system

Abstract

The system according to this embodiment aims to allow pet owners to understand their pet's behavior and vocalizations even when they are away from home, thereby enabling them to feel a stronger bond with their pet. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a reproduction unit, and a notification unit. The collection unit collects pet behavior data. The analysis unit analyzes the data collected by the collection unit. The reproduction unit reproduces the pet's vocalizations based on the data analyzed by the analysis unit. The notification unit notifies the owner of the vocalizations reproduced by the reproduction unit.

Description

Technical Field

[0006] , , ,

[0005] , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] <00000′16>

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to grasp the behavior and vocalization of a pet when away from home, and the opportunity for the owner to feel the bond with the pet is limited.

[0005] The system according to the embodiment aims to enable the owner to grasp the behavior and vocalization of the pet even when away from home and to feel the bond with the pet.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a reproduction unit, and a notification unit. The collection unit collects behavioral data of the pet. The analysis unit analyzes the data collected by the collection unit. The reproduction unit reproduces the pet's vocalizations based on the data analyzed by the analysis unit. The notification unit notifies the owner of the vocalizations reproduced by the reproduction unit. [Effects of the Invention]

[0007] The system according to this embodiment allows pet owners to monitor their pet's behavior and vocalizations even when they are away from home, enabling them to feel a stronger bond with their pet. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F conducts communication among a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The smart pet care system according to an embodiment of the present invention is an AI service that allows pet owners to feel connected to their pets even when they are away from home. This smart pet care system uses a multimodal AI to learn and record the pet's behavior and vocalizations, and provides alert notifications of videos and cute gestures of the pet even when the owner is away. Furthermore, the AI ​​can reproduce the pet's vocalizations, providing the owner with the experience of their pet being affectionate. This allows pet owners to feel connected to their pets even when they are away, providing comfort and a heartwarming moment. For example, the smart pet care system uses a multimodal AI to learn and record the pet's behavior and vocalizations. For instance, the system can capture images of the pet playing or sleeping with a camera, and the AI ​​analyzes this data. This allows the system to understand the pet's behavior patterns and vocalization characteristics. Next, the smart pet care system provides alert notifications of videos and cute gestures of the pet even when the owner is away from home. For example, if the camera captures the pet playing or being affectionate, the system notifies the owner's smartphone of the video. This allows the owner to check on their pet in real time. Furthermore, the smart pet care system uses AI to reproduce pet sounds, providing owners with an experience of being pampered by their pet. For example, if a pet barks, the AI ​​analyzes the sound and reproduces it on the smartphone. This allows owners to hear their pet's voice and experience it as if their pet were right beside them. This service is especially useful for busy business people and those who travel frequently, as it allows them to feel connected to their pets even when they are away. It also provides peace of mind by allowing owners to check on their pets in real time. In addition, the generative AI analyzes pet behavior and vocal data to generate text and voice messages expressing the pet's emotions. For example, if a pet wants to play, the AI ​​analyzes that emotion and generates a message such as "I want to play." This makes it easier for owners to understand their pet's feelings. Using speech synthesis technology, the system reproduces pet voices, providing owners with a simulated communication experience. For example, if a pet barks because it is hungry, the AI ​​reproduces the sound and tells the owner, "I'm hungry."This allows pet owners to respond to their pets' needs. Thus, the smart pet care system is an AI service that allows pet owners to feel connected to their pets even when they are away, making it an extremely useful tool for pet owners. In this way, the smart pet care system allows pet owners to feel connected to their pets even when they are away, providing comfort and a heartwarming moment.

[0029] The smart pet care system according to this embodiment comprises a data collection unit, an analysis unit, a reproduction unit, and a notification unit. The data collection unit collects pet behavior data. The data collection unit can collect pet behavior data, for example, using a camera. The data collection unit can take pictures of the pet playing or sleeping with a camera and collect the data. The data collection unit can also take pictures of the pet eating or going for a walk with a camera and collect the data. When collecting pet behavior data, the data collection unit can also simultaneously collect environmental data of the pet's surroundings. The data collection unit can record the temperature and humidity of the place where the pet is playing, for example. The data collection unit can also record the brightness of the room where the pet is. The data collection unit can also record the sound environment around the pet. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze pet behavior data, for example, using multimodal AI. The analysis unit can analyze the collected data to understand the pet's behavior patterns and vocalization characteristics. The analysis unit can, for example, analyze pet behavior data and generate the pet's emotions and messages in text or voice. When analyzing pet behavior data, the analysis unit can improve the accuracy of the analysis by referring to the pet's past behavior patterns. The analysis unit can, for example, predict current behavior based on the pet's past behavior data. The analysis unit can also detect abnormal behavior by referring to the pet's past behavior patterns. The analysis unit can also analyze changes in behavior using the pet's past behavior data. The reproduction unit reproduces the pet's barks based on the data analyzed by the analysis unit. The reproduction unit can reproduce the pet's barks using, for example, speech synthesis technology. The reproduction unit can use speech synthesis technology based on collected data to realistically reproduce the pet's barks. When reproducing the pet's barks, the reproduction unit can improve the accuracy of the reproduction by referring to the pet's past barking data. The reproduction unit can, for example, reproduce current barks based on the pet's past barking data. The reproduction unit can also detect abnormal barks by referring to the pet's past barking patterns.The reproduction unit can also analyze changes in the pet's vocalizations using past vocalization data. The notification unit notifies the owner of the vocalizations reproduced by the reproduction unit. The notification unit can, for example, alert the owner of the pet's cute behaviors. The notification unit can send alert notifications to the owner based on the pet's behavior data. The notification unit can analyze the pet's behavior data and send alert notifications for specific behaviors. For example, the notification unit can send alert notifications for cute behaviors such as the pet wagging its tail or jumping. The notification unit can send real-time notifications to the owner based on the pet's behavior data. For example, if the camera captures the pet playing or being affectionate, the notification unit can send the video to the smartphone. As a result, the smart pet care system according to this embodiment allows the owner to feel a bond with their pet even when away from home, providing comfort and a heartwarming moment.

[0030] The data collection unit collects pet behavior data. For example, the data collection unit can use a camera to collect pet behavior data. Specifically, the camera captures the pet's movements in high resolution, recording all daily activities such as playing, sleeping, eating, and walking. This allows for a detailed understanding of the pet's behavior patterns. Furthermore, the data collection unit can simultaneously collect environmental data surrounding the pet. For example, it records the temperature and humidity of the area where the pet is playing, the brightness of the room, and the surrounding sound environment. This allows for an understanding of the environmental conditions under which the pet's behavior occurs. The data collection unit collects this data in real time and transmits it to a central database. The data is stored on a cloud server, making it accessible to the analysis and reproduction units. By adjusting the data collection frequency and accuracy, the data collection unit can flexibly respond to specific situations and conditions. For example, it can collect data at a high frequency during times when the pet is active and reduce the collection frequency during times when the pet is resting. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze pet behavior data using multimodal AI. Specifically, the AI ​​uses image recognition technology to analyze camera footage and understand the pet's behavior patterns and vocal characteristics. For example, it can analyze the pet's movements while playing, its posture while eating, and its sleeping position to infer its emotions and health status. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to the pet's past behavior data. For example, it can predict current behavior based on the pet's past behavior patterns and detect abnormal behavior. This allows for real-time monitoring of the pet's health status and stress levels, enabling early detection of abnormalities. When analyzing pet behavior data, the analysis unit can also generate the pet's emotions and messages in text or voice. For example, it can analyze the pet's behavior patterns when it is happy or sad and communicate those emotions to the owner in text or voice. This allows the owner to understand their pet's feelings and provide better care.

