System

Abstract

An object of a system according to an embodiment is to convert a feeling of an animal into words and improve communication with an owner or a person related to the animal.SOLUTION: A system includes a collection unit, an analysis unit, and a notification unit. The collection unit collects information on behavior, facial expression, voice, body movement, and pose of an animal. The analysis unit analyzes the information collected by the collection unit and converts the animal's feeling into words. The notification unit notifies the word converted by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 is difficult to accurately understand an animal's feelings, and there is room for improvement in communication.

[0005] The system according to the embodiment aims to convert the feelings of animals into words and improve communication between owners and those involved with animals. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a notification unit. The collection unit collects information on the animal's behavior, facial expressions, voice, body movements, and poses. The analysis unit analyzes the information collected by the collection unit and converts the animal's feelings into words. The notification unit notifies the user of the words converted by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can convert the feelings of animals into words, thereby improving communication between owners and those involved with animals. [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) The system for converting animal feelings into words according to an embodiment of the present invention is a system that collects information on the behavior, facial expression, voice, body movement, and pose of an animal, analyzes the information, and converts the animal's feelings into words. As a result, the system for converting animal feelings into words can collect information on the behavior, facial expression, voice, body movement, and pose of an animal, analyze the information, and convert the animal's feelings into words.

[0029] According to an embodiment, a system for converting an animal's feelings into words includes an information collection unit, an analysis unit, and a notification unit. The information collection unit collects information on the animal's behavior, facial expression, voice, body movement, and pose. For example, a camera is used to capture the animal's facial expression and a microphone is used to collect the animal's voice. An acceleration sensor can also be used to detect the animal's body movement. The information collection unit can also use a posture sensor to detect the animal's pose. The analysis unit analyzes the information collected by the information collection unit to convert the animal's feelings into words. For example, a generation AI (such as a text generation AI or a multimodal generation AI) can be used to analyze data on the animal's behavior, facial expression, voice, body movement, and pose, and convert the animal's feelings into words. The analysis unit can also use a sentiment analysis algorithm to estimate the animal's feelings and convert them into words. The analysis unit can also use natural language generation technology to convert the animal's feelings into appropriate words. The notification unit notifies the user of the words converted by the analysis unit. For example, the animal's feelings can be conveyed by voice using a voice notification. The animal's feelings can also be displayed in text using a text notification. Furthermore, visual notifications can be used to visually display the animal's feelings, allowing the system to convert animal feelings into words by collecting and analyzing information on the animal's behavior, facial expressions, voice, body movements, and poses.

[0030] The information collection unit can also simultaneously collect physiological data such as the animal's body temperature or heart rate. For example, when collecting data on the animal's behavior, facial expressions, voice, body movements, and pose, the information collection unit incorporates a body temperature sensor or heart rate sensor into the collar or bracelet. This allows the animal's physiological data to be simultaneously collected and analyzed together with the behavioral data. The information collection unit also builds a system that monitors the animal's body temperature and heart rate in real time and links it with the behavioral data. For example, analyzing changes in the animal's heart rate when it is excited can be used to estimate its emotions. Furthermore, to collect physiological data, the information collection unit develops sensors that come into direct contact with the animal's skin and incorporates them into the collar or bracelet. This allows the animal's body temperature and heart rate to be accurately measured and integrated with the behavioral data for analysis. This allows for simultaneous collection of physiological data such as the animal's body temperature and heart rate, enabling a more detailed understanding of the animal's condition.

[0031] The information collection unit can use drones to monitor the behavior of animals over a wide area in real time and collect data. For example, the information collection unit can equip drones with cameras and microphones to build a system for monitoring the behavior of animals over a wide area in real time. This allows for efficient collection of animal behavior data. The information collection unit can also use drones to monitor animal behavior from the sky and collect behavior data. For example, the information collection unit can observe the behavior of a group of wild animals and analyze the data. Furthermore, the information collection unit can equip drones with acceleration sensors to record the movements of animals in detail. This allows for analysis of animal behavior patterns and helps to estimate emotions. This allows for real-time monitoring of animal behavior over a wide area and collection of data.

[0032] The information collection unit can install not only wearable devices but also environmental sensors to simultaneously collect surrounding environmental data. For example, to collect animal behavioral data, the information collection unit installs sensors to collect surrounding environmental data in addition to collars or bracelets. For example, data such as temperature, humidity, and light intensity is collected. The information collection unit also installs environmental sensors in the animal's living space and builds a system that simultaneously collects animal behavioral data and environmental data. For example, it analyzes the environmental conditions when an animal exhibits a specific behavior. Furthermore, the information collection unit links wearable devices and environmental sensors to develop a system that integrates and analyzes animal behavioral data and environmental data. For example, it identifies the environmental factors that cause an animal to exhibit a specific behavior. This allows for more detailed analysis by simultaneously collecting animal behavioral data and environmental data.

[0033] The information collection unit develops sensors specialized for each animal species to accommodate different types of animals, thereby improving the accuracy of data collection. The information collection unit develops sensors specialized for each animal species, such as for dogs, cats, and birds, to improve the accuracy of data collection. For example, a sensor for dogs analyzes barks, and a sensor for cats analyzes meows. The information collection unit also incorporates sensors specialized for each animal species into collars or bracelets to build a system that collects animal behavior data with high accuracy. For example, a sensor that analyzes bird flight patterns is developed. Furthermore, the information collection unit adjusts the sensitivity of the sensor and analysis algorithm to accommodate different animal species, improving the accuracy of data collection. For example, an acceleration sensor specialized for cat movements is developed. In this way, by developing sensors specialized for each animal species, the accuracy of data collection can be improved.

[0034] The analysis unit can compare the collected data with the animal's past data to analyze long-term behavioral patterns and emotional changes. For example, when analyzing collected data, the analysis unit compares the collected data with the animal's past behavioral data to identify long-term behavioral patterns. For example, it analyzes behavioral changes based on data from the past few months. The analysis unit also uses the animal's past data to build a system for long-term monitoring of emotional changes. For example, it analyzes changes in the frequency with which the animal exhibits a specific behavior. Furthermore, the analysis unit compares the past data with current data to develop an algorithm that identifies changes in the animal's behavioral patterns and emotional changes. For example, it analyzes changes in the animal's emotions when it exhibits a specific behavior. This makes it possible to analyze long-term behavioral patterns and emotional changes by comparing the collected data with the animal's past data.

