System

Abstract

To provide a system which accurately grasps a body condition and feelings of a pet, and proposes an appropriate breeding environment.SOLUTION: A system includes a collection part, an analysis part, a visualization part, and a proposal part. The collection part collects data of a pet's voice, expression, movement, body temperature, appetite, and feces. The analysis part analyzes the data collected by the collection part, and determines the body condition and feelings of the pet. The visualization part visualizes an analysis result obtained by the analysis part. The proposal part proposes an appropriate breeding environment on the basis of the information visualized by the visualization part.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 was difficult to accurately grasp a pet's physical condition and mood and to suggest an appropriate environment for keeping the pet.

[0005] The system according to the embodiment aims to accurately grasp the physical condition and mood of a pet and to propose an appropriate environment for keeping the pet. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a visualization unit, and a suggestion unit. The collection unit collects data on the pet's voice, facial expression, movement, body temperature, appetite, and feces. The analysis unit analyzes the data collected by the collection unit and determines the pet's physical condition and mood. The visualization unit visualizes the analysis results obtained by the analysis unit. The suggestion unit suggests an appropriate pet environment based on the information visualized by the visualization unit. Effect of the Invention

[0007] The system according to the embodiment can accurately grasp the physical condition and mood of a pet and can suggest an appropriate environment for keeping the pet. [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 care system according to the embodiment of the present invention is a system that visualizes the physical condition and feelings of a pet as data and proposes an optimal environment for keeping the pet. This pet care system collects data such as the pet's voice, facial expression, movement, body temperature, appetite, and feces, and has AI learn and analyze the data to determine and visualize the physical condition and feelings of the pet. Furthermore, by combining data on other animals and data on the home environment and the keeping environment, the system proposes optimal food and interaction for the pet. For example, the pet's voice is collected by a microphone, its facial expression and movement are photographed by a camera, and its body temperature is measured by a sensor. In addition, the pet's health condition can be understood by recording the appetite and feces state. Next, the collected data is made to learn the AI, and the physical condition and feelings of the pet are analyzed. The AI ​​analyzes the collected data and determines the physical condition and feelings of the pet. For example, it is possible to determine whether the pet is happy or sad from the tone of the pet's voice and changes in its facial expression. In addition, the pet's health condition can be understood from changes in its movement and body temperature. Based on the analysis results, the physical condition and feelings of the pet are visualized and provided to the owner. For example, the physical condition and feelings of the pet are displayed as graphs and icons, so that the owner can understand the pet's condition at a glance. In addition, by providing advice according to the physical condition and feelings of the pet, the owner can take appropriate measures. Furthermore, by combining data on other animals and data on the home and rearing environments, the system proposes the optimal diet and interaction for the pet. For example, data on the same type of pet can be compared to propose the optimal diet and amount of exercise. In addition, by providing advice according to the home and rearing environments, the system can provide an environment in which the pet can live comfortably. This makes the physical condition and feelings of the pet visible as data, making it easier for the owner to understand the condition of the pet. In addition, by proposing the optimal rearing environment for the pet, the system can provide an environment in which the pet can live happily. For example, when the pet becomes unwell, the system can detect abnormalities early and take appropriate measures. In addition, when the pet feels stressed, the pet's stress can be reduced by providing appropriate care. In this way, the pet care system can visualize the physical condition and feelings of the pet as data, making it easier for the owner to understand the condition of the pet.

[0029] The pet care system according to the embodiment includes a collection unit, an analysis unit, a visualization unit, and a proposal unit. The collection unit collects data such as the pet's voice, facial expression, movement, body temperature, appetite, and feces. The collection unit collects various data of the pet in real time using, for example, a sensor or a camera. For example, the collection unit collects the pet's voice with a microphone, captures the pet's facial expression and movement with a camera, and measures the pet's body temperature with a sensor. The collection unit can also record the pet's appetite and feces state. For example, the collection unit collects the frequency of the pet's voice, captures changes in facial expression with a camera, and analyzes the movement pattern. Furthermore, the collection unit can measure the pet's body temperature with a sensor and collect data based on an evaluation criterion for appetite. The analysis unit analyzes the data collected by the collection unit and determines the pet's physical condition and feelings. The analysis unit analyzes the collected data using, for example, AI. For example, the analysis unit can determine whether the pet is happy or sad from the tone of the pet's voice and changes in facial expression. The analysis unit can also grasp the pet's health condition from changes in movement and body temperature. For example, the analysis unit uses AI to analyze the tone of the pet's voice and determine the emotions. The analysis unit can also use AI to analyze changes in the pet's facial expressions and determine the emotions. Furthermore, the analysis unit can use AI to analyze the pet's movements and changes in body temperature to grasp the health condition. The visualization unit visualizes the analysis results obtained by the analysis unit. For example, the visualization unit displays the pet's physical condition and feelings using graphs and icons, so that the owner can grasp the pet's condition at a glance. For example, the visualization unit displays the pet's physical condition and feelings using line graphs and bar graphs. The visualization unit can also display using icons representing the pet's emotions. Furthermore, the visualization unit can display the pet's physical condition and feelings in real time. The suggestion unit suggests an optimal rearing environment based on the information visualized by the visualization unit. The suggestion unit suggests the optimal diet and interaction for the pet, for example, by combining data on other animals, the home environment, and the rearing environment. For example, the suggestion unit compares data on the same type of pet and suggests the optimal diet and amount of exercise. The suggestion unit can also provide advice according to the home environment and the rearing environment.Furthermore, the suggestion unit can provide advice according to the physical condition and mood of the pet. As a result, the pet care system according to the embodiment can visualize the physical condition and mood of the pet as data, making it easier for the owner to understand the condition of the pet. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make a suggestion using an AI model that inputs data about the pet and suggests an optimal pet care environment.

[0030] The collection unit collects data such as the pet's voice, facial expression, movement, body temperature, appetite, and feces. The collection unit collects various data on the pet in real time using, for example, a sensor or a camera. Specifically, the pet's voice is collected by a microphone, and the frequency, volume, and tone of the voice are analyzed to detect changes in the pet's emotions and physical condition. Facial expressions and movements are captured using a high-resolution camera, and detailed data is obtained using face recognition technology and motion analysis algorithms. For example, the camera captures subtle changes in the pet's facial expression and infers emotions from the movements of the ears, eyes, and mouth. In addition, to analyze movement patterns, the pet's movements such as walking, running, and jumping are recorded, and abnormal movements and behaviors that are different from normal are detected. Body temperature is measured using a non-contact infrared sensor to determine whether the pet is feeling stressed or unwell. Appetite and feces condition data are collected using a feeder that automatically records the pet's food amount and meal time, and a sensor that detects the condition of the feces. This allows the collection unit to grasp the pet's health condition and emotions from multiple angles and collect data in real time. Furthermore, the collection department can centrally manage these data and link them with other systems and departments as necessary. For example, the collected data can be stored on a cloud server and accessed by the analysis department and visualization department. 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 department to collect data efficiently and effectively and improve the performance of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit to determine the physical condition and feelings of the pet. The analysis unit analyzes the collected data using, for example, AI. Specifically, it is possible to determine whether the pet is happy or sad from the tone of the pet's voice and changes in facial expression. AI uses voice recognition technology to analyze the pet's voice and detect changes in frequency, volume, and tone. For example, if the pet's voice is higher, it may be happy, and if it is lower, it may be anxious or stressed. In addition, facial expression recognition technology is used to analyze subtle changes in the pet's face and determine emotions. For example, the pet's emotions can be inferred from the position of the ears, the degree of opening of the eyes, the shape of the mouth, etc. In addition, the pet's health condition can be understood from changes in movement and body temperature. A motion analysis algorithm is used to analyze the pet's movements such as walking, running, and jumping, and detect abnormal movements or behavior that is different from normal. For example, if the pet moves more slowly than usual or shows abnormal movements, it may be a sign of poor health or stress. Changes in body temperature are measured using a non-contact infrared sensor to determine whether the pet is stressed or in poor health. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the physical condition and feelings of the pet in real time. Furthermore, the analysis unit can also perform long-term risk assessment and trend analysis using past data and statistical information. For example, based on past data, it can predict fluctuations in the pet's behavioral patterns and health condition during specific seasons and times of the day, and plan future countermeasures. In addition, the analysis unit can use an anomaly detection algorithm to detect unusual patterns and abnormal data and issue an early warning. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The visualization unit visualizes the analysis results obtained by the analysis unit. For example, the visualization unit displays the pet's physical condition and feelings using graphs and icons, allowing the owner to understand the pet's condition at a glance. Specifically, the pet's physical condition and feelings are displayed using line graphs and bar graphs. For example, the pet's body temperature change is displayed using a line graph along the time axis, allowing the owner to visually understand the change in physical condition. In addition, the pet's emotions can be displayed using icons that represent the pet's emotions. For example, if the pet is happy, a smiling icon is displayed, and if the pet is sad, a tearful icon is displayed. Furthermore, the visualization unit can display the pet's physical condition and feelings in real time. For example, the pet's body temperature and movement data are updated in real time, allowing the owner to always be aware of the latest information. In this way, the visualization unit can enable the owner to understand the pet's condition at a glance and support rapid response. Furthermore, the visualization unit can provide an interactive dashboard to display the analysis results in an easy-to-understand manner. For example, when the owner clicks on a specific data point, detailed information and comparison with past data are displayed. The visualization unit can also provide advanced visualization techniques such as heat maps and trend lines to help owners better understand their pet's condition, thereby providing owners with information to intuitively understand their pet's condition and take appropriate action.