[0032] The reproduction unit reproduces the pet's barks based on the data analyzed by the analysis unit. The reproduction unit can reproduce the pet's barks using, for example, speech synthesis technology. Specifically, it uses speech synthesis technology to realistically reproduce the pet's barks based on the collected data. The reproduction unit can improve the reproduction accuracy by referring to the pet's past barking data. For example, it can reproduce the current bark based on the pet's past barking data and detect abnormal barks. This allows for a more accurate understanding of the pet's health and emotions. When reproducing the pet's barks, the reproduction unit can also analyze changes in the barks by referring to the pet's past barking patterns. For example, it can analyze changes in barks when the pet is sick or stressed and notify the owner. This allows the owner to understand the pet's health in real time and take early action.

[0033] The notification unit notifies the owner of the sounds reproduced by the reproduction unit. The notification unit can, for example, alert the owner of cute pet behaviors. Specifically, if the camera captures a pet wagging its tail or jumping, the video can be sent to the owner's smartphone as a notification. The notification unit can provide real-time notifications to the owner based on the pet's behavior data. For example, if the camera captures the pet playing or being affectionate, the video can be sent to the owner's smartphone as a notification. This allows the owner to feel connected to their pet even when away from home, providing comfort and a heartwarming moment. Furthermore, the notification unit can analyze the pet's behavior data and provide alert notifications for specific behaviors. For example, it can provide alert notifications for cute pet behaviors such as wagging its tail or jumping. The notification unit can collect user feedback and continuously improve the accuracy and effectiveness of notifications. For example, based on feedback from users who have received evacuation orders, it can revise evacuation routes and improve the content of the orders. In addition, the notification unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the notification system to provide users with quick and reliable instructions, minimizing the risk of disaster.

[0034] The analysis unit can analyze pet behavior data using multimodal AI. For example, the analysis unit can analyze pet behavior data using multimodal AI. For example, the multimodal AI can analyze pet behavior data using a combination of image recognition and speech recognition. When analyzing pet behavior data, the analysis unit can improve the accuracy of the analysis by referring to the pet's past behavior patterns. For example, the analysis unit can predict current behavior based on the pet's past behavior data. The analysis unit can also detect abnormal behavior by referring to the pet's past behavior patterns. The analysis unit can also analyze changes in behavior using the pet's past behavior data. As a result, the accuracy of the analysis of pet behavior data is improved by using multimodal AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input pet behavior data into a generating AI and have the generating AI perform the analysis of the behavior data.

[0035] The reproduction unit can reproduce pet sounds using speech synthesis technology. The reproduction unit can realistically reproduce pet sounds using, for example, speech synthesis technology. For example, speech synthesis technology can reproduce pet sounds using techniques such as text-to-speech synthesis and waveform-to-speech synthesis. When reproducing pet sounds, the reproduction unit can improve the reproduction accuracy by referring to the pet's past sound data. For example, the reproduction unit can reproduce current sounds based on the pet's past sound data. The reproduction unit can also detect abnormal sounds by referring to the pet's past sound patterns. The reproduction unit can also analyze changes in sounds using the pet's past sound data. As a result, pet sounds can be realistically reproduced using speech synthesis technology. Some or all of the above processing in the reproduction unit may be performed using, for example, AI, or without AI. For example, the reproduction unit can input pet sound data into a generating AI and have the generating AI perform the sound reproduction.

[0036] The notification unit can alert the owner of a pet for specific behaviors. For example, it can alert the owner of a pet for cute behaviors. For example, it can alert the owner of a pet for cute behaviors such as wagging its tail or jumping. The notification unit can notify the owner in real time based on the pet's behavior data. For example, if the camera captures the pet playing or being affectionate, the notification unit can notify the owner of the video on their smartphone. The notification unit can analyze the pet's behavior data and alert the owner of specific behaviors. This allows the owner to check on their pet in real time by alerting them of cute behaviors. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the pet's behavior data into a generating AI and have the generating AI detect and notify of specific behaviors.

[0037] The analysis unit can estimate a pet's emotions and generate a message in text or voice. For example, the analysis unit can estimate a pet's emotions by analyzing the pet's behavioral data. The analysis unit can use an emotion estimation algorithm to estimate a pet's emotions. The analysis unit can estimate a pet's emotions and generate a message based on those emotions. For example, if a pet wants to play, the analysis unit can analyze that emotion and generate a message such as "I want to play." The analysis unit can also estimate a pet's emotions and generate a voice message based on those emotions. For example, if a pet cries out "I'm hungry," the analysis unit can analyze that sound and generate a voice message such as "I'm hungry." This makes it easier for owners to understand their pets' feelings by generating messages about their pets' emotions in text or voice. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input pet behavior data into a generating AI, which can then perform emotion estimation and message generation.

[0038] The data collection unit can select a data collection method based on the pet's activity level and health condition. For example, the data collection unit can record the pet's behavior as a video if the pet is actively moving around. If the pet is resting, the data collection unit can record the pet's behavior as a still image. If the pet is in poor health, the data collection unit can increase the frequency of behavioral data collection. This allows for the collection of appropriate data by selecting a data collection method according to the pet's activity level and health condition. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the pet's activity level and health condition into a generating AI and have the generating AI select the data collection method.

[0039] The data collection unit can collect environmental data surrounding the pet when collecting behavioral data about the pet. For example, the data collection unit can record the temperature and humidity of the place where the pet is playing. The data collection unit can record the brightness of the room where the pet is. The data collection unit can record the sound environment around the pet. The data collection unit can collect environmental data surrounding the pet when collecting behavioral data about the pet. By collecting behavioral data and environmental data simultaneously, more detailed data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input pet behavioral data and environmental data into a generating AI and have the generating AI perform the data collection.

[0040] The data collection unit can select the data to collect based on the pet's location information when collecting pet behavior data. For example, if the pet is outdoors, the data collection unit can collect GPS data. If the pet is in a specific room, the data collection unit can collect environmental data for that room. If the pet is moving, the data collection unit can record the movement path. By collecting data while considering the pet's location information, more appropriate data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input pet location data into a generating AI and have the generating AI perform the data collection.

[0041] The data collection unit can collect data on the pet's diet and exercise while simultaneously collecting behavioral data. For example, the unit can record the content and amount of food the pet eats when it is eating. The unit can also record the type and duration of exercise the pet does when it is exercising. The unit can combine the data on the pet's diet and exercise to evaluate its health status. This allows for a more detailed understanding of the pet's health status by simultaneously collecting behavioral data and data on diet and exercise. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the pet's diet and exercise data into a generating AI, and have the generating AI perform data collection and health status evaluation.

[0042] The analysis unit can improve the accuracy of its analysis by referring to the pet's past behavioral patterns when analyzing pet behavioral data. For example, the analysis unit can predict current behavior based on the pet's past behavioral data. The analysis unit can detect abnormal behavior by referring to the pet's past behavioral patterns. The analysis unit can analyze changes in behavior using the pet's past behavioral data. This improves the accuracy of the analysis by referring to the pet's past behavioral patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the pet's past behavioral data into a generating AI and have the generating AI perform the analysis of the behavioral data.

[0043] The analysis unit can adjust its analysis algorithm based on the pet's health and age when analyzing pet behavior data. For example, if the pet is in good health, the analysis unit uses a standard analysis algorithm. If the pet is in poor health, the analysis unit can use an algorithm to detect abnormal behavior. Depending on the pet's age, the analysis unit can use an analysis algorithm that takes into account changes in behavioral patterns. By adjusting the analysis algorithm according to the pet's health and age, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the pet's health and age into a generating AI and have the generating AI perform the adjustment of the analysis algorithm.

