System

Abstract

To analyze behavior, an action, and a cry of a pet and to grasp physical conditions and mental conditions.SOLUTION: A system includes: an acquisition unit; an analysis unit; a provision unit; a collection unit; and a diagnosis unit. The acquisition unit acquires gesture or behavior or an action, and a cry of a pet. The analysis unit analyzes data acquired by the acquisition unit. The provision unit provides analysis results obtained by the analysis unit. The collection unit collects data for medical examination of the pet. The diagnosis unit analyzes the data collected by the collection unit and detects abnormality.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it is difficult to understand a pet's physical or mental state from its gestures, movements, and cries, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze a pet's behavior, movements, and cries to understand the physical and mental state of the pet. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a provision unit, a collection unit, and a diagnosis unit. The acquisition unit acquires the gestures or movements, actions, and cries of the pet. The analysis unit analyzes the data acquired by the acquisition unit. The provision unit provides the analysis results obtained by the analysis unit. The collection unit collects data for a health check of the pet. The diagnosis unit analyzes the data collected by the collection unit and detects abnormalities. Effect of the Invention

[0007] The system according to the embodiment can analyze a pet's behavior, movements, and cries to understand the pet's physical and mental state. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The pet health management system according to the embodiment of the present invention is a tool for capturing the gestures, movements, behavior, and cries of a pet as data using camera images and voice recognition, analyzing the current physical and mental state of the pet using generation AI, and improving communication with humans. This system can also be used for pet health checkups. For example, a camera and a microphone are used to record the gestures, movements, behavior, and cries of a pet in real time. Next, the recorded data is input into the generation AI to analyze the physical and mental state of the pet. Based on the analysis results, appropriate advice and information is provided to the pet owner. For example, if the pet is feeling stressed, the cause is identified and a method for reducing stress is proposed. In addition, as a pet health checkup, data is collected periodically, and if an abnormality is detected, it is possible to deal with it early. This tool makes it easier to manage the health of the pet and improves communication with the owner. As a result, the pet health management system can grasp the physical and mental state of the pet in detail and provide appropriate advice and information to the owner.

[0029] The pet health management system according to the embodiment includes an acquisition unit, an analysis unit, a provision unit, a collection unit, and a diagnosis unit. The acquisition unit acquires the gestures, movements, behavior, and cries of the pet. The acquisition unit can acquire the gestures, movements, behavior, and cries of the pet using, for example, a camera image and a voice recognition technology. The camera image records the pet's movements in detail using, for example, a high-resolution camera. The voice recognition technology accurately acquires the pet's cries using, for example, a high-sensitivity microphone. The analysis unit analyzes the data acquired by the acquisition unit using a generation AI. The generation AI analyzes the physical condition and mental state of the pet using, for example, a text generation AI (for example, LLM). The generation AI can learn the patterns of the pet's movements and cries and detect abnormalities. The provision unit provides the owner with the analysis results obtained by the analysis unit. The provision unit can display the analysis results as, for example, a text message or a graph. The collection unit periodically collects data for the pet's health check. The collection unit can collect data at a frequency of, for example, daily, weekly, or monthly. The diagnosis unit analyzes the data collected by the collection unit and detects abnormalities. For example, the diagnosis unit can detect abnormalities in the pet's body temperature or heart rate. This allows the pet health management system according to the embodiment to grasp the pet's physical and mental state in detail and provide appropriate advice and information to the pet owner.

[0030] The acquisition unit acquires the gestures, movements, behavior, and cries of the pet. The acquisition unit can acquire the gestures, movements, behavior, and cries of the pet using, for example, camera images and voice recognition technology. Specifically, a high-resolution camera can be used to record the pet's movements in detail, and minute changes in the pet's movements and facial expressions can be captured. The camera is installed in the pet's living space and can record images 24 hours a day. This allows the pet's behavior patterns and daily movements to be continuously monitored. In addition, a high-sensitivity microphone is used to accurately acquire the pet's cries. Voice recognition technology can analyze the frequency, volume, rhythm, etc. of the pet's cries and identify specific cry patterns. For example, it can detect cries when the pet is feeling anxious or stressed, or cries indicating poor health. Furthermore, the acquisition unit collects data on the pet's movements and cries in real time and transmits them to a central database. This allows the pet's behavior and condition to be always kept up to date. The acquisition unit can centrally manage these data and link with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis and diagnosis units. In addition, the frequency and accuracy of data collection can be adjusted to respond flexibly to specific situations and conditions. This allows the acquisition unit to collect data efficiently and effectively, improving the performance of the entire system.

[0031] The analysis unit uses the generation AI to analyze the data acquired by the acquisition unit. The generation AI analyzes the physical and mental states of the pet using, for example, a text generation AI (e.g., LLM). Specifically, the generation AI can learn the patterns of the pet's movements and cries and detect abnormalities. The generation AI models the pet's normal behavioral patterns based on a large amount of data and sets standards for detecting abnormal behavior and cries. For example, if the pet moves differently than usual or makes an abnormal cry, the generation AI can immediately detect the abnormality and issue a warning. Furthermore, the generation AI can analyze changes in the pet's behavior and cries in chronological order to grasp long-term trends and patterns. This makes it possible to detect changes in the pet's health and mental state early and take appropriate measures. The analysis unit evaluates the pet's physical and mental states in detail based on the analysis results obtained by the generation AI. For example, if the pet is feeling stressed or shows signs of poor health, the analysis unit can identify the cause and provide specific advice to the owner. In addition, the analysis unit can use past data and statistical information to analyze long-term trends in a pet's health and predict future risks, allowing the analysis unit to play an important role in pet health management and provide owners with reliable information.

[0032] The providing unit provides the owner with the analysis results obtained by the analysis unit. The providing unit can display the analysis results as text messages or graphs, for example. Specifically, the analysis results are notified to the owner's smartphone or computer, and the pet's health condition and behavioral patterns are visually displayed in an easy-to-understand manner. For example, information on the pet's physical condition and mental state is displayed as graphs or charts, allowing the owner to understand the pet's condition at a glance. In addition, specific advice and points of caution are provided as text messages, providing guidelines for the owner to take appropriate measures. Furthermore, the providing unit can update the analysis results in real time and provide the owner with the latest information. For example, if an abnormality is detected in the pet's behavior or cries, a notification is sent immediately, allowing the owner to respond quickly. In addition, the providing unit can collect feedback from the owner and use it to improve the accuracy of the analysis results and the content provided. For example, the owner can comment on or evaluate the provided information, thereby improving the performance of the entire system. As a result, the providing unit can provide reliable information to the owner and play an important role in supporting the health management of the pet.

[0033] The collection unit periodically collects data for the health check of the pet. The collection unit can collect data at a frequency of, for example, daily, weekly, or monthly. Specifically, the pet's biological data, such as body temperature, heart rate, and respiratory rate, are periodically measured and recorded. These data are important for continuously monitoring the health condition of the pet. The collection unit can collect data in real time using a wearable device attached to the pet. For example, a sensor attached to the pet's collar measures the body temperature and heart rate, and transmits the data to a central database via wireless communication. This allows the collection unit to always have the latest information on the pet's health condition. Furthermore, the collection unit can centrally manage the collected data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and can be accessed by the analysis unit and diagnosis unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system.