[0033] The suggestion unit proposes an optimal rearing environment based on the information visualized by the visualization unit. The suggestion unit proposes the optimal diet and interaction for the pet, for example, by combining data on other animals, the home environment, and the rearing environment. Specifically, the suggestion unit compares data on the same type of pet and proposes the optimal diet and amount of exercise. For example, the suggestion unit proposes an appropriate calorie intake and nutritional balance based on the age, weight, and activity level of the pet. The suggestion unit can also provide advice according to the home environment and rearing environment. For example, if the pet is in an environment that is prone to stress, advice is provided for creating a relaxing environment. Furthermore, the suggestion unit can provide advice according to the physical condition and feelings of the pet. For example, if data indicating that the pet is in poor health is collected, it is suggested that the pet be examined by a veterinarian early. Furthermore, if the pet is stressed, it is possible to suggest play and exercise to reduce stress. As a result, the suggestion unit can provide specific advice for the owner to maintain the health and happiness of the pet, and improve the quality of life of the pet. Furthermore, some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs pet data and suggests an optimal environment for keeping the pet. The AI ​​model can learn from past data and data of other pets to make optimal suggestions according to the pet's individual needs. This allows the suggestion unit to provide specific advice to owners to maintain the health and happiness of their pets, improving the quality of their pets' lives.

[0034] The collection unit can collect data on the pet's voice, facial expression, movement, body temperature, appetite, and feces in real time using a sensor or a camera. The collection unit collects various data on the pet in real time using, for example, a sensor or a camera. For example, the collection unit collects the pet's voice with a microphone, captures the pet's facial expression and movement with a camera, and measures the pet's body temperature with a sensor. The collection unit can also record the pet's appetite and feces state. For example, the collection unit collects the frequency of the pet's voice, captures changes in facial expression with a camera, and analyzes the movement pattern. Furthermore, the collection unit can measure the pet's body temperature with a sensor and collect data based on the evaluation criteria for appetite. This allows more accurate information to be obtained by collecting the pet's data in real time. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the pet's voice data to the generation AI and cause the generation AI to perform voice analysis.

[0035] The analysis unit can make the AI ​​learn the collected data and determine the physical condition and feelings of the pet. The analysis unit analyzes the data collected by the collection unit and determines the physical condition and feelings of the pet. The analysis unit, for example, analyzes the collected data using AI. For example, the analysis unit can determine whether the pet is happy or sad from the tone of the pet's voice and changes in facial expression. The analysis unit can also grasp the health condition of the pet from changes in movement and body temperature. For example, the analysis unit can analyze the tone of the pet's voice using AI and determine emotions. The analysis unit can also analyze changes in the pet's facial expression using AI and determine emotions. Furthermore, the analysis unit can analyze the movement and changes in body temperature of the pet using AI to grasp the health condition. As a result, by using AI, the physical condition and feelings of the pet can be determined with high accuracy. Part or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data of the pet to the generation AI and cause the generation AI to execute the determination of the physical condition and feelings.

[0036] The visualization unit can display the pet's physical condition and feelings with graphs and icons, so that the owner can understand the pet's condition at a glance. The visualization unit visualizes the analysis results obtained by the analysis unit. The visualization unit can display the pet's physical condition and feelings with graphs and icons, so that the owner can understand the pet's condition at a glance. For example, the visualization unit can display the pet's physical condition and feelings with line graphs and bar graphs. The visualization unit can also display using icons representing the pet's emotions. Furthermore, the visualization unit can display the pet's physical condition and feelings in real time. This makes it easier for the owner to take appropriate action by visually understanding the pet's condition. A part or all of the above-mentioned processing in the visualization unit may be performed, for example, using AI, or may be performed without using AI. For example, the visualization unit can input the pet's analysis results to the generation AI and cause the generation AI to execute the generation of visualization.

[0037] The suggestion unit can propose an appropriate diet or interaction for the pet in combination with data on other animals or data on the home environment and rearing environment. The suggestion unit proposes an optimal rearing environment based on the information visualized by the visualization unit. The suggestion unit proposes an optimal diet or interaction for the pet in combination with data on other animals or data on the home environment and rearing environment. For example, the suggestion unit compares data on pets of the same type and proposes an optimal diet or amount of exercise. The suggestion unit can also provide advice according to the home environment and rearing environment. Furthermore, the suggestion unit can provide advice according to the physical condition and feelings of the pet. This makes it possible to provide an environment in which the pet can live happily by proposing an optimal rearing environment for the pet. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input data on the pet to the generation AI and have the generation AI execute the data, which proposes the optimal rearing environment.

[0038] The collection unit can estimate the pet's emotion and adjust the frequency of data collection based on the estimated pet's emotion. The collection unit estimates the pet's emotion and adjusts the frequency of data collection based on the estimated pet's emotion. For example, when the pet is excited, the collection unit increases the frequency of data collection to obtain detailed data. Also, when the pet is relaxed, the collection unit can reduce the frequency of data collection to reduce the pet's stress. Furthermore, when the pet feels anxious, the collection unit can set the frequency of data collection to a medium level to ensure necessary data. This allows the pet's stress to be reduced while necessary data is collected by adjusting the frequency of data collection according to the pet's emotion. 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. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input emotional data of a pet into the generation AI and cause the generation AI to adjust the frequency of data collection.

[0039] The collection unit can analyze the pet's past health data and select the optimal collection timing. The collection unit analyzes the pet's past health data and selects the optimal collection timing. For example, the collection unit discovers from the pet's past health data that the physical condition is likely to change during a specific time period, and collects data during that time period. The collection unit can also analyze the pet's past eating patterns and collect data at a certain time period after eating. Furthermore, the collection unit can collect data during a time period when the pet is less active, referring to the pet's past activity level. This allows data to be collected efficiently by selecting the optimal collection timing based on the past health data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the pet's past health data to the generation AI and cause the generation AI to select the optimal collection timing.

[0040] The collection unit can dynamically change the type of data to be collected based on the activity level of the pet when collecting data. The collection unit dynamically changes the type of data to be collected based on the activity level of the pet when collecting data. For example, when the pet is actively moving, the collection unit preferentially collects data on movement and body temperature. In addition, when the pet is resting, the collection unit can collect data on body temperature and breathing. Furthermore, when the pet is eating, the collection unit can record the appetite and the amount of food eaten. This allows more appropriate data to be collected by changing the type of data to be collected according to the activity level of the pet. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input data on the activity level of the pet to the generation AI and cause the generation AI to change the type of data to be collected.

[0041] 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. The collection unit estimates the pet's emotion and determines the priority of data to be collected based on the estimated pet's emotion. For example, when the pet is excited, the collection unit preferentially collects data on movement and body temperature. Also, when the pet is relaxed, the collection unit can preferentially collect data on appetite and feces. Furthermore, when the pet is anxious, the collection unit can preferentially collect data on voice and facial expression. As a result, important data can be preferentially collected by determining the priority of data to be collected according to the pet's emotion. 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. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the pet's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of data to be collected.

[0042] The collection unit can adjust the data to be collected in consideration of changes in the pet's living environment when collecting data. The collection unit adjusts the data to be collected in consideration of changes in the pet's living environment when collecting data. For example, when a new pet comes to the house, the collection unit collects the pet's stress level. The collection unit can also collect the pet's adaptation status after moving. Furthermore, the collection unit can collect the pet's behavior pattern after rearranging the house. This makes it easier to grasp the pet's adaptation status by considering changes in the living environment. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data on the pet's living environment to the generation AI and cause the generation AI to adjust the data to be collected.

[0043] The collection unit can optimize the collection timing in consideration of the schedule of the pet owner when collecting data. The collection unit optimizes the collection timing in consideration of the schedule of the pet owner when collecting data. For example, the collection unit collects data before the owner goes to work. The collection unit can also collect data of the pet after the owner returns home. Furthermore, the collection unit can collect data in accordance with the pet sitter's schedule while the owner is traveling. This allows the burden on the owner to be reduced by collecting data in accordance with the owner's schedule. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the owner's schedule data to the generation AI and cause the generation AI to optimize the collection timing.

[0044] The analysis unit can estimate the pet's emotion and adjust the analysis algorithm based on the estimated pet's emotion. The analysis unit can estimate the pet's emotion and adjust the analysis algorithm based on the estimated pet's emotion. For example, when the pet is excited, the analysis unit can analyze the data of the movement and body temperature with emphasis. Furthermore, when the pet is relaxed, the analysis unit can analyze the data of the appetite and stool with emphasis. Furthermore, when the pet is anxious, the analysis unit can analyze the data of the voice and facial expression with emphasis. Thereby, the analysis accuracy is improved by adjusting the analysis algorithm according to the pet's emotion. The estimation of the emotion is realized by 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. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the pet's emotion data to the generation AI and cause the generation AI to adjust the analysis algorithm.

[0045] The analysis unit can improve the analysis accuracy by referring to the pet's past health data during the analysis. The analysis unit can improve the analysis accuracy by referring to the pet's past health data during the analysis. The analysis unit can, for example, refer to the pet's past body temperature data to detect an abnormality in the current body temperature. The analysis unit can also analyze the current change in appetite by referring to the pet's past appetite data. Furthermore, the analysis unit can analyze the health condition of the digestive system by referring to the pet's past stool data. As a result, the analysis accuracy is improved by referring to the past health data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the pet's past health data to the generation AI and cause the generation AI to improve the analysis accuracy.

