system

Abstract

The system according to the embodiment aims to enable pet owners to accurately understand their pets' emotions and needs. [Solution] A system according to an embodiment includes an acquisition unit, an analysis unit, and a notification unit. The acquisition unit acquires video or audio of a pet. The analysis unit analyzes the video or audio acquired by the acquisition unit to determine the pet's emotions and desired responses. The notification unit notifies the owner of the results determined by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to accurately understand a pet's emotions and needs, which can make it difficult for owners to respond appropriately.

[0005] The system according to the embodiment aims to enable pet owners to accurately understand their pets' emotions and needs. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, and a notification unit. The acquisition unit acquires video or audio of the pet. The analysis unit analyzes the video or audio acquired by the acquisition unit to determine the pet's emotions and desired responses. The notification unit notifies the owner of the results determined by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable pet owners to accurately understand their pet's emotions and needs. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A pet emotion analysis system according to an embodiment of the present invention allows owners to understand their pet's emotions and what it is trying to convey from its gestures and facial expressions. In this pet emotion analysis system, the owner captures video and audio of their pet using a camera and microphone installed on their smartphone or in their room. A generation AI then analyzes the captured video and audio to determine the pet's current emotions and desired behavior. The determination results are displayed on a smartphone or dedicated device, allowing the owner to understand their pet's emotions and desired behavior. For example, a camera can be installed indoors to check how the pet is behaving while the owner is out. This camera can record the pet's gestures and facial expressions in real time. A microphone can also be used to capture the pet's cries and sounds. The captured video and audio are then analyzed by a generation AI. The generation AI analyzes the pet's gestures, facial expressions, cries, etc. to determine the pet's current emotions and desired behavior. For example, if the pet wags its tail, the generation AI determines that the pet is happy. Additionally, if a pet meows, the generation AI determines that the pet is requesting something. The result of the determination is displayed on a smartphone or dedicated device. For example, a message such as "Your pet is happy" or "Your pet is requesting something" is displayed on the owner's smartphone. This message allows the owner to understand their pet's emotions and what they want. This system makes it easier for owners to understand their pet's emotions and what they are trying to communicate. For example, if a pet becomes anxious while the owner is out, the generation AI determines this and notifies the owner, allowing the owner to understand their pet's condition and take appropriate action. Also, if a pet requests something, the generation AI determines the request and notifies the owner, allowing the owner to respond to the pet's request. In this way, the pet emotion analysis system allows owners to understand their pet's emotions and what they are trying to communicate.

[0029] The pet emotion analysis system according to the embodiment includes an acquisition unit, an analysis unit, and a notification unit. The acquisition unit acquires video or audio of the pet. The video or audio of the pet may include, but is not limited to, a specific format, resolution, sound quality, etc. The acquisition unit acquires video or audio of the pet using, for example, a camera or a microphone. The camera can record the pet's gestures and facial expressions in real time. The microphone can capture the pet's cries and audio. The analysis unit analyzes the video or audio acquired by the acquisition unit to determine the pet's emotions and desired responses. The analysis unit uses, for example, a generation AI to analyze the pet's gestures, facial expressions, cries, etc. The generation AI analyzes the pet's gestures, facial expressions, cries, etc. to determine the pet's emotions at that time and desired responses. For example, if the pet is wagging its tail, the generation AI determines that the pet is happy. Also, if the pet is meowing, the generation AI determines that the pet is requesting something. The notification unit notifies the owner of the result determined by the analysis unit. The notification unit displays the determination result on, for example, a smartphone or a dedicated device. For example, a message such as "Your pet is happy" or "Your pet is asking for something" is displayed on the owner's smartphone. This allows the pet emotion analysis system according to the embodiment to allow the owner to understand the pet's emotions and what it is trying to communicate. Some or all of the above-described processing by the analysis unit may be performed using, for example, a generation AI. For example, the analysis unit may input the pet's gestures, facial expressions, cries, etc., and determine the pet's emotions and desired responses using a generation AI model that outputs the pet's emotions and desired responses. Some or all of the above-described processing by the notification unit may be performed using, for example, an AI. For example, the notification unit may input the result determined by the analysis unit and notify the owner using an AI model that outputs a notification.

[0030] The acquisition unit can acquire video and audio of the pet using a camera or microphone. The acquisition unit acquires video and audio of the pet using, for example, a camera or microphone. The camera can record the pet's behavior and facial expressions in real time. For example, a camera is installed indoors to check how the pet is behaving while the owner is out. This camera can record the pet's behavior and facial expressions in real time. The microphone can also be used to acquire the pet's cries and audio. For example, when the pet is barking, the microphone captures the cries. In this way, by using the camera or microphone, the pet's behavior, facial expressions, and cries can be acquired in detail. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input video and audio data acquired by the camera or microphone into a generation AI and convert it into a format that is easy for the generation AI to analyze.

[0031] The analysis unit analyzes the pet's gestures, facial expressions, and cries to determine the pet's emotions and the desired response. The analysis unit analyzes, for example, the pet's gestures, facial expressions, and cries to determine the pet's emotions and the desired response. For example, if the pet wags its tail, the analysis unit determines that the pet is happy. Also, if the pet meows, the analysis unit determines that the pet is requesting something. The analysis unit uses the generation AI to analyze the pet's gestures, facial expressions, and cries. The generation AI analyzes the pet's gestures, facial expressions, and cries to determine the pet's emotions at that time and the desired response. For example, if the pet wags its tail, the generation AI determines that the pet is happy. Also, if the pet meows, the generation AI determines that the pet is requesting something. In this way, by analyzing the pet's gestures, facial expressions, and cries, the pet's emotions and requests can be accurately determined. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can determine the pet's emotions and desired responses using a generation AI model that receives the pet's gestures, facial expressions, cries, etc. as input and outputs the pet's emotions and desired responses.

[0032] The notification unit can display the determination result on a smartphone or a dedicated device. For example, the notification unit displays the determination result on a smartphone or a dedicated device. For example, a message such as "Your pet is happy" or "Your pet is asking for something" is displayed on the owner's smartphone. By displaying the determination result on a smartphone or a dedicated device, the owner can instantly understand the pet's emotions and requests. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can notify the owner using an AI model that inputs the result determined by the analysis unit and outputs a notification.

