system

Abstract

The system according to the embodiment aims to automatically generate a picture diary based on the behavior and location information of a pet. [Solution] A system according to an embodiment includes an image analysis unit, a position analysis unit, a conversion unit, a generation unit, and a provision unit. The image analysis unit acquires image information. The position analysis unit acquires position information. The conversion unit converts the information acquired by the image analysis unit and the position analysis unit into text information. The generation unit generates a picture diary based on the text information converted by the conversion unit. The provision unit provides the picture diary generated by the generation 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] With conventional technology, it was difficult and time-consuming to automatically generate a picture diary based on a pet's behavior and location information.

[0005] The system according to the embodiment aims to automatically generate a picture diary based on the behavior and location information of a pet. [Means for solving the problem]

[0006] The system according to the embodiment includes an image analysis unit, a position analysis unit, a conversion unit, a generation unit, and a provision unit. The image analysis unit acquires image information. The position analysis unit acquires position information. The conversion unit converts the information acquired by the image analysis unit and the position analysis unit into text information. The generation unit generates a picture diary based on the text information converted by the conversion unit. The provision unit provides the picture diary generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate a picture diary based on the behavior and location information of a pet. [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 service according to an embodiment of the present invention is a service for pet owners. This service provides a function for compiling a pet's daily life in the form of a picture diary using a generation AI. The service uses a wearable camera and GPS attached to the pet to acquire image information captured by the camera and location information acquired by the GPS. The acquired image information and location information are then converted into text information, and the generation AI generates a picture diary of the pet's daily life. For example, the service records the pet playing in the park and the route taken during a walk. This allows the owner to easily look back on the pet's daily events. This allows the service to summarize the pet's daily events in the form of a picture diary. For example, the owner can view the generated picture diary on a smartphone or tablet, allowing them to enjoy reminiscing about their memories with their pet. The picture diary can also be shared on social media, allowing the owner to deepen interactions with other pet owners.

[0029] A pet daily record system according to an embodiment includes an image analysis unit, a position analysis unit, a conversion unit, a generation unit, and a provision unit. The image analysis unit analyzes image information acquired by a camera attached to the pet. For example, the image analysis unit analyzes images acquired by the camera to identify what and where the pet is looking. The image analysis unit can also use image analysis technology to identify objects the pet is looking at. The image analysis unit can also identify the pet's behavior based on the image information. The position analysis unit analyzes location information acquired by a GPS attached to the pet. For example, the position analysis unit analyzes GPS data to identify the pet's movement route and places visited. The position analysis unit can also identify the pet's movement pattern based on the location information. The conversion unit converts the information acquired by the image analysis unit and the position analysis unit into text information. For example, the conversion unit converts image information into text information and records what and where the pet is looking. The conversion unit can also convert location information into text information and record the pet's movement route and places visited. The generation unit generates a picture diary of the pet's daily life based on the text information converted by the conversion unit. For example, the generation unit summarizes the pet's daily events as a picture diary based on the text information. The generation unit can also generate the pet's daily life in picture diary format using a generation AI. The provision unit provides the picture diary generated by the generation unit. For example, the provision unit provides the generated picture diary so that it can be viewed on a smartphone or tablet. The provision unit can also provide the generated picture diary so that it can be shared on social media, etc. In this way, the pet daily life recording system according to the embodiment can summarize the pet's daily life in picture diary format.

[0030] The image analysis unit can analyze images acquired by the camera and identify the object and location that the pet is looking at. The image analysis unit can, for example, analyze images acquired by the camera and identify the object that the pet is looking at. For example, the image analysis unit can identify the person or object that the pet is looking at. The image analysis unit can also analyze images acquired by the camera and identify the location that the pet is looking at. For example, the image analysis unit can identify the location, such as a park or home, that the pet is looking at. This makes it possible to accurately obtain information from the pet's perspective. Some or all of the above-mentioned processing in the image analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the image analysis unit can input image data acquired by the camera to a generation AI and cause the generation AI to identify the object and location from the image data.

[0031] The position analysis unit can analyze the GPS data and identify the pet's movement route and the places visited. The position analysis unit can, for example, analyze the GPS data and identify the pet's movement route. For example, the position analysis unit can identify the pet's movement route from home to the park. The position analysis unit can also analyze the GPS data and identify the places visited by the pet. For example, the position analysis unit can identify the park, veterinary clinic, or other places visited by the pet. This makes it possible to accurately obtain pet movement information. Some or all of the above-described processing in the position analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the position analysis unit can input GPS data to the generation AI and cause the generation AI to identify the movement route and the places visited.

[0032] The conversion unit can convert information acquired by the image analysis unit and the position analysis unit into text information. The conversion unit, for example, converts image information acquired by the image analysis unit into text information. For example, the conversion unit records what the pet is looking at and where it is located as text information. The conversion unit can also convert location information acquired by the position analysis unit into text information. For example, the conversion unit records the pet's travel route and the places visited as text information. This allows the acquired information to be converted into text information. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input image information and location information to a generation AI and have the generation AI convert the information into text information.

[0033] The generation unit can generate the pet's daily life in a picture diary format based on the character information converted by the conversion unit. The generation unit, for example, generates the pet's daily life in a picture diary format based on the character information converted by the conversion unit. For example, the generation unit compiles a picture diary of the pet playing in the park. The generation unit can also generate the pet's daily life in a picture diary format using a generation AI. For example, the generation unit inputs character information to the generation AI and generates the pet's daily life in a picture diary format. In this way, the pet's daily life can be generated in a picture diary format. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the generation unit can input character information to the generation AI and cause the generation AI to generate a picture diary.

[0034] The providing unit can provide the generated pictorial diary so that it can be viewed on a smartphone or tablet. The providing unit, for example, provides the generated pictorial diary so that it can be viewed on a smartphone or tablet. For example, the providing unit provides the generated pictorial diary through a smartphone application. The providing unit can also provide the generated pictorial diary through a tablet application. This allows the generated pictorial diary to be viewed on a smartphone or tablet. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can provide the generated pictorial diary in an optimal format using AI.

[0035] The image analysis unit can analyze the movement patterns of the pet during image analysis and identify specific behaviors. For example, the image analysis unit can analyze the movement patterns of the pet during image analysis and identify specific behaviors. For example, if the pet is running, the movement patterns can be analyzed to identify the behavior of "running." Also, if the pet is eating, the movement patterns can be analyzed to identify the behavior of "eating." Also, if the pet is sleeping, the movement patterns can be analyzed to identify the behavior of "sleeping." In this way, the movement patterns of the pet can be analyzed and specific behaviors can be identified. Some or all of the above-described processing in the image analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the image analysis unit can input data of the pet's movement to a generation AI and have the generation AI perform behavior identification.