[0035] The analysis unit can use an individually optimized analysis model that takes into account the individual differences of animals. The analysis unit, for example, uses generative AI to develop an analysis model that takes into account the individual differences of animals and analyzes behavioral data. For example, the analysis model is optimized based on the behavioral patterns of each individual animal. The analysis unit also builds a system that takes into account the individual differences of animals and uses individually optimized analysis models. For example, it develops analysis models that correspond to the age and sex of the animal. Furthermore, the analysis unit uses generative AI to develop a system that automatically generates analysis models that reflect the individual differences of animals. For example, it generates individually optimized models based on the animal's past data. This enables more accurate analysis by taking into account the individual differences of animals and using individually optimized analysis models.

[0036] The analysis unit can integrate data from different animal species to reveal common behavioral patterns and emotional tendencies. The analysis unit, for example, builds a system that integrates data from different animal species to identify common behavioral patterns. For example, it analyzes behavioral data from dogs and cats to reveal common behavioral patterns. The analysis unit also analyzes data from different animal species to develop an algorithm that identifies common emotional tendencies. For example, it integrates data from dogs and birds to analyze common emotional tendencies. The analysis unit also develops a system that integrates data from different animal species to reveal behavioral patterns and emotional tendencies. For example, it analyzes data from cats and birds to identify common behavioral patterns. This makes it possible to integrate data from different animal species to reveal common behavioral patterns and emotional tendencies.

[0037] The analysis unit uses generative AI to analyze data on an animal's behavior, facial expressions, voice, body movements, and poses, and can simultaneously verbalize not only its emotions but also its health condition and stress level. For example, the analysis unit uses generative AI to analyze animal behavior data and build a system that simultaneously verbalizes not only its emotions but also its health condition and stress level. For example, it converts the data into words such as "happy," "healthy," and "no stress." The analysis unit also analyzes animal behavior data and develops an algorithm that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "anxious," "unwell," and "stressed." Furthermore, the analysis unit uses generative AI to analyze the animal's behavioral data and physiological data and develops a system that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "excited," "healthy," and "no stress." This makes it possible to use generative AI to simultaneously verbalize not only an animal's emotions but also its health condition and stress level.

[0038] The analysis unit can integrate data from different animal species to reveal common behavioral patterns and emotional tendencies. The analysis unit, for example, builds a system that integrates data from different animal species to identify common behavioral patterns. For example, it analyzes behavioral data from dogs and cats to reveal common behavioral patterns. The analysis unit also analyzes data from different animal species to develop an algorithm that identifies common emotional tendencies. For example, it integrates data from dogs and birds to analyze common emotional tendencies. The analysis unit also develops a system that integrates data from different animal species to reveal behavioral patterns and emotional tendencies. For example, it analyzes data from cats and birds to identify common behavioral patterns. This makes it possible to integrate data from different animal species to reveal common behavioral patterns and emotional tendencies.

[0039] The analysis unit uses generative AI to analyze data on an animal's behavior, facial expressions, voice, body movements, and poses, and can simultaneously verbalize not only its emotions but also its health condition and stress level. For example, the analysis unit uses generative AI to analyze animal behavior data and build a system that simultaneously verbalizes not only its emotions but also its health condition and stress level. For example, it converts the data into words such as "happy," "healthy," and "no stress." The analysis unit also analyzes animal behavior data and develops an algorithm that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "anxious," "unwell," and "stressed." Furthermore, the analysis unit uses generative AI to analyze the animal's behavioral data and physiological data and develops a system that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "excited," "healthy," and "no stress." This makes it possible to use generative AI to simultaneously verbalize not only an animal's emotions but also its health condition and stress level.

[0040] The analysis unit can integrate data from different animal species to reveal common behavioral patterns and emotional tendencies. The analysis unit, for example, builds a system that integrates data from different animal species to identify common behavioral patterns. For example, it analyzes behavioral data from dogs and cats to reveal common behavioral patterns. The analysis unit also analyzes data from different animal species to develop an algorithm that identifies common emotional tendencies. For example, it integrates data from dogs and birds to analyze common emotional tendencies. The analysis unit also develops a system that integrates data from different animal species to reveal behavioral patterns and emotional tendencies. For example, it analyzes data from cats and birds to identify common behavioral patterns. This makes it possible to integrate data from different animal species to reveal common behavioral patterns and emotional tendencies.

[0041] The analysis unit uses generative AI to analyze data on an animal's behavior, facial expressions, voice, body movements, and poses, and can simultaneously verbalize not only its emotions but also its health condition and stress level. For example, the analysis unit uses generative AI to analyze animal behavior data and build a system that simultaneously verbalizes not only its emotions but also its health condition and stress level. For example, it converts the data into words such as "happy," "healthy," and "no stress." The analysis unit also analyzes animal behavior data and develops an algorithm that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "anxious," "unwell," and "stressed." Furthermore, the analysis unit uses generative AI to analyze the animal's behavioral data and physiological data and develops a system that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "excited," "healthy," and "no stress." This makes it possible to use generative AI to simultaneously verbalize not only an animal's emotions but also its health condition and stress level.

[0042] The analysis unit can integrate data from different animal species to reveal common behavioral patterns and emotional tendencies. The analysis unit, for example, builds a system that integrates data from different animal species to identify common behavioral patterns. For example, it analyzes behavioral data from dogs and cats to reveal common behavioral patterns. The analysis unit also analyzes data from different animal species to develop an algorithm that identifies common emotional tendencies. For example, it integrates data from dogs and birds to analyze common emotional tendencies. The analysis unit also develops a system that integrates data from different animal species to reveal behavioral patterns and emotional tendencies. For example, it analyzes data from cats and birds to identify common behavioral patterns. This makes it possible to integrate data from different animal species to reveal common behavioral patterns and emotional tendencies.

[0043] The analysis unit uses generative AI to analyze data on an animal's behavior, facial expressions, voice, body movements, and poses, and can simultaneously verbalize not only its emotions but also its health condition and stress level. For example, the analysis unit uses generative AI to analyze animal behavior data and build a system that simultaneously verbalizes not only its emotions but also its health condition and stress level. For example, it converts the data into words such as "happy," "healthy," and "no stress." The analysis unit also analyzes animal behavior data and develops an algorithm that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "anxious," "unwell," and "stressed." Furthermore, the analysis unit uses generative AI to analyze the animal's behavioral data and physiological data and develops a system that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "excited," "healthy," and "no stress." This makes it possible to use generative AI to simultaneously verbalize not only an animal's emotions but also its health condition and stress level.