[0033] The analysis unit can learn using past pet behavior data. The analysis unit learns using, for example, past pet behavior data. Past behavior data includes, for example, a behavior recording method, a data storage format, and the like, but is not limited to these examples. For example, the analysis unit collects past pet behavior data and learns based on that data. By using the past behavior data, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can improve the accuracy of the analysis by using a generation AI model that inputs past pet behavior data and learns.

[0034] The acquisition unit can install the camera in a position that can cover a wide range of the pet's activities and adjust the microphone's sensitivity to clearly pick up the pet's cries. For example, the acquisition unit installs the camera in a position that can cover a wide range of the pet's activities and adjusts the microphone's sensitivity to clearly pick up the pet's cries. The camera's installation position includes, but is not limited to, the center of the room or a high position. For example, installing the camera in the center of the room can cover a wide range of the pet's activities. The microphone's sensitivity can be adjusted to, but is not limited to, a decibel value or a frequency band. For example, increasing the microphone's sensitivity can clearly pick up the pet's cries. This allows the pet's activities and cries to be acquired in detail by installing and adjusting the camera and microphone. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit uses an AI model to optimize the placement and sensitivity of the cameras and microphones, enabling it to cover a wide range of your pet's movements and clearly pick up its cries.

[0035] The analysis unit can analyze the pet's gestures and facial expressions, such as wagging its tail, pricking up its ears, squinting its eyes, meowing, and barking. The analysis unit analyzes, for example, the pet's gestures and facial expressions, such as wagging its tail, pricking up its ears, squinting its eyes, meowing, and barking. Specific criteria for determining gestures and facial expressions include, but are not limited to, the frequency and duration of the behavior. For example, if the pet wags its tail, the analysis is performed based on the frequency and duration of the behavior. Similarly, if the pet pricks up its ears, the analysis is performed based on the frequency and duration of the behavior. This allows for a detailed analysis of the pet's gestures and facial expressions, thereby accurately determining its emotions and requests. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can determine the pet's emotions and desired responses using a generation AI model that inputs the pet's gestures and facial expressions and outputs emotions and desired responses.

[0036] The notification unit can display messages such as "Your pet is happy" or "Your pet is asking for something" on the owner's smartphone. For example, the notification unit can display messages such as "Your pet is happy" or "Your pet is asking for something" on the owner's smartphone. This allows the owner to immediately understand their pet's emotions and requests. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can notify the owner using an AI model that inputs the results determined by the analysis unit and outputs a notification.

[0037] The acquisition unit can analyze the pet's past behavioral history and select the optimal acquisition method. The acquisition unit, for example, analyzes the pet's past behavioral history and selects the optimal acquisition method. Methods for analyzing behavioral history and criteria for selecting an acquisition method include, but are not limited to, data mining techniques and selection algorithms. For example, if the pet was active during a specific time period in the past, recording can be concentrated on that time period. Also, if the pet often meowed in a specific location in the past, a microphone can be installed in that location. Furthermore, if the pet frequently performed a specific behavior in the past, that behavior can be recorded intensively. This allows the optimal acquisition method to be selected by analyzing the past behavioral history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can select the optimal acquisition method using an AI model that analyzes the pet's past behavioral history.

[0038] The acquisition unit can perform filtering based on the pet's current activity status and environment when acquiring video and audio. For example, when acquiring video and audio, the acquisition unit performs filtering based on the pet's current activity status and environment. Specific filtering criteria and methods include, but are not limited to, the type of activity and environmental conditions. For example, if the pet is quiet, recording is paused and resumed when there is movement. Furthermore, if the pet makes a specific sound, that sound is used as a trigger to start recording. Furthermore, if the pet enters a specific area, the behavior in that area is preferentially recorded. This allows important data to be efficiently acquired by filtering based on the activity status and environment. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can perform filtering using an AI model that analyzes the pet's activity status and environment.

[0039] The acquisition unit can prioritize acquisition of highly relevant data by taking into consideration the geographical location information of the pet when acquiring video or audio. For example, the acquisition unit prioritizes acquisition of highly relevant data by taking into consideration the geographical location information of the pet when acquiring video or audio. Specific methods of using the geographical location information and criteria for relevance include, but are not limited to, the accuracy of the location information and a method for evaluating relevance. For example, if the pet is in the garden, its behavior in the garden is prioritized for recording. Also, if the pet is in the living room, its cries in the living room are prioritized for recording. Furthermore, if the pet enters a specific room, its behavior in that room is prioritized for recording. This allows highly relevant data to be acquired efficiently by taking into consideration the geographical location information. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can prioritize acquisition of highly relevant data by using an AI model that analyzes the geographical location information of the pet.

[0040] The acquisition unit can analyze the social media activity of the pet owner when acquiring video or audio, and acquire related data. For example, the acquisition unit can analyze the social media activity of the pet owner when acquiring video or audio, and acquire related data. Specific analysis methods and relevance criteria for social media activity include, but are not limited to, analysis of posted content and methods of evaluating relevance. For example, if the owner mentions a specific behavior of the pet on social media, the system prioritizes recording of that behavior. Furthermore, if the owner mentions a sound made by the pet on social media, the system prioritizes recording of the sound. Furthermore, if the owner mentions a specific location of the pet on social media, the system prioritizes recording of the pet's behavior at that location. This allows for efficient acquisition of related data by analyzing the owner's social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can acquire related data using an AI model that analyzes the owner's social media activity.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the pet's behavior during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the pet's behavior during analysis. Criteria and specific methods for adjusting the level of detail include, but are not limited to, importance evaluation criteria and levels of detail. For example, if the pet frequently performs a particular behavior, a detailed analysis of that behavior is performed. Furthermore, if the pet rarely performs that behavior, a simplified analysis is performed. Furthermore, if the pet's behavior is important to the owner, a detailed analysis of that behavior is performed. Thus, by adjusting the level of detail of the analysis based on the importance of the behavior, important behaviors can be analyzed in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can adjust the level of detail of the analysis using a generation AI model that evaluates the importance of the pet's behavior.