[0036] The image analysis unit can track the direction of the pet's gaze during image analysis and identify the object the pet is focusing on. For example, the image analysis unit can track the direction of the pet's gaze during image analysis and identify the object the pet is focusing on. For example, if the pet is looking at a ball, the direction of the gaze can be tracked to identify the ball. Also, if the pet is looking at another animal, the direction of the gaze can be tracked to identify the other animal. Also, if the pet is looking at its owner, the direction of the gaze can be tracked to identify the owner. In this way, the direction of the pet's gaze can be tracked to identify the object the pet is focusing on. Some or all of the above-described processing in the image analysis unit can be performed using, for example, AI, or without AI. For example, the image analysis unit can input the pet's gaze data into the generation AI and have the generation AI identify the object the pet is focusing on.

[0037] The image analysis unit can correct the analysis results based on environmental information about the pet's surroundings during image analysis. The image analysis unit, for example, corrects the analysis results by taking into account environmental information about the pet's surroundings during image analysis. For example, if the pet is indoors, the analysis results can be corrected by taking into account indoor lighting and furniture arrangement. If the pet is outdoors, the analysis results can also be corrected by taking into account the weather and time of day. If the pet is in a park, the analysis results can also be corrected by taking into account the park's topography and the presence of other animals. In this way, the analysis results can be corrected by taking into account environmental information about the pet's surroundings. Some or all of the above-described processing in the image analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the image analysis unit can input environmental information data into a generation AI and cause the generation AI to correct the analysis results.

[0038] The location analysis unit can analyze the pet's movement speed and direction during location analysis and detect abnormal behavior. The location analysis unit, for example, analyzes the pet's movement speed and direction during location analysis and detects abnormal behavior. For example, if the pet is moving at a speed faster than normal, it can detect this as abnormal behavior. It can also detect this as abnormal behavior if the pet deviates from its normal movement route. It can also detect this as abnormal behavior if the pet stays in a specific location for a long time. In this way, it is possible to analyze the pet's movement speed and direction and detect abnormal behavior. Some or all of the above-mentioned processing in the location analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the location analysis unit can input data on the movement speed and direction to the generation AI and cause the generation AI to detect abnormal behavior.

[0039] The location analysis unit can learn the pet's movement patterns during location analysis and construct a prediction model. The location analysis unit, for example, learns the pet's movement patterns during location analysis and constructs a prediction model. For example, the location analysis unit learns the pet's normal movement patterns based on the pet's past movement data. A model that predicts the pet's next destination can also be constructed based on the pet's movement patterns. A model that predicts abnormal behavior can also be constructed based on the pet's movement patterns. In this way, the pet's movement patterns can be learned and a prediction model can be constructed. Some or all of the above-mentioned processing in the location analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the location analysis unit can input movement pattern data to a generation AI and have the generation AI construct a prediction model.

[0040] The location analysis unit can correct the analysis result based on geographic information about the pet's surroundings during location analysis. The location analysis unit, for example, corrects the analysis result by taking into account geographic information about the pet's surroundings during location analysis. For example, if the pet is in a park, the analysis result can be corrected by taking into account geographic information about the park. Furthermore, if the pet is in a residential area, the analysis result can also be corrected by taking into account geographic information about the residential area. Furthermore, if the pet is in a mountainous area, the analysis result can also be corrected by taking into account geographic information about the mountainous area. In this way, the analysis result can be corrected by taking into account geographic information about the pet's surroundings. Some or all of the above-described processing in the location analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the location analysis unit can input geographic information data to a generation AI and cause the generation AI to correct the analysis result.

[0041] The conversion unit can analyze the relationship between image information and location information during conversion and generate integrated text information. For example, the conversion unit can generate integrated text information by integrating "playing in the park" from image information with "at the park" from location information. Alternatively, the conversion unit can generate "pet playing in the park" by integrating "chasing a ball" from image information with "at the yard" from location information. Alternatively, the conversion unit can generate "pet chasing a ball in the yard" by integrating "sleeping" from image information with "at home" from location information. This allows the conversion unit to analyze the relationship between image information and location information and generate integrated text information. Some or all of the above-described processing in the conversion unit may be performed using, or without, AI. For example, the conversion unit can input image information and location information to a generation AI and cause the generation AI to generate integrated text information.

[0042] The conversion unit can learn the behavioral patterns of the pet during conversion and generate natural-sounding sentences. For example, the conversion unit can learn the behavioral patterns of the pet during conversion and generate natural-sounding sentences. For example, if a pet goes for a walk at the same time every day, the conversion unit can learn the behavioral patterns and generate natural-sounding sentences such as "I go for a walk every morning." If a pet behaves in a specific place, the conversion unit can learn the behavioral patterns and generate natural-sounding sentences such as "I chase a ball in the park." If a pet eats at a specific time, the conversion unit can learn the behavioral patterns and generate natural-sounding sentences such as "I eat in the evening." This allows the conversion unit to learn the behavioral patterns of the pet and generate natural-sounding sentences. Some or all of the above-described processing in the conversion unit may be performed using, or without, AI. For example, the conversion unit can input the pet's behavioral data into a generation AI and cause the generation AI to generate natural-sounding sentences.

[0043] The conversion unit can correct the text information based on environmental information about the pet's surroundings during conversion. The conversion unit, for example, corrects the text information taking into account environmental information about the pet's surroundings during conversion. For example, if the pet is out on a rainy day, the conversion unit can correct the text information to "taking a walk in the rain" taking into account weather information. If the pet is active at night, the conversion unit can correct the text information to "taking a walk at night" taking into account time information. If the pet is in a noisy place, the conversion unit can correct the text information to "playing in a noisy place" taking into account environmental sound information. In this way, the text information can be corrected taking into account environmental information about the pet's surroundings. Some or all of the above-described processing in the conversion unit may be performed using AI, for example, or may be performed without using AI. For example, the conversion unit can input environmental information data to a generation AI and cause the generation AI to correct the text information.

[0044] The generation unit can learn the behavioral patterns of the pet at the time of generation and generate a natural-looking picture diary. For example, the generation unit can learn the behavioral patterns of the pet at the time of generation and generate a natural-looking picture diary. For example, if the pet goes for a walk at the same time every day, the generation unit can learn that behavioral pattern and generate a natural-looking picture diary such as "Going for a walk every morning." If the pet behaves in a specific place, the generation unit can learn that behavioral pattern and generate a natural-looking picture diary such as "Chases a ball in the park." If the pet eats at a specific time, the generation unit can learn that behavioral pattern and generate a natural-looking picture diary such as "Eats in the evening." In this way, the behavioral patterns of the pet can be learned and a natural-looking picture diary can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the behavioral data of the pet into the generation AI and cause the generation AI to generate a natural-looking picture diary.