[0034] The diagnostic unit analyzes the data collected by the collection unit and detects abnormalities. The diagnostic unit can detect, for example, abnormalities in the pet's body temperature or heart rate. Specifically, based on the collected data, the diagnostic unit evaluates the pet's health condition in detail and identifies abnormal patterns or signs. For example, if the pet's body temperature is higher than normal or the heart rate is abnormally fast, the diagnostic unit can immediately detect the abnormality and issue a warning. Furthermore, the diagnostic unit can also use past data and statistical information to analyze long-term trends in the pet's health condition and predict future risks. In this way, the diagnostic unit can play an important role in pet health management and provide reliable information to the owner. If an abnormality is detected, the diagnostic unit provides the owner with specific advice and countermeasures. For example, if the pet's body temperature is high, the diagnostic unit recommends cooling methods or consulting a veterinarian. In addition, the diagnostic unit can identify the cause of the abnormality and propose measures to prevent recurrence. In this way, the diagnostic unit can play an important role in supporting pet health management and providing appropriate advice and information to the owner.

[0035] The acquisition unit can acquire the pet's gestures, movements, behavior, and cries using camera images and voice recognition. The camera images are recorded in detail using a high-resolution camera, for example. The resolution of the camera is high resolution, such as 1080p or 4K, and can accurately capture the pet's detailed movements. The camera is preferably installed in a place where the pet is active or rests, for example. The voice recognition technology accurately acquires the pet's cries using a high-sensitivity microphone, for example. The types of microphones include directional microphones and omnidirectional microphones, and can separate and acquire the pet's cries from surrounding noise. The voice recognition algorithm can analyze the pet's cries using deep learning technology, for example, and identify specific cries. As a result, the pet's gestures, movements, behavior, and cries can be accurately acquired using the camera images and voice recognition.

[0036] The analysis unit can analyze the data acquired by the generation AI and analyze the physical condition or mental condition of the pet. The generation AI can, for example, use a text generation AI (for example, LLM) to analyze the physical condition or mental condition of the pet. The generation AI can learn the patterns of the pet's movements and cries and detect abnormalities. The generation AI can, for example, receive the pet's movement data as an input and analyze the pet's physical condition based on the data. The generation AI can, for example, detect abnormal movement patterns from the pet's movement data and determine whether there is an abnormality in the pet's physical condition. The generation AI can also receive the pet's cry data as an input and analyze the pet's mental state based on the data. The generation AI can, for example, detect signs of stress or anxiety from the pet's cry data and determine whether there is an abnormality in the pet's mental state. As a result, the use of the generation AI can analyze the physical condition or mental state of the pet with high accuracy.

[0037] The providing unit can provide the owner with appropriate advice or information based on the analysis result. The providing unit can display the analysis result as a text message or a graph, for example. The providing unit provides specific advice to the owner based on the analysis result. For example, if the pet is stressed, the providing unit can identify the cause of the stress and suggest a method for reducing the stress. For example, if the cause of the pet's stress is a change in the environment, the providing unit can provide the owner with specific advice to improve the environment. The providing unit can also provide information on health management of the pet. For example, if an abnormality is detected in the pet's physical condition, the providing unit can advise the owner to visit a veterinary clinic early. This improves communication with the pet by providing the owner with appropriate advice or information based on the analysis result.

[0038] The collection unit can periodically collect data for the health check of the pet. The collection unit can collect data, for example, daily, weekly, monthly, etc. The collection unit periodically collects data in order to continuously monitor the health condition of the pet. For example, the collection unit can quickly detect changes in the physical condition of the pet by collecting data daily. Furthermore, the collection unit can grasp long-term trends in the health condition of the pet by collecting data weekly. Furthermore, the collection unit can periodically check the results of the health check of the pet by collecting data monthly. In this way, the health condition of the pet can be continuously monitored by collecting data periodically.

[0039] The diagnosis unit can analyze the collected data and detect an abnormality. The diagnosis unit can detect, for example, an abnormality in the pet's body temperature or heart rate. The diagnosis unit analyzes the pet's health condition based on the data collected by the collection unit. For example, the diagnosis unit can analyze the pet's body temperature data and detect an abnormality when the body temperature exceeds the normal range. The diagnosis unit can also analyze the pet's heart rate data and detect an abnormality when the heart rate is abnormally high or low. Furthermore, the diagnosis unit can analyze the pet's behavior data and detect an abnormal behavior pattern. For example, the diagnosis unit can detect an abnormality when the pet exhibits behavior that is different from normal. In this way, by analyzing the collected data and detecting an abnormality, the pet's health problem can be addressed early.

[0040] The acquisition unit can analyze the past behavior history of the pet and select an appropriate acquisition method. For example, the acquisition unit preferentially acquires behaviors that the pet frequently performed in the past. The acquisition unit analyzes the past behavior history of the pet and selects the optimal acquisition method based on the history. For example, based on the past behavior patterns of the pet, data can be acquired with a focus on a specific time period. In addition, abnormal behaviors can be detected from the past behavior history of the pet and the behaviors can be acquired with a focus. In this way, the optimal acquisition method can be selected by analyzing the past behavior history of the pet.

[0041] The acquisition unit can perform filtering based on the pet's current environment or situation at the time of acquisition. For example, when the pet is outdoors, the acquisition unit filters the environmental sounds to acquire the pet's cries. The acquisition unit performs filtering based on the pet's current environment and situation. For example, when the pet is indoors, the acquisition unit can filter the background movements to acquire the pet's behavior. Also, when the pet is with other animals, the acquisition unit can filter and acquire the behavior of the specific animal. This allows data with less noise to be acquired by filtering based on the pet's current environment and situation.

[0042] The acquisition unit can preferentially acquire highly relevant data based on the geographical location information of the pet at the time of acquisition. For example, when the pet is in a park, the acquisition unit preferentially acquires playing behavior. The acquisition unit preferentially acquires highly relevant data taking into account the geographical location information of the pet. For example, when the pet is in a veterinary clinic, signs of stress can be preferentially acquired. Also, when the pet is at home, relaxing behavior can be preferentially acquired. In this way, highly relevant data can be preferentially acquired by taking into account the geographical location information of the pet.

[0043] At the time of acquisition, the acquisition unit can analyze the social media activity of the pet owner and acquire related data. For example, if the owner frequently posts photos of the pet, the acquisition unit acquires data by referring to the status of the photos. The acquisition unit analyzes the social media activity of the pet owner and acquires related data based on the activity. For example, if the owner posts about the pet's health, the acquisition unit can acquire data by referring to the content of the posts. Also, if the owner posts about the pet's behavior, the acquisition unit can acquire data with a focus on the behavior. In this way, related data can be acquired by analyzing the social media activity of the pet owner.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit adjusts the level of detail of the analysis based on the importance of the data. For example, a simplified analysis can be performed on general data. Furthermore, a particularly detailed analysis can be performed on abnormal data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0045] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a motion analysis algorithm to motion data. The analysis unit applies different analysis algorithms depending on the category of data. For example, a sound analysis algorithm can be applied to bird cry data. Also, a behavior analysis algorithm can be applied to behavior data. In this way, highly accurate analysis is possible by applying different analysis algorithms depending on the category of data.

[0046] During analysis, the analysis unit can determine the analysis priority based on the time of data acquisition. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit determines the analysis priority based on the time of data acquisition. For example, analysis can be performed while referring to past data. Also, data acquired during a specific period can be focused on for analysis. In this way, by determining the analysis priority based on the time of data acquisition, the most recent data can be prioritized for analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit adjusts the order of analysis based on the relevance of the data. For example, data with low relevance can be postponed. In addition, the analysis order can be dynamically adjusted based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data.

[0048] The providing unit can adjust the level of detail of the provided information based on the importance of the analysis result at the time of providing the information. For example, the providing unit provides detailed information for important analysis results. The providing unit adjusts the level of detail of the provided information based on the importance of the analysis result. For example, simple information can be provided for general analysis results. Furthermore, particularly detailed information can be provided for abnormal analysis results. This enables efficient information provision by adjusting the level of detail of the provided information based on the importance of the analysis results.