[0042] The analysis unit can apply different analysis algorithms depending on the type and individual differences of the pet during analysis. For example, the analysis unit can apply different analysis algorithms depending on the type and individual differences of the pet during analysis. Criteria for selecting and applying analysis algorithms and specific application methods include, but are not limited to, methods for evaluating the characteristics of each type and individual differences. For example, different analysis algorithms can be used for dogs and cats to perform analysis according to their respective characteristics. Furthermore, even for the same breed of dog, the analysis algorithm can be adjusted according to individual differences. Furthermore, the analysis algorithm can be changed depending on the age and health condition of the pet. By applying an analysis algorithm depending on the type and individual differences, more accurate analysis results can be obtained. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can apply different analysis algorithms using a generation AI model that evaluates the type and individual differences of the pet.

[0043] The analysis unit can determine the analysis priority based on the timing of the pet's behavior during analysis. The analysis unit, for example, determines the analysis priority based on the timing of the pet's behavior during analysis. Criteria and specific methods for determining the priority include, but are not limited to, criteria for evaluating the timing of occurrence and methods for setting priorities. For example, the analysis may prioritize recent behaviors performed by the pet. Also, the analysis may prioritize behaviors performed by the pet during a specific time period. Furthermore, the analysis may prioritize behaviors performed by the pet at a specific event (e.g., when the owner returns home). In this way, by determining the priority based on the timing of behavior occurrence, important behaviors can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can determine the analysis priority using a generation AI model that evaluates the timing of pet behavior occurrence.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the pet's behavior during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the pet's behavior during analysis. Criteria and specific methods for adjusting the order include, but are not limited to, relevance evaluation criteria and adjustment algorithms. For example, if the pet's behavior is continuous, the analysis is performed taking into account that continuity. Furthermore, if the pet's behavior follows a specific pattern, the analysis is performed taking into account that pattern. Furthermore, if the pet's behavior is related to other behaviors, the analysis is performed taking into account that relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the behaviors. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the analysis unit can adjust the order of analysis using a generative AI model that evaluates the relevance of the pet's behavior.

[0045] The notification unit can adjust the level of detail of the notification based on the importance of the pet's behavior when providing a notification. For example, the notification unit adjusts the level of detail of the notification based on the importance of the pet's behavior when providing a notification. Criteria and specific methods for adjusting the level of detail include, but are not limited to, importance evaluation criteria and levels of detail. For example, if the pet frequently performs a specific behavior, a detailed notification of the behavior is provided. Furthermore, if the pet rarely performs the behavior, a simplified notification is provided. Furthermore, if the pet's behavior is important to the owner, a detailed notification of the behavior is provided. In this way, by adjusting the level of detail of the notification based on the importance of the behavior, important behavior can be notified in detail. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can adjust the level of detail of the notification using an AI model that evaluates the importance of the pet's behavior.

[0046] The notification unit can select the optimal notification method by referring to the pet owner's past reaction history when notifying the pet. For example, the notification unit can select the optimal notification method by referring to the pet owner's past reaction history when notifying the pet. Specific methods for referring to the reaction history and criteria for selecting the notification method include, but are not limited to, a history analysis method and a selection algorithm. For example, if the pet owner has previously preferred a specific notification method, that method is used preferentially. Furthermore, if the pet owner has previously responded quickly to a specific notification, that notification method is used preferentially. Furthermore, if the pet owner has previously responded slowly to a specific notification, the notification method is changed. In this way, the optimal notification method can be selected by referring to the pet owner's past reaction history. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can select the optimal notification method using an AI model that analyzes the pet owner's past reaction history.

[0047] The notification unit can adjust the order of notifications based on the time of occurrence of the pet's behavior when notifying. For example, the notification unit adjusts the order of notifications based on the time of occurrence of the pet's behavior when notifying. Criteria and specific methods for adjusting the order include, but are not limited to, evaluation criteria for the time of occurrence and methods for setting the order, for example. For example, the notification unit prioritizes notification of recent behaviors performed by the pet. Also, the notification unit prioritizes notification of behaviors performed by the pet during a specific time period. Furthermore, the notification unit prioritizes notification of behaviors performed by the pet during a specific event (e.g., when the owner returns home). In this way, by adjusting the order of notifications based on the time of occurrence of the behavior, important behaviors can be prioritized. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can adjust the order of notifications using an AI model that evaluates the time of occurrence of the pet's behavior.

[0048] The notification unit can select the optimal notification method by taking into account the device information of the pet owner when sending a notification. For example, the notification unit selects the optimal notification method by taking into account the device information of the pet owner when sending a notification. Specific methods for considering the device information and criteria for selecting the notification method include, but are not limited to, the type and function of the device, and usage status of the device. For example, if the owner uses a smartphone, push notifications are used preferentially. Also, if the owner uses a tablet, a notification method optimized for a large screen is used. Furthermore, if the owner uses a smartwatch, a notification method that is simple and highly visible is used. This allows the optimal notification method to be selected by taking into account the device information of the owner. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can select the optimal notification method using an AI model that analyzes the device information of the owner.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The acquisition unit can store pet behavior data in the cloud and make it accessible from other devices. For example, an owner can check their pet's behavior on a smartphone while they are out. Storing pet behavior data in the cloud also allows data to be accumulated over a long period of time and used for later analysis. Furthermore, by sharing data stored in the cloud, multiple owners can check the behavior of the same pet. Storing pet behavior data in the cloud thus improves convenience.

[0051] The acquisition unit can stream the pet's behavior data in real time, allowing the owner to check it live. For example, the owner can check the pet's behavior live on their smartphone while they are out. Streaming the pet's behavior data in real time also allows the owner to instantly understand the pet's current situation. Furthermore, through live streaming, the owner can respond to the pet's behavior in real time. Streaming the pet's behavior data in real time thus improves convenience for the owner.