[0045] The generation unit can correct the picture diary based on environmental information about the pet's surroundings when generating the picture diary. For example, the generation unit corrects the picture diary by taking into account environmental information about the pet's surroundings when generating the picture diary. For example, if the pet is out on a rainy day, the generation unit can correct the picture diary to "taking a walk in the rain" by taking into account weather information. If the pet is active at night, the generation unit can correct the picture diary to "taking a walk at night" by taking into account time information. If the pet is in a noisy place, the generation unit can correct the picture diary to "playing in a noisy place" by taking into account environmental sound information. In this way, the picture diary can be corrected by taking into account environmental information about the pet's surroundings. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input environmental information data into the generation AI and cause the generation AI to correct the picture diary.

[0046] The generation unit can customize the style of the picture diary according to the preferences of the pet owner at the time of generation. The generation unit, for example, customizes the style of the picture diary according to the preferences of the pet owner at the time of generation. For example, the generation unit generates a picture diary that reflects the owner's preferred colors and designs. The generation unit can also generate a picture diary that reflects the owner's preferred fonts and layouts. The generation unit can also suggest an optimal style based on the owner's past selection history. This allows the style of the picture diary to be customized according to the pet owner's preferences. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the owner's preference data into the generation AI and cause the generation AI to customize the style of the picture diary.

[0047] The providing unit can select an appropriate providing method by referring to the browsing history of the pet owner at the time of providing. The providing unit, for example, selects an appropriate providing method by referring to the browsing history of the pet owner at the time of providing. For example, the providing unit selects the optimal providing method based on the style of picture diaries that the owner has previously viewed. It is also possible to provide a picture diary that reflects the owner's preferred content from the owner's browsing history. It is also possible to provide a picture diary at the optimal timing based on the owner's browsing history. This makes it possible to select the optimal providing method by referring to the pet owner's browsing history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the owner's browsing history data to the generation AI and cause the generation AI to select the providing method.

[0048] The providing unit can select an appropriate display method by taking into consideration the device information of the pet owner when providing the data. For example, the providing unit selects an appropriate display method by taking into consideration the device information of the pet owner when providing the data. For example, if the owner uses a smartphone, a display method that matches the screen size can be provided. Also, if the owner uses a tablet, a display method optimized for a large screen can be provided. Also, if the owner uses a smartwatch, a display method that is simple and highly visible can be provided. This makes it possible to select an optimal display method by taking into consideration the device information of the pet owner. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the owner's device information data to the generation AI and cause the generation AI to select a display method.

[0049] The providing unit can improve the providing method by reflecting the feedback of the pet owner at the time of providing. The providing unit, for example, improves the providing method by reflecting the feedback of the pet owner at the time of providing. For example, the providing method of the picture diary is improved based on the feedback provided by the owner. Furthermore, the providing method of the picture diary regarding specific behaviors or situations can be detailed by reflecting the feedback of the owner. Furthermore, the providing algorithm can be adjusted based on the feedback of the owner. In this way, the providing method can be improved by reflecting the feedback of the pet owner. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the feedback data of the owner into the generating AI and cause the generating AI to improve the providing method.

[0050] The providing unit can analyze the social media activity of the pet owner at the time of providing the data and enhance the sharing function. For example, the providing unit can analyze the social media activity of the pet owner at the time of providing the data and enhance the sharing function. For example, the providing unit can analyze the content that the pet owner often shares on social media and enhance the sharing function of the picture diary. The providing unit can also suggest the optimal timing for sharing based on the pet owner's social media activity. It can also share related picture diaries by referring to the activity of the pet owner's friends on social media. In this way, the social media activity of the pet owner can be analyzed and the sharing function can be enhanced. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the pet owner's social media activity data into the generating AI and cause the generating AI to enhance the sharing function.

[0051] The providing unit can provide a function for customizing the picture diary according to the preferences of the pet owner at the time of provision. The providing unit, for example, provides a function for customizing the picture diary according to the preferences of the pet owner at the time of provision. For example, the providing unit customizes the picture diary to reflect the owner's preferred colors and designs. The providing unit can also customize the picture diary to reflect the owner's preferred fonts and layouts. The providing unit can also suggest optimal customization options based on the owner's past selection history. This makes it possible to provide a function for customizing the picture diary according to the preferences of the pet owner. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the owner's preference data into the generating AI and cause the generating AI to provide the customization function.

[0052] The providing unit can customize the providing method by reflecting the feedback of the pet owner at the time of providing. The providing unit, for example, customizes the providing method by reflecting the feedback of the pet owner at the time of providing. For example, the providing unit customizes the providing method of the picture diary based on the feedback provided by the owner. Furthermore, the providing method of the picture diary regarding a specific behavior or situation can be detailed by reflecting the feedback of the owner. Furthermore, the providing algorithm can be adjusted based on the feedback of the owner. In this way, the providing method can be customized by reflecting the feedback of the pet owner. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the feedback data of the owner into the generating AI and cause the generating AI to customize the providing method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The image analysis unit can also be equipped with a function to monitor the health of pets. For example, it can estimate a pet's body temperature and heart rate using image analysis technology and notify the owner if any abnormalities are detected. It can also record a pet's food and water intake using image analysis, which can be used to help manage health. It can also regularly monitor changes in a pet's weight and body shape to detect changes in health at an early stage. This allows you to constantly understand your pet's health and provide appropriate care.

[0055] The location analysis unit can also be equipped with functions to ensure the safety of pets. For example, a function can be added to issue an alert if a pet strays from a specific area. It can also issue a warning if a pet approaches a dangerous location. Furthermore, if a pet gets lost, it can be equipped with a function to assist in searching for the pet based on its last known location information. This ensures the safety of pets and allows owners to watch over them with peace of mind.

[0056] The conversion unit can also have the function of analyzing pet behavior in real time and notifying the owner. For example, if a pet exhibits abnormal behavior, the owner can be notified in real time. Also, if a pet exhibits a specific behavior, the behavior can be recorded in real time and reported to the owner. Furthermore, it can learn the pet's behavior patterns and notify the owner of predicted behavior in advance. This allows the owner to understand the pet's behavior in real time and take appropriate action.