[0044] The analysis unit can integrate data from different animal species to reveal common behavioral patterns and emotional tendencies. The analysis unit, for example, builds a system that integrates data from different animal species to identify common behavioral patterns. For example, it analyzes behavioral data from dogs and cats to reveal common behavioral patterns. The analysis unit also analyzes data from different animal species to develop an algorithm that identifies common emotional tendencies. For example, it integrates data from dogs and birds to analyze common emotional tendencies. The analysis unit also develops a system that integrates data from different animal species to reveal behavioral patterns and emotional tendencies. For example, it analyzes data from cats and birds to identify common behavioral patterns. This makes it possible to integrate data from different animal species to reveal common behavioral patterns and emotional tendencies.

[0045] The analysis unit uses generative AI to analyze data on an animal's behavior, facial expressions, voice, body movements, and poses, and can simultaneously verbalize not only its emotions but also its health condition and stress level. For example, the analysis unit uses generative AI to analyze animal behavior data and build a system that simultaneously verbalizes not only its emotions but also its health condition and stress level. For example, it converts the data into words such as "happy," "healthy," and "no stress." The analysis unit also analyzes animal behavior data and develops an algorithm that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "anxious," "unwell," and "stressed." Furthermore, the analysis unit uses generative AI to analyze the animal's behavioral data and physiological data and develops a system that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "excited," "healthy," and "no stress." This makes it possible to use generative AI to simultaneously verbalize not only an animal's emotions but also its health condition and stress level.

[0046] The analysis unit can integrate data from different animal species to reveal common behavioral patterns and emotional tendencies. The analysis unit, for example, builds a system that integrates data from different animal species to identify common behavioral patterns. For example, it analyzes behavioral data from dogs and cats to reveal common behavioral patterns. The analysis unit also analyzes data from different animal species to develop an algorithm that identifies common emotional tendencies. For example, it integrates data from dogs and birds to analyze common emotional tendencies. The analysis unit also develops a system that integrates data from different animal species to reveal behavioral patterns and emotional tendencies. For example, it analyzes data from cats and birds to identify common behavioral patterns. This makes it possible to integrate data from different animal species to reveal common behavioral patterns and emotional tendencies.

[0047] The analysis unit uses generative AI to analyze data on an animal's behavior, facial expressions, voice, body movements, and poses, and can simultaneously verbalize not only its emotions but also its health condition and stress level. For example, the analysis unit uses generative AI to analyze animal behavior data and build a system that simultaneously verbalizes not only its emotions but also its health condition and stress level. For example, it converts the data into words such as "happy," "healthy," and "no stress." The analysis unit also analyzes animal behavior data and develops an algorithm that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "anxious," "unwell," and "stressed." Furthermore, the analysis unit uses generative AI to analyze the animal's behavioral data and physiological data and develops a system that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "excited," "healthy," and "no stress." This makes it possible to use generative AI to simultaneously verbalize not only an animal's emotions but also its health condition and stress level.

[0048] The analysis unit can integrate data from different animal species to reveal common behavioral patterns and emotional tendencies. The analysis unit, for example, builds a system that integrates data from different animal species to identify common behavioral patterns. For example, it analyzes behavioral data from dogs and cats to reveal common behavioral patterns. The analysis unit also analyzes data from different animal species to develop an algorithm that identifies common emotional tendencies. For example, it integrates data from dogs and birds to analyze common emotional tendencies. The analysis unit also develops a system that integrates data from different animal species to reveal behavioral patterns and emotional tendencies. For example, it analyzes data from cats and birds to identify common behavioral patterns. This makes it possible to integrate data from different animal species to reveal common behavioral patterns and emotional tendencies.

[0049] The analysis unit uses generative AI to analyze data on an animal's behavior, facial expressions, voice, body movements, and poses, and can simultaneously verbalize not only its emotions but also its health condition and stress level. For example, the analysis unit uses generative AI to analyze animal behavior data and build a system that simultaneously verbalizes not only its emotions but also its health condition and stress level. For example, it converts the data into words such as "happy," "healthy," and "no stress." The analysis unit also analyzes animal behavior data and develops an algorithm that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "anxious," "unwell," and "stressed." Furthermore, the analysis unit uses generative AI to analyze the animal's behavioral data and physiological data and develops a system that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "excited," "healthy," and "no stress." This makes it possible to use generative AI to simultaneously verbalize not only an animal's emotions but also its health condition and stress level.

[0050] The analysis unit can integrate data from different animal species to reveal common behavioral patterns and emotional tendencies. The analysis unit, for example, builds a system that integrates data from different animal species to identify common behavioral patterns. For example, it analyzes behavioral data from dogs and cats to reveal common behavioral patterns. The analysis unit also analyzes data from different animal species to develop an algorithm that identifies common emotional tendencies. For example, it integrates data from dogs and birds to analyze common emotional tendencies. The analysis unit also develops a system that integrates data from different animal species to reveal behavioral patterns and emotional tendencies. For example, it analyzes data from cats and birds to identify common behavioral patterns. This makes it possible to integrate data from different animal species to reveal common behavioral patterns and emotional tendencies.

[0051] The analysis unit uses generative AI to analyze data on an animal's behavior, facial expressions, voice, body movements, and poses, and can simultaneously verbalize not only its emotions but also its health condition and stress level. For example, the analysis unit uses generative AI to analyze animal behavior data and build a system that simultaneously verbalizes not only its emotions but also its health condition and stress level. For example, it converts the data into words such as "happy," "healthy," and "no stress." The analysis unit also analyzes animal behavior data and develops an algorithm that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "anxious," "unwell," and "stressed." Furthermore, the analysis unit uses generative AI to analyze the animal's behavioral data and physiological data and develops a system that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "excited," "healthy," and "no stress." This makes it possible to use generative AI to simultaneously verbalize not only an animal's emotions but also its health condition and stress level.

[0052] The analysis unit can integrate data from different animal species to reveal common behavioral patterns and emotional tendencies. The analysis unit, for example, builds a system that integrates data from different animal species to identify common behavioral patterns. For example, it analyzes behavioral data from dogs and cats to reveal common behavioral patterns. The analysis unit also analyzes data from different animal species to develop an algorithm that identifies common emotional tendencies. For example, it integrates data from dogs and birds to analyze common emotional tendencies. The analysis unit also develops a system that integrates data from different animal species to reveal behavioral patterns and emotional tendencies. For example, it analyzes data from cats and birds to identify common behavioral patterns. This makes it possible to integrate data from different animal species to reveal common behavioral patterns and emotional tendencies.