[0049] The providing unit can apply different providing methods depending on the category of the analysis result when providing the information. For example, the providing unit provides information about the movement for the movement analysis result. The providing unit applies different providing methods depending on the category of the analysis result. For example, information about the cry can be provided for the cry analysis result. Also, information about the behavior can be provided for the behavior analysis result. In this way, by applying different providing methods depending on the category of the analysis result, it becomes possible to provide appropriate information.

[0050] At the time of provision, the providing unit can determine the priority of provision based on the time when the analysis results were acquired. The providing unit, for example, provides the latest analysis results preferentially. The providing unit determines the priority of provision based on the time when the analysis results were acquired. For example, information can be provided while referring to past analysis results. Also, analysis results acquired during a specific period can be provided with a focus. In this way, by determining the priority of provision based on the time when the analysis results were acquired, the latest information can be provided preferentially.

[0051] The providing unit can adjust the order of provision based on the relevance of the analysis results at the time of provision. For example, the providing unit provides analysis results with high relevance preferentially. The providing unit adjusts the order of provision based on the relevance of the analysis results. For example, analysis results with low relevance can be postponed. In addition, the order of provision can be dynamically adjusted based on the relevance of the analysis results. This enables efficient information provision by adjusting the order of provision based on the relevance of the analysis results.

[0052] The collection unit can select an appropriate collection method based on the pet's past health data at the time of collection. For example, the collection unit selects the optimal collection method for a specific health condition based on the pet's past health data. The collection unit refers to the pet's past health data and selects the optimal collection method based on the data. For example, the collection unit can refer to the pet's past health data and focus on collecting data when an abnormality is detected. In addition, the pet's past health data can be analyzed to determine the optimal collection timing. In this way, the optimal collection method can be selected by referring to the pet's past health data.

[0053] The collection unit can adjust the level of detail of the collection based on the pet's current health condition at the time of collection. For example, if the pet is healthy, the collection unit collects data at a normal level of detail. The collection unit adjusts the level of detail of the collection based on the pet's current health condition. For example, if the pet is in poor health, detailed data can be collected. Also, if the pet has a specific health problem, data related to the problem can be collected with a focus. In this way, appropriate data can be collected by adjusting the level of detail of the collection based on the pet's current health condition.

[0054] The collection unit can prioritize collecting highly relevant data based on the geographical location information of the pet at the time of collection. For example, when the pet is in a park, the collection unit prioritizes collecting play behavior data. The collection unit prioritizes collecting highly relevant data taking into account the geographical location information of the pet. For example, when the pet is in a veterinary clinic, data related to the health condition can be prioritized. Also, when the pet is at home, relaxing behavior data can be prioritized. In this way, highly relevant data can be prioritized by taking into account the geographical location information of the pet.

[0055] At the time of collection, the collection unit can analyze the social media activity of the pet owner and collect related data. For example, if the owner posts about the pet's health, the collection unit collects data with reference to the content. The collection unit analyzes the social media activity of the pet owner and collects related data based on the activity. For example, if the owner posts about the pet's behavior, the collection unit can focus on collecting the behavior. Also, if the owner frequently posts photos of the pet, the collection unit can collect data with reference to the status of the photos. In this way, related data can be collected by analyzing the social media activity of the pet owner.

[0056] The diagnostic unit can select an appropriate diagnostic method based on the pet's past health data at the time of diagnosis. For example, the diagnostic unit selects the optimal diagnostic method for a specific health condition based on the pet's past health data. The diagnostic unit refers to the pet's past health data and selects the optimal diagnostic method based on the data. For example, by referring to the pet's past health data, the diagnostic unit can focus on diagnosis when an abnormality is detected. In addition, the pet's past health data can be analyzed to determine the optimal timing for diagnosis. In this way, the optimal diagnostic method can be selected by referring to the pet's past health data.

[0057] The diagnosis unit can adjust the level of detail of the diagnosis based on the pet's current health condition at the time of diagnosis. For example, if the pet is healthy, the diagnosis unit performs a diagnosis with a normal level of detail. The diagnosis unit adjusts the level of detail of the diagnosis based on the pet's current health condition. For example, if the pet is in poor health, a detailed diagnosis can be performed. Also, if the pet has a specific health problem, a detailed diagnosis related to the problem can be performed. This allows for an appropriate diagnosis by adjusting the level of detail of the diagnosis based on the pet's current health condition.

[0058] The diagnosis unit can select an appropriate diagnosis method based on the geographical location information of the pet at the time of diagnosis. For example, when the pet is in a park, the diagnosis unit performs a diagnosis related to play behavior. The diagnosis unit selects the optimal diagnosis method in consideration of the geographical location information of the pet. For example, when the pet is at a veterinary clinic, a diagnosis related to a health condition can be performed. Also, when the pet is at home, a diagnosis related to a relaxing behavior can be performed. In this way, the optimal diagnosis method can be selected by considering the geographical location information of the pet.

[0059] The diagnostic unit can analyze the social media activity of the pet owner at the time of diagnosis and provide related diagnostic information. For example, if the owner posts about the pet's health, the diagnostic unit makes a diagnosis with reference to the content of the posts. The diagnostic unit analyzes the social media activity of the pet owner and provides related diagnostic information based on the activities. For example, if the owner posts about the pet's behavior, the diagnosis can be made with reference to the behavior. Also, if the owner frequently posts photos of the pet, the diagnosis can be made with reference to the status of the photos. In this way, by analyzing the social media activity of the pet owner, related diagnostic information can be provided.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The pet health management system can further include a pet diet management section. The diet management section records the pet's dietary content and intake amount, and provides the data to the analysis section. For example, if the pet has an allergy to a particular food ingredient, the dietary content can be adjusted based on that information. Also, appropriate food amounts can be suggested according to the pet's weight and health condition. This allows for more comprehensive health management of pets.

[0062] The acquisition unit may include an acceleration sensor for measuring the amount of exercise of the pet. The acceleration sensor measures the movement of the pet in real time and collects data on the amount of exercise. For example, it is possible to record how much exercise the pet gets in a day and detect insufficient or excessive exercise. In addition, the amount of exercise data may be provided to the analysis unit to grasp the health condition of the pet in more detail. This makes it easier to manage the exercise of the pet.

[0063] The analysis unit can analyze the sleep patterns of pets. By collecting the sleep data of pets and providing it to the analysis unit, the quality and quantity of the pet's sleep can be evaluated. For example, if the pet is not getting enough sleep, the cause can be identified and measures to improve the condition can be proposed. In addition, the pet's health condition can be evaluated comprehensively based on the sleep data. This makes it possible to manage the sleep of pets.

[0064] The providing unit can suggest an appropriate exercise program based on the pet's health condition. Based on the data obtained from the analyzing unit, an exercise program is created according to the pet's physical condition and amount of exercise. For example, if the pet is not getting enough exercise, an appropriate amount of exercise can be suggested and an exercise program can be provided. Also, if the pet is overexercising, advice can be provided to adjust the amount of exercise. This allows for more effective management of the pet's exercise.

[0065] The collection unit can include an infrared sensor for non-contact measurement of the pet's body temperature. The infrared sensor measures the pet's body temperature in real time and collects the data. For example, if the pet has a fever, the information is provided to the analysis unit, allowing early detection of abnormalities. In addition, the pet's health condition can be evaluated comprehensively based on the body temperature data. This makes it easier to manage the pet's body temperature.

[0066] The process flow of the first embodiment will be briefly described below.