[0057] The generation unit may further have a function to provide advice based on the behavior of the pet. For example, if the pet is not getting enough exercise, advice to encourage exercise can be provided. Also, if the pet is feeling stressed, advice to help the pet relax can be provided. Furthermore, advice regarding the pet's diet and health care can be provided. This allows owners to receive appropriate advice to maintain the health and happiness of their pets.

[0058] The image analysis unit can also provide a training program based on the pet's behavior. For example, if the pet repeatedly behaves in a certain way, it can provide a training program to improve that behavior. It can also provide a step-by-step guide for the pet to learn a new trick. It can also monitor the pet's behavior and evaluate the training progress. This allows owners to effectively train their pets to improve their behavior and learn new skills.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The image analysis unit analyzes image information acquired by a camera attached to the pet. For example, the image analysis unit analyzes images acquired by the camera to identify what or where the pet is looking. The image analysis unit can also use image analysis technology to identify the object the pet is looking at. Furthermore, the image analysis unit can also identify the pet's behavior based on the image information. Step 2: The location analysis unit analyzes the location information obtained by the GPS attached to the pet. For example, the location analysis unit analyzes the GPS data to identify the pet's movement route and the places visited. The location analysis unit can also identify the pet's movement pattern based on the location information. Step 3: The conversion unit converts the information acquired by the image analysis unit and the position analysis unit into text information. For example, the conversion unit converts image information into text information and records what the pet is looking at and where it is located. The conversion unit can also convert position information into text information and record the pet's movement route and the places it has visited. Step 4: The generation unit generates a picture diary of the pet's daily life based on the text information converted by the conversion unit. For example, the generation unit may compile the pet's daily events as a picture diary based on the text information. The generation unit may also use a generation AI to generate a picture diary of the pet's daily life. Step 5: The providing unit provides the picture diary generated by the generating unit. For example, the providing unit provides the generated picture diary so that it can be viewed on a smartphone or tablet. The providing unit can also provide the generated picture diary so that it can be shared on social networking sites or the like.

[0061] (Example 2) A service according to an embodiment of the present invention is a service for pet owners. This service provides a function for compiling a pet's daily life in the form of a picture diary using a generation AI. The service uses a wearable camera and GPS attached to the pet to acquire image information captured by the camera and location information acquired by the GPS. The acquired image information and location information are then converted into text information, and the generation AI generates a picture diary of the pet's daily life. For example, the service records the pet playing in the park and the route taken during a walk. This allows the owner to easily look back on the pet's daily events. This allows the service to summarize the pet's daily events in the form of a picture diary. For example, the owner can view the generated picture diary on a smartphone or tablet, allowing them to enjoy reminiscing about their memories with their pet. The picture diary can also be shared on social media, allowing the owner to deepen interactions with other pet owners.

[0062] A pet daily record system according to an embodiment includes an image analysis unit, a position analysis unit, a conversion unit, a generation unit, and a provision unit. The image analysis unit analyzes image information acquired by a camera attached to the pet. For example, the image analysis unit analyzes images acquired by the camera to identify what and where the pet is looking. The image analysis unit can also use image analysis technology to identify objects the pet is looking at. The image analysis unit can also identify the pet's behavior based on the image information. The position analysis unit analyzes location information acquired by a GPS attached to the pet. For example, the position analysis unit analyzes GPS data to identify the pet's movement route and places visited. The position analysis unit can also identify the pet's movement pattern based on the location information. The conversion unit converts the information acquired by the image analysis unit and the position analysis unit into text information. For example, the conversion unit converts image information into text information and records what and where the pet is looking. The conversion unit can also convert location information into text information and record the pet's movement route and places visited. The generation unit generates a picture diary of the pet's daily life based on the text information converted by the conversion unit. For example, the generation unit summarizes the pet's daily events as a picture diary based on the text information. The generation unit can also generate the pet's daily life in picture diary format using a generation AI. The provision unit provides the picture diary generated by the generation unit. For example, the provision unit provides the generated picture diary so that it can be viewed on a smartphone or tablet. The provision unit can also provide the generated picture diary so that it can be shared on social media, etc. In this way, the pet daily life recording system according to the embodiment can summarize the pet's daily life in picture diary format.

[0063] The image analysis unit can analyze images acquired by the camera and identify the object and location that the pet is looking at. The image analysis unit can, for example, analyze images acquired by the camera and identify the object that the pet is looking at. For example, the image analysis unit can identify the person or object that the pet is looking at. The image analysis unit can also analyze images acquired by the camera and identify the location that the pet is looking at. For example, the image analysis unit can identify the location, such as a park or home, that the pet is looking at. This makes it possible to accurately obtain information from the pet's perspective. Some or all of the above-mentioned processing in the image analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the image analysis unit can input image data acquired by the camera to a generation AI and cause the generation AI to identify the object and location from the image data.

[0064] The position analysis unit can analyze the GPS data and identify the pet's movement route and the places visited. The position analysis unit can, for example, analyze the GPS data and identify the pet's movement route. For example, the position analysis unit can identify the pet's movement route from home to the park. The position analysis unit can also analyze the GPS data and identify the places visited by the pet. For example, the position analysis unit can identify the park, veterinary clinic, or other places visited by the pet. This makes it possible to accurately obtain pet movement information. Some or all of the above-described processing in the position analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the position analysis unit can input GPS data to the generation AI and cause the generation AI to identify the movement route and the places visited.

[0065] The conversion unit can convert information acquired by the image analysis unit and the position analysis unit into text information. The conversion unit, for example, converts image information acquired by the image analysis unit into text information. For example, the conversion unit records what the pet is looking at and where it is located as text information. The conversion unit can also convert location information acquired by the position analysis unit into text information. For example, the conversion unit records the pet's travel route and the places visited as text information. This allows the acquired information to be converted into text information. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input image information and location information to a generation AI and have the generation AI convert the information into text information.

[0066] The generation unit can generate the pet's daily life in a picture diary format based on the character information converted by the conversion unit. The generation unit, for example, generates the pet's daily life in a picture diary format based on the character information converted by the conversion unit. For example, the generation unit compiles a picture diary of the pet playing in the park. The generation unit can also generate the pet's daily life in a picture diary format using a generation AI. For example, the generation unit inputs character information to the generation AI and generates the pet's daily life in a picture diary format. In this way, the pet's daily life can be generated in a picture diary format. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the generation unit can input character information to the generation AI and cause the generation AI to generate a picture diary.