[0044] The analysis unit can improve the accuracy of its analysis by referring to the pet's living environment data when analyzing pet behavior data. For example, the analysis unit can analyze behavior data while considering the temperature and humidity of the room where the pet is located. The analysis unit can analyze behavior data while considering the sound environment around the pet. The analysis unit can detect behavioral abnormalities based on the pet's living environment data. As a result, the accuracy of the analysis is improved by referring to the pet's living environment data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input pet living environment data into a generating AI and have the generating AI perform the analysis of behavior data.

[0045] The analysis unit can supplement its analysis results by referring to the pet's diet and exercise data when analyzing pet behavioral data. For example, the analysis unit can analyze changes in behavior based on the pet's diet data. The analysis unit can detect behavioral abnormalities based on the pet's exercise data. The analysis unit can evaluate the pet's health status by combining the pet's diet and exercise data. This allows for a more accurate analysis by supplementing the analysis results by referring to the pet's diet and exercise data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input pet diet and exercise data into a generating AI and have the generating AI perform the data analysis.

[0046] The reproduction unit can improve reproduction accuracy by referring to past pet vocalization data. For example, the reproduction unit can reproduce the current vocalization based on the pet's past vocalization data. The reproduction unit can detect abnormal vocalizations by referring to the pet's past vocalization patterns. The reproduction unit can analyze changes in vocalizations using the pet's past vocalization data. This improves reproduction accuracy by referring to the pet's past vocalization data. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the pet's past vocalization data into a generating AI and have the generating AI perform the vocalization reproduction.

[0047] The reproduction unit can adjust the reproduction algorithm based on the pet's emotions and situation. For example, if the pet is relaxed, the reproduction unit can reproduce a relaxed bark. If the pet is excited, the reproduction unit can reproduce an energetic bark. If the pet is feeling lonely, the reproduction unit can reproduce a pleading bark. By adjusting the reproduction algorithm according to the pet's emotions and situation, more accurate barks can be reproduced. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input data on the pet's emotions and situation into a generating AI and have the generating AI adjust the reproduction algorithm.

[0048] The reproduction unit can improve reproduction accuracy by referring to data on the pet's living environment. For example, the reproduction unit can reproduce vocalizations considering the temperature and humidity of the room where the pet is located. The reproduction unit can reproduce vocalizations considering the sound environment around the pet. The reproduction unit can detect abnormalities in vocalizations based on the pet's living environment data. As a result, reproduction accuracy is improved by referring to the pet's living environment data. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the pet's living environment data into a generating AI and have the generating AI perform the reproduction of vocalizations.

[0049] The reproduction unit can supplement the reproduction results by referring to the pet's diet and exercise data. For example, the reproduction unit can analyze changes in vocalizations based on the pet's diet data. The reproduction unit can detect abnormalities in vocalizations based on the pet's exercise data. The reproduction unit can improve the accuracy of vocalization reproduction by combining the pet's diet and exercise data. As a result, by referring to the pet's diet and exercise data, the reproduction results can be supplemented, and more accurate vocalizations can be reproduced. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the pet's diet and exercise data into a generating AI and have the generating AI perform the vocalization reproduction.

[0050] The notification unit can improve notification accuracy by referring to the pet's past behavior patterns when alerting about the pet's cute behavior. For example, the notification unit can predict and notify about cute behavior based on the pet's past behavior data. The notification unit can detect and notify about abnormal behavior by referring to the pet's past behavior patterns. The notification unit can analyze and notify about changes in behavior using the pet's past behavior data. This improves notification accuracy by referring to the pet's past behavior patterns. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the pet's past behavior data into a generating AI and have the generating AI perform the analysis of the behavior data and the notification.

[0051] The notification unit can adjust its notification algorithm based on the pet's health and age when alerting users about their pet's cute behavior. For example, if the pet is in good health, the notification unit uses a standard notification algorithm. If the pet is in poor health, the notification unit can use a notification algorithm to detect abnormal behavior. Depending on the pet's age, the notification unit can use a notification algorithm that takes into account changes in behavioral patterns. This allows for more accurate notifications by adjusting the notification algorithm according to the pet's health and age. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on the pet's health and age into a generating AI and have the generating AI perform the adjustment of the notification algorithm.

[0052] The notification unit can improve notification accuracy by referring to the pet's living environment data when issuing alert notifications for the pet's cute behavior. For example, the notification unit can consider the temperature and humidity of the room where the pet is located when issuing notifications. The notification unit can also consider the sound environment around the pet when issuing notifications. Based on the pet's living environment data, the notification unit can detect and notify of abnormal behavior. As a result, the notification accuracy is improved by referring to the pet's living environment data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the pet's living environment data into a generating AI and have the generating AI perform the task of improving notification accuracy.

[0053] The notification unit can supplement its notification results by referring to the pet's diet and exercise data when it alerts the pet about its cute behavior. For example, the notification unit can analyze and notify about changes in behavior based on the pet's diet data. The notification unit can detect and notify about abnormal behavior based on the pet's exercise data. The notification unit can analyze and notify about changes in behavior by combining the pet's diet and exercise data. This allows for more accurate notifications by supplementing the notification results by referring to the pet's diet and exercise data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the pet's diet and exercise data into a generating AI and have the generating AI execute the notification results.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The smart pet care system can also include a health management unit that monitors the pet's health. The health management unit collects vital data such as the pet's body temperature, heart rate, and respiratory rate, and transmits it to the analysis unit. The analysis unit evaluates the pet's health based on this vital data, and if an abnormality is detected, it can send an alert to the owner through the notification unit. For example, if the pet's body temperature is higher than normal, the analysis unit can detect a possible fever and notify the owner. If the heart rate is abnormally high, it can be analyzed as a sign of stress or excitement and the owner can be notified. If the respiratory rate is abnormally low, it may indicate a health problem, and a notification can be sent to urge immediate action. This allows owners to understand their pet's health in real time and take appropriate action.

[0056] The smart pet care system can also include a learning unit that learns the pet's preferences and habits based on behavioral data. The learning unit records the pet's preferred behaviors at specific times of day and activities in specific locations, and transmits this data to the analysis unit. The analysis unit uses this data to understand the pet's preferences and habits and can make suggestions to the owner. For example, if the pet likes to go for a walk at a specific time every morning, the analysis unit will notify the owner of a suggestion to go for a walk at that time. If the pet likes to play in a specific place, the analysis unit can suggest playing in that place. If the pet likes a particular food, the analysis unit can suggest the best time to give that food. This allows owners to provide care tailored to their pet's preferences and habits, deepening their bond with their pet.

[0057] The smart pet care system can also include a training support unit that assists with pet training based on pet behavior data. The training support unit analyzes the pet's behavior data and evaluates the training progress. The analysis unit can then evaluate the effectiveness of the training based on this data and provide feedback to the owner. For example, it can record the frequency and success rate of the pet following specific commands to evaluate training progress. It can analyze the difficulties the pet faces in learning specific behaviors and suggest improvements. If the pet is experiencing stress during training, it can detect signs of this and suggest a review of the training method. This allows owners to conduct effective training and support their pet's growth.

[0058] The smart pet care system can also include a sociability assessment unit that evaluates the pet's sociability based on behavioral data. The sociability assessment unit collects data on the pet's interactions with other animals and people and transmits it to the analysis unit. The analysis unit can evaluate the pet's sociability based on this data and provide feedback to the owner. For example, it can record the frequency and duration of the pet's play with other animals to evaluate sociability. It can also analyze how the pet reacts to new environments and people and reflect this in the sociability evaluation. If the pet experiences stress in a particular situation, it can suggest avoiding that situation. This allows owners to understand their pet's sociability and provide appropriate support.