[0052] The acquisition unit can analyze the behavioral data of the pet and identify the pet's likes and dislikes. For example, if the pet often plays with a particular toy, the acquisition unit identifies that toy as the pet's favorite. Also, if the pet avoids a particular food, the acquisition unit identifies that food as the pet's dislike. Furthermore, if the pet avoids a particular place, the acquisition unit identifies that place as the pet's dislike. In this way, by analyzing the behavioral data of the pet, the likes and dislikes of the pet can be identified, allowing the owner to improve the living environment of their pet.

[0053] The acquisition unit can measure the amount of exercise of a pet using the behavioral data of the pet. For example, it can measure how far the pet walks in a day, how much time the pet spends playing in a day, and how much time the pet spends resting in a day. In this way, measuring the amount of exercise using the behavioral data of the pet can be useful for pet owners in managing their pet's health.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The acquisition unit acquires video or audio of the pet. The video or audio of the pet may have, but is not limited to, a specific format, resolution, sound quality, etc. The acquisition unit acquires video or audio of the pet using a camera and a microphone. The camera records the pet's movements and facial expressions in real time, and the microphone captures the pet's cries and audio. Step 2: The analysis unit analyzes the video or audio captured by the acquisition unit to determine the pet's emotions and the desired response. Using the generation AI, the analysis unit analyzes the pet's gestures, facial expressions, cries, etc. to determine the pet's emotions at that time and the desired response. For example, if the pet is wagging its tail, the generation AI determines that the pet is happy, and if the pet is meowing, the generation AI determines that the pet is requesting something. Step 3: The notification unit notifies the owner of the results determined by the analysis unit. The notification unit displays the results on a smartphone or dedicated device. For example, a message such as "Your pet is happy" or "Your pet wants something" may be displayed on the owner's smartphone.

[0056] (Example 2) A pet emotion analysis system according to an embodiment of the present invention allows owners to understand their pet's emotions and what it is trying to convey from its gestures and facial expressions. In this pet emotion analysis system, the owner captures video and audio of their pet using a camera and microphone installed on their smartphone or in their room. A generation AI then analyzes the captured video and audio to determine the pet's current emotions and desired behavior. The determination results are displayed on a smartphone or dedicated device, allowing the owner to understand their pet's emotions and desired behavior. For example, a camera can be installed indoors to check how the pet is behaving while the owner is out. This camera can record the pet's gestures and facial expressions in real time. A microphone can also be used to capture the pet's cries and sounds. The captured video and audio are then analyzed by a generation AI. The generation AI analyzes the pet's gestures, facial expressions, cries, etc. to determine the pet's current emotions and desired behavior. For example, if the pet wags its tail, the generation AI determines that the pet is happy. Additionally, if a pet meows, the generation AI determines that the pet is requesting something. The result of the determination is displayed on a smartphone or dedicated device. For example, a message such as "Your pet is happy" or "Your pet is requesting something" is displayed on the owner's smartphone. This message allows the owner to understand their pet's emotions and what they want. This system makes it easier for owners to understand their pet's emotions and what they are trying to communicate. For example, if a pet becomes anxious while the owner is out, the generation AI determines this and notifies the owner, allowing the owner to understand their pet's condition and take appropriate action. Also, if a pet requests something, the generation AI determines the request and notifies the owner, allowing the owner to respond to the pet's request. In this way, the pet emotion analysis system allows owners to understand their pet's emotions and what they are trying to communicate.

[0057] The pet emotion analysis system according to the embodiment includes an acquisition unit, an analysis unit, and a notification unit. The acquisition unit acquires video or audio of the pet. The video or audio of the pet may include, but is not limited to, a specific format, resolution, sound quality, etc. The acquisition unit acquires video or audio of the pet using, for example, a camera or a microphone. The camera can record the pet's gestures and facial expressions in real time. The microphone can capture the pet's cries and audio. The analysis unit analyzes the video or audio acquired by the acquisition unit to determine the pet's emotions and desired responses. The analysis unit uses, for example, a generation AI to analyze the pet's gestures, facial expressions, cries, etc. The generation AI analyzes the pet's gestures, facial expressions, cries, etc. to determine the pet's emotions at that time and desired responses. For example, if the pet is wagging its tail, the generation AI determines that the pet is happy. Also, if the pet is meowing, the generation AI determines that the pet is requesting something. The notification unit notifies the owner of the result determined by the analysis unit. The notification unit displays the determination result on, for example, a smartphone or a dedicated device. For example, a message such as "Your pet is happy" or "Your pet is asking for something" is displayed on the owner's smartphone. This allows the pet emotion analysis system according to the embodiment to allow the owner to understand the pet's emotions and what it is trying to communicate. Some or all of the above-described processing by the analysis unit may be performed using, for example, a generation AI. For example, the analysis unit may input the pet's gestures, facial expressions, cries, etc., and determine the pet's emotions and desired responses using a generation AI model that outputs the pet's emotions and desired responses. Some or all of the above-described processing by the notification unit may be performed using, for example, an AI. For example, the notification unit may input the result determined by the analysis unit and notify the owner using an AI model that outputs a notification.

[0058] The acquisition unit can acquire video and audio of the pet using a camera or microphone. The acquisition unit acquires video and audio of the pet using, for example, a camera or microphone. The camera can record the pet's behavior and facial expressions in real time. For example, a camera is installed indoors to check how the pet is behaving while the owner is out. This camera can record the pet's behavior and facial expressions in real time. The microphone can also be used to acquire the pet's cries and audio. For example, when the pet is barking, the microphone captures the cries. In this way, by using the camera or microphone, the pet's behavior, facial expressions, and cries can be acquired in detail. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input video and audio data acquired by the camera or microphone into a generation AI and convert it into a format that is easy for the generation AI to analyze.

[0059] The analysis unit analyzes the pet's gestures, facial expressions, and cries to determine the pet's emotions and the desired response. The analysis unit analyzes, for example, the pet's gestures, facial expressions, and cries to determine the pet's emotions and the desired response. For example, if the pet wags its tail, the analysis unit determines that the pet is happy. Also, if the pet meows, the analysis unit determines that the pet is requesting something. The analysis unit uses the generation AI to analyze the pet's gestures, facial expressions, and cries. The generation AI analyzes the pet's gestures, facial expressions, and cries to determine the pet's emotions at that time and the desired response. For example, if the pet wags its tail, the generation AI determines that the pet is happy. Also, if the pet meows, the generation AI determines that the pet is requesting something. In this way, by analyzing the pet's gestures, facial expressions, and cries, the pet's emotions and requests can be accurately determined. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can determine the pet's emotions and desired responses using a generation AI model that receives the pet's gestures, facial expressions, cries, etc. as input and outputs the pet's emotions and desired responses.