[0067] The providing unit can provide the generated pictorial diary so that it can be viewed on a smartphone or tablet. The providing unit, for example, provides the generated pictorial diary so that it can be viewed on a smartphone or tablet. For example, the providing unit provides the generated pictorial diary through a smartphone application. The providing unit can also provide the generated pictorial diary through a tablet application. This allows the generated pictorial diary to be viewed on a smartphone or tablet. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can provide the generated pictorial diary in an optimal format using AI.

[0068] The image analysis unit can estimate the pet's emotions and adjust the accuracy of the image analysis based on the estimated pet's emotions. For example, the image analysis unit can estimate the pet's emotions and adjust the accuracy of the image analysis based on the estimated pet's emotions. For example, if the pet is excited, the accuracy of the image analysis can be increased to accurately capture fast-moving objects. Furthermore, if the pet is relaxed, the accuracy of the image analysis can be adjusted to analyze stationary objects in detail. Furthermore, if the pet is anxious, the accuracy of the image analysis can be adjusted to capture a wider range of the surrounding environment. This allows the accuracy of the image analysis to be adjusted according to the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the image analysis unit can be performed using, for example, an AI, or without an AI. For example, the image analysis unit can input the pet's emotion data into the generative AI and cause the generative AI to adjust the accuracy of the image analysis.

[0069] The image analysis unit can analyze the movement patterns of the pet during image analysis and identify specific behaviors. For example, the image analysis unit can analyze the movement patterns of the pet during image analysis and identify specific behaviors. For example, if the pet is running, the movement patterns can be analyzed to identify the behavior of "running." Also, if the pet is eating, the movement patterns can be analyzed to identify the behavior of "eating." Also, if the pet is sleeping, the movement patterns can be analyzed to identify the behavior of "sleeping." In this way, the movement patterns of the pet can be analyzed and specific behaviors can be identified. Some or all of the above-described processing in the image analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the image analysis unit can input data of the pet's movement to a generation AI and have the generation AI perform behavior identification.

[0070] The image analysis unit can track the direction of the pet's gaze during image analysis and identify the object the pet is focusing on. For example, the image analysis unit can track the direction of the pet's gaze during image analysis and identify the object the pet is focusing on. For example, if the pet is looking at a ball, the direction of the gaze can be tracked to identify the ball. Also, if the pet is looking at another animal, the direction of the gaze can be tracked to identify the other animal. Also, if the pet is looking at its owner, the direction of the gaze can be tracked to identify the owner. In this way, the direction of the pet's gaze can be tracked to identify the object the pet is focusing on. Some or all of the above-described processing in the image analysis unit can be performed using, for example, AI, or without AI. For example, the image analysis unit can input the pet's gaze data into the generation AI and have the generation AI identify the object the pet is focusing on.

[0071] The image analysis unit can correct the analysis results based on environmental information about the pet's surroundings during image analysis. The image analysis unit, for example, corrects the analysis results by taking into account environmental information about the pet's surroundings during image analysis. For example, if the pet is indoors, the analysis results can be corrected by taking into account indoor lighting and furniture arrangement. If the pet is outdoors, the analysis results can also be corrected by taking into account the weather and time of day. If the pet is in a park, the analysis results can also be corrected by taking into account the park's topography and the presence of other animals. In this way, the analysis results can be corrected by taking into account environmental information about the pet's surroundings. Some or all of the above-described processing in the image analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the image analysis unit can input environmental information data into a generation AI and cause the generation AI to correct the analysis results.

[0072] The location analysis unit can estimate the pet's emotion and adjust the frequency of location information acquisition based on the estimated pet's emotion. For example, the location analysis unit estimates the pet's emotion and adjusts the frequency of location information acquisition based on the estimated pet's emotion. For example, if the pet is excited, the frequency of location information acquisition can be increased to record a detailed movement route. Furthermore, if the pet is relaxed, the frequency of location information acquisition can be reduced to reduce battery consumption. Furthermore, if the pet is anxious, the frequency of location information acquisition can be appropriately adjusted to accurately record the movement route. This allows the frequency of location information acquisition to be adjusted according to the pet's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the location analysis unit can be performed using, for example, an AI, or without an AI. For example, the location analysis unit can input the pet's emotion data into the generation AI and cause the generation AI to adjust the frequency of location information acquisition.

[0073] The location analysis unit can analyze the pet's movement speed and direction during location analysis and detect abnormal behavior. The location analysis unit, for example, analyzes the pet's movement speed and direction during location analysis and detects abnormal behavior. For example, if the pet is moving at a speed faster than normal, it can detect this as abnormal behavior. It can also detect this as abnormal behavior if the pet deviates from its normal movement route. It can also detect this as abnormal behavior if the pet stays in a specific location for a long time. In this way, it is possible to analyze the pet's movement speed and direction and detect abnormal behavior. Some or all of the above-mentioned processing in the location analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the location analysis unit can input data on the movement speed and direction to the generation AI and cause the generation AI to detect abnormal behavior.

[0074] The location analysis unit can learn the pet's movement patterns during location analysis and construct a prediction model. The location analysis unit, for example, learns the pet's movement patterns during location analysis and constructs a prediction model. For example, the location analysis unit learns the pet's normal movement patterns based on the pet's past movement data. A model that predicts the pet's next destination can also be constructed based on the pet's movement patterns. A model that predicts abnormal behavior can also be constructed based on the pet's movement patterns. In this way, the pet's movement patterns can be learned and a prediction model can be constructed. Some or all of the above-mentioned processing in the location analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the location analysis unit can input movement pattern data to a generation AI and have the generation AI construct a prediction model.

[0075] The location analysis unit can correct the analysis result based on geographic information about the pet's surroundings during location analysis. The location analysis unit, for example, corrects the analysis result by taking into account geographic information about the pet's surroundings during location analysis. For example, if the pet is in a park, the analysis result can be corrected by taking into account geographic information about the park. Furthermore, if the pet is in a residential area, the analysis result can also be corrected by taking into account geographic information about the residential area. Furthermore, if the pet is in a mountainous area, the analysis result can also be corrected by taking into account geographic information about the mountainous area. In this way, the analysis result can be corrected by taking into account geographic information about the pet's surroundings. Some or all of the above-described processing in the location analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the location analysis unit can input geographic information data to a generation AI and cause the generation AI to correct the analysis result.

[0076] The conversion unit can estimate the pet's emotion and adjust the method for generating text information based on the estimated pet's emotion. For example, the conversion unit can estimate the pet's emotion and adjust the method for generating text information based on the estimated pet's emotion. For example, if the pet is excited, the conversion unit can generate vivid text information that reflects the emotion. If the pet is relaxed, the conversion unit can generate text information in a calm tone. If the pet is anxious, the conversion unit can generate text information that includes a detailed description of the situation. This allows the method for generating text information to be adjusted according to the pet's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the conversion unit can be performed using, for example, AI, or without AI. For example, the conversion unit can input the pet's emotion data to the generation AI and cause the generation AI to adjust the method for generating text information.