[0067] Step 1: The acquisition unit acquires the pet's gestures, movements, behavior, and cries. The acquisition unit can acquire the pet's gestures, movements, behavior, and cries, for example, using camera footage and voice recognition technology. The camera footage, for example, is used to record the pet's movements in detail using a high-resolution camera. The voice recognition technology, for example, is used to accurately acquire the pet's cries using a high-sensitivity microphone. Step 2: The analysis unit uses the generation AI to analyze the data acquired by the acquisition unit. The generation AI analyzes the physical and mental state of the pet using, for example, a text generation AI (e.g., LLM). The generation AI can learn the patterns of the pet's movements and cries and detect abnormalities. Step 3: The providing unit provides the analysis result obtained by the analysis unit to the owner. The providing unit can display the analysis result as a text message or a graph, for example. Step 4: The collection unit periodically collects data for the pet's health check. The collection unit can collect data, for example, daily, weekly, monthly, etc. Step 5: The diagnostic unit analyzes the data collected by the collection unit and detects abnormalities. For example, the diagnostic unit can detect abnormalities in the pet's body temperature or heart rate.

[0068] (Example 2) The pet health management system according to the embodiment of the present invention is a tool for capturing the gestures, movements, behavior, and cries of a pet as data using camera images and voice recognition, analyzing the current physical and mental state of the pet using generation AI, and improving communication with humans. This system can also be used for pet health checkups. For example, a camera and a microphone are used to record the gestures, movements, behavior, and cries of a pet in real time. Next, the recorded data is input into the generation AI to analyze the physical and mental state of the pet. Based on the analysis results, appropriate advice and information is provided to the pet owner. For example, if the pet is feeling stressed, the cause is identified and a method for reducing stress is proposed. In addition, as a pet health checkup, data is collected periodically, and if an abnormality is detected, it is possible to deal with it early. This tool makes it easier to manage the health of the pet and improves communication with the owner. As a result, the pet health management system can grasp the physical and mental state of the pet in detail and provide appropriate advice and information to the owner.

[0069] The pet health management system according to the embodiment includes an acquisition unit, an analysis unit, a provision unit, a collection unit, and a diagnosis unit. The acquisition unit acquires the gestures, movements, behavior, and cries of the pet. The acquisition unit can acquire the gestures, movements, behavior, and cries of the pet using, for example, a camera image and a voice recognition technology. The camera image records the pet's movements in detail using, for example, a high-resolution camera. The voice recognition technology accurately acquires the pet's cries using, for example, a high-sensitivity microphone. The analysis unit analyzes the data acquired by the acquisition unit using a generation AI. The generation AI analyzes the physical condition and mental state of the pet using, for example, a text generation AI (for example, LLM). The generation AI can learn the patterns of the pet's movements and cries and detect abnormalities. The provision unit provides the owner with the analysis results obtained by the analysis unit. The provision unit can display the analysis results as, for example, a text message or a graph. The collection unit periodically collects data for the pet's health check. The collection unit can collect data at a frequency of, for example, daily, weekly, or monthly. The diagnosis unit analyzes the data collected by the collection unit and detects abnormalities. For example, the diagnosis unit can detect abnormalities in the pet's body temperature or heart rate. This allows the pet health management system according to the embodiment to grasp the pet's physical and mental state in detail and provide appropriate advice and information to the pet owner.

[0070] The acquisition unit acquires the gestures, movements, behavior, and cries of the pet. The acquisition unit can acquire the gestures, movements, behavior, and cries of the pet using, for example, camera images and voice recognition technology. Specifically, a high-resolution camera can be used to record the pet's movements in detail, and minute changes in the pet's movements and facial expressions can be captured. The camera is installed in the pet's living space and can record images 24 hours a day. This allows the pet's behavior patterns and daily movements to be continuously monitored. In addition, a high-sensitivity microphone is used to accurately acquire the pet's cries. Voice recognition technology can analyze the frequency, volume, rhythm, etc. of the pet's cries and identify specific cry patterns. For example, it can detect cries when the pet is feeling anxious or stressed, or cries indicating poor health. Furthermore, the acquisition unit collects data on the pet's movements and cries in real time and transmits them to a central database. This allows the pet's behavior and condition to be always kept up to date. The acquisition unit can centrally manage these data and link with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis and diagnosis units. In addition, the frequency and accuracy of data collection can be adjusted to respond flexibly to specific situations and conditions. This allows the acquisition unit to collect data efficiently and effectively, improving the performance of the entire system.

[0071] The analysis unit uses the generation AI to analyze the data acquired by the acquisition unit. The generation AI analyzes the physical and mental states of the pet using, for example, a text generation AI (e.g., LLM). Specifically, the generation AI can learn the patterns of the pet's movements and cries and detect abnormalities. The generation AI models the pet's normal behavioral patterns based on a large amount of data and sets standards for detecting abnormal behavior and cries. For example, if the pet moves differently than normal or makes an abnormal cry, the generation AI can immediately detect the abnormality and issue a warning. Furthermore, the generation AI can analyze changes in the pet's behavior and cries in chronological order to grasp long-term trends and patterns. This makes it possible to detect changes in the pet's health and mental state early and take appropriate measures. The analysis unit evaluates the pet's physical and mental states in detail based on the analysis results obtained by the generation AI. For example, if the pet is feeling stressed or shows signs of poor health, the analysis unit can identify the cause and provide specific advice to the owner. In addition, the analysis unit can use past data and statistical information to analyze long-term trends in a pet's health and predict future risks, allowing the analysis unit to play an important role in pet health management and provide owners with reliable information.

[0072] The providing unit provides the owner with the analysis results obtained by the analysis unit. The providing unit can display the analysis results as text messages or graphs, for example. Specifically, the analysis results are notified to the owner's smartphone or computer, and the pet's health condition and behavioral patterns are visually displayed in an easy-to-understand manner. For example, information on the pet's physical condition and mental state is displayed as graphs or charts, allowing the owner to understand the pet's condition at a glance. In addition, specific advice and points of caution are provided as text messages, providing guidelines for the owner to take appropriate measures. Furthermore, the providing unit can update the analysis results in real time and provide the owner with the latest information. For example, if an abnormality is detected in the pet's behavior or cries, a notification is sent immediately, allowing the owner to respond quickly. In addition, the providing unit can collect feedback from the owner and use it to improve the accuracy of the analysis results and the content provided. For example, the owner can comment on or evaluate the provided information, thereby improving the performance of the entire system. As a result, the providing unit can provide reliable information to the owner and play an important role in supporting the health management of the pet.

[0073] The collection unit periodically collects data for the health check of the pet. The collection unit can collect data at a frequency of, for example, daily, weekly, or monthly. Specifically, the pet's biological data, such as body temperature, heart rate, and respiratory rate, are periodically measured and recorded. These data are important for continuously monitoring the health condition of the pet. The collection unit can collect data in real time using a wearable device attached to the pet. For example, a sensor attached to the pet's collar measures the body temperature and heart rate, and transmits the data to a central database via wireless communication. This allows the collection unit to always have the latest information on the pet's health condition. Furthermore, the collection unit can centrally manage the collected data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and can be accessed by the analysis unit and diagnosis unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system.

[0074] The diagnostic unit analyzes the data collected by the collection unit and detects abnormalities. The diagnostic unit can detect, for example, abnormalities in the pet's body temperature or heart rate. Specifically, based on the collected data, the diagnostic unit evaluates the pet's health condition in detail and identifies abnormal patterns or signs. For example, if the pet's body temperature is higher than normal or the heart rate is abnormally fast, the diagnostic unit can immediately detect the abnormality and issue a warning. Furthermore, the diagnostic unit can also use past data and statistical information to analyze long-term trends in the pet's health condition and predict future risks. In this way, the diagnostic unit can play an important role in pet health management and provide reliable information to the owner. If an abnormality is detected, the diagnostic unit provides the owner with specific advice and countermeasures. For example, if the pet's body temperature is high, the diagnostic unit recommends cooling methods or consulting a veterinarian. In addition, the diagnostic unit can identify the cause of the abnormality and propose measures to prevent recurrence. In this way, the diagnostic unit can play an important role in supporting pet health management and providing appropriate advice and information to the owner.