[0060] The notification unit can display the determination result on a smartphone or a dedicated device. For example, the notification unit displays the determination result on a smartphone or a dedicated device. For example, a message such as "Your pet is happy" or "Your pet is asking for something" is displayed on the owner's smartphone. By displaying the determination result on a smartphone or a dedicated device, the owner can instantly understand the pet's emotions and requests. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can notify the owner using an AI model that inputs the result determined by the analysis unit and outputs a notification.

[0061] The analysis unit can learn using past pet behavior data. The analysis unit learns using, for example, past pet behavior data. Past behavior data includes, for example, a behavior recording method, a data storage format, and the like, but is not limited to these examples. For example, the analysis unit collects past pet behavior data and learns based on that data. By using the past behavior data, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can improve the accuracy of the analysis by using a generation AI model that inputs past pet behavior data and learns.

[0062] The acquisition unit can install the camera in a position that can cover a wide range of the pet's activities and adjust the microphone's sensitivity to clearly pick up the pet's cries. For example, the acquisition unit installs the camera in a position that can cover a wide range of the pet's activities and adjusts the microphone's sensitivity to clearly pick up the pet's cries. The camera's installation position includes, but is not limited to, the center of the room or a high position. For example, installing the camera in the center of the room can cover a wide range of the pet's activities. The microphone's sensitivity can be adjusted to, but is not limited to, a decibel value or a frequency band. For example, increasing the microphone's sensitivity can clearly pick up the pet's cries. This allows the pet's activities and cries to be acquired in detail by installing and adjusting the camera and microphone. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit uses an AI model to optimize the placement and sensitivity of the cameras and microphones, enabling it to cover a wide range of your pet's movements and clearly pick up its cries.

[0063] The analysis unit can analyze the pet's gestures and facial expressions, such as wagging its tail, pricking up its ears, squinting its eyes, meowing, and barking. The analysis unit analyzes, for example, the pet's gestures and facial expressions, such as wagging its tail, pricking up its ears, squinting its eyes, meowing, and barking. Specific criteria for determining gestures and facial expressions include, but are not limited to, the frequency and duration of the behavior. For example, if the pet wags its tail, the analysis is performed based on the frequency and duration of the behavior. Similarly, if the pet pricks up its ears, the analysis is performed based on the frequency and duration of the behavior. This allows for a detailed analysis of the pet's gestures and facial expressions, thereby accurately determining its emotions and requests. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can determine the pet's emotions and desired responses using a generation AI model that inputs the pet's gestures and facial expressions and outputs emotions and desired responses.

[0064] The notification unit can display messages such as "Your pet is happy" or "Your pet is asking for something" on the owner's smartphone. For example, the notification unit can display messages such as "Your pet is happy" or "Your pet is asking for something" on the owner's smartphone. This allows the owner to immediately understand their pet's emotions and requests. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can notify the owner using an AI model that inputs the results determined by the analysis unit and outputs a notification.

[0065] The acquisition unit can estimate the pet's emotions and adjust the timing of video and audio acquisition based on the estimated pet emotions. The acquisition unit, for example, estimates the pet's emotions and adjusts the timing of video and audio acquisition based on the estimated pet emotions. The emotion estimation method and the criteria for adjusting the acquisition timing include, but are not limited to, real-time analysis and timing thresholds. For example, if the pet is excited, the camera starts recording and records detailed behavior. If the pet is relaxed, the recording frequency is reduced and recording is performed only when necessary. Furthermore, if the pet is anxious, the microphone sensitivity is increased to record detailed sounds. By adjusting the acquisition timing according to the pet's emotions, important moments can be recorded without missing any important moments. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can adjust the timing of video and audio acquisition using an AI model that estimates the pet's emotions.

[0066] The acquisition unit can analyze the pet's past behavioral history and select the optimal acquisition method. The acquisition unit, for example, analyzes the pet's past behavioral history and selects the optimal acquisition method. Methods for analyzing behavioral history and criteria for selecting an acquisition method include, but are not limited to, data mining techniques and selection algorithms. For example, if the pet was active during a specific time period in the past, recording can be concentrated on that time period. Also, if the pet often meowed in a specific location in the past, a microphone can be installed in that location. Furthermore, if the pet frequently performed a specific behavior in the past, that behavior can be recorded intensively. This allows the optimal acquisition method to be selected by analyzing the past behavioral history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can select the optimal acquisition method using an AI model that analyzes the pet's past behavioral history.

[0067] The acquisition unit can perform filtering based on the pet's current activity status and environment when acquiring video and audio. For example, when acquiring video and audio, the acquisition unit performs filtering based on the pet's current activity status and environment. Specific filtering criteria and methods include, but are not limited to, the type of activity and environmental conditions. For example, if the pet is quiet, recording is paused and resumed when there is movement. Furthermore, if the pet makes a specific sound, that sound is used as a trigger to start recording. Furthermore, if the pet enters a specific area, the behavior in that area is preferentially recorded. This allows important data to be efficiently acquired by filtering based on the activity status and environment. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can perform filtering using an AI model that analyzes the pet's activity status and environment.

[0068] The acquisition unit can estimate the pet's emotions and determine the priority of the videos and audio to be acquired based on the estimated pet emotions. The acquisition unit can, for example, estimate the pet's emotions and determine the priority of the videos and audio to be acquired based on the estimated pet emotions. Criteria and methods for determining the priority include, but are not limited to, the intensity and importance of emotions. For example, if the pet is excited, the pet's behavior is recorded with priority. Furthermore, if the pet is relaxed, the recording priority is lowered. Furthermore, if the pet is anxious, the pet's cries are recorded with priority. In this way, by determining the priority based on the pet's emotions, important data can be acquired with priority. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can determine the priority of the videos and audio to be acquired using an AI model that estimates the pet's emotions.