[0046] The analysis unit can apply different analysis methods depending on the type and age of the pet during analysis. The analysis unit can apply different analysis methods depending on the type and age of the pet during analysis. For example, the analysis unit can analyze young pets by focusing on activity level data. Also, the analysis unit can analyze old pets by focusing on body temperature and stool data. Furthermore, the analysis unit can analyze taking into consideration health problems specific to specific dog breeds and cat breeds. As a result, more appropriate analysis results can be obtained by applying an analysis method according to the type and age of the pet. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the type and age of the pet to the generation AI and cause the generation AI to apply the analysis method.

[0047] The analysis unit can estimate the pet's emotion and adjust the display method of the analysis result based on the estimated pet's emotion. The analysis unit can estimate the pet's emotion and adjust the display method of the analysis result based on the estimated pet's emotion. For example, when the pet is excited, the analysis unit displays a detailed analysis result. Furthermore, when the pet is relaxed, the analysis unit can display a concise analysis result. Furthermore, when the pet is anxious, the analysis unit can display an analysis result that gives a sense of security. This allows the display to be easily understood by the owner by adjusting the display method of the analysis result according to the pet's emotion. 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. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the pet's emotion data to the generation AI and cause the generation AI to adjust the display method of the analysis result.

[0048] The analysis unit can complement the analysis result by referring to the pet's living environment data during the analysis. The analysis unit complements the analysis result by referring to the pet's living environment data during the analysis. The analysis unit complements the analysis result of the body temperature data by referring to, for example, the temperature and humidity of the pet's living environment. The analysis unit can also complement the analysis result of the activity level by referring to the pet's exercise environment. Furthermore, the analysis unit can complement the analysis result of the appetite data by referring to the pet's eating environment. This makes it possible to obtain a more accurate analysis result by referring to the living environment data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the pet's living environment data to the generation AI and cause the generation AI to execute the complement of the analysis result.

[0049] The analysis unit can improve the analysis accuracy by incorporating feedback from the pet owner during the analysis. The analysis unit can improve the analysis accuracy by incorporating feedback from the pet owner during the analysis. For example, the analysis unit incorporates behavioral observation data of the pet provided by the owner into the analysis. The analysis unit can also adjust the analysis algorithm based on the owner's feedback. Furthermore, the analysis unit can improve the reliability of the analysis result by referring to the owner's feedback. As a result, the analysis accuracy is improved by incorporating the owner's feedback. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the owner's feedback data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0050] The visualization unit can estimate the pet's emotion and adjust the visualization expression method based on the estimated pet's emotion. The visualization unit can estimate the pet's emotion and adjust the visualization expression method based on the estimated pet's emotion. For example, when the pet is excited, the visualization unit visualizes the pet using a dynamic graph or animation. Also, when the pet is relaxed, the visualization unit can visualize the pet using a static graph or icon. Furthermore, when the pet is anxious, the visualization unit can visualize the pet using a color or design that gives a sense of security. This allows the pet owner to easily understand the visualization expression method by adjusting the visualization expression method according to the pet's emotion. 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. A part or all of the above-mentioned processing in the visualization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the visualization unit can input the pet's emotion data to the generation AI and cause the generation AI to adjust the visualization expression method.

[0051] The visualization unit can adjust the level of detail of the display based on the importance of the pet's health data at the time of visualization. The visualization unit adjusts the level of detail of the display based on the importance of the pet's health data at the time of visualization. For example, the visualization unit displays important health data (such as body temperature and appetite) in detail. The visualization unit can also display less important data (such as the state of stool) concisely. Furthermore, the visualization unit can preferentially display data in which the owner is interested in detail. This allows the owner to efficiently obtain necessary information by adjusting the level of detail of the display according to the importance of the health data. A part or all of the above-mentioned processing in the visualization unit may be performed, for example, using AI or may be performed without using AI. For example, the visualization unit can input the pet's health data to the generation AI and cause the generation AI to adjust the level of detail of the display.

[0052] The visualization unit can apply different visualization methods depending on the type and age of the pet at the time of visualization. The visualization unit can apply different visualization methods depending on the type and age of the pet at the time of visualization. For example, the visualization unit dynamically visualizes activity level data for young pets. Also, the visualization unit can statically visualize body temperature and stool data for older pets. Furthermore, the visualization unit can visualize taking into consideration health problems specific to specific dog and cat breeds. As a result, by applying a visualization method according to the type and age of the pet, it is possible to display information that is easy for the owner to understand. A part or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input data on the type and age of the pet to the generation AI and cause the generation AI to apply the visualization method.

[0053] The visualization unit can estimate the pet's emotion and determine the visualization priority based on the estimated pet's emotion. The visualization unit can estimate the pet's emotion and determine the visualization priority based on the estimated pet's emotion. For example, when the pet is excited, the visualization unit prioritizes visualization of movement and body temperature data. Also, when the pet is relaxed, the visualization unit can prioritize visualization of appetite and stool data. Furthermore, when the pet is anxious, the visualization unit can prioritize visualization of voice and facial expression data. As a result, important information can be preferentially displayed by determining the visualization priority according to the pet's emotion. 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. A part or all of the above-mentioned processing in the visualization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the visualization unit can input the pet's emotion data to the generation AI and cause the generation AI to execute the determination of the visualization priority.

[0054] The visualization unit can select the optimal display method in consideration of the device information of the pet owner at the time of visualization. The visualization unit selects the optimal display method in consideration of the device information of the pet owner at the time of visualization. For example, when the owner uses a smartphone, the visualization unit provides a display method that matches the screen size. In addition, when the owner uses a tablet, the visualization unit can provide a display method optimized for a large screen. Furthermore, when the owner uses a personal computer, the visualization unit can display detailed data. As a result, by selecting the optimal display method based on the device information of the owner, it is possible to provide a display that is easy for the owner to use. Part or all of the above-mentioned processing in the visualization unit may be performed, for example, using AI or may be performed without using AI. For example, the visualization unit can input the device information of the owner to the generation AI and cause the generation AI to select the optimal display method.

[0055] The visualization unit can select the optimal display method by referring to the past operation history of the pet owner at the time of visualization. The visualization unit can select the optimal display method by referring to the past operation history of the pet owner at the time of visualization. For example, the visualization unit preferentially displays data that the owner frequently referenced in the past. In addition, the visualization unit can propose an optimal display layout from the owner's past operation history. Furthermore, the visualization unit can provide an optimal display method based on the display method used by the owner in the past. As a result, by referring to the owner's past operation history, a display that is easy for the owner to use can be provided. A part or all of the above-mentioned processing in the visualization unit may be performed, for example, using AI, or may be performed without using AI. For example, the visualization unit can input the owner's operation history data to the generation AI and cause the generation AI to select the optimal display method.

[0056] The suggestion unit can estimate the pet's emotion and adjust the proposed content based on the estimated pet's emotion. The suggestion unit can estimate the pet's emotion and adjust the proposed content based on the estimated pet's emotion. For example, when the pet is excited, the suggestion unit can suggest a relaxing environment. Furthermore, when the pet is relaxed, the suggestion unit can suggest moderate exercise. Furthermore, when the pet is anxious, the suggestion unit can suggest an environment that gives a sense of security. This allows for more appropriate suggestions by adjusting the proposed content according to the pet's emotion. 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. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the pet's emotion data to the generation AI and cause the generation AI to adjust the proposed content.

[0057] The suggestion unit can provide an optimal suggestion by referring to the pet's past health data when making a suggestion. The suggestion unit can provide an optimal suggestion by referring to the pet's past health data when making a suggestion. The suggestion unit can, for example, refer to the pet's past body temperature data and suggest appropriate temperature management. The suggestion unit can also refer to the pet's past appetite data and suggest an optimal meal plan. Furthermore, the suggestion unit can refer to the pet's past stool data and suggest digestive health management. This makes it possible to make more appropriate suggestions by referring to the past health data. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without using AI. For example, the suggestion unit can input the pet's past health data into the generation AI and cause the generation AI to provide an optimal suggestion.

[0058] The suggestion unit can apply different suggestion methods according to the type and age of the pet at the time of suggestion. The suggestion unit can apply different suggestion methods according to the type and age of the pet at the time of suggestion. For example, the suggestion unit can suggest active play and exercise to a young pet. Also, the suggestion unit can suggest a calm environment and moderate exercise to an elderly pet. Furthermore, the suggestion unit can make a suggestion taking into consideration health problems specific to a particular dog breed or cat breed. This makes it possible to make a more appropriate suggestion by applying a suggestion method according to the type and age of the pet. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input data on the type and age of the pet to the generation AI and cause the generation AI to apply the suggestion method.

[0059] The suggestion unit can estimate the pet's emotion and determine the priority of suggestions based on the estimated pet's emotion. The suggestion unit can estimate the pet's emotion and determine the priority of suggestions based on the estimated pet's emotion. For example, when the pet is excited, the suggestion unit can preferentially suggest an environment that allows the pet to relax. Furthermore, when the pet is relaxed, the suggestion unit can preferentially suggest moderate exercise. Furthermore, when the pet is anxious, the suggestion unit can preferentially suggest an environment that gives a sense of security. This allows important suggestions to be provided preferentially by determining the priority of suggestions according to the pet's emotion. The estimation of emotions 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. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the pet's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of suggestions.