[0075] The acquisition unit can acquire the pet's gestures, movements, behavior, and cries using camera images and voice recognition. The camera images are recorded in detail using a high-resolution camera, for example. The resolution of the camera is high resolution, such as 1080p or 4K, and can accurately capture the pet's detailed movements. The camera is preferably installed in a place where the pet is active or rests, for example. The voice recognition technology accurately acquires the pet's cries using a high-sensitivity microphone, for example. The types of microphones include directional microphones and omnidirectional microphones, and can separate and acquire the pet's cries from surrounding noise. The voice recognition algorithm can analyze the pet's cries using deep learning technology, for example, and identify specific cries. As a result, the pet's gestures, movements, behavior, and cries can be accurately acquired using the camera images and voice recognition.

[0076] The analysis unit can analyze the data acquired by the generation AI and analyze the physical condition or mental condition of the pet. The generation AI can, for example, use a text generation AI (for example, LLM) to analyze the physical condition or mental condition of the pet. The generation AI can learn the patterns of the pet's movements and cries and detect abnormalities. The generation AI can, for example, receive the pet's movement data as an input and analyze the pet's physical condition based on the data. The generation AI can, for example, detect abnormal movement patterns from the pet's movement data and determine whether there is an abnormality in the pet's physical condition. The generation AI can also receive the pet's cry data as an input and analyze the pet's mental state based on the data. The generation AI can, for example, detect signs of stress or anxiety from the pet's cry data and determine whether there is an abnormality in the pet's mental state. As a result, the use of the generation AI can analyze the physical condition or mental state of the pet with high accuracy.

[0077] The providing unit can provide the owner with appropriate advice or information based on the analysis result. The providing unit can display the analysis result as a text message or a graph, for example. The providing unit provides specific advice to the owner based on the analysis result. For example, if the pet is stressed, the providing unit can identify the cause of the stress and suggest a method for reducing the stress. For example, if the cause of the pet's stress is a change in the environment, the providing unit can provide the owner with specific advice to improve the environment. The providing unit can also provide information on health management of the pet. For example, if an abnormality is detected in the pet's physical condition, the providing unit can advise the owner to visit a veterinary clinic early. This improves communication with the pet by providing the owner with appropriate advice or information based on the analysis result.

[0078] The collection unit can periodically collect data for the health check of the pet. The collection unit can collect data, for example, daily, weekly, monthly, etc. The collection unit periodically collects data in order to continuously monitor the health condition of the pet. For example, the collection unit can quickly detect changes in the physical condition of the pet by collecting data daily. Furthermore, the collection unit can grasp long-term trends in the health condition of the pet by collecting data weekly. Furthermore, the collection unit can periodically check the results of the health check of the pet by collecting data monthly. In this way, the health condition of the pet can be continuously monitored by collecting data periodically.

[0079] The diagnosis unit can analyze the collected data and detect an abnormality. The diagnosis unit can detect, for example, an abnormality in the pet's body temperature or heart rate. The diagnosis unit analyzes the pet's health condition based on the data collected by the collection unit. For example, the diagnosis unit can analyze the pet's body temperature data and detect an abnormality when the body temperature exceeds the normal range. The diagnosis unit can also analyze the pet's heart rate data and detect an abnormality when the heart rate is abnormally high or low. Furthermore, the diagnosis unit can analyze the pet's behavior data and detect an abnormal behavior pattern. For example, the diagnosis unit can detect an abnormality when the pet exhibits behavior that is different from normal. In this way, by analyzing the collected data and detecting an abnormality, the pet's health problem can be addressed early.

[0080] The acquisition unit can estimate the pet's emotion and adjust the timing of acquiring gestures and actions based on the estimated pet's emotion. For example, when the pet is excited, the acquisition unit increases the frame rate of the camera to acquire the actions in detail. The acquisition unit estimates the pet's emotion and adjusts the timing of acquisition based on the emotion. For example, when the pet is relaxed, the pet's gestures can be acquired at a normal frame rate. Also, when the pet is stressed, a specific action can be acquired with a focus. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows more accurate data to be acquired by adjusting the timing of acquisition based on the pet's emotion.

[0081] The acquisition unit can analyze the past behavior history of the pet and select an appropriate acquisition method. For example, the acquisition unit preferentially acquires behaviors that the pet frequently performed in the past. The acquisition unit analyzes the past behavior history of the pet and selects the optimal acquisition method based on the history. For example, based on the past behavior patterns of the pet, data can be acquired with a focus on a specific time period. In addition, abnormal behaviors can be detected from the past behavior history of the pet and the behaviors can be acquired with a focus. In this way, the optimal acquisition method can be selected by analyzing the past behavior history of the pet.

[0082] The acquisition unit can perform filtering based on the pet's current environment or situation at the time of acquisition. For example, when the pet is outdoors, the acquisition unit filters the environmental sounds to acquire the pet's cries. The acquisition unit performs filtering based on the pet's current environment and situation. For example, when the pet is indoors, the acquisition unit can filter the background movements to acquire the pet's behavior. Also, when the pet is with other animals, the acquisition unit can filter and acquire the behavior of the specific animal. This allows data with less noise to be acquired by filtering based on the pet's current environment and situation.

[0083] The acquisition unit can estimate the pet's emotion and determine the priority of data to be acquired based on the estimated pet's emotion. For example, when the pet is excited, the acquisition unit preferentially acquires motion data. The acquisition unit estimates the pet's emotion and determines the priority of data to be acquired based on the emotion. For example, when the pet is relaxed, the acquisition unit can preferentially acquire barking data. Also, when the pet is stressed, the acquisition unit can preferentially acquire behavior data. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, important data can be preferentially acquired by determining the priority of data based on the pet's emotion.

[0084] The acquisition unit can preferentially acquire highly relevant data based on the geographical location information of the pet at the time of acquisition. For example, when the pet is in a park, the acquisition unit preferentially acquires playing behavior. The acquisition unit preferentially acquires highly relevant data taking into account the geographical location information of the pet. For example, when the pet is in a veterinary clinic, signs of stress can be preferentially acquired. Also, when the pet is at home, relaxing behavior can be preferentially acquired. In this way, highly relevant data can be preferentially acquired by taking into account the geographical location information of the pet.

[0085] At the time of acquisition, the acquisition unit can analyze the social media activity of the pet owner and acquire related data. For example, if the owner frequently posts photos of the pet, the acquisition unit acquires data by referring to the status of the photos. The acquisition unit analyzes the social media activity of the pet owner and acquires related data based on the activity. For example, if the owner posts about the pet's health, the acquisition unit can acquire data by referring to the content of the posts. Also, if the owner posts about the pet's behavior, the acquisition unit can acquire data with a focus on the behavior. In this way, related data can be acquired by analyzing the social media activity of the pet owner.

[0086] The analysis unit can estimate the pet's emotion and adjust the expression method of the analysis based on the estimated pet's emotion. For example, if the pet is relaxed, the analysis unit displays the analysis result in a calm expression. The analysis unit estimates the pet's emotion and adjusts the expression method of the analysis based on the emotion. For example, if the pet is excited, a detailed analysis result can be displayed. Also, if the pet is stressed, an analysis result that identifies the cause of stress can be displayed. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, a more appropriate analysis result can be provided by adjusting the expression method of the analysis based on the pet's emotion.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit adjusts the level of detail of the analysis based on the importance of the data. For example, a simplified analysis can be performed on general data. Furthermore, a particularly detailed analysis can be performed on abnormal data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0088] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a motion analysis algorithm to motion data. The analysis unit applies different analysis algorithms depending on the category of data. For example, a sound analysis algorithm can be applied to bird cry data. Also, a behavior analysis algorithm can be applied to behavior data. In this way, highly accurate analysis is possible by applying different analysis algorithms depending on the category of data.

