.

[0110] When collecting health data of a pet, the collection unit can estimate the pet's emotions and adjust the data collection method based on the estimated emotions. For example, if the pet is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the pet. Also, if the pet is relaxed, the collection unit can collect more detailed data to obtain more information. Furthermore, if the pet is excited, the collection unit can prioritize the collection of exercise data. This makes it possible to collect data according to the pet's emotions and obtain more accurate health data.

[0111] When analyzing the pet's health data, the analysis unit can estimate the owner's emotions and adjust the way the analysis results are presented based on the estimated emotions. For example, if the owner is feeling stressed, the analysis unit can provide simple and easy-to-understand analysis results. If the owner is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the owner is busy, the analysis unit can provide analysis results that focus on the main points. This allows the analysis results to be provided according to the owner's emotions, allowing the owner to check the health data in a format that is easy to understand.

[0112] When displaying the pet's health data, the display unit can estimate the owner's emotions and adjust the display method based on the estimated emotions. For example, the display unit can provide a simple display method if the owner is stressed. Alternatively, the display unit can provide a detailed display method if the owner is relaxed. Furthermore, the display unit can provide a display method that focuses on the main points if the owner is busy. This provides a display method that corresponds to the owner's emotions, allowing the owner to check the health data in an easy-to-read format.

[0113] The providing unit can provide the owner with advice that takes into consideration their emotions based on the pet's health data. For example, if the owner is feeling stressed, the providing unit can provide simple, easy-to-understand advice. If the owner is relaxed, the providing unit can also provide detailed advice. Furthermore, if the owner is busy, the providing unit can also provide advice that focuses on the main points. This allows the owner to receive advice that is in line with their emotions, enabling them to manage their health in a way that is easy for them to understand.

[0114] The providing unit can provide the owner with advice that takes into consideration their emotions based on the pet's health data. For example, if the owner is feeling stressed, the providing unit can provide simple, easy-to-understand advice. If the owner is relaxed, the providing unit can also provide detailed advice. Furthermore, if the owner is busy, the providing unit can also provide advice that focuses on the main points. This allows the owner to receive advice that is in line with their emotions, enabling them to manage their health in a way that is easy for them to understand.

[0115] The providing unit can provide the owner with advice that takes into consideration their emotions based on the pet's health data. For example, if the owner is feeling stressed, the providing unit can provide simple, easy-to-understand advice. If the owner is relaxed, the providing unit can also provide detailed advice. Furthermore, if the owner is busy, the providing unit can also provide advice that focuses on the main points. This allows the owner to receive advice that is in line with their emotions, enabling them to manage their health in a way that is easy for them to understand.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The collection unit collects health data of the pet. The pet's health data includes, for example, weight, amount of food eaten, amount of exercise, and body temperature. The collection unit not only collects data entered by the owner, but can also collect data automatically using sensors. For example, data is collected using a weighing scale, activity monitor, temperature sensor, etc. Step 2: The analysis unit analyzes the data collected by the collection unit and evaluates the pet's health condition. The analysis unit analyzes fluctuations in weight, changes in food intake, increases or decreases in exercise, etc., and makes a comprehensive assessment of the pet's health condition. The analysis unit can analyze the data using statistical analysis and machine learning algorithms. Step 3: The display unit visually displays the health status evaluated by the analysis unit. The display unit displays the evaluation results in a visual format such as a graph or chart. For example, the display unit may display weight fluctuations as a line graph, or food intake changes as a bar graph. Step 4: The providing unit provides health management advice to the owner based on the evaluation results displayed by the display unit. The providing unit provides specific health management advice, such as adjusting the amount of food or exercise, based on the evaluation results. For example, if the pet is gaining weight, the providing unit may advise the owner to reduce the amount of food, or if the pet is not getting enough exercise, the providing unit may advise the owner to exercise more.

[0118] 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.

[0119] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0141] The 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.

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

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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 evaluates the health condition of the pet; a display unit that visually displays the health condition evaluated by the analysis unit; and a providing unit that provides health care advice to the owner based on the evaluation result displayed by the display unit. A system characterized by:

2. The collecting unit Collect data entered by owners 2. The system of claim 1.

3. The collecting unit Automatically collect data using sensors 2. The system of claim 1.

4. The analysis unit Analyze changes in weight, food intake, and exercise to comprehensively assess your pet's health.

2. The system of claim 1.

5. The display unit Display the evaluation results in a visual format of graphs or charts 2. The system of claim 1.

6. The providing unit Based on the results of the assessment, specific health management advice will be provided, such as adjusting diet and exercise.

2. The system of claim 1.

7. The collecting unit Estimate the owner's emotions and adjust the timing of data collection based on the estimated owner's emotions.

2. The system of claim 1.

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

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