[0053] The analysis unit uses generative AI to analyze data on an animal's behavior, facial expressions, voice, body movements, and poses, and can simultaneously verbalize not only its emotions but also its health condition and stress level. For example, the analysis unit uses generative AI to analyze animal behavior data and build a system that simultaneously verbalizes not only its emotions but also its health condition and stress level. For example, it converts the data into words such as "happy," "healthy," and "no stress." The analysis unit also analyzes animal behavior data and develops an algorithm that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "anxious," "unwell," and "stressed." Furthermore, the analysis unit uses generative AI to analyze the animal's behavioral data and physiological data and develops a system that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "excited," "healthy," and "no stress." This makes it possible to use generative AI to simultaneously verbalize not only an animal's emotions but also its health condition and stress level.

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

[0055] The information collection unit collects information on the animal's behavior, facial expressions, voice, body movements, and pose. For example, a camera may be used to capture the animal's facial expressions and a microphone may be used to collect the animal's voice. The information collection unit may also use an acceleration sensor to detect the animal's body movements. Furthermore, the information collection unit may also use a posture sensor to detect the animal's pose. The analysis unit analyzes the information collected by the information collection unit and converts the animal's feelings into words. For example, a generative AI (such as a text generation AI or a multimodal generation AI) may be used to analyze data on the animal's behavior, facial expressions, voice, body movements, and pose, and convert the animal's feelings into words. The analysis unit may also use an emotion analysis algorithm to estimate the animal's emotions and convert them into words. Furthermore, the analysis unit may also use natural language generation technology to convert the animal's feelings into appropriate words. The notification unit notifies the user of the words converted by the analysis unit. For example, the animal's feelings can be conveyed by voice using a voice notification. The animal's feelings can also be displayed in text using a text notification. The animal's feelings can also be displayed visually using a visual notification. This allows the system that converts animal feelings into words to collect information on the animal's behavior, facial expressions, voice, body movements, and poses, and analyzes this information to convert the animal's feelings into words.

[0056] For example, when collecting data on an animal's behavior, facial expressions, voice, body movements, and poses, the information collection unit incorporates body temperature sensors and heart rate sensors into the collar or bracelet. This allows the animal's physiological data to be collected simultaneously and analyzed together with the behavioral data. The information collection unit also builds a system that monitors the animal's body temperature and heart rate in real time and links this with the behavioral data. For example, it analyzes changes in the animal's heart rate when the animal is excited, which helps to estimate its emotions. Furthermore, to collect physiological data, the information collection unit develops sensors that come into direct contact with the animal's skin and incorporates them into the collar or bracelet. This allows the animal's body temperature and heart rate to be accurately measured and integrated with the behavioral data for analysis. This allows for simultaneous collection of physiological data such as the animal's body temperature and heart rate, enabling a more detailed understanding of the animal's condition.

[0057] The information collection unit, for example, builds a system that equips drones with cameras and microphones and monitors the behavior of animals over a wide area in real time. This allows for efficient collection of animal behavior data. The information collection unit also uses drones to monitor animal behavior from the sky and collect behavior data. For example, it observes the behavior of a group of wild animals and analyzes the data. Furthermore, the information collection unit equips drones with acceleration sensors and records the movements of animals in detail. This allows for analysis of animal behavior patterns and helps in estimating emotions. This makes it possible to use drones to monitor the behavior of animals over a wide area in real time and collect data.

[0058] For example, the information collection unit may install sensors to collect environmental data in addition to collars or bracelets to collect animal behavioral data. For example, data such as temperature, humidity, and light intensity may be collected. The information collection unit may also install environmental sensors in the animal's living space to build a system that simultaneously collects both animal behavioral data and environmental data. For example, it may analyze the environmental conditions when an animal exhibits a specific behavior. Furthermore, the information collection unit may link wearable devices with environmental sensors to develop a system that integrates and analyzes animal behavioral data and environmental data. For example, it may identify the environmental factors that cause an animal to exhibit a specific behavior. This allows for more detailed analysis by simultaneously collecting animal behavioral data and environmental data.

[0059] The information collection unit develops sensors specialized for each animal species, such as for dogs, cats, and birds, to improve the accuracy of data collection. For example, a sensor for dogs analyzes barks, and a sensor for cats analyzes meows. The information collection unit also incorporates sensors specialized for each animal species into collars or bracelets to build a system that collects animal behavior data with high accuracy. For example, it develops a sensor that analyzes bird flight patterns. Furthermore, the information collection unit adjusts the sensitivity of the sensor and analysis algorithms to accommodate different animal species, improving the accuracy of data collection. For example, it develops an acceleration sensor specialized for cat movements. In this way, by developing sensors specialized for each animal species, the accuracy of data collection can be improved.

[0060] For example, when analyzing collected data, the analysis unit compares it with the animal's past behavioral data to identify long-term behavioral patterns. For example, it analyzes behavioral changes based on data from the past few months. The analysis unit also uses the animal's past data to build a system for long-term monitoring of emotional changes. For example, it analyzes changes in the frequency with which the animal exhibits a specific behavior. Furthermore, the analysis unit compares past data with current data to develop an algorithm that identifies changes in the animal's behavioral patterns and emotions. For example, it analyzes changes in emotions when the animal exhibits a specific behavior. This makes it possible to analyze long-term behavioral patterns and emotional changes by comparing it with the animal's past data.

[0061] The analysis unit, for example, uses generative AI to develop an analytical model that takes into account individual differences in animals and analyzes behavioral data. For example, the analytical model is optimized based on the behavioral patterns of each individual animal. The analysis unit also builds a system that takes into account individual differences in animals and uses individually optimized analytical models. For example, it develops analytical models that correspond to the age and sex of the animal. Furthermore, the analysis unit uses generative AI to develop a system that automatically generates analytical models that reflect individual differences in animals. For example, it generates individually optimized models based on the animal's past data. This enables more accurate analysis by taking into account individual differences in animals and using individually optimized analytical models.