[0069] The acquisition unit can prioritize acquisition of highly relevant data by taking into consideration the geographical location information of the pet when acquiring video or audio. For example, the acquisition unit prioritizes acquisition of highly relevant data by taking into consideration the geographical location information of the pet when acquiring video or audio. Specific methods of using the geographical location information and criteria for relevance include, but are not limited to, the accuracy of the location information and a method for evaluating relevance. For example, if the pet is in the garden, its behavior in the garden is prioritized for recording. Also, if the pet is in the living room, its cries in the living room are prioritized for recording. Furthermore, if the pet enters a specific room, its behavior in that room is prioritized for recording. This allows highly relevant data to be acquired efficiently by taking into consideration the geographical location information. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can prioritize acquisition of highly relevant data by using an AI model that analyzes the geographical location information of the pet.

[0070] The acquisition unit can analyze the social media activity of the pet owner when acquiring video or audio, and acquire related data. For example, the acquisition unit can analyze the social media activity of the pet owner when acquiring video or audio, and acquire related data. Specific analysis methods and relevance criteria for social media activity include, but are not limited to, analysis of posted content and methods of evaluating relevance. For example, if the owner mentions a specific behavior of the pet on social media, the system prioritizes recording of that behavior. Furthermore, if the owner mentions a sound made by the pet on social media, the system prioritizes recording of the sound. Furthermore, if the owner mentions a specific location of the pet on social media, the system prioritizes recording of the pet's behavior at that location. This allows for efficient acquisition of related data by analyzing the owner's social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can acquire related data using an AI model that analyzes the owner's social media activity.

[0071] The analysis unit can estimate the pet's emotion and adjust the analysis presentation method based on the estimated pet's emotion. The analysis unit, for example, estimates the pet's emotion and adjusts the analysis presentation method based on the estimated pet's emotion. The criteria and specific methods for adjusting the presentation method include, but are not limited to, an expression format and an adjustment algorithm according to the type of emotion. For example, if the pet is happy, the analysis results are displayed in bright colors. If the pet is anxious, the analysis results are displayed in subdued colors. If the pet is excited, the analysis results are displayed with dynamic effects added. By adjusting the analysis presentation method based on the pet's emotion, the analysis results can be easily understood by the owner. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the analysis unit can adjust the analysis presentation method using a generative AI model that estimates the pet's emotion.

[0072] The analysis unit can adjust the level of detail of the analysis based on the importance of the pet's behavior during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the pet's behavior during analysis. Criteria and specific methods for adjusting the level of detail include, but are not limited to, importance evaluation criteria and levels of detail. For example, if the pet frequently performs a particular behavior, a detailed analysis of that behavior is performed. Furthermore, if the pet rarely performs that behavior, a simplified analysis is performed. Furthermore, if the pet's behavior is important to the owner, a detailed analysis of that behavior is performed. Thus, by adjusting the level of detail of the analysis based on the importance of the behavior, important behaviors can be analyzed in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can adjust the level of detail of the analysis using a generation AI model that evaluates the importance of the pet's behavior.

[0073] The analysis unit can apply different analysis algorithms depending on the type and individual differences of the pet during analysis. For example, the analysis unit can apply different analysis algorithms depending on the type and individual differences of the pet during analysis. Criteria for selecting and applying analysis algorithms and specific application methods include, but are not limited to, methods for evaluating the characteristics of each type and individual differences. For example, different analysis algorithms can be used for dogs and cats to perform analysis according to their respective characteristics. Furthermore, even for the same breed of dog, the analysis algorithm can be adjusted according to individual differences. Furthermore, the analysis algorithm can be changed depending on the age and health condition of the pet. By applying an analysis algorithm depending on the type and individual differences, more accurate analysis results can be obtained. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can apply different analysis algorithms using a generation AI model that evaluates the type and individual differences of the pet.

[0074] The analysis unit can estimate the pet's emotions and adjust the length of the analysis based on the estimated pet's emotions. The analysis unit, for example, estimates the pet's emotions and adjusts the length of the analysis based on the estimated pet's emotions. Criteria and specific methods for adjusting the length of the analysis include, but are not limited to, length adjustment algorithms and adjustment algorithms based on the intensity of the emotions. For example, if the pet is excited, a detailed analysis is performed and a longer report is generated. If the pet is relaxed, a concise analysis is performed and a shorter report is generated. Furthermore, if the pet is anxious, a detailed analysis is performed and a longer report is generated. By adjusting the length of the analysis based on the pet's emotions, an analysis result of an appropriate length can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can adjust the length of the analysis using a generation AI model that estimates the pet's emotions.

[0075] The analysis unit can determine the analysis priority based on the timing of the pet's behavior during analysis. The analysis unit, for example, determines the analysis priority based on the timing of the pet's behavior during analysis. Criteria and specific methods for determining the priority include, but are not limited to, criteria for evaluating the timing of occurrence and methods for setting priorities. For example, the analysis may prioritize recent behaviors performed by the pet. Also, the analysis may prioritize behaviors performed by the pet during a specific time period. Furthermore, the analysis may prioritize behaviors performed by the pet at a specific event (e.g., when the owner returns home). In this way, by determining the priority based on the timing of behavior occurrence, important behaviors can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can determine the analysis priority using a generation AI model that evaluates the timing of pet behavior occurrence.

[0076] The analysis unit can adjust the order of analysis based on the relevance of the pet's behavior during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the pet's behavior during analysis. Criteria and specific methods for adjusting the order include, but are not limited to, relevance evaluation criteria and adjustment algorithms. For example, if the pet's behavior is continuous, the analysis is performed taking into account that continuity. Furthermore, if the pet's behavior follows a specific pattern, the analysis is performed taking into account that pattern. Furthermore, if the pet's behavior is related to other behaviors, the analysis is performed taking into account that relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the behaviors. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the analysis unit can adjust the order of analysis using a generative AI model that evaluates the relevance of the pet's behavior.