[0060] The suggestion unit can provide an optimal suggestion by referring to the living environment data of the pet owner when making a suggestion. The suggestion unit can provide an optimal suggestion by referring to the living environment data of the pet owner when making a suggestion. The suggestion unit can, for example, suggest a meal time for the pet that matches the owner's life rhythm. The suggestion unit can also suggest an exercise method for the pet that matches the owner's living environment. Furthermore, the suggestion unit can suggest a care method for the pet that matches the owner's life style. This makes it possible to make a more appropriate suggestion by referring to the living environment data of the owner. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without using AI. For example, the suggestion unit can input the living environment data of the owner to the generation AI and cause the generation AI to provide an optimal suggestion.

[0061] The suggestion unit can improve the proposed content by incorporating feedback from the pet owner at the time of suggestion. The suggestion unit can improve the proposed content by incorporating feedback from the pet owner at the time of suggestion. For example, the suggestion unit customizes the proposed content based on the owner's feedback. Also, the suggestion unit can adjust the proposal algorithm with reference to the owner's feedback. Furthermore, the suggestion unit can improve the accuracy of the proposal by incorporating the owner's feedback. As a result, the accuracy of the proposed content is improved by incorporating the owner's feedback. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the owner's feedback data to the generation AI and cause the generation AI to execute improvement of the proposed content.

[0062] The suggestion unit can provide an optimal suggestion by referring to the living environment data of the pet owner when making a suggestion. The suggestion unit can provide an optimal suggestion by referring to the living environment data of the pet owner when making a suggestion. The suggestion unit can, for example, suggest a meal time for the pet that matches the owner's life rhythm. The suggestion unit can also suggest an exercise method for the pet that matches the owner's living environment. Furthermore, the suggestion unit can suggest a care method for the pet that matches the owner's life style. This makes it possible to make a more appropriate suggestion by referring to the living environment data of the owner. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without using AI. For example, the suggestion unit can input the living environment data of the owner to the generation AI and cause the generation AI to provide an optimal suggestion.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The pet care system can also be equipped with a function to monitor the pet's sleep patterns. For example, by measuring the pet's sleep time and sleep quality with a sensor and analyzing the data, the pet's health condition can be understood in more detail. Also, if the pet is not getting enough sleep, the pet care system can provide the owner with advice on how to improve the pet's sleeping environment. Furthermore, it can also suggest optimal feeding and exercise timing based on the pet's sleep patterns. This can improve the overall health of the pet.

[0065] The pet care system can also be equipped with a function to monitor allergic reactions of pets. For example, if a pet has an allergic reaction to a certain food ingredient or environmental factor, the data can be collected and analyzed to identify the cause of the allergy. In addition, when an allergic reaction occurs, an alert can be sent quickly to the owner and an appropriate response can be suggested. Furthermore, based on the pet's allergy history, it is possible to suggest a meal plan and environmental improvement measures to avoid allergies. This can reduce health risks for pets and support a comfortable life.

[0066] The pet care system can further include a function to monitor the amount of exercise a pet gets and propose an appropriate exercise plan. For example, by using a sensor to measure how much exercise a pet gets on a daily basis and analyzing the data, it is possible to detect whether the pet is exercising too much or not. It can also suggest the optimal amount and method of exercise depending on the pet's age and health condition. Furthermore, if a pet likes a particular type of exercise, it is possible to propose a plan that incorporates that exercise. This helps maintain the pet's health and reduce health risks caused by obesity and lack of exercise.

[0067] The pet care system can further include a function to monitor the dietary content of a pet and optimize the nutritional balance. For example, by recording the dietary content of a pet on a daily basis and analyzing the data, it is possible to detect imbalances in nutritional balance. It can also propose optimal meal plans according to the pet's age and health condition. Furthermore, if a pet has a preference for a particular ingredient, it can also propose a well-balanced meal plan that includes that ingredient. This helps maintain the pet's health and reduce health risks caused by nutritional deficiencies and overconsumption.

[0068] The process flow of the first embodiment will be briefly described below.

[0069] Step 1: The collection unit collects data such as the pet's voice, facial expressions, movements, body temperature, appetite, and feces. The collection unit uses sensors and cameras to collect various data about the pet in real time. For example, the collection unit collects the pet's voice with a microphone, captures its facial expressions and movements with a camera, and measures its body temperature with a sensor. The collection unit can also record the pet's appetite and feces condition. Step 2: The analysis unit analyzes the data collected by the collection unit and determines the pet's physical condition and mood. The analysis unit analyzes the collected data using AI. For example, the analysis unit determines whether the pet is happy or sad from the tone of the pet's voice and changes in facial expression. The analysis unit can also grasp the pet's health condition from changes in movement and body temperature. Step 3: The visualization unit visualizes the analysis results obtained by the analysis unit. The visualization unit displays the pet's physical condition and emotions using graphs and icons, allowing the owner to understand the pet's condition at a glance. For example, the visualization unit displays the pet's physical condition and emotions using line graphs and bar graphs. The visualization unit can also display the pet's emotions using icons. Step 4: The suggestion unit suggests an optimal pet environment based on the information visualized by the visualization unit. The suggestion unit suggests the optimal diet and interaction for the pet by combining data on other animals and data on the home and pet environments. For example, the suggestion unit compares data on pets of the same species and suggests the optimal diet and amount of exercise. The suggestion unit can also provide advice according to the home and pet environments.

[0070] (Example 2) The pet care system according to the embodiment of the present invention is a system that visualizes the physical condition and feelings of a pet as data and proposes an optimal environment for keeping the pet. This pet care system collects data such as the pet's voice, facial expression, movement, body temperature, appetite, and feces, and has AI learn and analyze the data to determine and visualize the physical condition and feelings of the pet. Furthermore, by combining data on other animals and data on the home environment and the keeping environment, the system proposes optimal food and interaction for the pet. For example, the pet's voice is collected by a microphone, its facial expression and movement are photographed by a camera, and its body temperature is measured by a sensor. In addition, the pet's health condition can be understood by recording the appetite and feces state. Next, the collected data is made to learn the AI, and the physical condition and feelings of the pet are analyzed. The AI ​​analyzes the collected data and determines the physical condition and feelings of the pet. For example, it is possible to determine whether the pet is happy or sad from the tone of the pet's voice and changes in its facial expression. In addition, the pet's health condition can be understood from changes in its movement and body temperature. Based on the analysis results, the physical condition and feelings of the pet are visualized and provided to the owner. For example, the physical condition and feelings of the pet are displayed as graphs and icons, so that the owner can understand the pet's condition at a glance. In addition, by providing advice according to the physical condition and feelings of the pet, the owner can take appropriate measures. Furthermore, by combining data on other animals and data on the home and rearing environments, the system proposes the optimal diet and interaction for the pet. For example, data on the same type of pet can be compared to propose the optimal diet and amount of exercise. In addition, by providing advice according to the home and rearing environments, the system can provide an environment in which the pet can live comfortably. This makes the physical condition and feelings of the pet visible as data, making it easier for the owner to understand the condition of the pet. In addition, by proposing the optimal rearing environment for the pet, the system can provide an environment in which the pet can live happily. For example, when the pet becomes unwell, the system can detect abnormalities early and take appropriate measures. In addition, when the pet feels stressed, the pet's stress can be reduced by providing appropriate care. In this way, the pet care system can visualize the physical condition and feelings of the pet as data, making it easier for the owner to understand the condition of the pet.

[0071] The pet care system according to the embodiment includes a collection unit, an analysis unit, a visualization unit, and a proposal unit. The collection unit collects data such as the pet's voice, facial expression, movement, body temperature, appetite, and feces. The collection unit collects various data of the pet in real time using, for example, a sensor or a camera. For example, the collection unit collects the pet's voice with a microphone, captures the pet's facial expression and movement with a camera, and measures the pet's body temperature with a sensor. The collection unit can also record the pet's appetite and feces state. For example, the collection unit collects the frequency of the pet's voice, captures changes in facial expression with a camera, and analyzes the movement pattern. Furthermore, the collection unit can measure the pet's body temperature with a sensor and collect data based on an evaluation criterion for appetite. The analysis unit analyzes the data collected by the collection unit and determines the pet's physical condition and feelings. The analysis unit analyzes the collected data using, for example, AI. For example, the analysis unit can determine whether the pet is happy or sad from the tone of the pet's voice and changes in facial expression. The analysis unit can also grasp the pet's health condition from changes in movement and body temperature. For example, the analysis unit uses AI to analyze the tone of the pet's voice and determine the emotions. The analysis unit can also use AI to analyze changes in the pet's facial expressions and determine the emotions. Furthermore, the analysis unit can use AI to analyze the pet's movements and changes in body temperature to grasp the health condition. The visualization unit visualizes the analysis results obtained by the analysis unit. For example, the visualization unit displays the pet's physical condition and feelings using graphs and icons, so that the owner can grasp the pet's condition at a glance. For example, the visualization unit displays the pet's physical condition and feelings using line graphs and bar graphs. The visualization unit can also display using icons representing the pet's emotions. Furthermore, the visualization unit can display the pet's physical condition and feelings in real time. The suggestion unit suggests an optimal rearing environment based on the information visualized by the visualization unit. The suggestion unit suggests the optimal diet and interaction for the pet, for example, by combining data on other animals, the home environment, and the rearing environment. For example, the suggestion unit compares data on the same type of pet and suggests the optimal diet and amount of exercise. The suggestion unit can also provide advice according to the home environment and the rearing environment.Furthermore, the suggestion unit can provide advice according to the physical condition and mood of the pet. As a result, the pet care system according to the embodiment can visualize the physical condition and mood of the pet as data, making it easier for the owner to understand the condition of the pet. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make a suggestion using an AI model that inputs data about the pet and suggests an optimal pet care environment.