[0077] The conversion unit can analyze the relationship between image information and location information during conversion and generate integrated text information. For example, the conversion unit can generate integrated text information by integrating "playing in the park" from image information with "at the park" from location information. Alternatively, the conversion unit can generate "pet playing in the park" by integrating "chasing a ball" from image information with "at the yard" from location information. Alternatively, the conversion unit can generate "pet chasing a ball in the yard" by integrating "sleeping" from image information with "at home" from location information. This allows the conversion unit to analyze the relationship between image information and location information and generate integrated text information. Some or all of the above-described processing in the conversion unit may be performed using, or without, AI. For example, the conversion unit can input image information and location information to a generation AI and cause the generation AI to generate integrated text information.

[0078] The conversion unit can learn the behavioral patterns of the pet during conversion and generate natural-sounding sentences. For example, the conversion unit can learn the behavioral patterns of the pet during conversion and generate natural-sounding sentences. For example, if a pet goes for a walk at the same time every day, the conversion unit can learn the behavioral patterns and generate natural-sounding sentences such as "I go for a walk every morning." If a pet behaves in a specific place, the conversion unit can learn the behavioral patterns and generate natural-sounding sentences such as "I chase a ball in the park." If a pet eats at a specific time, the conversion unit can learn the behavioral patterns and generate natural-sounding sentences such as "I eat in the evening." This allows the conversion unit to learn the behavioral patterns of the pet and generate natural-sounding sentences. Some or all of the above-described processing in the conversion unit may be performed using, or without, AI. For example, the conversion unit can input the pet's behavioral data into a generation AI and cause the generation AI to generate natural-sounding sentences.

[0079] The conversion unit can correct the text information based on environmental information about the pet's surroundings during conversion. The conversion unit, for example, corrects the text information taking into account environmental information about the pet's surroundings during conversion. For example, if the pet is out on a rainy day, the conversion unit can correct the text information to "taking a walk in the rain" taking into account weather information. If the pet is active at night, the conversion unit can correct the text information to "taking a walk at night" taking into account time information. If the pet is in a noisy place, the conversion unit can correct the text information to "playing in a noisy place" taking into account environmental sound information. In this way, the text information can be corrected taking into account environmental information about the pet's surroundings. Some or all of the above-described processing in the conversion unit may be performed using AI, for example, or may be performed without using AI. For example, the conversion unit can input environmental information data to a generation AI and cause the generation AI to correct the text information.

[0080] The generation unit can estimate the pet's emotions and adjust the way the picture diary is expressed based on the estimated pet's emotions. The generation unit, for example, estimates the pet's emotions and adjusts the way the picture diary is expressed based on the estimated pet's emotions. For example, if the pet is excited, a lively picture diary that reflects the emotions can be generated. Also, if the pet is relaxed, a calm-toned picture diary can be generated. Also, if the pet is anxious, a picture diary that includes a detailed description of the situation can be generated. This allows the way the picture diary is expressed to be adjusted according to the pet's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the pet's emotion data into the generation AI and cause the generation AI to adjust the way the picture diary is expressed.

[0081] The generation unit can learn the behavioral patterns of the pet at the time of generation and generate a natural-looking picture diary. For example, the generation unit can learn the behavioral patterns of the pet at the time of generation and generate a natural-looking picture diary. For example, if the pet goes for a walk at the same time every day, the generation unit can learn that behavioral pattern and generate a natural-looking picture diary such as "Going for a walk every morning." If the pet behaves in a specific place, the generation unit can learn that behavioral pattern and generate a natural-looking picture diary such as "Chases a ball in the park." If the pet eats at a specific time, the generation unit can learn that behavioral pattern and generate a natural-looking picture diary such as "Eats in the evening." In this way, the behavioral patterns of the pet can be learned and a natural-looking picture diary can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the behavioral data of the pet into the generation AI and cause the generation AI to generate a natural-looking picture diary.

[0082] The generation unit can correct the picture diary based on environmental information about the pet's surroundings when generating the picture diary. For example, the generation unit corrects the picture diary by taking into account environmental information about the pet's surroundings when generating the picture diary. For example, if the pet is out on a rainy day, the generation unit can correct the picture diary to "taking a walk in the rain" by taking into account weather information. If the pet is active at night, the generation unit can correct the picture diary to "taking a walk at night" by taking into account time information. If the pet is in a noisy place, the generation unit can correct the picture diary to "playing in a noisy place" by taking into account environmental sound information. In this way, the picture diary can be corrected by taking into account environmental information about the pet's surroundings. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input environmental information data into the generation AI and cause the generation AI to correct the picture diary.

[0083] The generation unit can customize the style of the picture diary according to the preferences of the pet owner at the time of generation. The generation unit, for example, customizes the style of the picture diary according to the preferences of the pet owner at the time of generation. For example, the generation unit generates a picture diary that reflects the owner's preferred colors and designs. The generation unit can also generate a picture diary that reflects the owner's preferred fonts and layouts. The generation unit can also suggest an optimal style based on the owner's past selection history. This allows the style of the picture diary to be customized according to the pet owner's preferences. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the owner's preference data into the generation AI and cause the generation AI to customize the style of the picture diary.

[0084] The providing unit can estimate the pet's emotions and adjust the method of providing the pictorial diary based on the estimated pet's emotions. For example, the providing unit can estimate the pet's emotions and adjust the method of providing the pictorial diary based on the estimated pet's emotions. For example, if the pet is excited, a lively pictorial diary reflecting the emotions can be provided. If the pet is relaxed, a calm-toned pictorial diary can be provided. If the pet is anxious, a pictorial diary including a detailed description of the situation can be provided. This allows the method of providing the pictorial diary to be adjusted according to the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the pet's emotion data into the generation AI and cause the generation AI to adjust the method of providing the pictorial diary.

[0085] The providing unit can select an appropriate providing method by referring to the browsing history of the pet owner at the time of providing. The providing unit, for example, selects an appropriate providing method by referring to the browsing history of the pet owner at the time of providing. For example, the providing unit selects the optimal providing method based on the style of picture diaries that the owner has previously viewed. It is also possible to provide a picture diary that reflects the owner's preferred content from the owner's browsing history. It is also possible to provide a picture diary at the optimal timing based on the owner's browsing history. This makes it possible to select the optimal providing method by referring to the pet owner's browsing history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the owner's browsing history data to the generation AI and cause the generation AI to select the providing method.