[0059] The smart pet care system can also include a health prediction unit that predicts the pet's health status based on behavioral data. The health prediction unit analyzes the pet's behavioral and vital data to predict its future health status. The analysis unit evaluates the pet's health risks based on this data and can notify the owner of the prediction results. For example, if a pet's appetite decreases, it can predict future health risks and suggest early intervention. If a pet's exercise level decreases, it can predict a deterioration in health status and suggest increasing exercise. If abnormalities are found in the pet's vital data, it can evaluate health risks and send a notification encouraging early veterinary examination. This allows owners to predict their pet's health status and provide appropriate care.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects pet behavior data. The data collection unit can collect pet behavior data using, for example, a camera. The data collection unit can take pictures of the pet playing, sleeping, eating, walking, etc., and collect the data. The data collection unit can also simultaneously collect environmental data of the pet's surroundings, such as recording the temperature and humidity of the place where the pet is playing, the brightness of the room, and the sound environment. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze pet behavior data using multimodal AI to understand the pet's behavior patterns and vocal characteristics. The analysis unit can generate the pet's emotions and messages in text or voice, and can improve the accuracy of the analysis by referring to the pet's past behavior patterns. In addition, the analysis unit can predict current behavior, detect abnormal behavior, and analyze changes in behavior based on the pet's past behavior data. Step 3: The reproduction unit reproduces the pet's barks based on the data analyzed by the analysis unit. The reproduction unit can reproduce the pet's barks using, for example, speech synthesis technology. The reproduction unit can realistically reproduce the pet's barks using speech synthesis technology based on the collected data, and can improve the reproduction accuracy by referring to the pet's past barking data. In addition, the reproduction unit can detect abnormal barks by referring to the pet's past barking patterns and analyze changes in barks. Step 4: The notification unit notifies the owner of the sounds reproduced by the reproduction unit. The notification unit can, for example, alert the owner of cute pet behaviors. The notification unit can alert the owner based on the pet's behavior data and can alert for specific behaviors. For example, it can alert the owner of cute behaviors such as the pet wagging its tail or jumping. In addition, the notification unit can notify the owner in real time based on the pet's behavior data, and if the camera captures the pet playing or being affectionate, it can notify the owner of the video on their smartphone.

[0062] (Example of form 2) The smart pet care system according to an embodiment of the present invention is an AI service that allows pet owners to feel connected to their pets even when they are away from home. This smart pet care system uses a multimodal AI to learn and record the pet's behavior and vocalizations, and provides alert notifications of videos and cute gestures of the pet even when the owner is away. Furthermore, the AI ​​can reproduce the pet's vocalizations, providing the owner with the experience of their pet being affectionate. This allows pet owners to feel connected to their pets even when they are away, providing comfort and a heartwarming moment. For example, the smart pet care system uses a multimodal AI to learn and record the pet's behavior and vocalizations. For instance, the system can capture images of the pet playing or sleeping with a camera, and the AI ​​analyzes this data. This allows the system to understand the pet's behavior patterns and vocalization characteristics. Next, the smart pet care system provides alert notifications of videos and cute gestures of the pet even when the owner is away from home. For example, if the camera captures the pet playing or being affectionate, the system notifies the owner's smartphone of the video. This allows the owner to check on their pet in real time. Furthermore, the smart pet care system uses AI to reproduce pet sounds, providing owners with an experience of being pampered by their pet. For example, if a pet barks, the AI ​​analyzes the sound and reproduces it on the smartphone. This allows owners to hear their pet's voice and experience it as if their pet were right beside them. This service is especially useful for busy business people and those who travel frequently, as it allows them to feel connected to their pets even when they are away. It also provides peace of mind by allowing owners to check on their pets in real time. In addition, the generative AI analyzes pet behavior and vocal data to generate text and voice messages expressing the pet's emotions. For example, if a pet wants to play, the AI ​​analyzes that emotion and generates a message such as "I want to play." This makes it easier for owners to understand their pet's feelings. Using speech synthesis technology, the system reproduces pet voices, providing owners with a simulated communication experience. For example, if a pet barks because it is hungry, the AI ​​reproduces the sound and tells the owner, "I'm hungry."This allows pet owners to respond to their pets' needs. Thus, the smart pet care system is an AI service that allows pet owners to feel connected to their pets even when they are away, making it an extremely useful tool for pet owners. In this way, the smart pet care system allows pet owners to feel connected to their pets even when they are away, providing comfort and a heartwarming moment.

[0063] The smart pet care system according to this embodiment comprises a data collection unit, an analysis unit, a reproduction unit, and a notification unit. The data collection unit collects pet behavior data. The data collection unit can collect pet behavior data, for example, using a camera. The data collection unit can take pictures of the pet playing or sleeping with a camera and collect the data. The data collection unit can also take pictures of the pet eating or going for a walk with a camera and collect the data. When collecting pet behavior data, the data collection unit can also simultaneously collect environmental data of the pet's surroundings. The data collection unit can record the temperature and humidity of the place where the pet is playing, for example. The data collection unit can also record the brightness of the room where the pet is. The data collection unit can also record the sound environment around the pet. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze pet behavior data, for example, using multimodal AI. The analysis unit can analyze the collected data to understand the pet's behavior patterns and vocalization characteristics. The analysis unit can, for example, analyze pet behavior data and generate the pet's emotions and messages in text or voice. When analyzing pet behavior data, the analysis unit can improve the accuracy of the analysis by referring to the pet's past behavior patterns. The analysis unit can, for example, predict current behavior based on the pet's past behavior data. The analysis unit can also detect abnormal behavior by referring to the pet's past behavior patterns. The analysis unit can also analyze changes in behavior using the pet's past behavior data. The reproduction unit reproduces the pet's barks based on the data analyzed by the analysis unit. The reproduction unit can reproduce the pet's barks using, for example, speech synthesis technology. The reproduction unit can use speech synthesis technology based on collected data to realistically reproduce the pet's barks. When reproducing the pet's barks, the reproduction unit can improve the accuracy of the reproduction by referring to the pet's past barking data. The reproduction unit can, for example, reproduce current barks based on the pet's past barking data. The reproduction unit can also detect abnormal barks by referring to the pet's past barking patterns.The reproduction unit can also analyze changes in the pet's vocalizations using past vocalization data. The notification unit notifies the owner of the vocalizations reproduced by the reproduction unit. The notification unit can, for example, alert the owner of the pet's cute behaviors. The notification unit can send alert notifications to the owner based on the pet's behavior data. The notification unit can analyze the pet's behavior data and send alert notifications for specific behaviors. For example, the notification unit can send alert notifications for cute behaviors such as the pet wagging its tail or jumping. The notification unit can send real-time notifications to the owner based on the pet's behavior data. For example, if the camera captures the pet playing or being affectionate, the notification unit can send the video to the smartphone. As a result, the smart pet care system according to this embodiment allows the owner to feel a bond with their pet even when away from home, providing comfort and a heartwarming moment.