[0072] The collection unit collects data such as the pet's voice, facial expression, movement, body temperature, appetite, and feces. The collection unit collects various data on the pet in real time using, for example, a sensor or a camera. Specifically, the pet's voice is collected by a microphone, and the frequency, volume, and tone of the voice are analyzed to detect changes in the pet's emotions and physical condition. Facial expressions and movements are captured using a high-resolution camera, and detailed data is obtained using face recognition technology and motion analysis algorithms. For example, the camera captures subtle changes in the pet's facial expression and infers emotions from the movements of the ears, eyes, and mouth. In addition, to analyze movement patterns, the pet's movements such as walking, running, and jumping are recorded, and abnormal movements and behaviors that are different from normal are detected. Body temperature is measured using a non-contact infrared sensor to determine whether the pet is feeling stressed or unwell. Appetite and feces condition data are collected using a feeder that automatically records the pet's food amount and meal time, and a sensor that detects the condition of the feces. This allows the collection unit to grasp the pet's health condition and emotions from multiple angles and collect data in real time. Furthermore, the collection department can centrally manage these data and link them with other systems and departments as necessary. For example, the collected data can be stored on a cloud server and accessed by the analysis department and visualization department. 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 department to collect data efficiently and effectively and improve the performance of the entire system.

[0073] The analysis unit analyzes the data collected by the collection unit to determine the physical condition and feelings of the pet. The analysis unit analyzes the collected data using, for example, AI. Specifically, it is possible to determine whether the pet is happy or sad from the tone of the pet's voice and changes in facial expression. AI uses voice recognition technology to analyze the pet's voice and detect changes in frequency, volume, and tone. For example, if the pet's voice is higher, it may be happy, and if it is lower, it may be anxious or stressed. In addition, facial expression recognition technology is used to analyze subtle changes in the pet's face and determine emotions. For example, the pet's emotions can be inferred from the position of the ears, the degree of opening of the eyes, the shape of the mouth, etc. In addition, the pet's health condition can be understood from changes in movement and body temperature. A motion analysis algorithm is used to analyze the pet's movements such as walking, running, and jumping, and detect abnormal movements or behavior that is different from normal. For example, if the pet moves more slowly than usual or shows abnormal movements, it may be a sign of poor health or stress. Changes in body temperature are measured using a non-contact infrared sensor to determine whether the pet is stressed or in poor health. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the physical condition and feelings of the pet in real time. Furthermore, the analysis unit can also perform long-term risk assessment and trend analysis using past data and statistical information. For example, based on past data, it can predict fluctuations in the pet's behavioral patterns and health condition during specific seasons and times of the day, and plan future countermeasures. In addition, the analysis unit can use an anomaly detection algorithm to detect unusual patterns and abnormal data and issue an early warning. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0074] The visualization unit visualizes the analysis results obtained by the analysis unit. For example, the visualization unit displays the pet's physical condition and feelings using graphs and icons, allowing the owner to understand the pet's condition at a glance. Specifically, the pet's physical condition and feelings are displayed using line graphs and bar graphs. For example, the pet's body temperature change is displayed using a line graph along the time axis, allowing the owner to visually understand the change in physical condition. In addition, the pet's emotions can be displayed using icons that represent the pet's emotions. For example, if the pet is happy, a smiling icon is displayed, and if the pet is sad, a tearful icon is displayed. Furthermore, the visualization unit can display the pet's physical condition and feelings in real time. For example, the pet's body temperature and movement data are updated in real time, allowing the owner to always be aware of the latest information. In this way, the visualization unit can enable the owner to understand the pet's condition at a glance and support rapid response. Furthermore, the visualization unit can provide an interactive dashboard to display the analysis results in an easy-to-understand manner. For example, when the owner clicks on a specific data point, detailed information and comparison with past data are displayed. The visualization unit can also provide advanced visualization techniques such as heat maps and trend lines to help owners better understand their pet's condition, thereby providing owners with information to intuitively understand their pet's condition and take appropriate action.

[0075] The suggestion unit proposes an optimal rearing environment based on the information visualized by the visualization unit. The suggestion unit proposes the optimal diet and interaction for the pet, for example, by combining data on other animals, the home environment, and the rearing environment. Specifically, the suggestion unit compares data on the same type of pet and proposes the optimal diet and amount of exercise. For example, the suggestion unit proposes an appropriate calorie intake and nutritional balance based on the age, weight, and activity level of the pet. The suggestion unit can also provide advice according to the home environment and rearing environment. For example, if the pet is in an environment that is prone to stress, advice is provided for creating a relaxing environment. Furthermore, the suggestion unit can provide advice according to the physical condition and feelings of the pet. For example, if data indicating that the pet is in poor health is collected, it is suggested that the pet be examined by a veterinarian early. Furthermore, if the pet is stressed, it is possible to suggest play and exercise to reduce stress. As a result, the suggestion unit can provide specific advice for the owner to maintain the health and happiness of the pet, and improve the quality of life of the pet. Furthermore, some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs pet data and suggests an optimal environment for keeping the pet. The AI ​​model can learn from past data and data of other pets to make optimal suggestions according to the pet's individual needs. This allows the suggestion unit to provide specific advice to owners to maintain the health and happiness of their pets, improving the quality of their pets' lives.

[0076] The collection unit can collect data on the pet's voice, facial expression, movement, body temperature, appetite, and feces in real time using a sensor or a camera. The collection unit collects various data on the pet in real time using, for example, a sensor or a camera. For example, the collection unit collects the pet's voice with a microphone, captures the pet's facial expression and movement with a camera, and measures the pet's body temperature with a sensor. The collection unit can also record the pet's appetite and feces state. For example, the collection unit collects the frequency of the pet's voice, captures changes in facial expression with a camera, and analyzes the movement pattern. Furthermore, the collection unit can measure the pet's body temperature with a sensor and collect data based on the evaluation criteria for appetite. This allows more accurate information to be obtained by collecting the pet's data in real time. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the pet's voice data to the generation AI and cause the generation AI to perform voice analysis.

[0077] The analysis unit can make the AI ​​learn the collected data and determine the physical condition and feelings of the pet. The analysis unit analyzes the data collected by the collection unit and determines the physical condition and feelings of the pet. The analysis unit, for example, analyzes the collected data using AI. For example, the analysis unit can determine whether the pet is happy or sad from the tone of the pet's voice and changes in facial expression. The analysis unit can also grasp the health condition of the pet from changes in movement and body temperature. For example, the analysis unit can analyze the tone of the pet's voice using AI and determine emotions. The analysis unit can also analyze changes in the pet's facial expression using AI and determine emotions. Furthermore, the analysis unit can analyze the movement and changes in body temperature of the pet using AI to grasp the health condition. As a result, by using AI, the physical condition and feelings of the pet can be determined with high accuracy. Part or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data of the pet to the generation AI and cause the generation AI to execute the determination of the physical condition and feelings.

[0078] The visualization unit can display the pet's physical condition and feelings with graphs and icons, so that the owner can understand the pet's condition at a glance. The visualization unit visualizes the analysis results obtained by the analysis unit. The visualization unit can display the pet's physical condition and feelings with graphs and icons, so that the owner can understand the pet's condition at a glance. For example, the visualization unit can display the pet's physical condition and feelings with line graphs and bar graphs. The visualization unit can also display using icons representing the pet's emotions. Furthermore, the visualization unit can display the pet's physical condition and feelings in real time. This makes it easier for the owner to take appropriate action by visually understanding the pet's condition. A part or all of the above-mentioned processing in the visualization unit may be performed, for example, using AI, or may be performed without using AI. For example, the visualization unit can input the pet's analysis results to the generation AI and cause the generation AI to execute the generation of visualization.

[0079] The suggestion unit can propose an appropriate diet or interaction for the pet in combination with data on other animals or data on the home environment and rearing environment. The suggestion unit proposes an optimal rearing environment based on the information visualized by the visualization unit. The suggestion unit proposes an optimal diet or interaction for the pet in combination with data on other animals or data on the home environment and rearing environment. For example, the suggestion unit compares data on pets of the same type and proposes an optimal diet or amount of exercise. The suggestion unit can also provide advice according to the home environment and rearing environment. Furthermore, the suggestion unit can provide advice according to the physical condition and feelings of the pet. This makes it possible to provide an environment in which the pet can live happily by proposing an optimal rearing environment for the pet. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input data on the pet to the generation AI and have the generation AI execute the data, which proposes the optimal rearing environment.

[0080] The collection unit can estimate the pet's emotion and adjust the frequency of data collection based on the estimated pet's emotion. The collection unit estimates the pet's emotion and adjusts the frequency of data collection based on the estimated pet's emotion. For example, when the pet is excited, the collection unit increases the frequency of data collection to obtain detailed data. Also, when the pet is relaxed, the collection unit can reduce the frequency of data collection to reduce the pet's stress. Furthermore, when the pet feels anxious, the collection unit can set the frequency of data collection to a medium level to ensure necessary data. This allows the pet's stress to be reduced while necessary data is collected by adjusting the frequency of data collection according to the pet's emotion. 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. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input emotional data of a pet into the generation AI and cause the generation AI to adjust the frequency of data collection.