[0086] The providing unit can select an appropriate display method by taking into consideration the device information of the pet owner when providing the data. For example, the providing unit selects an appropriate display method by taking into consideration the device information of the pet owner when providing the data. For example, if the owner uses a smartphone, a display method that matches the screen size can be provided. Also, if the owner uses a tablet, a display method optimized for a large screen can be provided. Also, if the owner uses a smartwatch, a display method that is simple and highly visible can be provided. This makes it possible to select an optimal display method by taking into consideration the device information of the pet owner. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the owner's device information data to the generation AI and cause the generation AI to select a display method.

[0087] The providing unit can improve the providing method by reflecting the feedback of the pet owner at the time of providing. The providing unit, for example, improves the providing method by reflecting the feedback of the pet owner at the time of providing. For example, the providing method of the picture diary is improved based on the feedback provided by the owner. Furthermore, the providing method of the picture diary regarding specific behaviors or situations can be detailed by reflecting the feedback of the owner. Furthermore, the providing algorithm can be adjusted based on the feedback of the owner. In this way, the providing method can be improved by reflecting the feedback of the pet owner. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the feedback data of the owner into the generating AI and cause the generating AI to improve the providing method.

[0088] The providing unit can estimate the pet's emotions and adjust the display order of the picture diary based on the estimated pet's emotions. The providing unit, for example, estimates the pet's emotions and adjusts the display order of the picture diary based on the estimated pet's emotions. For example, if the pet is excited, lively content that reflects the emotion can be preferentially displayed. Also, if the pet is relaxed, content with a calm tone can be preferentially displayed. Also, if the pet is anxious, content including a detailed description of the situation can be preferentially displayed. This allows the display order of the picture diary to be adjusted according to the pet's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the pet's emotion data into the generation AI and cause the generation AI to adjust the display order.

[0089] The providing unit can analyze the social media activity of the pet owner at the time of providing the data and enhance the sharing function. For example, the providing unit can analyze the social media activity of the pet owner at the time of providing the data and enhance the sharing function. For example, the providing unit can analyze the content that the pet owner often shares on social media and enhance the sharing function of the picture diary. The providing unit can also suggest the optimal timing for sharing based on the pet owner's social media activity. It can also share related picture diaries by referring to the activity of the pet owner's friends on social media. In this way, the social media activity of the pet owner can be analyzed and the sharing function can be enhanced. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the pet owner's social media activity data into the generating AI and cause the generating AI to enhance the sharing function.

[0090] The providing unit can provide a function for customizing the picture diary according to the preferences of the pet owner at the time of provision. The providing unit, for example, provides a function for customizing the picture diary according to the preferences of the pet owner at the time of provision. For example, the providing unit customizes the picture diary to reflect the owner's preferred colors and designs. The providing unit can also customize the picture diary to reflect the owner's preferred fonts and layouts. The providing unit can also suggest optimal customization options based on the owner's past selection history. This makes it possible to provide a function for customizing the picture diary according to the preferences of the pet owner. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the owner's preference data into the generating AI and cause the generating AI to provide the customization function.

[0091] The providing unit can customize the providing method by reflecting the feedback of the pet owner at the time of providing. The providing unit, for example, customizes the providing method by reflecting the feedback of the pet owner at the time of providing. For example, the providing unit customizes the providing method of the picture diary based on the feedback provided by the owner. Furthermore, the providing method of the picture diary regarding a specific behavior or situation can be detailed by reflecting the feedback of the owner. Furthermore, the providing algorithm can be adjusted based on the feedback of the owner. In this way, the providing method can be customized by reflecting the feedback of the pet owner. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the feedback data of the owner into the generating AI and cause the generating AI to customize the providing method. === Hard Collateral 1-1 === Each of the multiple elements, including the image analysis unit, position analysis unit, conversion unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the image analysis unit uses the camera 42 of the smart device 14 to identify what and where the pet is looking, and the control unit 46A analyzes the image information. The position analysis unit uses the GPS function of the smart device 14 to identify the pet's travel route and the places visited, and the control unit 46A analyzes the image information. The conversion unit converts image information and position information into text information using the specific processing unit 290 of the data processing device 12. The generation unit generates the pet's daily life in the form of a picture diary using a generation AI by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated picture diary using the output device 40 of the smart device 14, allowing it to be shared on social media, etc. === Hard Collateral 1-2 === Each of the multiple elements, including the image analysis unit, position analysis unit, conversion unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the image analysis unit uses the camera 42 of the smart glasses 214 to identify what and where the pet is looking, and the control unit 46A analyzes the image information. The position analysis unit uses the GPS function of the smart glasses 214 to identify the pet's travel route and the places visited, and the control unit 46A analyzes the image information and position information. The conversion unit converts image information and position information into text information using the specific processing unit 290 of the data processing device 12. The generation unit generates the pet's daily life in the form of a picture diary using generation AI by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated picture diary using the output device of the smart glasses 214, allowing it to be shared on social media, etc. === Hard Collateral 1-3 === Each of the multiple elements, including the image analysis unit, position analysis unit, conversion unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the image analysis unit uses the camera 42 of the headset-type terminal 314 to identify what and where the pet is looking, and the control unit 46A analyzes the image information and the position analysis unit uses the GPS function of the headset-type terminal 314 to identify the pet's travel route and the places visited, and the control unit 46A analyzes the image information and the position information. The conversion unit converts the image information and the position information into text information using the specific processing unit 290 of the data processing device 12. The generation unit generates the pet's daily life in the form of a picture diary using a generation AI by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated picture diary using the output device of the headset-type terminal 314, allowing it to be shared on social media, etc. === Hard Collateral 1-4 === Each of the multiple elements, including the image analysis unit, position analysis unit, conversion unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the image analysis unit uses the camera 42 of the robot 414 to identify what and where the pet is looking, and the control unit 46A analyzes the image information and the position analysis unit uses the GPS function of the robot 414 to identify the pet's travel route and the places visited, and the control unit 46A analyzes the image information and the position information. The conversion unit converts the image information and the position information into text information using the specific processing unit 290 of the data processing device 12. The generation unit generates the pet's daily life in the form of a picture diary using a generation AI by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated picture diary using the output device of the robot 414, allowing it to be shared on social media, etc.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The pet daily record system can further include an audio analysis unit. The audio analysis unit can analyze the pet's cries and surrounding sounds to estimate the pet's behavior and emotions. For example, if the pet is barking, the audio can be analyzed to estimate that the pet is alert. Also, if the pet is barking, the audio can be analyzed to estimate that the pet is trying to communicate something to its owner. Furthermore, the audio analysis unit can analyze surrounding sounds to identify the environment in which the pet is located. This allows the pet's behavior and emotions to be understood in more detail by analyzing the pet's cries and surrounding sounds.