[0077] The notification unit can estimate the pet's emotion and adjust the notification display method based on the estimated pet's emotion. The notification unit, for example, estimates the pet's emotion and adjusts the notification display method based on the estimated pet's emotion. The display method adjustment criteria and specific methods include, but are not limited to, display formats and adjustment algorithms according to the type of emotion. For example, if the pet is happy, the notification is displayed in bright colors. If the pet is anxious, the notification is displayed in subdued colors. If the pet is excited, the notification is displayed with dynamic effects. By adjusting the notification display method based on the pet's emotion, the owner can be provided with a notification that is easy to understand. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can adjust the notification display method using an AI model that estimates the pet's emotion.

[0078] The notification unit can adjust the level of detail of the notification based on the importance of the pet's behavior when providing a notification. For example, the notification unit adjusts the level of detail of the notification based on the importance of the pet's behavior when providing a notification. Criteria and specific methods for adjusting the level of detail include, but are not limited to, importance evaluation criteria and levels of detail. For example, if the pet frequently performs a specific behavior, a detailed notification of the behavior is provided. Furthermore, if the pet rarely performs the behavior, a simplified notification is provided. Furthermore, if the pet's behavior is important to the owner, a detailed notification of the behavior is provided. In this way, by adjusting the level of detail of the notification based on the importance of the behavior, important behavior can be notified in detail. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can adjust the level of detail of the notification using an AI model that evaluates the importance of the pet's behavior.

[0079] The notification unit can select the optimal notification method by referring to the pet owner's past reaction history when notifying the pet. For example, the notification unit can select the optimal notification method by referring to the pet owner's past reaction history when notifying the pet. Specific methods for referring to the reaction history and criteria for selecting the notification method include, but are not limited to, a history analysis method and a selection algorithm. For example, if the pet owner has previously preferred a specific notification method, that method is used preferentially. Furthermore, if the pet owner has previously responded quickly to a specific notification, that notification method is used preferentially. Furthermore, if the pet owner has previously responded slowly to a specific notification, the notification method is changed. In this way, the optimal notification method can be selected by referring to the pet owner's past reaction history. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can select the optimal notification method using an AI model that analyzes the pet owner's past reaction history.

[0080] The notification unit can estimate the pet's emotion and determine the priority of notifications based on the estimated pet's emotion. The notification unit can, for example, estimate the pet's emotion and determine the priority of notifications based on the estimated pet's emotion. Priority determination criteria and specific methods include, but are not limited to, the intensity and importance of the emotion. For example, if the pet is excited, the notification is displayed with priority. Furthermore, if the pet is relaxed, the priority of the notification is lowered. Furthermore, if the pet is feeling anxious, the notification is displayed with priority. In this way, by determining the priority of notifications based on the pet's emotion, important notifications can be displayed with priority. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can determine the priority of notifications using an AI model that estimates the pet's emotion.

[0081] The notification unit can adjust the order of notifications based on the time of occurrence of the pet's behavior when notifying. For example, the notification unit adjusts the order of notifications based on the time of occurrence of the pet's behavior when notifying. Criteria and specific methods for adjusting the order include, but are not limited to, evaluation criteria for the time of occurrence and methods for setting the order, for example. For example, the notification unit prioritizes notification of recent behaviors performed by the pet. Also, the notification unit prioritizes notification of behaviors performed by the pet during a specific time period. Furthermore, the notification unit prioritizes notification of behaviors performed by the pet during a specific event (e.g., when the owner returns home). In this way, by adjusting the order of notifications based on the time of occurrence of the behavior, important behaviors can be prioritized. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can adjust the order of notifications using an AI model that evaluates the time of occurrence of the pet's behavior.

[0082] The notification unit can select the optimal notification method by taking into account the device information of the pet owner when sending a notification. For example, the notification unit selects the optimal notification method by taking into account the device information of the pet owner when sending a notification. Specific methods for considering the device information and criteria for selecting the notification method include, but are not limited to, the type and function of the device, and usage status of the device. For example, if the owner uses a smartphone, push notifications are used preferentially. Also, if the owner uses a tablet, a notification method optimized for a large screen is used. Furthermore, if the owner uses a smartwatch, a notification method that is simple and highly visible is used. This allows the optimal notification method to be selected by taking into account the device information of the owner. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can select the optimal notification method using an AI model that analyzes the device information of the owner. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, analysis unit, and notification unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires video and audio of the pet using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and determines the pet's emotions and desired responses using a generation AI. The notification unit is realized by the control unit 46A of the smart device 14 and displays the determination result on a smartphone or dedicated device. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, analysis unit, and notification unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires video and audio of the pet using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and determines the pet's emotions and desired responses using a generation AI. The notification unit is realized by the control unit 46A of the smart glasses 214 and displays the determination result on a smartphone or dedicated device. === Hard Collateral 1-3 === Each of the multiple elements including the acquisition unit, analysis unit, and notification unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the acquisition unit acquires video and audio of the pet using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and determines the pet's emotions and desired responses using a generation AI. The notification unit is realized by the control unit 46A of the headset-type terminal 314, and displays the determination result on a smartphone or dedicated device. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, analysis unit, and notification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires video and audio of the pet using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and determines the pet's emotions and desired responses using a generation AI. The notification unit is realized by the control unit 46A of the robot 414, and displays the determination result on a smartphone or dedicated device.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The analysis unit can estimate the pet's emotions and evaluate the pet's health condition based on the estimated pet's emotions. For example, if the pet frequently feels anxious, the analysis unit suggests that there may be a problem with the pet's health. If the pet is happy, the analysis unit evaluates the pet's health condition as good. Furthermore, if the pet is excited, the analysis unit indicates that the pet's activity level is high. This allows owners to manage their pet's health by evaluating the pet's health condition based on the pet's emotions.

[0085] The acquisition unit can store pet behavior data in the cloud and make it accessible from other devices. For example, an owner can check their pet's behavior on a smartphone while they are out. Storing pet behavior data in the cloud also allows data to be accumulated over a long period of time and used for later analysis. Furthermore, by sharing data stored in the cloud, multiple owners can check the behavior of the same pet. Storing pet behavior data in the cloud thus improves convenience.