[0081] The collection unit can analyze the pet's past health data and select the optimal collection timing. The collection unit analyzes the pet's past health data and selects the optimal collection timing. For example, the collection unit discovers from the pet's past health data that the physical condition is likely to change during a specific time period, and collects data during that time period. The collection unit can also analyze the pet's past eating patterns and collect data at a certain time period after eating. Furthermore, the collection unit can collect data during a time period when the pet is less active, referring to the pet's past activity level. This allows data to be collected efficiently by selecting the optimal collection timing based on the past health data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the pet's past health data to the generation AI and cause the generation AI to select the optimal collection timing.

[0082] The collection unit can dynamically change the type of data to be collected based on the activity level of the pet when collecting data. The collection unit dynamically changes the type of data to be collected based on the activity level of the pet when collecting data. For example, when the pet is actively moving, the collection unit preferentially collects data on movement and body temperature. In addition, when the pet is resting, the collection unit can collect data on body temperature and breathing. Furthermore, when the pet is eating, the collection unit can record the appetite and the amount of food eaten. This allows more appropriate data to be collected by changing the type of data to be collected according to the activity level of the pet. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input data on the activity level of the pet to the generation AI and cause the generation AI to change the type of data to be collected.

[0083] 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. The collection unit estimates the pet's emotion and determines the priority of data to be collected based on the estimated pet's emotion. For example, when the pet is excited, the collection unit preferentially collects data on movement and body temperature. Also, when the pet is relaxed, the collection unit can preferentially collect data on appetite and feces. Furthermore, when the pet is anxious, the collection unit can preferentially collect data on voice and facial expression. As a result, important data can be preferentially collected by determining the priority of data to be collected according to the pet's emotion. 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. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the pet's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of data to be collected.

[0084] The collection unit can adjust the data to be collected in consideration of changes in the pet's living environment when collecting data. The collection unit adjusts the data to be collected in consideration of changes in the pet's living environment when collecting data. For example, when a new pet comes to the house, the collection unit collects the pet's stress level. The collection unit can also collect the pet's adaptation status after moving. Furthermore, the collection unit can collect the pet's behavior pattern after rearranging the house. This makes it easier to grasp the pet's adaptation status by considering changes in the living environment. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data on the pet's living environment to the generation AI and cause the generation AI to adjust the data to be collected.

[0085] The collection unit can optimize the collection timing in consideration of the schedule of the pet owner when collecting data. The collection unit optimizes the collection timing in consideration of the schedule of the pet owner when collecting data. For example, the collection unit collects data before the owner goes to work. The collection unit can also collect data of the pet after the owner returns home. Furthermore, the collection unit can collect data in accordance with the pet sitter's schedule while the owner is traveling. This allows the burden on the owner to be reduced by collecting data in accordance with the owner's schedule. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the owner's schedule data to the generation AI and cause the generation AI to optimize the collection timing.

[0086] The analysis unit can estimate the pet's emotion and adjust the analysis algorithm based on the estimated pet's emotion. The analysis unit can estimate the pet's emotion and adjust the analysis algorithm based on the estimated pet's emotion. For example, when the pet is excited, the analysis unit can analyze the data of the movement and body temperature with emphasis. Furthermore, when the pet is relaxed, the analysis unit can analyze the data of the appetite and stool with emphasis. Furthermore, when the pet is anxious, the analysis unit can analyze the data of the voice and facial expression with emphasis. Thereby, the analysis accuracy is improved by adjusting the analysis algorithm according to the pet's emotion. The estimation of the emotion is realized by 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. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the pet's emotion data to the generation AI and cause the generation AI to adjust the analysis algorithm.

[0087] The analysis unit can improve the analysis accuracy by referring to the pet's past health data during the analysis. The analysis unit can improve the analysis accuracy by referring to the pet's past health data during the analysis. The analysis unit can, for example, refer to the pet's past body temperature data to detect abnormalities in the current body temperature. The analysis unit can also analyze the current appetite change by referring to the pet's past appetite data. Furthermore, the analysis unit can analyze the health condition of the digestive system by referring to the pet's past stool data. As a result, the analysis accuracy is improved by referring to the past health data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the pet's past health data to the generation AI and cause the generation AI to improve the analysis accuracy.

[0088] The analysis unit can apply different analysis methods depending on the type and age of the pet during analysis. The analysis unit can apply different analysis methods depending on the type and age of the pet during analysis. For example, the analysis unit can analyze young pets by focusing on activity level data. Also, the analysis unit can analyze old pets by focusing on body temperature and stool data. Furthermore, the analysis unit can analyze taking into consideration health problems specific to specific dog breeds and cat breeds. As a result, more appropriate analysis results can be obtained by applying an analysis method according to the type and age of the pet. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the type and age of the pet to the generation AI and cause the generation AI to apply the analysis method.

[0089] The analysis unit can estimate the pet's emotion and adjust the display method of the analysis result based on the estimated pet's emotion. The analysis unit can estimate the pet's emotion and adjust the display method of the analysis result based on the estimated pet's emotion. For example, when the pet is excited, the analysis unit displays a detailed analysis result. Furthermore, when the pet is relaxed, the analysis unit can display a concise analysis result. Furthermore, when the pet is anxious, the analysis unit can display an analysis result that gives a sense of security. This allows the display to be easily understood by the owner by adjusting the display method of the analysis result according to the pet's emotion. 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. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the pet's emotion data to the generation AI and cause the generation AI to adjust the display method of the analysis result.

[0090] The analysis unit can complement the analysis result by referring to the pet's living environment data during the analysis. The analysis unit complements the analysis result by referring to the pet's living environment data during the analysis. The analysis unit complements the analysis result of the body temperature data by referring to, for example, the temperature and humidity of the pet's living environment. The analysis unit can also complement the analysis result of the activity level by referring to the pet's exercise environment. Furthermore, the analysis unit can complement the analysis result of the appetite data by referring to the pet's eating environment. This makes it possible to obtain a more accurate analysis result by referring to the living environment data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the pet's living environment data to the generation AI and cause the generation AI to execute the complement of the analysis result.

[0091] The analysis unit can improve the analysis accuracy by incorporating feedback from the pet owner during the analysis. The analysis unit can improve the analysis accuracy by incorporating feedback from the pet owner during the analysis. For example, the analysis unit incorporates behavioral observation data of the pet provided by the owner into the analysis. The analysis unit can also adjust the analysis algorithm based on the owner's feedback. Furthermore, the analysis unit can improve the reliability of the analysis result by referring to the owner's feedback. As a result, the analysis accuracy is improved by incorporating the owner's feedback. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the owner's feedback data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0092] The visualization unit can estimate the pet's emotion and adjust the visualization expression method based on the estimated pet's emotion. The visualization unit can estimate the pet's emotion and adjust the visualization expression method based on the estimated pet's emotion. For example, when the pet is excited, the visualization unit visualizes the pet using a dynamic graph or animation. Also, when the pet is relaxed, the visualization unit can visualize the pet using a static graph or icon. Furthermore, when the pet is anxious, the visualization unit can visualize the pet using a color or design that gives a sense of security. This allows the pet owner to easily understand the visualization expression method by adjusting the visualization expression method according to the pet's emotion. 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. A part or all of the above-mentioned processing in the visualization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the visualization unit can input the pet's emotion data to the generation AI and cause the generation AI to adjust the visualization expression method.

[0093] The visualization unit can adjust the level of detail of the display based on the importance of the pet's health data at the time of visualization. The visualization unit adjusts the level of detail of the display based on the importance of the pet's health data at the time of visualization. For example, the visualization unit displays important health data (such as body temperature and appetite) in detail. The visualization unit can also display less important data (such as the state of stool) concisely. Furthermore, the visualization unit can preferentially display data in which the owner is interested in detail. This allows the owner to efficiently obtain necessary information by adjusting the level of detail of the display according to the importance of the health data. A part or all of the above-mentioned processing in the visualization unit may be performed, for example, using AI or may be performed without using AI. For example, the visualization unit can input the pet's health data to the generation AI and cause the generation AI to adjust the level of detail of the display.

[0094] The visualization unit can apply different visualization methods depending on the type and age of the pet at the time of visualization. The visualization unit can apply different visualization methods depending on the type and age of the pet at the time of visualization. For example, the visualization unit dynamically visualizes activity level data for young pets. Also, the visualization unit can statically visualize body temperature and stool data for older pets. Furthermore, the visualization unit can visualize taking into consideration health problems specific to specific dog and cat breeds. As a result, by applying a visualization method according to the type and age of the pet, it is possible to display information that is easy for the owner to understand. A part or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input data on the type and age of the pet to the generation AI and cause the generation AI to apply the visualization method.

[0095] The visualization unit can estimate the pet's emotion and determine the visualization priority based on the estimated pet's emotion. The visualization unit can estimate the pet's emotion and determine the visualization priority based on the estimated pet's emotion. For example, when the pet is excited, the visualization unit prioritizes visualization of movement and body temperature data. Also, when the pet is relaxed, the visualization unit can prioritize visualization of appetite and stool data. Furthermore, when the pet is anxious, the visualization unit can prioritize visualization of voice and facial expression data. As a result, important information can be preferentially displayed by determining the visualization priority according to the pet's emotion. 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. A part or all of the above-mentioned processing in the visualization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the visualization unit can input the pet's emotion data to the generation AI and cause the generation AI to execute the determination of the visualization priority.