[0062] The analysis unit, for example, builds a system that integrates data from different animal species and identifies common behavioral patterns. For example, it analyzes behavioral data from dogs and cats to identify common behavioral patterns. The analysis unit also analyzes data from different animal species and develops an algorithm that identifies common emotional tendencies. For example, it integrates data from dogs and birds to analyze common emotional tendencies. The analysis unit also develops a system that integrates data from different animal species and identifies behavioral patterns and emotional tendencies. For example, it analyzes data from cats and birds to identify common behavioral patterns. This makes it possible to integrate data from different animal species and identify common behavioral patterns and emotional tendencies.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The collection unit collects information on the animal's behavior, facial expressions, voice, body movements, and pose. For example, a camera can be used to capture the animal's facial expressions and a microphone can be used to collect the animal's voice. It is also possible to use an acceleration sensor to detect the animal's body movements and a posture sensor to detect the animal's pose. Step 2: The analysis unit analyzes the information collected by the collection unit and converts the animal's feelings into words. For example, it uses generative AI (text generation AI or multimodal generation AI) to analyze data on the animal's behavior, facial expressions, voice, body movements, and poses, and converts the animal's feelings into words. It can also use emotion analysis algorithms to estimate the animal's emotions and convert them into words. It can also use natural language generation technology to convert the animal's feelings into appropriate words. Step 3: The notification unit notifies the user of the words converted by the analysis unit. For example, the animal's feelings can be conveyed by voice using a voice notification, or the animal's feelings can be displayed in text using a text notification, or the animal's feelings can be displayed visually using a visual notification.

[0065] (Example 2) The system for converting animal feelings into words according to an embodiment of the present invention is a system that collects information on the behavior, facial expression, voice, body movement, and pose of an animal, analyzes the information, and converts the animal's feelings into words. As a result, the system for converting animal feelings into words can collect information on the behavior, facial expression, voice, body movement, and pose of an animal, analyze the information, and convert the animal's feelings into words.

[0066] According to an embodiment, a system for converting an animal's feelings into words includes an information collection unit, an analysis unit, and a notification unit. The information collection unit collects information on the animal's behavior, facial expression, voice, body movement, and pose. For example, a camera is used to capture the animal's facial expression and a microphone is used to collect the animal's voice. An acceleration sensor can also be used to detect the animal's body movement. The information collection unit can also use a posture sensor to detect the animal's pose. The analysis unit analyzes the information collected by the information collection unit to convert the animal's feelings into words. For example, a generation AI (such as a text generation AI or a multimodal generation AI) can be used to analyze data on the animal's behavior, facial expression, voice, body movement, and pose, and convert the animal's feelings into words. The analysis unit can also use a sentiment analysis algorithm to estimate the animal's feelings and convert them into words. The analysis unit can also use natural language generation technology to convert the animal's feelings into appropriate words. The notification unit notifies the user of the words converted by the analysis unit. For example, the animal's feelings can be conveyed by voice using a voice notification. The animal's feelings can also be displayed in text using a text notification. Furthermore, visual notifications can be used to visually display the animal's feelings, allowing the system to convert animal feelings into words by collecting and analyzing information on the animal's behavior, facial expressions, voice, body movements, and poses.

[0067] The information collection unit can also simultaneously collect physiological data such as the animal's body temperature or heart rate. For example, when collecting data on the animal's behavior, facial expressions, voice, body movements, and pose, the information collection unit incorporates a body temperature sensor or heart rate sensor into the collar or bracelet. This allows the animal's physiological data to be simultaneously collected and analyzed together with the behavioral data. The information collection unit also builds a system that monitors the animal's body temperature and heart rate in real time and links it with the behavioral data. For example, analyzing changes in the animal's heart rate when it is excited can be used to estimate its emotions. Furthermore, to collect physiological data, the information collection unit develops sensors that come into direct contact with the animal's skin and incorporates them into the collar or bracelet. This allows the animal's body temperature and heart rate to be accurately measured and integrated with the behavioral data for analysis. This allows for simultaneous collection of physiological data such as the animal's body temperature and heart rate, enabling a more detailed understanding of the animal's condition.

[0068] The information collection unit can use drones to monitor the behavior of animals over a wide area in real time and collect data. For example, the information collection unit can equip drones with cameras and microphones to build a system for monitoring the behavior of animals over a wide area in real time. This allows for efficient collection of animal behavior data. The information collection unit can also use drones to monitor animal behavior from the sky and collect behavior data. For example, the information collection unit can observe the behavior of a group of wild animals and analyze the data. Furthermore, the information collection unit can equip drones with acceleration sensors to record the movements of animals in detail. This allows for analysis of animal behavior patterns and helps to estimate emotions. This allows for real-time monitoring of animal behavior over a wide area and collection of data.

[0069] The analysis unit analyzes data on the animal's behavior, facial expressions, voice, body movements, and poses, and can monitor changes in emotion in real time and instantly verbalize them. The analysis unit, for example, uses an emotion estimation algorithm to build a system that estimates the emotion an animal is feeling when it behaves in a specific way in real time. For example, it analyzes the emotion an animal feels when it wags its tail and collects data. The analysis unit also analyzes animal behavior data and develops a system that displays emotion estimation results in real time. For example, it estimates the emotion an animal feels when it barks and collects data. The analysis unit also uses an emotion estimation function to build a system that simultaneously collects animal behavior data and emotion data. For example, it analyzes the emotion an animal feels when it jumps and collects data. This makes it possible to monitor changes in an animal's emotion in real time and instantly verbalize them.

[0070] The information collection unit can install not only wearable devices but also environmental sensors to simultaneously collect surrounding environmental data. For example, to collect animal behavioral data, the information collection unit installs sensors to collect surrounding environmental data in addition to collars or bracelets. For example, data such as temperature, humidity, and light intensity is collected. The information collection unit also installs environmental sensors in the animal's living space and builds a system that simultaneously collects animal behavioral data and environmental data. For example, it analyzes the environmental conditions when an animal exhibits a specific behavior. Furthermore, the information collection unit links wearable devices and environmental sensors to develop a system that integrates and analyzes animal behavioral data and environmental data. For example, it identifies the environmental factors that cause an animal to exhibit a specific behavior. This allows for more detailed analysis by simultaneously collecting animal behavioral data and environmental data.