[0089] The analysis unit can estimate the pet's emotion and adjust the length of the analysis based on the estimated pet's emotion. For example, when the pet is relaxed, the analysis unit performs a detailed analysis. The analysis unit estimates the pet's emotion and adjusts the length of the analysis based on the emotion. For example, when the pet is excited, an analysis that covers the main points can be performed in a short time. Also, when the pet is stressed, a detailed analysis can be performed to identify the cause of the stress. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, an appropriate analysis result can be provided by adjusting the length of the analysis based on the pet's emotion.

[0090] During analysis, the analysis unit can determine the analysis priority based on the time of data acquisition. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit determines the analysis priority based on the time of data acquisition. For example, analysis can be performed while referring to past data. Also, data acquired during a specific period can be focused on for analysis. In this way, by determining the analysis priority based on the time of data acquisition, the most recent data can be prioritized for analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit adjusts the order of analysis based on the relevance of the data. For example, data with low relevance can be postponed. In addition, the analysis order can be dynamically adjusted based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data.

[0092] The providing unit can estimate the pet's emotion and adjust the way of expressing the information to be provided based on the estimated pet's emotion. For example, when the pet is relaxed, the providing unit provides information in a calm expression. The providing unit estimates the pet's emotion and adjusts the way of expressing the information to be provided based on the emotion. For example, when the pet is excited, detailed information can be provided. Also, when the pet is stressed, information identifying the cause of the stress can be provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, by adjusting the way of expressing the information based on the pet's emotion, it is possible to provide information that is easy for the owner to understand.

[0093] The providing unit can adjust the level of detail of the provided information based on the importance of the analysis result at the time of providing the information. For example, the providing unit provides detailed information for important analysis results. The providing unit adjusts the level of detail of the provided information based on the importance of the analysis result. For example, simple information can be provided for general analysis results. Furthermore, particularly detailed information can be provided for abnormal analysis results. This enables efficient information provision by adjusting the level of detail of the provided information based on the importance of the analysis results.

[0094] The providing unit can apply different providing methods depending on the category of the analysis result when providing the information. For example, the providing unit provides information about the movement for the movement analysis result. The providing unit applies different providing methods depending on the category of the analysis result. For example, information about the cry can be provided for the cry analysis result. Also, information about the behavior can be provided for the behavior analysis result. In this way, by applying different providing methods depending on the category of the analysis result, it becomes possible to provide appropriate information.

[0095] The providing unit can estimate the pet's emotion and determine the priority of information to be provided based on the estimated pet's emotion. For example, when the pet is relaxed, the providing unit provides general information preferentially. The providing unit estimates the pet's emotion and determines the priority of information to be provided based on the emotion. For example, when the pet is excited, detailed information can be provided preferentially. Also, when the pet is stressed, information identifying the cause of the stress can be provided preferentially. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, important information can be provided preferentially by determining the priority of information based on the pet's emotion.

[0096] At the time of provision, the providing unit can determine the priority of provision based on the time when the analysis results were acquired. The providing unit, for example, provides the latest analysis results preferentially. The providing unit determines the priority of provision based on the time when the analysis results were acquired. For example, information can be provided while referring to past analysis results. Also, analysis results acquired during a specific period can be provided with a focus. In this way, by determining the priority of provision based on the time when the analysis results were acquired, the latest information can be provided preferentially.

[0097] The providing unit can adjust the order of provision based on the relevance of the analysis results at the time of provision. For example, the providing unit provides analysis results with high relevance preferentially. The providing unit adjusts the order of provision based on the relevance of the analysis results. For example, analysis results with low relevance can be postponed. In addition, the order of provision can be dynamically adjusted based on the relevance of the analysis results. This enables efficient information provision by adjusting the order of provision based on the relevance of the analysis results.

[0098] The collection unit can estimate the pet's emotion and adjust the timing of data collection based on the estimated pet's emotion. For example, when the pet is relaxed, the collection unit collects data at a normal timing. The collection unit estimates the pet's emotion and adjusts the timing of collection based on the emotion. For example, when the pet is excited, data can be collected frequently. Also, when the pet is stressed, data can be collected at a specific timing. The estimation of emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, data can be collected at an appropriate timing by adjusting the collection timing based on the pet's emotion.

[0099] The collection unit can select an appropriate collection method based on the pet's past health data at the time of collection. For example, the collection unit selects the optimal collection method for a specific health condition based on the pet's past health data. The collection unit refers to the pet's past health data and selects the optimal collection method based on the data. For example, the collection unit can refer to the pet's past health data and focus on collecting data when an abnormality is detected. In addition, the pet's past health data can be analyzed to determine the optimal collection timing. In this way, the optimal collection method can be selected by referring to the pet's past health data.

[0100] The collection unit can adjust the level of detail of the collection based on the pet's current health condition at the time of collection. For example, if the pet is healthy, the collection unit collects data at a normal level of detail. The collection unit adjusts the level of detail of the collection based on the pet's current health condition. For example, if the pet is in poor health, detailed data can be collected. Also, if the pet has a specific health problem, data related to the problem can be collected with a focus. In this way, appropriate data can be collected by adjusting the level of detail of the collection based on the pet's current health condition.

[0101] The collection unit can estimate the pet's emotion and determine the priority of data to be collected based on the estimated pet's emotion. For example, when the pet is relaxed, the collection unit prioritizes the collection of general health data. The collection unit estimates the pet's emotion and determines the priority of data to be collected based on the emotion. For example, when the pet is excited, the collection unit can prioritize the collection of behavioral data. Also, when the pet is stressed, the collection unit can prioritize the collection of data related to the cause of stress. The estimation of emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. As a result, important data can be preferentially collected by determining the priority of data based on the pet's emotion.

[0102] The collection unit can prioritize collecting highly relevant data based on the geographical location information of the pet at the time of collection. For example, when the pet is in a park, the collection unit prioritizes collecting play behavior data. The collection unit prioritizes collecting highly relevant data taking into account the geographical location information of the pet. For example, when the pet is in a veterinary clinic, data related to the health condition can be prioritized. Also, when the pet is at home, relaxing behavior data can be prioritized. In this way, highly relevant data can be prioritized by taking into account the geographical location information of the pet.

[0103] At the time of collection, the collection unit can analyze the social media activity of the pet owner and collect related data. For example, if the owner posts about the pet's health, the collection unit collects data with reference to the content. The collection unit analyzes the social media activity of the pet owner and collects related data based on the activity. For example, if the owner posts about the pet's behavior, the collection unit can focus on collecting the behavior. Also, if the owner frequently posts photos of the pet, the collection unit can collect data with reference to the status of the photos. In this way, related data can be collected by analyzing the social media activity of the pet owner.

[0104] The diagnosis unit can estimate the pet's emotion and adjust the diagnosis method based on the estimated pet's emotion. For example, when the pet is relaxed, the diagnosis unit applies a normal diagnosis method. The diagnosis unit estimates the pet's emotion and adjusts the diagnosis method based on the emotion. For example, when the pet is excited, a detailed diagnosis method can be applied. Also, when the pet is stressed, a diagnosis method for identifying the cause of stress can be applied. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows for an appropriate diagnosis by adjusting the diagnosis method based on the pet's emotion.