[0096] The visualization unit can select the optimal display method in consideration of the device information of the pet owner at the time of visualization. The visualization unit selects the optimal display method in consideration of the device information of the pet owner at the time of visualization. For example, when the owner uses a smartphone, the visualization unit provides a display method that matches the screen size. In addition, when the owner uses a tablet, the visualization unit can provide a display method optimized for a large screen. Furthermore, when the owner uses a personal computer, the visualization unit can display detailed data. As a result, by selecting the optimal display method based on the device information of the owner, it is possible to provide a display that is easy for the owner to use. Part or all of the above-mentioned processing in the visualization unit may be performed, for example, using AI or may be performed without using AI. For example, the visualization unit can input the device information of the owner to the generation AI and cause the generation AI to select the optimal display method.

[0097] The visualization unit can select the optimal display method by referring to the past operation history of the pet owner at the time of visualization. The visualization unit can select the optimal display method by referring to the past operation history of the pet owner at the time of visualization. For example, the visualization unit preferentially displays data that the owner frequently referenced in the past. In addition, the visualization unit can propose an optimal display layout from the owner's past operation history. Furthermore, the visualization unit can provide an optimal display method based on the display method used by the owner in the past. As a result, by referring to the owner's past operation history, a display that is easy for the owner to use can be provided. A part or all of the above-mentioned processing in the visualization unit may be performed, for example, using AI, or may be performed without using AI. For example, the visualization unit can input the owner's operation history data to the generation AI and cause the generation AI to select the optimal display method.

[0098] The suggestion unit can estimate the pet's emotion and adjust the proposed content based on the estimated pet's emotion. The suggestion unit can estimate the pet's emotion and adjust the proposed content based on the estimated pet's emotion. For example, when the pet is excited, the suggestion unit can suggest a relaxing environment. Furthermore, when the pet is relaxed, the suggestion unit can suggest moderate exercise. Furthermore, when the pet is anxious, the suggestion unit can suggest an environment that gives a sense of security. This allows for more appropriate suggestions by adjusting the proposed content according to the pet's emotion. 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. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the pet's emotion data to the generation AI and cause the generation AI to adjust the proposed content.

[0099] The suggestion unit can provide an optimal suggestion by referring to the pet's past health data when making a suggestion. The suggestion unit can provide an optimal suggestion by referring to the pet's past health data when making a suggestion. The suggestion unit can, for example, refer to the pet's past body temperature data and suggest appropriate temperature management. The suggestion unit can also refer to the pet's past appetite data and suggest an optimal meal plan. Furthermore, the suggestion unit can refer to the pet's past stool data and suggest digestive health management. This makes it possible to make more appropriate suggestions by referring to the past health data. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without using AI. For example, the suggestion unit can input the pet's past health data into the generation AI and cause the generation AI to provide an optimal suggestion.

[0100] The suggestion unit can apply different suggestion methods according to the type and age of the pet at the time of suggestion. The suggestion unit can apply different suggestion methods according to the type and age of the pet at the time of suggestion. For example, the suggestion unit can suggest active play and exercise to a young pet. Also, the suggestion unit can suggest a calm environment and moderate exercise to an elderly pet. Furthermore, the suggestion unit can make a suggestion taking into consideration health problems specific to a particular dog breed or cat breed. This makes it possible to make a more appropriate suggestion by applying a suggestion method according to the type and age of the pet. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input data on the type and age of the pet to the generation AI and cause the generation AI to apply the suggestion method.

[0101] The suggestion unit can estimate the pet's emotion and determine the priority of suggestions based on the estimated pet's emotion. The suggestion unit can estimate the pet's emotion and determine the priority of suggestions based on the estimated pet's emotion. For example, when the pet is excited, the suggestion unit can preferentially suggest an environment that allows the pet to relax. Furthermore, when the pet is relaxed, the suggestion unit can preferentially suggest moderate exercise. Furthermore, when the pet is anxious, the suggestion unit can preferentially suggest an environment that gives a sense of security. This allows important suggestions to be provided preferentially by determining the priority of suggestions according to the pet's emotion. The estimation of emotions 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. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the pet's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of suggestions.

[0102] The suggestion unit can provide an optimal suggestion by referring to the living environment data of the pet owner when making a suggestion. The suggestion unit can provide an optimal suggestion by referring to the living environment data of the pet owner when making a suggestion. The suggestion unit can, for example, suggest a meal time for the pet that matches the owner's life rhythm. The suggestion unit can also suggest an exercise method for the pet that matches the owner's living environment. Furthermore, the suggestion unit can suggest a care method for the pet that matches the owner's life style. This makes it possible to make a more appropriate suggestion by referring to the living environment data of the owner. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without using AI. For example, the suggestion unit can input the living environment data of the owner to the generation AI and cause the generation AI to provide an optimal suggestion.

[0103] The suggestion unit can improve the proposed content by incorporating feedback from the pet owner at the time of suggestion. The suggestion unit can improve the proposed content by incorporating feedback from the pet owner at the time of suggestion. For example, the suggestion unit customizes the proposed content based on the owner's feedback. Also, the suggestion unit can adjust the proposal algorithm with reference to the owner's feedback. Furthermore, the suggestion unit can improve the accuracy of the proposal by incorporating the owner's feedback. As a result, the accuracy of the proposed content is improved by incorporating the owner's feedback. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the owner's feedback data to the generation AI and cause the generation AI to execute improvement of the proposed content.

[0104] The suggestion unit can provide an optimal suggestion by referring to the living environment data of the pet owner when making a suggestion. The suggestion unit can provide an optimal suggestion by referring to the living environment data of the pet owner when making a suggestion. The suggestion unit can, for example, suggest a meal time for the pet that matches the owner's life rhythm. The suggestion unit can also suggest an exercise method for the pet that matches the owner's living environment. Furthermore, the suggestion unit can suggest a care method for the pet that matches the owner's life style. This makes it possible to make a more appropriate suggestion by referring to the living environment data of the owner. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without using AI. For example, the suggestion unit can input the living environment data of the owner to the generation AI and cause the generation AI to provide an optimal suggestion.

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

[0106] The pet care system can further include a function to monitor the social behavior of pets. For example, by observing how pets interact with other pets and humans using cameras and sensors and analyzing the data, the social health of pets can be understood. If a pet feels lonely, the system can suggest that the owner increase the amount of playtime with the pet. Furthermore, if a pet likes to play with other pets, it can suggest social events between pets. This can help maintain the social health of pets and reduce stress.

[0107] The pet care system can also be equipped with a function to monitor the pet's sleep patterns. For example, by measuring the pet's sleep time and sleep quality with a sensor and analyzing the data, the pet's health condition can be understood in more detail. Also, if the pet is not getting enough sleep, the pet care system can provide the owner with advice on how to improve the pet's sleeping environment. Furthermore, it can also suggest optimal feeding and exercise timing based on the pet's sleep patterns. This can improve the overall health of the pet.

[0108] The pet care system can also be equipped with a function to monitor allergic reactions of pets. For example, if a pet has an allergic reaction to a certain food ingredient or environmental factor, the data can be collected and analyzed to identify the cause of the allergy. In addition, when an allergic reaction occurs, an alert can be sent quickly to the owner and an appropriate response can be suggested. Furthermore, based on the pet's allergy history, it is possible to suggest a meal plan and environmental improvement measures to avoid allergies. This can reduce health risks for pets and support a comfortable life.

[0109] The pet care system can further include a function to monitor the amount of exercise a pet gets and propose an appropriate exercise plan. For example, by using a sensor to measure how much exercise a pet gets on a daily basis and analyzing the data, it is possible to detect whether the pet is exercising too much or not. It can also suggest the optimal amount and method of exercise depending on the pet's age and health condition. Furthermore, if a pet likes a particular type of exercise, it is possible to propose a plan that incorporates that exercise. This helps maintain the pet's health and reduce health risks caused by obesity and lack of exercise.

[0110] The pet care system can further include a function to monitor the dietary content of a pet and optimize the nutritional balance. For example, by recording the dietary content of a pet on a daily basis and analyzing the data, it is possible to detect imbalances in nutritional balance. It can also propose optimal meal plans according to the pet's age and health condition. Furthermore, if a pet has a preference for a particular ingredient, it can also propose a well-balanced meal plan that includes that ingredient. This helps maintain the pet's health and reduce health risks caused by nutritional deficiencies and overconsumption.

[0111] The pet care system can further include a function to estimate the pet's emotions and evaluate the pet's stress level based on the estimated emotions. For example, it can analyze the tone of the pet's voice and changes in facial expressions to determine whether the pet is stressed. If the pet is stressed, it can provide the owner with advice on how to reduce the pet's stress. It can also suggest appropriate play and relaxation methods based on the pet's stress level. This can reduce the pet's stress and support a comfortable life.

[0112] The pet care system can further include a function of estimating the emotions of the pet and analyzing the behavioral patterns of the pet based on the estimated emotions. For example, if the pet is excited, it can analyze whether the pet tends to behave actively and grasp the behavioral patterns of the pet. Also, if the pet is relaxed, it can analyze whether the pet tends to rest in quiet places. Furthermore, it can suggest appropriate environmental settings and play based on the behavioral patterns of the pet. This makes it possible to understand the behavior of the pet and provide more appropriate care.

[0113] The pet care system can further include a function to estimate the pet's emotions and predict the pet's health condition based on the estimated emotions. For example, if the pet frequently feels anxious, it can predict that the pet's health is likely to deteriorate and suggest an early health check to the owner. Also, if the pet is always relaxed, it can suggest that the pet is in good health. Furthermore, it can predict future health risks based on the pet's emotion data and suggest preventive measures. This can help maintain the pet's health and detect problems early.