[0071] The information collection unit develops sensors specialized for each animal species to accommodate different types of animals, thereby improving the accuracy of data collection. The information collection unit develops sensors specialized for each animal species, such as for dogs, cats, and birds, to improve the accuracy of data collection. For example, a sensor for dogs analyzes barks, and a sensor for cats analyzes meows. The information collection unit also incorporates sensors specialized for each animal species into collars or bracelets to build a system that collects animal behavior data with high accuracy. For example, a sensor that analyzes bird flight patterns is developed. Furthermore, the information collection unit adjusts the sensitivity of the sensor and analysis algorithm to accommodate different animal species, improving the accuracy of data collection. For example, an acceleration sensor specialized for cat movements is developed. In this way, by developing sensors specialized for each animal species, the accuracy of data collection can be improved.

[0072] The analysis unit can compare the collected data with the animal's past data to analyze long-term behavioral patterns and emotional changes. For example, when analyzing collected data, the analysis unit compares the collected data with the animal's past behavioral data to identify long-term behavioral patterns. For example, it analyzes behavioral changes based on data from the past few months. The analysis unit also uses the animal's past data to build a system for long-term monitoring of emotional changes. For example, it analyzes changes in the frequency with which the animal exhibits a specific behavior. Furthermore, the analysis unit compares the past data with current data to develop an algorithm that identifies changes in the animal's behavioral patterns and emotional changes. For example, it analyzes changes in the animal's emotions when it exhibits a specific behavior. This makes it possible to analyze long-term behavioral patterns and emotional changes by comparing the collected data with the animal's past data.

[0073] The analysis unit can use an individually optimized analysis model that takes into account the individual differences of animals. The analysis unit, for example, uses generative AI to develop an analysis model that takes into account the individual differences of animals and analyzes behavioral data. For example, the analysis model is optimized based on the behavioral patterns of each individual animal. The analysis unit also builds a system that takes into account the individual differences of animals and uses individually optimized analysis models. For example, it develops analysis models that correspond to the age and sex of the animal. Furthermore, the analysis unit uses generative AI to develop a system that automatically generates analysis models that reflect the individual differences of animals. For example, it generates individually optimized models based on the animal's past data. This enables more accurate analysis by taking into account the individual differences of animals and using individually optimized analysis models.

[0074] The analysis unit uses the emotion estimation function to analyze data on the animal's behavior, facial expressions, voice, body movements, and poses, and monitors changes in emotion in real time and can instantly verbalize them. The analysis unit, for example, uses the emotion estimation function to analyze animal behavior data in real time and build a system that instantly verbalizes changes in emotion. For example, the emotion an animal feels when it barks is analyzed in real time and converted into words such as "anxiety." The analysis unit also develops an algorithm that monitors animal behavior data in real time and instantly verbalizes changes in emotion. For example, the emotion an animal feels when it wags its tail is analyzed and converted into words such as "happy." The analysis unit also uses the emotion estimation function to analyze animal behavior data and emotion data in real time and develop a system that instantly verbalizes changes in emotion. For example, the emotion an animal feels when it jumps is analyzed and converted into words such as "excited." In this way, the emotion estimation function can be used to monitor changes in animal emotion in real time and instantly verbalize them.

[0075] The analysis unit can integrate data from different animal species to reveal common behavioral patterns and emotional tendencies. The analysis unit, for example, builds a system that integrates data from different animal species to identify common behavioral patterns. For example, it analyzes behavioral data from dogs and cats to reveal common behavioral patterns. The analysis unit also analyzes data from different animal species to develop an algorithm that identifies common emotional tendencies. For example, it integrates data from dogs and birds to analyze common emotional tendencies. The analysis unit also develops a system that integrates data from different animal species to reveal behavioral patterns and emotional tendencies. For example, it analyzes data from cats and birds to identify common behavioral patterns. This makes it possible to integrate data from different animal species to reveal common behavioral patterns and emotional tendencies.

[0076] The analysis unit uses generative AI to analyze data on an animal's behavior, facial expressions, voice, body movements, and poses, and can simultaneously verbalize not only its emotions but also its health condition and stress level. For example, the analysis unit uses generative AI to analyze animal behavior data and build a system that simultaneously verbalizes not only its emotions but also its health condition and stress level. For example, it converts the data into words such as "happy," "healthy," and "no stress." The analysis unit also analyzes animal behavior data and develops an algorithm that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "anxious," "unwell," and "stressed." Furthermore, the analysis unit uses generative AI to analyze the animal's behavioral data and physiological data and develops a system that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "excited," "healthy," and "no stress." This makes it possible to use generative AI to simultaneously verbalize not only an animal's emotions but also its health condition and stress level.

[0077] The analysis unit can use the emotion estimation function to collect users' emotional reactions to summarized ideas and improve the accuracy of summaries based on the collected data. For example, the analysis unit collects users' emotional reactions to summarized ideas in real time and improves the accuracy of summaries based on the data. For example, it prioritizes the adoption of summaries with a high number of positive reactions. The analysis unit also uses the emotion estimation function to collect feedback on the summarized ideas and regenerates the summary if there are a high number of negative reactions. Furthermore, the analysis unit analyzes the users' emotional reaction data and identifies areas for improvement in the summary based on the results. For example, it makes suggestions to revise parts with low emotion scores. In this way, the emotion estimation function can be used to improve the accuracy of summaries.

[0078] The analysis unit can integrate data from different animal species to reveal common behavioral patterns and emotional tendencies. The analysis unit, for example, builds a system that integrates data from different animal species to identify common behavioral patterns. For example, it analyzes behavioral data from dogs and cats to reveal common behavioral patterns. The analysis unit also analyzes data from different animal species to develop an algorithm that identifies common emotional tendencies. For example, it integrates data from dogs and birds to analyze common emotional tendencies. The analysis unit also develops a system that integrates data from different animal species to reveal behavioral patterns and emotional tendencies. For example, it analyzes data from cats and birds to identify common behavioral patterns. This makes it possible to integrate data from different animal species to reveal common behavioral patterns and emotional tendencies.

[0079] The analysis unit uses generative AI to analyze data on an animal's behavior, facial expressions, voice, body movements, and poses, and can simultaneously verbalize not only its emotions but also its health condition and stress level. For example, the analysis unit uses generative AI to analyze animal behavior data and build a system that simultaneously verbalizes not only its emotions but also its health condition and stress level. For example, it converts the data into words such as "happy," "healthy," and "no stress." The analysis unit also analyzes animal behavior data and develops an algorithm that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "anxious," "unwell," and "stressed." Furthermore, the analysis unit uses generative AI to analyze the animal's behavioral data and physiological data and develops a system that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "excited," "healthy," and "no stress." This makes it possible to use generative AI to simultaneously verbalize not only an animal's emotions but also its health condition and stress level.