[0064] The data collection unit collects pet behavior data. For example, the data collection unit can use a camera to collect pet behavior data. Specifically, the camera captures the pet's movements in high resolution, recording all daily activities such as playing, sleeping, eating, and walking. This allows for a detailed understanding of the pet's behavior patterns. Furthermore, the data collection unit can simultaneously collect environmental data surrounding the pet. For example, it records the temperature and humidity of the area where the pet is playing, the brightness of the room, and the surrounding sound environment. This allows for an understanding of the environmental conditions under which the pet's behavior occurs. The data collection unit collects this data in real time and transmits it to a central database. The data is stored on a cloud server, making it accessible to the analysis and reproduction units. By adjusting the data collection frequency and accuracy, the data collection unit can flexibly respond to specific situations and conditions. For example, it can collect data at a high frequency during times when the pet is active and reduce the collection frequency during times when the pet is resting. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0065] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze pet behavior data using multimodal AI. Specifically, the AI ​​uses image recognition technology to analyze camera footage and understand the pet's behavior patterns and vocal characteristics. For example, it can analyze the pet's movements while playing, its posture while eating, and its sleeping position to infer its emotions and health status. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to the pet's past behavior data. For example, it can predict current behavior based on the pet's past behavior patterns and detect abnormal behavior. This allows for real-time monitoring of the pet's health status and stress levels, enabling early detection of abnormalities. When analyzing pet behavior data, the analysis unit can also generate the pet's emotions and messages in text or voice. For example, it can analyze the pet's behavior patterns when it is happy or sad and communicate those emotions to the owner in text or voice. This allows the owner to understand their pet's feelings and provide better care.

[0066] The reproduction unit reproduces the pet's barks based on the data analyzed by the analysis unit. The reproduction unit can reproduce the pet's barks using, for example, speech synthesis technology. Specifically, it uses speech synthesis technology to realistically reproduce the pet's barks based on the collected data. The reproduction unit can improve the reproduction accuracy by referring to the pet's past barking data. For example, it can reproduce the current bark based on the pet's past barking data and detect abnormal barks. This allows for a more accurate understanding of the pet's health and emotions. When reproducing the pet's barks, the reproduction unit can also analyze changes in the barks by referring to the pet's past barking patterns. For example, it can analyze changes in barks when the pet is sick or stressed and notify the owner. This allows the owner to understand the pet's health in real time and take early action.

[0067] The notification unit notifies the owner of the sounds reproduced by the reproduction unit. The notification unit can, for example, alert the owner of cute pet behaviors. Specifically, if the camera captures a pet wagging its tail or jumping, the video can be sent to the owner's smartphone as a notification. The notification unit can provide real-time notifications to the owner based on the pet's behavior data. For example, if the camera captures the pet playing or being affectionate, the video can be sent to the owner's smartphone as a notification. This allows the owner to feel connected to their pet even when away from home, providing comfort and a heartwarming moment. Furthermore, the notification unit can analyze the pet's behavior data and provide alert notifications for specific behaviors. For example, it can provide alert notifications for cute pet behaviors such as wagging its tail or jumping. The notification unit can collect user feedback and continuously improve the accuracy and effectiveness of notifications. For example, based on feedback from users who have received evacuation orders, it can revise evacuation routes and improve the content of the orders. In addition, the notification unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the notification system to provide users with quick and reliable instructions, minimizing the risk of disaster.

[0068] The analysis unit can analyze pet behavior data using multimodal AI. For example, the analysis unit can analyze pet behavior data using multimodal AI. For example, the multimodal AI can analyze pet behavior data using a combination of image recognition and speech recognition. When analyzing pet behavior data, the analysis unit can improve the accuracy of the analysis by referring to the pet's past behavior patterns. For example, the analysis unit can predict current behavior based on the pet's past behavior data. The analysis unit can also detect abnormal behavior by referring to the pet's past behavior patterns. The analysis unit can also analyze changes in behavior using the pet's past behavior data. As a result, the accuracy of the analysis of pet behavior data is improved by using multimodal AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input pet behavior data into a generating AI and have the generating AI perform the analysis of the behavior data.

[0069] The reproduction unit can reproduce pet sounds using speech synthesis technology. The reproduction unit can realistically reproduce pet sounds using, for example, speech synthesis technology. For example, speech synthesis technology can reproduce pet sounds using techniques such as text-to-speech synthesis and waveform-to-speech synthesis. When reproducing pet sounds, the reproduction unit can improve the reproduction accuracy by referring to the pet's past sound data. For example, the reproduction unit can reproduce current sounds based on the pet's past sound data. The reproduction unit can also detect abnormal sounds by referring to the pet's past sound patterns. The reproduction unit can also analyze changes in sounds using the pet's past sound data. As a result, pet sounds can be realistically reproduced using speech synthesis technology. Some or all of the above processing in the reproduction unit may be performed using, for example, AI, or without AI. For example, the reproduction unit can input pet sound data into a generating AI and have the generating AI perform the sound reproduction.

[0070] The notification unit can alert the owner of a pet for specific behaviors. For example, it can alert the owner of a pet for cute behaviors. For example, it can alert the owner of a pet for cute behaviors such as wagging its tail or jumping. The notification unit can notify the owner in real time based on the pet's behavior data. For example, if the camera captures the pet playing or being affectionate, the notification unit can notify the owner of the video on their smartphone. The notification unit can analyze the pet's behavior data and alert the owner of specific behaviors. This allows the owner to check on their pet in real time by alerting them of cute behaviors. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the pet's behavior data into a generating AI and have the generating AI detect and notify of specific behaviors.

[0071] The analysis unit can estimate a pet's emotions and generate a message in text or voice. For example, the analysis unit can estimate a pet's emotions by analyzing the pet's behavioral data. The analysis unit can use an emotion estimation algorithm to estimate a pet's emotions. The analysis unit can estimate a pet's emotions and generate a message based on those emotions. For example, if a pet wants to play, the analysis unit can analyze that emotion and generate a message such as "I want to play." The analysis unit can also estimate a pet's emotions and generate a voice message based on those emotions. For example, if a pet cries out "I'm hungry," the analysis unit can analyze that sound and generate a voice message such as "I'm hungry." This makes it easier for owners to understand their pets' feelings by generating messages about their pets' emotions in text or voice. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input pet behavior data into a generating AI, which can then perform emotion estimation and message generation.

[0072] The data collection unit can estimate the owner's emotions and adjust the timing of collecting pet behavior data based on the estimated owner's emotions. For example, if the owner is stressed, the data collection unit can prioritize collecting relaxed pet behaviors. If the owner is having fun, the data collection unit can collect moments of the pet playing. If the owner is feeling lonely, the data collection unit can collect affectionate behaviors of the pet. By adjusting the timing of collecting pet behavior data according to the owner's emotions, more appropriate data can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the owner's emotional data into a generating AI, which can then perform emotion estimation and adjust the timing of data collection.

[0073] The data collection unit can select a data collection method based on the pet's activity level and health condition. For example, the data collection unit can record the pet's behavior as a video if the pet is actively moving around. If the pet is resting, the data collection unit can record the pet's behavior as a still image. If the pet is in poor health, the data collection unit can increase the frequency of behavioral data collection. This allows for the collection of appropriate data by selecting a data collection method according to the pet's activity level and health condition. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the pet's activity level and health condition into a generating AI and have the generating AI select the data collection method.

[0074] The data collection unit can collect environmental data surrounding the pet when collecting behavioral data about the pet. For example, the data collection unit can record the temperature and humidity of the place where the pet is playing. The data collection unit can record the brightness of the room where the pet is. The data collection unit can record the sound environment around the pet. The data collection unit can collect environmental data surrounding the pet when collecting behavioral data about the pet. By collecting behavioral data and environmental data simultaneously, more detailed data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input pet behavioral data and environmental data into a generating AI and have the generating AI perform the data collection.

[0075] The data collection unit can estimate the owner's emotions and determine the priority of data to collect based on the estimated owner's emotions. For example, if the owner is stressed, the data collection unit can prioritize collecting data of the pet's relaxed behavior. If the owner is having fun, the data collection unit can prioritize collecting data of the pet's playful behavior. If the owner is feeling lonely, the data collection unit can prioritize collecting data of the pet's affectionate behavior. By prioritizing the data to collect according to the owner's emotions, more important data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the owner's emotional data into a generating AI, which can then perform emotion estimation and determine the priority of the data.