[0105] The diagnostic unit can select an appropriate diagnostic method based on the pet's past health data at the time of diagnosis. For example, the diagnostic unit selects the optimal diagnostic method for a specific health condition based on the pet's past health data. The diagnostic unit refers to the pet's past health data and selects the optimal diagnostic method based on the data. For example, by referring to the pet's past health data, the diagnostic unit can focus on diagnosis when an abnormality is detected. In addition, the pet's past health data can be analyzed to determine the optimal timing for diagnosis. In this way, the optimal diagnostic method can be selected by referring to the pet's past health data.

[0106] The diagnosis unit can adjust the level of detail of the diagnosis based on the pet's current health condition at the time of diagnosis. For example, if the pet is healthy, the diagnosis unit performs a diagnosis with a normal level of detail. The diagnosis unit adjusts the level of detail of the diagnosis based on the pet's current health condition. For example, if the pet is in poor health, a detailed diagnosis can be performed. Also, if the pet has a specific health problem, a detailed diagnosis related to the problem can be performed. This allows for an appropriate diagnosis by adjusting the level of detail of the diagnosis based on the pet's current health condition.

[0107] The diagnosis unit can estimate the pet's emotion and determine the priority of diagnosis based on the estimated pet's emotion. For example, if the pet is relaxed, the diagnosis unit prioritizes a general health check. The diagnosis unit estimates the pet's emotion and determines the priority of diagnosis based on the emotion. For example, if the pet is excited, a diagnosis related to the behavior can be prioritized. Also, if the pet is stressed, a diagnosis related to the cause of stress can be prioritized. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, important diagnoses can be prioritized by determining the priority of diagnosis based on the pet's emotion.

[0108] The diagnosis unit can select an appropriate diagnosis method based on the geographical location information of the pet at the time of diagnosis. For example, when the pet is in a park, the diagnosis unit performs a diagnosis related to play behavior. The diagnosis unit selects the optimal diagnosis method in consideration of the geographical location information of the pet. For example, when the pet is at a veterinary clinic, a diagnosis related to a health condition can be performed. Also, when the pet is at home, a diagnosis related to a relaxing behavior can be performed. In this way, the optimal diagnosis method can be selected by considering the geographical location information of the pet.

[0109] The diagnostic unit can analyze the social media activity of the pet owner at the time of diagnosis and provide related diagnostic information. For example, if the owner posts about the pet's health, the diagnostic unit makes a diagnosis with reference to the content of the posts. The diagnostic unit analyzes the social media activity of the pet owner and provides related diagnostic information based on the activities. For example, if the owner posts about the pet's behavior, the diagnosis can be made with reference to the behavior. Also, if the owner frequently posts photos of the pet, the diagnosis can be made with reference to the status of the photos. In this way, by analyzing the social media activity of the pet owner, related diagnostic information can be provided.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The pet health management system can further include a pet diet management section. The diet management section records the pet's dietary content and intake amount, and provides the data to the analysis section. For example, if the pet has an allergy to a particular food ingredient, the dietary content can be adjusted based on that information. Also, appropriate food amounts can be suggested according to the pet's weight and health condition. This allows for more comprehensive health management of pets.

[0112] The acquisition unit may include an acceleration sensor for measuring the amount of exercise of the pet. The acceleration sensor measures the movement of the pet in real time and collects data on the amount of exercise. For example, it is possible to record how much exercise the pet gets in a day and detect insufficient or excessive exercise. In addition, the amount of exercise data may be provided to the analysis unit to grasp the health condition of the pet in more detail. This makes it easier to manage the exercise of the pet.

[0113] The analysis unit can analyze the sleep patterns of pets. By collecting the sleep data of pets and providing it to the analysis unit, the quality and quantity of the pet's sleep can be evaluated. For example, if the pet is not getting enough sleep, the cause can be identified and measures to improve the condition can be proposed. In addition, the pet's health condition can be evaluated comprehensively based on the sleep data. This makes it possible to manage the sleep of pets.

[0114] The providing unit can suggest an appropriate exercise program based on the pet's health condition. Based on the data obtained from the analyzing unit, an exercise program is created according to the pet's physical condition and amount of exercise. For example, if the pet is not getting enough exercise, an appropriate amount of exercise can be suggested and an exercise program can be provided. Also, if the pet is overexercising, advice can be provided to adjust the amount of exercise. This allows for more effective management of the pet's exercise.

[0115] The collection unit can include an infrared sensor for non-contact measurement of the pet's body temperature. The infrared sensor measures the pet's body temperature in real time and collects the data. For example, if the pet has a fever, the information is provided to the analysis unit, allowing early detection of abnormalities. In addition, the pet's health condition can be evaluated comprehensively based on the body temperature data. This makes it easier to manage the pet's body temperature.

[0116] The acquisition unit can estimate the pet's emotion and select the type of data to acquire based on the estimated pet's emotion. For example, if the pet is excited, movement data can be acquired preferentially. If the pet is relaxed, barking data can be acquired preferentially. The emotion estimation is realized using an emotion engine or a generative AI. This allows more appropriate data to be acquired by selecting the type of data based on the pet's emotion.

[0117] The analysis unit can estimate the pet's emotions and determine the priority of the analysis based on the estimated pet's emotions. For example, if the pet is stressed, an analysis to identify the cause of the stress can be prioritized. If the pet is relaxed, an analysis of the pet's general health condition can be performed. The estimation of emotions is achieved using an emotion engine or generative AI. This allows important analyses to be prioritized by determining the priority of the analysis based on the pet's emotions.

[0118] The providing unit can estimate the pet's emotion and adjust the format of the information to be provided based on the estimated pet's emotion. For example, if the pet is excited, detailed information can be provided. If the pet is relaxed, concise information can be provided. The emotion estimation is realized using an emotion engine or a generation AI. This makes it possible to provide information that is easy for the owner to understand by adjusting the format of the information based on the pet's emotion.

[0119] The collection unit can estimate the pet's emotion and adjust the frequency of data collection based on the estimated pet's emotion. For example, if the pet is stressed, data can be collected frequently. If the pet is relaxed, data can be collected at a normal frequency. The emotion estimation is realized using an emotion engine or generative AI. This allows data to be collected at an appropriate time by adjusting the frequency of data collection based on the pet's emotion.

[0120] The diagnosis unit can estimate the pet's emotions and adjust the level of detail of the diagnosis based on the estimated pet's emotions. For example, if the pet is excited, a detailed diagnosis can be made. If the pet is relaxed, a simplified diagnosis can be made. The estimation of emotions is realized using an emotion engine or generative AI. This allows for an appropriate diagnosis by adjusting the level of detail of the diagnosis based on the pet's emotions.

[0121] The process flow of the second embodiment will be briefly described below.

[0122] Step 1: The acquisition unit acquires the pet's gestures, movements, behavior, and cries. The acquisition unit can acquire the pet's gestures, movements, behavior, and cries, for example, using camera footage and voice recognition technology. The camera footage, for example, is used to record the pet's movements in detail using a high-resolution camera. The voice recognition technology, for example, is used to accurately acquire the pet's cries using a high-sensitivity microphone. Step 2: The analysis unit uses the generation AI to analyze the data acquired by the acquisition unit. The generation AI analyzes the physical and mental state of the pet using, for example, a text generation AI (e.g., LLM). The generation AI can learn the patterns of the pet's movements and cries and detect abnormalities. Step 3: The providing unit provides the analysis result obtained by the analysis unit to the owner. The providing unit can display the analysis result as a text message or a graph, for example. Step 4: The collection unit periodically collects data for the pet's health check. The collection unit can collect data, for example, daily, weekly, monthly, etc. Step 5: The diagnostic unit analyzes the data collected by the collection unit and detects abnormalities. For example, the diagnostic unit can detect abnormalities in the pet's body temperature or heart rate.