[0086] The analysis unit can estimate the pet's emotions and evaluate the pet's stress level based on the estimated pet's emotions. For example, if the pet frequently feels anxious, the analysis unit evaluates the pet's stress level as high. If the pet is relaxed, the analysis unit evaluates the pet's stress level as low. If the pet is excited, the analysis unit evaluates the pet's stress level as medium. This allows owners to manage their pet's stress by evaluating the pet's stress level based on the pet's emotions.

[0087] The notification unit can estimate the pet's emotions and suggest appropriate actions to the owner based on the estimated pet's emotions. For example, if the pet feels anxious, the notification unit suggests to the owner, "Pat your pet to make it feel safe." If the pet is happy, the notification unit suggests to the owner, "Play with your pet." If the pet is excited, the notification unit suggests to the owner, "Take your pet to a quiet place to calm it down." This helps owners care for their pets by suggesting appropriate actions based on the pet's emotions.

[0088] The acquisition unit can stream the pet's behavior data in real time, allowing the owner to check it live. For example, the owner can check the pet's behavior live on their smartphone while they are out. Streaming the pet's behavior data in real time also allows the owner to instantly understand the pet's current situation. Furthermore, through live streaming, the owner can respond to the pet's behavior in real time. Streaming the pet's behavior data in real time thus improves convenience for the owner.

[0089] The analysis unit can estimate the pet's emotions and predict the pet's behavioral patterns based on the estimated pet's emotions. For example, if the pet frequently feels anxious, the analysis unit predicts that the pet is likely to feel anxious in certain situations. Also, if the pet is happy, the analysis unit predicts that the pet is likely to be happy in certain situations. Furthermore, if the pet is excited, the analysis unit predicts that the pet is likely to be excited in certain situations. Thus, predicting behavioral patterns based on the pet's emotions makes it easier for owners to understand their pet's behavior in advance.

[0090] The acquisition unit can analyze the behavioral data of the pet and identify the pet's likes and dislikes. For example, if the pet often plays with a particular toy, the acquisition unit identifies that toy as the pet's favorite. Also, if the pet avoids a particular food, the acquisition unit identifies that food as the pet's dislike. Furthermore, if the pet avoids a particular place, the acquisition unit identifies that place as the pet's dislike. In this way, by analyzing the behavioral data of the pet, the likes and dislikes of the pet can be identified, allowing the owner to improve the living environment of their pet.

[0091] The analysis unit can estimate the pet's emotions and evaluate the pet's sociability based on the estimated pet's emotions. For example, if the pet is happy when it is with other animals, the analysis unit evaluates the pet's sociability as high. If the pet feels anxious when it is with other animals, the analysis unit evaluates the pet's sociability as low. Furthermore, if the pet is excited when it is with other animals, the analysis unit evaluates the pet's sociability as medium. Thus, evaluating the pet's sociability based on the pet's emotions makes it easier for owners to understand their pet's sociability.

[0092] The acquisition unit can measure the amount of exercise of a pet using the behavioral data of the pet. For example, it can measure how far the pet walks in a day, how much time the pet spends playing in a day, and how much time the pet spends resting in a day. In this way, measuring the amount of exercise using the behavioral data of the pet can be useful for pet owners in managing their pet's health.

[0093] The analysis unit can estimate the pet's emotions and detect changes in the pet's behavior based on the estimated pet's emotions. For example, if the pet begins to feel anxious in response to a behavior that previously made the pet happy, the analysis unit detects the change. Also, if the pet begins to feel relaxed in response to a behavior that previously made the pet excited, the analysis unit detects the change. Furthermore, if the pet begins to feel excited in response to a behavior that previously made the pet anxious, the analysis unit detects the change. In this way, by detecting changes in behavior based on the pet's emotions, owners can more accurately understand the state of their pet.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The acquisition unit acquires video or audio of the pet. The video or audio of the pet may have, but is not limited to, a specific format, resolution, sound quality, etc. The acquisition unit acquires video or audio of the pet using a camera and a microphone. The camera records the pet's movements and facial expressions in real time, and the microphone captures the pet's cries and audio. Step 2: The analysis unit analyzes the video or audio captured by the acquisition unit to determine the pet's emotions and the desired response. Using the generation AI, the analysis unit analyzes the pet's gestures, facial expressions, cries, etc. to determine the pet's emotions at that time and the desired response. For example, if the pet is wagging its tail, the generation AI determines that the pet is happy, and if the pet is meowing, the generation AI determines that the pet is requesting something. Step 3: The notification unit notifies the owner of the results determined by the analysis unit. The notification unit displays the results on a smartphone or dedicated device. For example, a message such as "Your pet is happy" or "Your pet wants something" may be displayed on the owner's smartphone.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0098] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0099] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0114] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0130] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0140] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0157] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] 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.

[0159] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0160] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0167] [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system characterized by comprising an acquisition unit that acquires video or audio of a pet, an analysis unit that analyzes the video or audio acquired by the acquisition unit and determines the pet's emotions and desired response, and a notification unit that notifies the owner of the results determined by the analysis unit.

2. The acquisition unit Capture video and audio of your pet using a camera and microphone 2. The system of claim 1.

3. 2. The system according to claim 1, wherein the analysis unit analyzes the gestures, facial expressions, and cries of the pet to determine the pet's emotions and the desired response.

4. The system according to claim 1 , wherein the notification unit displays the judgment result on a smartphone or a dedicated device.

5. The analysis unit Learn using past pet behavior data 2. The system of claim 1.

6. The system according to claim 1, wherein the acquisition unit installs a camera in a position that can cover a wide range of the pet's movement and adjusts the sensitivity of a microphone to a level that can clearly pick up the pet's cries.

7. 2. The system according to claim 1, wherein the analysis unit analyzes gestures and facial expressions of the pet, such as wagging a tail, erecting ears, squinting, meowing, and barking.

8. The acquisition unit Estimate the pet's emotions and adjust the timing of video and audio capture based on the estimated pet's emotions.

2. The system of claim 1.

9. The acquisition unit Analyze your pet's past behavioral history and select the best acquisition method 2. The system of claim 1.

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