[0076] The data collection unit can select the data to collect based on the pet's location information when collecting pet behavior data. For example, if the pet is outdoors, the data collection unit can collect GPS data. If the pet is in a specific room, the data collection unit can collect environmental data for that room. If the pet is moving, the data collection unit can record the movement path. By collecting data while considering the pet's location information, more appropriate data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input pet location data into a generating AI and have the generating AI perform the data collection.

[0077] The data collection unit can collect data on the pet's diet and exercise while simultaneously collecting behavioral data. For example, the unit can record the content and amount of food the pet eats when it is eating. The unit can also record the type and duration of exercise the pet does when it is exercising. The unit can combine the data on the pet's diet and exercise to evaluate its health status. This allows for a more detailed understanding of the pet's health status by simultaneously collecting behavioral data and data on diet and exercise. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the pet's diet and exercise data into a generating AI, and have the generating AI perform data collection and health status evaluation.

[0078] The analysis unit can estimate the owner's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the owner is stressed, the analysis unit can provide a simple and easy-to-read display method. If the owner is having fun, the analysis unit can provide a display method that includes detailed information. If the owner is feeling lonely, the analysis unit can provide a display method that highlights the pet's cute moments. By adjusting the display method of the analysis results according to the owner's emotions, it becomes possible to provide a display that is easy for the owner to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the owner's emotional data into the generating AI, which can then perform emotion estimation and adjust the display method.

[0079] The analysis unit can improve the accuracy of its analysis by referring to the pet's past behavioral patterns when analyzing pet behavioral data. For example, the analysis unit can predict current behavior based on the pet's past behavioral data. The analysis unit can detect abnormal behavior by referring to the pet's past behavioral patterns. The analysis unit can analyze changes in behavior using the pet's past behavioral data. This improves the accuracy of the analysis by referring to the pet's past behavioral patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the pet's past behavioral data into a generating AI and have the generating AI perform the analysis of the behavioral data.

[0080] The analysis unit can adjust its analysis algorithm based on the pet's health and age when analyzing pet behavior data. For example, if the pet is in good health, the analysis unit uses a standard analysis algorithm. If the pet is in poor health, the analysis unit can use an algorithm to detect abnormal behavior. Depending on the pet's age, the analysis unit can use an analysis algorithm that takes into account changes in behavioral patterns. By adjusting the analysis algorithm according to the pet's health and age, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the pet's health and age into a generating AI and have the generating AI perform the adjustment of the analysis algorithm.

[0081] The analysis unit can estimate the owner's emotions and determine the priority of the analysis results based on the estimated emotions. For example, if the owner is stressed, the analysis unit can prioritize displaying analysis results of relaxed behaviors. If the owner is having fun, the analysis unit can prioritize displaying analysis results of playful behaviors. If the owner is feeling lonely, the analysis unit can prioritize displaying analysis results of affectionate behaviors. In this way, by prioritizing the analysis results according to the owner's emotions, information important to the owner can be displayed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the owner's emotional data into a generating AI, which can then perform emotion estimation and determine the priority of the analysis results.

[0082] The analysis unit can improve the accuracy of its analysis by referring to the pet's living environment data when analyzing pet behavior data. For example, the analysis unit can analyze behavior data while considering the temperature and humidity of the room where the pet is located. The analysis unit can analyze behavior data while considering the sound environment around the pet. The analysis unit can detect behavioral abnormalities based on the pet's living environment data. As a result, the accuracy of the analysis is improved by referring to the pet's living environment data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input pet living environment data into a generating AI and have the generating AI perform the analysis of behavior data.

[0083] The analysis unit can supplement its analysis results by referring to the pet's diet and exercise data when analyzing pet behavioral data. For example, the analysis unit can analyze changes in behavior based on the pet's diet data. The analysis unit can detect behavioral abnormalities based on the pet's exercise data. The analysis unit can evaluate the pet's health status by combining the pet's diet and exercise data. This allows for a more accurate analysis by supplementing the analysis results by referring to the pet's diet and exercise data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input pet diet and exercise data into a generating AI and have the generating AI perform the data analysis.

[0084] The reproduction unit can estimate the owner's emotions and adjust the expression of the reproduced vocalizations based on the estimated emotions. For example, if the owner is stressed, the reproduction unit can reproduce relaxed vocalizations. If the owner is having fun, the reproduction unit can reproduce energetic vocalizations. If the owner is feeling lonely, the reproduction unit can reproduce affectionate vocalizations. By adjusting the expression of vocalizations according to the owner's emotions, more appropriate vocalizations can be reproduced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the owner's emotional data into the generating AI, which can then perform emotion estimation and adjust the way the vocalizations are expressed.

[0085] The reproduction unit can improve reproduction accuracy by referring to past pet vocalization data. For example, the reproduction unit can reproduce the current vocalization based on the pet's past vocalization data. The reproduction unit can detect abnormal vocalizations by referring to the pet's past vocalization patterns. The reproduction unit can analyze changes in vocalizations using the pet's past vocalization data. This improves reproduction accuracy by referring to the pet's past vocalization data. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the pet's past vocalization data into a generating AI and have the generating AI perform the vocalization reproduction.

[0086] The reproduction unit can adjust the reproduction algorithm based on the pet's emotions and situation. For example, if the pet is relaxed, the reproduction unit can reproduce a relaxed bark. If the pet is excited, the reproduction unit can reproduce an energetic bark. If the pet is feeling lonely, the reproduction unit can reproduce a pleading bark. By adjusting the reproduction algorithm according to the pet's emotions and situation, more accurate barks can be reproduced. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input data on the pet's emotions and situation into a generating AI and have the generating AI adjust the reproduction algorithm.

[0087] The reproduction unit can estimate the owner's emotions and determine the priority of the sounds to reproduce based on the estimated emotions. For example, if the owner is stressed, the reproduction unit can prioritize reproducing relaxed sounds. If the owner is having fun, the reproduction unit can prioritize reproducing energetic sounds. If the owner is feeling lonely, the reproduction unit can prioritize reproducing affectionate sounds. By prioritizing sounds according to the owner's emotions, more appropriate sounds can be reproduced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the owner's emotional data into a generating AI, which can then perform emotion estimation and determine the priority of vocalizations.

[0088] The reproduction unit can improve reproduction accuracy by referring to data on the pet's living environment. For example, the reproduction unit can reproduce vocalizations considering the temperature and humidity of the room where the pet is located. The reproduction unit can reproduce vocalizations considering the sound environment around the pet. The reproduction unit can detect abnormalities in vocalizations based on the pet's living environment data. As a result, reproduction accuracy is improved by referring to the pet's living environment data. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the pet's living environment data into a generating AI and have the generating AI perform the reproduction of vocalizations.

[0089] The reproduction unit can supplement the reproduction results by referring to the pet's diet and exercise data. For example, the reproduction unit can analyze changes in vocalizations based on the pet's diet data. The reproduction unit can detect abnormalities in vocalizations based on the pet's exercise data. The reproduction unit can improve the accuracy of vocalization reproduction by combining the pet's diet and exercise data. As a result, by referring to the pet's diet and exercise data, the reproduction results can be supplemented, and more accurate vocalizations can be reproduced. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the pet's diet and exercise data into a generating AI and have the generating AI perform the vocalization reproduction.

[0090] The notification unit can estimate the owner's emotions and adjust the way notifications are displayed based on the estimated emotions. For example, if the owner is stressed, the notification unit can provide a simple and highly visible notification. If the owner is having fun, the notification unit can provide a notification with detailed information. If the owner is feeling lonely, the notification unit can provide a notification that highlights cute moments of the pet. By adjusting the way notifications are displayed according to the owner's emotions, notifications that are easy for the owner to see can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the owner's emotional data into a generating AI, which can then perform emotion estimation and adjust the way notifications are displayed.