[0123] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0124] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0125] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, provision unit, collection unit, and diagnosis unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires the gestures, movements, behavior, and cries of the pet using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the acquired data using the generation AI. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the analysis result to the owner. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and periodically collects data. The diagnosis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to detect abnormalities. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0130] 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.

[0131] 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 the voice according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0133] 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 performed in a secure state.

[0134] Fig. 4 shows an example of 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0139] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0140] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0141] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, provision unit, collection unit, and diagnosis unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires the gestures, movements, behavior, and cries of the pet using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the acquired data using the generation AI. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the analysis result to the owner. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and periodically collects data. The diagnosis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to detect abnormalities. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0146] 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.

[0147] 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 the voice according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0149] 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 performed in a secure state.

[0150] 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.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0153] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0154] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0155] 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 voice indicating a user input for 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.

[0156] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0157] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, provision unit, collection unit, and diagnosis unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit acquires the gestures, movements, behavior, and cries of the pet using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the acquired data using the generation AI. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the analysis result to the owner. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and periodically collects data. The diagnosis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to detect abnormalities. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0162] 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.

[0163] 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 the voice according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0165] 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 performed in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0167] 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.

[0168] 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.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0170] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0171] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0172] 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 a voice indicating a user input for 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.

[0173] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0174] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0175] Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, provision unit, collection unit, and diagnosis unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires the gestures, movements, behavior, and cries of the pet using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the acquired data using the generation AI. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the analysis result to the owner. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and periodically collects data. The diagnosis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to detect abnormalities. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0176] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0177] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0179] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0180] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0181] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0183] 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.

[0184] 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 Universal Serial Bus (USB) 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.

[0185] In addition, 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 upon request from the data processing device 12.

[0186] 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.

[0187] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0188] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0189] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0190] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0192] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0194] (Appendix 1) An acquisition unit that acquires the gestures, movements, actions, and cries of a pet; an analysis unit that analyzes the data acquired by the acquisition unit; a providing unit that provides an analysis result obtained by the analysis unit; A collection unit for collecting data for pet health checks; A diagnostic unit that analyzes the data collected by the collection unit and detects an abnormality. A system characterized by: (Appendix 2) The acquisition unit is Capture your pet's gestures, movements, behavior, and cries using camera footage and voice recognition 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze the data acquired by the generative AI to analyze the physical and mental state of the pet 2. The system of claim 1, (Appendix 4) The providing unit is Providing appropriate advice or information to owners based on the analysis results 2. The system of claim 1, (Appendix 5) The collecting unit includes: Collect data regularly for pet health checks 2. The system of claim 1, (Appendix 6) The diagnosis unit includes: Analyze collected data and detect anomalies 2. The system of claim 1, (Appendix 7) The acquisition unit is Estimate your pet's emotions and adjust the timing of its gestures and movements based on the estimated emotions. 2. The system of claim 1, (Appendix 8) The acquisition unit is Analyze your pet's past behavioral history and choose the appropriate acquisition method 2. The system of claim 1, (Appendix 9) The acquisition unit is Upon retrieval, filtering occurs based on the pet's current environment or situation 2. The system of claim 1, (Appendix 10) The acquisition unit is Estimate the pet's emotions and prioritize the data to be acquired based on the estimated pet's emotions. 2. The system of claim 1, (Appendix 11) The acquisition unit is At the time of capture, the most relevant data is prioritized based on the pet's geolocation 2. The system of claim 1, (Appendix 12) The acquisition unit is Upon capture, we analyze the social media activity of pet owners to capture relevant data. 2. The system of claim 1, (Appendix 13) The analysis unit is Estimate the pet's emotions and adjust the way the analysis is presented based on the estimated pet's emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail based on the importance of the data 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1, (Appendix 16) The analysis unit is Estimate the pet's emotion and adjust the length of the analysis based on the estimated pet's emotion 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when the data was obtained 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 19) The providing unit is Estimate the pet's emotions and adjust the way information is presented based on the estimated pet's emotions 2. The system of claim 1, (Appendix 20) The providing unit is At the time of provision, the level of detail provided will be adjusted based on the importance of the analysis results. 2. The system of claim 1, (Appendix 21) The providing unit is When providing, apply different provision methods depending on the category of analysis results. 2. The system of claim 1, (Appendix 22) The providing unit is Estimate the pet's emotions and prioritize the information to be provided based on the estimated pet's emotions 2. The system of claim 1, (Appendix 23) The providing unit is When provided, the priority of provision will be determined based on when the analysis results were obtained. 2. The system of claim 1, (Appendix 24) The providing unit is At the time of serving, adjust the order of serving based on the relevance of the analysis results 2. The system of claim 1, (Appendix 25) The collecting unit includes: Estimate the pet's emotions and adjust the timing of data collection based on the estimated pet's emotions 2. The system of claim 1, (Appendix 26) The collecting unit includes: At the time of collection, select the appropriate collection method based on the pet's past health data. 2. The system of claim 1, (Appendix 27) The collecting unit includes: When harvesting, adjust the level of detail of the harvest based on the current health of the pet 2. The system of claim 1, (Appendix 28) The collecting unit includes: Estimate the emotions of your pet and prioritize the data you collect based on the emotions you infer 2. The system of claim 1, (Appendix 29) The collecting unit includes: At the time of collection, we prioritize the most relevant data based on your pet's geolocation 2. The system of claim 1, (Appendix 30) The collecting unit includes: At the time of collection, we analyze the social media activity of pet owners and collect relevant data 2. The system of claim 1, (Appendix 31) The diagnosis unit includes: Estimate the pet's emotions and adjust the diagnostic method based on the estimated pet's emotions 2. The system of claim 1, (Appendix 32) The diagnosis unit includes: At the time of diagnosis, select the appropriate diagnostic method based on your pet's past health data 2. The system of claim 1, (Appendix 33) The diagnosis unit includes: During diagnostics, adjust diagnostic detail based on your pet's current health status 2. The system of claim 1, (Appendix 34) The diagnosis unit includes: Estimate the emotions of your pet and prioritize diagnoses based on the estimated emotions of your pet 2. The system of claim 1, (Appendix 35) The diagnosis unit includes: At the time of diagnosis, choose the appropriate diagnostic method based on the pet's geographic location 2. The system of claim 1, (Appendix 36) The diagnosis unit includes: At the time of diagnosis, analyze pet owners' social media activity to provide relevant diagnostic information 2. The system of claim 1, [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An acquisition unit that acquires the gestures, movements, actions, and cries of a pet; an analysis unit that analyzes the data acquired by the acquisition unit; a providing unit that provides an analysis result obtained by the analysis unit; A collection unit for collecting data for pet health checks; A diagnostic unit that analyzes the data collected by the collection unit and detects an abnormality. A system characterized by:

2. The acquisition unit is Capture your pet's gestures, movements, behavior, and cries using camera footage and voice recognition 2. The system of claim 1 .

3. The analysis unit is Analyze the data acquired by the generated AI to analyze the physical or mental state of the pet.

2. The system of claim 1 .

4. The providing unit is Providing appropriate advice or information to owners based on the analysis results 2. The system of claim 1 .

5. The collecting unit includes: Collect data regularly for pet health checks 2. The system of claim 1 .

6. The acquisition unit is Estimate your pet's emotions and adjust the timing of its gestures and movements based on the estimated emotions.

2. The system of claim 1 .

7. The acquisition unit is Analyze your pet's past behavioral history and choose the appropriate acquisition method 2. The system of claim 1 .

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