[0114] The pet care system can further include a function of estimating the pet's emotions and suggesting activities for the pet based on the estimated emotions. For example, if the pet is excited, active activities for releasing energy can be suggested. If the pet is relaxed, relaxing activities in a quiet environment can be suggested. Furthermore, if the pet is feeling anxious, activities that give a sense of security can be suggested. In this way, activities that correspond to the pet's emotions can be offered, reducing the pet's stress.

[0115] The pet care system may further include a function of estimating the pet's emotions and adjusting the pet's health care plan based on the estimated emotions. For example, if the pet is frequently stressed, a special care plan for stress reduction may be proposed. If the pet is relaxed, the regular health care plan may be continued. Furthermore, based on the pet's emotion data, the effectiveness of the health care plan may be evaluated and the plan may be adjusted as necessary. This allows health care to be performed according to the pet's emotions and the pet's health to be maintained.

[0116] The process flow of the second embodiment will be briefly described below.

[0117] Step 1: The collection unit collects data such as the pet's voice, facial expressions, movements, body temperature, appetite, and feces. The collection unit uses sensors and cameras to collect various data about the pet in real time. For example, the collection unit collects the pet's voice with a microphone, captures its facial expressions and movements with a camera, and measures its body temperature with a sensor. The collection unit can also record the pet's appetite and feces condition. Step 2: The analysis unit analyzes the data collected by the collection unit and determines the pet's physical condition and mood. The analysis unit analyzes the collected data using AI. For example, the analysis unit determines whether the pet is happy or sad from the tone of the pet's voice and changes in facial expression. The analysis unit can also grasp the pet's health condition from changes in movement and body temperature. Step 3: The visualization unit visualizes the analysis results obtained by the analysis unit. The visualization unit displays the pet's physical condition and emotions using graphs and icons, allowing the owner to understand the pet's condition at a glance. For example, the visualization unit displays the pet's physical condition and emotions using line graphs and bar graphs. The visualization unit can also display the pet's emotions using icons. Step 4: The suggestion unit suggests an optimal pet environment based on the information visualized by the visualization unit. The suggestion unit suggests the optimal diet and interaction for the pet by combining data on other animals and data on the home and pet environments. For example, the suggestion unit compares data on pets of the same species and suggests the optimal diet and amount of exercise. The suggestion unit can also provide advice according to the home and pet environments.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires 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.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<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.

[0120] 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.

[0121] Each of the multiple elements including the above-mentioned collection unit, analysis unit, visualization unit, and proposal unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the voice, facial expression, and movement of the pet using the camera 42 and microphone 38B of the smart device 14, and measures the body temperature with a body temperature sensor. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data with AI. The visualization unit displays the physical condition and feelings of the pet as graphs and icons using, for example, the display 40A of the smart device 14. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal breeding environment based on the analysis results. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0123] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0127] 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).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0129] 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.

[0130] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0132] 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.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may 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.

[0137] Each of the multiple elements including the above-mentioned collection unit, analysis unit, visualization unit, and proposal unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the voice, facial expression, and movement of the pet using the camera 42 and microphone 238 of the smart glasses 214, and measures the body temperature with a body temperature sensor. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data with AI. The visualization unit displays the physical condition and feelings of the pet as graphs and icons using, for example, the display of the smart glasses 214. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal breeding environment based on the analysis results. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

[0143] 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).

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

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0148] 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.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may 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.

[0153] Each of the multiple elements including the above-mentioned collection unit, analysis unit, visualization unit, and proposal unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects the voice, facial expression, and movement of the pet using the camera 42 and microphone 238 of the headset type terminal 314, and measures the body temperature with a body temperature sensor. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data with AI. The visualization unit displays the physical condition and feelings of the pet as graphs and icons using, for example, the display 343 of the headset type terminal 314. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal breeding environment based on the analysis results. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0155] 7, 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.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0159] 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).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0161] 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.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0165] 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.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires 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.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may 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.

[0170] Each of the multiple elements including the above-mentioned collection unit, analysis unit, visualization unit, and proposal unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the voice, facial expression, and movement of the pet using the camera 42 and microphone 238 of the robot 414, and measures the body temperature with a body temperature sensor. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data with AI. The visualization unit displays the physical condition and feelings of the pet as graphs and icons using the display of the robot 414. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal breeding environment based on the analysis results. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0171] 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.

[0172] 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.

[0173] These emotions are distributed in the 3 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a 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.

[0180] 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.

[0181] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] (Appendix 1) A system comprising: a collection unit that collects data on a pet's voice, facial expressions, movements, body temperature, appetite, and feces; an analysis unit that analyzes the data collected by the collection unit and determines the pet's physical condition and mood; a visualization unit that visualizes the analysis results obtained by the analysis unit; and a proposal unit that proposes an appropriate pet environment based on the information visualized by the visualization unit. (Appendix 2) The system described in Appendix 1, wherein the collection unit uses a sensor or camera to collect data on the pet's voice, facial expressions, movements, body temperature, appetite, and feces in real time. (Appendix 3) The analysis unit is AI learns from collected data to determine pet health and mood 2. The system of claim 1, (Appendix 4) The visualization unit is Displays a pet's physical condition and mood using graphs and icons, allowing owners to understand their pet's condition at a glance 2. The system of claim 1, (Appendix 5) The system described in Appendix 1, characterized in that the suggestion unit combines data on other animals or data on the home environment or rearing environment to suggest appropriate diet or ways of interacting with the pet. (Appendix 6) The suggestion unit, Providing advice based on your pet's physical condition and mood 2. The system of claim 1, (Appendix 7) The collecting unit includes: Inferring pet emotions and adjusting the frequency of data collection based on the estimated pet emotions 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze your pet's past health data and choose the best time to collect it 2. The system of claim 1, (Appendix 9) The collecting unit includes: During data collection, dynamically change the type of data you collect based on your pet's activity level 2. The system of claim 1, (Appendix 10) 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 11) The collecting unit includes: When collecting data, adjust the data you collect to account for changes in your pet's living environment. 2. The system of claim 1, (Appendix 12) The collecting unit includes: Optimize collection timing based on pet owners' schedules when collecting data 2. The system of claim 1, (Appendix 13) The analysis unit is Estimate the pet's emotions and adjust the analysis algorithm based on the estimated pet's emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, the accuracy of the analysis can be improved by referring to the pet's past health data. 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis methods are applied depending on the type and age of the pet. 2. The system of claim 1, (Appendix 16) The analysis unit is Estimate your pet's emotions and adjust how the analysis results are displayed based on the estimated pet's emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, data on pets' living environments is referenced to complement the analysis results. 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, feedback from pet owners will be incorporated to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 19) The visualization unit is Infer the pet's emotions and adjust the visualization based on the inferred emotions 2. The system of claim 1, (Appendix 20) The visualization unit is When visualizing, adjust the level of detail displayed based on the importance of pet health data 2. The system of claim 1, (Appendix 21) The visualization unit is When visualizing, different visualization techniques are applied depending on the type and age of the pet. 2. The system of claim 1, (Appendix 22) The visualization unit is Infer the pet's emotions and prioritize visualizations based on the inferred pet's emotions 2. The system of claim 1, (Appendix 23) The visualization unit is When visualizing, the optimal display method is selected taking into account the pet owner's device information. 2. The system of claim 1, (Appendix 24) The visualization unit is When visualizing, the system selects the optimal display method by referring to the pet owner's past operation history. 2. The system of claim 1, (Appendix 25) The suggestion unit, Estimate your pet's emotions and tailor suggestions based on your pet's emotions 2. The system of claim 1, (Appendix 26) The suggestion unit, When making suggestions, the system will refer to your pet's past health data to provide optimal suggestions. 2. The system of claim 1, (Appendix 27) The suggestion unit, When making suggestions, different suggestion methods are applied depending on the type and age of the pet. 2. The system of claim 1, (Appendix 28) The suggestion unit, Estimate pet emotions and prioritize suggestions based on the estimated pet emotions 2. The system of claim 1, (Appendix 29) The suggestion unit, When making suggestions, the system will refer to the pet owner's living environment data to provide optimal suggestions. 2. The system of claim 1, (Appendix 30) The suggestion unit, When making suggestions, incorporate feedback from pet owners to improve the suggestions 2. The system of claim 1, [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects data on your pet's voice, facial expression, movement, body temperature, appetite, and feces. an analysis unit that analyzes the data collected by the collection unit and determines the physical condition and mood of the pet; a visualization unit that visualizes the analysis results obtained by the analysis unit; A suggestion unit that suggests an appropriate breeding environment based on the information visualized by the visualization unit. A system characterized by:

2. The collecting unit includes: Using sensors or cameras, data on pets' voices, facial expressions, movements, body temperature, appetite, and feces is collected in real time.

2. The system of claim 1 .

3. The analysis unit is The data collected by the collection unit is learned by AI to determine the pet's physical condition and mood.

2. The system of claim 1 .

4. The visualization unit is Displays a pet's physical condition and mood using graphs and icons, allowing owners to understand their pet's condition at a glance 2. The system of claim 1 .

5. The suggestion unit, Combined with data from other animals or from the home and environment, it can suggest appropriate diets or interactions for pets.

2. The system of claim 1 .

6. The suggestion unit, Providing advice based on your pet's physical condition and mood 2. The system of claim 1 .

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