[0080] The analysis unit can use the emotion estimation function to collect users' emotional reactions to summarized ideas and improve the accuracy of summaries based on the collected data. For example, the analysis unit collects users' emotional reactions to summarized ideas in real time and improves the accuracy of summaries based on the data. For example, it prioritizes the adoption of summaries with a high number of positive reactions. The analysis unit also uses the emotion estimation function to collect feedback on the summarized ideas and regenerates the summary if there are a high number of negative reactions. Furthermore, the analysis unit analyzes the users' emotional reaction data and identifies areas for improvement in the summary based on the results. For example, it makes suggestions to revise parts with low emotion scores. In this way, the emotion estimation function can be used to improve the accuracy of summaries.

[0081] The analysis unit can integrate data from different animal species to reveal common behavioral patterns and emotional tendencies. The analysis unit, for example, builds a system that integrates data from different animal species to identify common behavioral patterns. For example, it analyzes behavioral data from dogs and cats to reveal common behavioral patterns. The analysis unit also analyzes data from different animal species to develop an algorithm that identifies common emotional tendencies. For example, it integrates data from dogs and birds to analyze common emotional tendencies. The analysis unit also develops a system that integrates data from different animal species to reveal behavioral patterns and emotional tendencies. For example, it analyzes data from cats and birds to identify common behavioral patterns. This makes it possible to integrate data from different animal species to reveal common behavioral patterns and emotional tendencies.

[0082] The analysis unit uses generative AI to analyze data on an animal's behavior, facial expressions, voice, body movements, and poses, and can simultaneously verbalize not only its emotions but also its health condition and stress level. For example, the analysis unit uses generative AI to analyze animal behavior data and build a system that simultaneously verbalizes not only its emotions but also its health condition and stress level. For example, it converts the data into words such as "happy," "healthy," and "no stress." The analysis unit also analyzes animal behavior data and develops an algorithm that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "anxious," "unwell," and "stressed." Furthermore, the analysis unit uses generative AI to analyze the animal's behavioral data and physiological data and develops a system that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "excited," "healthy," and "no stress." This makes it possible to use generative AI to simultaneously verbalize not only an animal's emotions but also its health condition and stress level.

[0083] The analysis unit can use the emotion estimation function to collect users' emotional reactions to summarized ideas and improve the accuracy of summaries based on the collected data. For example, the analysis unit collects users' emotional reactions to summarized ideas in real time and improves the accuracy of summaries based on the data. For example, it prioritizes the adoption of summaries with a high number of positive reactions. The analysis unit also uses the emotion estimation function to collect feedback on the summarized ideas and regenerates the summary if there are a high number of negative reactions. Furthermore, the analysis unit analyzes the users' emotional reaction data and identifies areas for improvement in the summary based on the results. For example, it makes suggestions to revise parts with low emotion scores. In this way, the emotion estimation function can be used to improve the accuracy of summaries.

[0084] The analysis unit can integrate data from different animal species to reveal common behavioral patterns and emotional tendencies. The analysis unit, for example, builds a system that integrates data from different animal species to identify common behavioral patterns. For example, it analyzes behavioral data from dogs and cats to reveal common behavioral patterns. The analysis unit also analyzes data from different animal species to develop an algorithm that identifies common emotional tendencies. For example, it integrates data from dogs and birds to analyze common emotional tendencies. The analysis unit also develops a system that integrates data from different animal species to reveal behavioral patterns and emotional tendencies. For example, it analyzes data from cats and birds to identify common behavioral patterns. This makes it possible to integrate data from different animal species to reveal common behavioral patterns and emotional tendencies.

[0085] The analysis unit uses generative AI to analyze data on an animal's behavior, facial expressions, voice, body movements, and poses, and can simultaneously verbalize not only its emotions but also its health condition and stress level. For example, the analysis unit uses generative AI to analyze animal behavior data and build a system that simultaneously verbalizes not only its emotions but also its health condition and stress level. For example, it converts the data into words such as "happy," "healthy," and "no stress." The analysis unit also analyzes animal behavior data and develops an algorithm that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "anxious," "unwell," and "stressed." Furthermore, the analysis unit uses generative AI to analyze the animal's behavioral data and physiological data and develops a system that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "excited," "healthy," and "no stress." This makes it possible to use generative AI to simultaneously verbalize not only an animal's emotions but also its health condition and stress level.

[0086] The analysis unit uses the emotion estimation function to analyze data on the animal's behavior, facial expressions, voice, body movements, and poses, and monitors changes in emotion in real time and can instantly verbalize them. The analysis unit, for example, uses the emotion estimation function to analyze animal behavior data in real time and build a system that instantly verbalizes changes in emotion. For example, the emotion an animal feels when it barks is analyzed in real time and converted into words such as "anxiety." The analysis unit also develops an algorithm that monitors animal behavior data in real time and instantly verbalizes changes in emotion. For example, the emotion an animal feels when it wags its tail is analyzed and converted into words such as "happy." The analysis unit also uses the emotion estimation function to analyze animal behavior data and emotion data in real time and develop a system that instantly verbalizes changes in emotion. For example, the emotion an animal feels when it jumps is analyzed and converted into words such as "excited." In this way, the emotion estimation function can be used to monitor changes in animal emotion in real time and instantly verbalize them.

[0087] The analysis unit can integrate data from different animal species to reveal common behavioral patterns and emotional tendencies. The analysis unit, for example, builds a system that integrates data from different animal species to identify common behavioral patterns. For example, it analyzes behavioral data from dogs and cats to reveal common behavioral patterns. The analysis unit also analyzes data from different animal species to develop an algorithm that identifies common emotional tendencies. For example, it integrates data from dogs and birds to analyze common emotional tendencies. The analysis unit also develops a system that integrates data from different animal species to reveal behavioral patterns and emotional tendencies. For example, it analyzes data from cats and birds to identify common behavioral patterns. This makes it possible to integrate data from different animal species to reveal common behavioral patterns and emotional tendencies.