[0094] The image analysis unit can also be equipped with a function to monitor the health of pets. For example, it can estimate a pet's body temperature and heart rate using image analysis technology and notify the owner if any abnormalities are detected. It can also record a pet's food and water intake using image analysis, which can be used to help manage health. It can also regularly monitor changes in a pet's weight and body shape to detect changes in health at an early stage. This allows you to constantly understand your pet's health and provide appropriate care.

[0095] The location analysis unit can also be equipped with functions to ensure the safety of pets. For example, a function can be added to issue an alert if a pet strays from a specific area. It can also issue a warning if a pet approaches a dangerous location. Furthermore, if a pet gets lost, it can be equipped with a function to assist in searching for the pet based on its last known location information. This ensures the safety of pets and allows owners to watch over them with peace of mind.

[0096] The conversion unit can also have the function of analyzing pet behavior in real time and notifying the owner. For example, if a pet exhibits abnormal behavior, the owner can be notified in real time. Also, if a pet exhibits a specific behavior, the behavior can be recorded in real time and reported to the owner. Furthermore, it can learn the pet's behavior patterns and notify the owner of predicted behavior in advance. This allows the owner to understand the pet's behavior in real time and take appropriate action.

[0097] The generation unit may further have a function to provide advice based on the behavior of the pet. For example, if the pet is not getting enough exercise, advice to encourage exercise can be provided. Also, if the pet is feeling stressed, advice to help the pet relax can be provided. Furthermore, advice regarding the pet's diet and health care can be provided. This allows owners to receive appropriate advice to maintain the health and happiness of their pets.

[0098] The providing unit can further estimate the pet's emotions and customize the contents of the picture diary based on the estimated emotions. For example, if the pet is happy, a picture diary with fun content that reflects the pet's emotions can be generated. Also, if the pet is sad, a picture diary with comforting content that reflects the pet's emotions can be generated. Furthermore, if the pet is excited, a picture diary with lively content that reflects the pet's emotions can be generated. This allows the pet owner to provide a picture diary that corresponds to the pet's emotions, allowing them to have a deeper understanding of their pet's emotions.

[0099] The image analysis unit can also provide a training program based on the pet's behavior. For example, if the pet repeatedly behaves in a certain way, it can provide a training program to improve that behavior. It can also provide a step-by-step guide for the pet to learn a new trick. It can also monitor the pet's behavior and evaluate the training progress. This allows owners to effectively train their pets to improve their behavior and learn new skills.

[0100] The location analysis unit can further estimate the pet's emotions and optimize the travel route based on the estimated emotions. For example, if the pet is feeling stressed, a quiet route can be suggested. If the pet is excited, a walking route in an open area can be suggested. Furthermore, if the pet is relaxed, a route rich in nature can be suggested. This allows the system to provide the optimal travel route according to the pet's emotions and reduce stress for the pet.

[0101] The conversion unit can further estimate the pet's emotion and adjust the tone of the text information based on the estimated emotion. For example, if the pet is excited, text information with a lively tone that reflects the pet's emotion can be generated. Alternatively, if the pet is relaxed, text information with a calm tone that reflects the pet's emotion can be generated. Furthermore, if the pet is feeling anxious, text information including a detailed description of the situation that reflects the pet's emotion can be generated. This allows the pet owner to understand their pet's emotion more deeply by providing text information that corresponds to the pet's emotion.

[0102] The providing unit can further estimate the pet's emotions and adjust the display method of the picture diary based on the estimated emotions. For example, if the pet is excited, a dynamic display method that reflects the pet's emotions can be provided. Alternatively, if the pet is relaxed, a static display method that reflects the pet's emotions can be provided. Furthermore, if the pet is feeling anxious, a display method that includes a detailed description of the situation that reflects the pet's emotions can be provided. This allows the optimal display method to be provided according to the pet's emotions, allowing owners to gain a deeper understanding of their pet's emotions.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The image analysis unit analyzes image information acquired by a camera attached to the pet. For example, the image analysis unit analyzes images acquired by the camera to identify what or where the pet is looking. The image analysis unit can also use image analysis technology to identify the object the pet is looking at. Furthermore, the image analysis unit can also identify the pet's behavior based on the image information. Step 2: The location analysis unit analyzes the location information obtained by the GPS attached to the pet. For example, the location analysis unit analyzes the GPS data to identify the pet's movement route and the places visited. The location analysis unit can also identify the pet's movement pattern based on the location information. Step 3: The conversion unit converts the information acquired by the image analysis unit and the position analysis unit into text information. For example, the conversion unit converts image information into text information and records what the pet is looking at and where it is located. The conversion unit can also convert position information into text information and record the pet's movement route and the places it has visited. Step 4: The generation unit generates a picture diary of the pet's daily life based on the text information converted by the conversion unit. For example, the generation unit may compile the pet's daily events as a picture diary based on the text information. The generation unit may also use a generation AI to generate a picture diary of the pet's daily life. Step 5: The providing unit provides the picture diary generated by the generating unit. For example, the providing unit provides the generated picture diary so that it can be viewed on a smartphone or tablet. The providing unit can also provide the generated picture diary so that it can be shared on social networking sites or the like.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image analysis unit that acquires image information; a location analysis unit that acquires location information; a conversion unit that converts the information acquired by the image analysis unit and the position analysis unit into text information; a generation unit that generates a picture diary based on the character information converted by the conversion unit; a providing unit that provides the picture diary generated by the generating unit; Equipped with A system characterized by:

2. The image analysis unit Analyzes images captured by the camera to identify what and where your pet is looking 2. The system of claim 1.

3. The position analysis unit Analyze GPS data to identify your pet's travel routes and the places they visit 2. The system of claim 1.

4. The conversion unit The information acquired by the image analysis unit and the position analysis unit is converted into character information.

2. The system of claim 1.

5. The generation unit A picture diary of the pet's daily life is generated based on the character information converted by the conversion unit.

2. The system of claim 1.

6. The providing unit The created picture diary will be made available for viewing on smartphones and tablets.

2. The system of claim 1.

7. The image analysis unit Estimate the pet's emotions and adjust the accuracy of image analysis based on the estimated pet's emotions.

2. The system of claim 1.

8. The image analysis unit During image analysis, it analyzes pet movement patterns and identifies specific behaviors 2. The system of claim 1.

Images