[0091] The notification unit can improve notification accuracy by referring to the pet's past behavior patterns when alerting about the pet's cute behavior. For example, the notification unit can predict and notify about cute behavior based on the pet's past behavior data. The notification unit can detect and notify about abnormal behavior by referring to the pet's past behavior patterns. The notification unit can analyze and notify about changes in behavior using the pet's past behavior data. This improves notification accuracy by referring to the pet's past behavior patterns. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the pet's past behavior data into a generating AI and have the generating AI perform the analysis of the behavior data and the notification.

[0092] The notification unit can adjust its notification algorithm based on the pet's health and age when alerting users about their pet's cute behavior. For example, if the pet is in good health, the notification unit uses a standard notification algorithm. If the pet is in poor health, the notification unit can use a notification algorithm to detect abnormal behavior. Depending on the pet's age, the notification unit can use a notification algorithm that takes into account changes in behavioral patterns. This allows for more accurate notifications by adjusting the notification algorithm according to the pet's health and age. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on the pet's health and age into a generating AI and have the generating AI perform the adjustment of the notification algorithm.

[0093] The notification unit can estimate the owner's emotions and determine the priority of notifications based on the estimated emotions. For example, if the owner is stressed, the notification unit can prioritize notifications of relaxing behaviors. If the owner is having fun, the notification unit can prioritize notifications of playful behaviors. If the owner is feeling lonely, the notification unit can prioritize notifications of affectionate behaviors. By prioritizing notifications according to the owner's emotions, important information for the owner can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the owner's emotional data into a generating AI, which can then perform emotion estimation and determine the priority of notifications.

[0094] The notification unit can improve notification accuracy by referring to the pet's living environment data when issuing alert notifications for the pet's cute behavior. For example, the notification unit can consider the temperature and humidity of the room where the pet is located when issuing notifications. The notification unit can also consider the sound environment around the pet when issuing notifications. Based on the pet's living environment data, the notification unit can detect and notify of abnormal behavior. As a result, the notification accuracy is improved by referring to the pet's living environment data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the pet's living environment data into a generating AI and have the generating AI perform the task of improving notification accuracy.

[0095] The notification unit can supplement its notification results by referring to the pet's diet and exercise data when it alerts the pet about its cute behavior. For example, the notification unit can analyze and notify about changes in behavior based on the pet's diet data. The notification unit can detect and notify about abnormal behavior based on the pet's exercise data. The notification unit can analyze and notify about changes in behavior by combining the pet's diet and exercise data. This allows for more accurate notifications by supplementing the notification results by referring to the pet's diet and exercise data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the pet's diet and exercise data into a generating AI and have the generating AI execute the notification results.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The smart pet care system can also include a health management unit that monitors the pet's health. The health management unit collects vital data such as the pet's body temperature, heart rate, and respiratory rate, and transmits it to the analysis unit. The analysis unit evaluates the pet's health based on this vital data, and if an abnormality is detected, it can send an alert to the owner through the notification unit. For example, if the pet's body temperature is higher than normal, the analysis unit can detect a possible fever and notify the owner. If the heart rate is abnormally high, it can be analyzed as a sign of stress or excitement and the owner can be notified. If the respiratory rate is abnormally low, it may indicate a health problem, and a notification can be sent to urge immediate action. This allows owners to understand their pet's health in real time and take appropriate action.

[0098] The smart pet care system can also include a learning unit that learns the pet's preferences and habits based on behavioral data. The learning unit records the pet's preferred behaviors at specific times of day and activities in specific locations, and transmits this data to the analysis unit. The analysis unit uses this data to understand the pet's preferences and habits and can make suggestions to the owner. For example, if the pet likes to go for a walk at a specific time every morning, the analysis unit will notify the owner of a suggestion to go for a walk at that time. If the pet likes to play in a specific place, the analysis unit can suggest playing in that place. If the pet likes a particular food, the analysis unit can suggest the best time to give that food. This allows owners to provide care tailored to their pet's preferences and habits, deepening their bond with their pet.

[0099] The smart pet care system can also include a stress assessment unit that evaluates the pet's stress level based on behavioral data. The stress assessment unit analyzes the pet's behavioral and vital data to detect signs of stress. Based on this data, the analysis unit evaluates the pet's stress level and can notify the owner. For example, if a pet barks frequently, is restless, or has a decreased appetite, the stress assessment unit analyzes this as a sign of stress and notifies the owner. If the pet's heart rate or respiratory rate is abnormally high, it can suggest the possibility of stress and inform the owner. If a pet experiences stress in a particular environment or situation, it can suggest avoiding that environment or situation. This allows owners to reduce their pet's stress and support a healthy life.

[0100] The smart pet care system can also include a training support unit that assists with pet training based on pet behavior data. The training support unit analyzes the pet's behavior data and evaluates the training progress. The analysis unit can then evaluate the effectiveness of the training based on this data and provide feedback to the owner. For example, it can record the frequency and success rate of the pet following specific commands to evaluate training progress. It can analyze the difficulties the pet faces in learning specific behaviors and suggest improvements. If the pet is experiencing stress during training, it can detect signs of this and suggest a review of the training method. This allows owners to conduct effective training and support their pet's growth.

[0101] The smart pet care system can also include a sociability assessment unit that evaluates the pet's sociability based on behavioral data. The sociability assessment unit collects data on the pet's interactions with other animals and people and transmits it to the analysis unit. The analysis unit can evaluate the pet's sociability based on this data and provide feedback to the owner. For example, it can record the frequency and duration of the pet's play with other animals to evaluate sociability. It can also analyze how the pet reacts to new environments and people and reflect this in the sociability evaluation. If the pet experiences stress in a particular situation, it can suggest avoiding that situation. This allows owners to understand their pet's sociability and provide appropriate support.

[0102] The smart pet care system can also include a prediction unit that estimates the pet's emotions based on behavioral data and predicts the pet's behavior based on those emotions. The prediction unit analyzes the pet's behavioral and emotional data to predict future behavior. The analysis unit uses this data to understand the pet's behavioral patterns and can notify the owner of the prediction results. For example, if the pet wants to play at a specific time, the system can suggest playtime at that time. If the pet feels stressed in a particular situation, the system can suggest avoiding that situation. If the pet likes a particular food, the system can suggest the best time to give that food. This allows owners to predict their pet's behavior and take appropriate action.

[0103] The smart pet care system can also include a health prediction unit that predicts the pet's health status based on behavioral data. The health prediction unit analyzes the pet's behavioral and vital data to predict its future health status. The analysis unit evaluates the pet's health risks based on this data and can notify the owner of the prediction results. For example, if a pet's appetite decreases, it can predict future health risks and suggest early intervention. If a pet's exercise level decreases, it can predict a deterioration in health status and suggest increasing exercise. If abnormalities are found in the pet's vital data, it can evaluate health risks and send a notification encouraging early veterinary examination. This allows owners to predict their pet's health status and provide appropriate care.

[0104] The smart pet care system can also include a guidance unit that estimates the pet's emotions based on behavioral data and guides the pet's behavior based on those emotions. The guidance unit analyzes the pet's behavioral and emotional data and makes suggestions for guiding the pet's behavior. The analysis unit understands the pet's behavioral patterns based on this data and can notify the owner of guidance methods. For example, if the pet is stressed, it can suggest a relaxing environment. If the pet wants to play, it can suggest appropriate play activities. If the pet wants to eat, it can suggest the appropriate timing for feeding. This allows owners to guide their pets' behavior based on their emotions and support a comfortable life for their pets.

[0105] The smart pet care system can also include a reinforcement unit that estimates the pet's emotions based on behavioral data and reinforces the pet's behavior based on those emotions. The reinforcement unit analyzes the pet's behavioral and emotional data and makes suggestions for reinforcing the pet's behavior. The analysis unit can understand the pet's behavioral patterns based on this data and notify the owner of reinforcement methods. For example, it can suggest when to praise the pet when it performs a specific behavior, when to give a reward when it performs a specific behavior, or when to offer playtime when it performs a specific behavior. This allows owners to reinforce their pet's behavior based on its emotions and support the pet's learning.

[0106] The smart pet care system can also include a modification unit that estimates the pet's emotions based on behavioral data and modifies the pet's behavior based on those emotions. The modification unit analyzes the pet's behavioral and emotional data and makes suggestions for modifying the pet's behavior. The analysis unit can understand the pet's behavioral patterns based on this data and notify the owner of how to make corrections. For example, it can suggest when to pay attention when the pet performs a specific behavior. It can suggest alternative behaviors when the pet performs a specific behavior. It can suggest changing the environment when the pet performs a specific behavior. This allows owners to modify their pet's behavior based on its emotions and support appropriate behavior for their pets.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects pet behavior data. The data collection unit can collect pet behavior data using, for example, a camera. The data collection unit can take pictures of the pet playing, sleeping, eating, walking, etc., and collect the data. The data collection unit can also simultaneously collect environmental data of the pet's surroundings, such as recording the temperature and humidity of the place where the pet is playing, the brightness of the room, and the sound environment. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze pet behavior data using multimodal AI to understand the pet's behavior patterns and vocal characteristics. The analysis unit can generate the pet's emotions and messages in text or voice, and can improve the accuracy of the analysis by referring to the pet's past behavior patterns. In addition, the analysis unit can predict current behavior, detect abnormal behavior, and analyze changes in behavior based on the pet's past behavior data. Step 3: The reproduction unit reproduces the pet's barks based on the data analyzed by the analysis unit. The reproduction unit can reproduce the pet's barks using, for example, speech synthesis technology. The reproduction unit can realistically reproduce the pet's barks using speech synthesis technology based on the collected data, and can improve the reproduction accuracy by referring to the pet's past barking data. In addition, the reproduction unit can detect abnormal barks by referring to the pet's past barking patterns and analyze changes in barks. Step 4: The notification unit notifies the owner of the sounds reproduced by the reproduction unit. The notification unit can, for example, alert the owner of cute pet behaviors. The notification unit can alert the owner based on the pet's behavior data and can alert for specific behaviors. For example, it can alert the owner of cute behaviors such as the pet wagging its tail or jumping. In addition, the notification unit can notify the owner in real time based on the pet's behavior data, and if the camera captures the pet playing or being affectionate, it can notify the owner of the video on their smartphone.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, reproduction unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit can collect pet behavior data using the camera 42 of the smart device 14. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The reproduction unit is implemented in the identification processing unit 290 of the data processing unit 12 and reproduces the pet's barks using speech synthesis technology. The notification unit is implemented in the control unit 46A of the smart device 14 and can notify the owner of the reproduced barks or the pet's cute gestures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, reproduction unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit can collect pet behavior data using the camera 42 of the smart glasses 214. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The reproduction unit is implemented in the identification processing unit 290 of the data processing unit 12 and reproduces the pet's barks using speech synthesis technology. The notification unit is implemented in the control unit 46A of the smart glasses 214 and can notify the owner of the reproduced barks or the pet's cute gestures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, reproduction unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit can collect pet behavior data using the camera 42 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The reproduction unit is implemented in the specific processing unit 290 of the data processing unit 12 and reproduces the pet's barks using speech synthesis technology. The notification unit is implemented in the control unit 46A of the headset terminal 314 and can notify the owner of the reproduced barks or the pet's cute gestures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the data collection unit, analysis unit, reproduction unit, and notification unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit can collect pet behavior data using the camera 42 of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data. The reproduction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and reproduces the pet's barks using speech synthesis technology. The notification unit is implemented, for example, by the control unit 46A of the robot 414, and can notify the owner of the reproduced barks or the pet's cute gestures. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A data collection unit that collects pet behavior data, An analysis unit analyzes the data collected by the aforementioned collection unit, A reproduction unit reproduces the sound of a pet based on the data analyzed by the aforementioned analysis unit, The device includes a notification unit that notifies the owner of the sound reproduced by the reproduction unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect pet behavior data using cameras. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Using multimodal AI to analyze pet behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The reproduction unit is, Reproducing pet sounds using speech synthesis technology. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, Alerts you when your pet exhibits specific behaviors. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Estimate your pet's emotions and generate messages in text or voice. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the owner's emotions and adjusts the timing of pet behavior data collection based on the estimated owner's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The collection method will be selected based on the pet's activity level and health condition. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting pet behavioral data, we also collect data on the surrounding environment of the pet. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the owner's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting pet behavioral data, the data to be collected is selected based on the pet's location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting pet behavioral data, we also collect data on pet diet and exercise at the same time. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the owner's emotions and adjusts the display method of the analysis results based on the estimated emotions of the owner. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing pet behavioral data, referencing the pet's past behavioral patterns improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing pet behavioral data, the analysis algorithm is adjusted based on the pet's health status and age. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the owner's emotions and prioritizes the analysis results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing pet behavioral data, referencing data on the pet's living environment improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When analyzing pet behavioral data, supplement the analysis results by referring to pet diet and exercise data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The reproduction unit is, It estimates the owner's emotions and adjusts the way it reproduces vocalizations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The reproduction unit is, We improve the accuracy of reproduction by referencing past pet vocalization data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The reproduction unit is, Adjust the reproduction algorithm based on the pet's emotions and circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 22) The reproduction unit is, It estimates the owner's emotions and determines the priority of the sounds to reproduce based on the estimated owner's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The reproduction unit is, Improve reproduction accuracy by referencing pet living environment data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The reproduction unit is, Supplement the reproduction results by referring to pet diet and exercise data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, The system estimates the owner's emotions and adjusts how notifications are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, When sending alert notifications for your pet's cute behaviors, we improve notification accuracy by referencing your pet's past behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When alerting you about your pet's cute behavior, the notification algorithm will be adjusted based on your pet's health condition and age. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, The system estimates the owner's emotions and prioritizes notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, When sending alert notifications for your pet's cute behavior, we improve notification accuracy by referencing data about your pet's living environment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When alerting you to your pet's cute behavior, the system supplements the notification results by referencing your pet's diet and exercise data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects pet behavior data, An analysis unit analyzes the data collected by the aforementioned collection unit, A reproduction unit reproduces the sound of a pet based on the data analyzed by the aforementioned analysis unit, The device includes a notification unit that notifies the owner of the sound reproduced by the reproduction unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect pet behavior data using cameras. The system according to feature 1.

3. The aforementioned analysis unit, Using multimodal AI to analyze pet behavior data. The system according to feature 1.

4. The reproduction unit is, Reproducing pet sounds using speech synthesis technology. The system according to feature 1.

5. The aforementioned notification unit, Alerts you when your pet exhibits specific behaviors. The system according to feature 1.

6. The aforementioned analysis unit, Estimate your pet's emotions and generate messages in text or voice. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the owner's emotions and adjusts the timing of pet behavior data collection based on the estimated owner's emotions. The system according to feature 1.

8. The aforementioned collection unit is The collection method will be selected based on the pet's activity level and health condition. The system according to feature 1.

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