[0088] The analysis unit uses generative AI to analyze data on an animal's behavior, facial expressions, voice, body movements, and poses, and can simultaneously verbalize not only its emotions but also its health condition and stress level. For example, the analysis unit uses generative AI to analyze animal behavior data and build a system that simultaneously verbalizes not only its emotions but also its health condition and stress level. For example, it converts the data into words such as "happy," "healthy," and "no stress." The analysis unit also analyzes animal behavior data and develops an algorithm that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "anxious," "unwell," and "stressed." Furthermore, the analysis unit uses generative AI to analyze the animal's behavioral data and physiological data and develops a system that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "excited," "healthy," and "no stress." This makes it possible to use generative AI to simultaneously verbalize not only an animal's emotions but also its health condition and stress level.

[0089] The analysis unit uses the emotion estimation function to analyze data on the animal's behavior, facial expressions, voice, body movements, and poses, and monitors changes in emotion in real time and can instantly verbalize them. The analysis unit, for example, uses the emotion estimation function to analyze animal behavior data in real time and build a system that instantly verbalizes changes in emotion. For example, the emotion an animal feels when it barks is analyzed in real time and converted into words such as "anxiety." The analysis unit also develops an algorithm that monitors animal behavior data in real time and instantly verbalizes changes in emotion. For example, the emotion an animal feels when it wags its tail is analyzed and converted into words such as "happy." The analysis unit also uses the emotion estimation function to analyze animal behavior data and emotion data in real time and develop a system that instantly verbalizes changes in emotion. For example, the emotion an animal feels when it jumps is analyzed and converted into words such as "excited." In this way, the emotion estimation function can be used to monitor changes in animal emotion in real time and instantly verbalize them.

[0090] The analysis unit can integrate data from different animal species to reveal common behavioral patterns and emotional tendencies. The analysis unit, for example, builds a system that integrates data from different animal species to identify common behavioral patterns. For example, it analyzes behavioral data from dogs and cats to reveal common behavioral patterns. The analysis unit also analyzes data from different animal species to develop an algorithm that identifies common emotional tendencies. For example, it integrates data from dogs and birds to analyze common emotional tendencies. The analysis unit also develops a system that integrates data from different animal species to reveal behavioral patterns and emotional tendencies. For example, it analyzes data from cats and birds to identify common behavioral patterns. This makes it possible to integrate data from different animal species to reveal common behavioral patterns and emotional tendencies.

[0091] The analysis unit uses generative AI to analyze data on an animal's behavior, facial expressions, voice, body movements, and poses, and can simultaneously verbalize not only its emotions but also its health condition and stress level. For example, the analysis unit uses generative AI to analyze animal behavior data and build a system that simultaneously verbalizes not only its emotions but also its health condition and stress level. For example, it converts the data into words such as "happy," "healthy," and "no stress." The analysis unit also analyzes animal behavior data and develops an algorithm that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "anxious," "unwell," and "stressed." Furthermore, the analysis unit uses generative AI to analyze the animal's behavioral data and physiological data and develops a system that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "excited," "healthy," and "no stress." This makes it possible to use generative AI to simultaneously verbalize not only an animal's emotions but also its health condition and stress level.

[0092] The analysis unit uses the emotion estimation function to analyze data on the animal's behavior, facial expressions, voice, body movements, and poses, and monitors changes in emotion in real time and can instantly verbalize them. The analysis unit, for example, uses the emotion estimation function to analyze animal behavior data in real time and build a system that instantly verbalizes changes in emotion. For example, the emotion an animal feels when it barks is analyzed in real time and converted into words such as "anxiety." The analysis unit also develops an algorithm that monitors animal behavior data in real time and instantly verbalizes changes in emotion. For example, the emotion an animal feels when it wags its tail is analyzed and converted into words such as "happy." The analysis unit also uses the emotion estimation function to analyze animal behavior data and emotion data in real time and develop a system that instantly verbalizes changes in emotion. For example, the emotion an animal feels when it jumps is analyzed and converted into words such as "excited." In this way, the emotion estimation function can be used to monitor changes in animal emotion in real time and instantly verbalize them.

[0093] The analysis unit can integrate data from different animal species to reveal common behavioral patterns and emotional tendencies. The analysis unit, for example, builds a system that integrates data from different animal species to identify common behavioral patterns. For example, it analyzes behavioral data from dogs and cats to reveal common behavioral patterns. The analysis unit also analyzes data from different animal species to develop an algorithm that identifies common emotional tendencies. For example, it integrates data from dogs and birds to analyze common emotional tendencies. The analysis unit also develops a system that integrates data from different animal species to reveal behavioral patterns and emotional tendencies. For example, it analyzes data from cats and birds to identify common behavioral patterns. This makes it possible to integrate data from different animal species to reveal common behavioral patterns and emotional tendencies.

[0094] The analysis unit uses generative AI to analyze data on an animal's behavior, facial expressions, voice, body movements, and poses, and can simultaneously verbalize not only its emotions but also its health condition and stress level. For example, the analysis unit uses generative AI to analyze animal behavior data and build a system that simultaneously verbalizes not only its emotions but also its health condition and stress level. For example, it converts the data into words such as "happy," "healthy," and "no stress." The analysis unit also analyzes animal behavior data and develops an algorithm that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "anxious," "unwell," and "stressed." Furthermore, the analysis unit uses generative AI to analyze the animal's behavioral data and physiological data and develops a system that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "excited," "healthy," and "no stress." This makes it possible to use generative AI to simultaneously verbalize not only an animal's emotions but also its health condition and stress level.

[0095] The analysis unit uses the emotion estimation function to analyze data on the animal's behavior, facial expressions, voice, body movements, and poses, and monitors changes in emotion in real time and can instantly verbalize them. The analysis unit, for example, uses the emotion estimation function to analyze animal behavior data in real time and build a system that instantly verbalizes changes in emotion. For example, the emotion an animal feels when it barks is analyzed in real time and converted into words such as "anxiety." The analysis unit also develops an algorithm that monitors animal behavior data in real time and instantly verbalizes changes in emotion. For example, the emotion an animal feels when it wags its tail is analyzed and converted into words such as "happy." The analysis unit also uses the emotion estimation function to analyze animal behavior data and emotion data in real time and develop a system that instantly verbalizes changes in emotion. For example, the emotion an animal feels when it jumps is analyzed and converted into words such as "excited." In this way, the emotion estimation function can be used to monitor changes in animal emotion in real time and instantly verbalize them.

[0096] The analysis unit can integrate data from different animal species to reveal common behavioral patterns and emotional tendencies. The analysis unit, for example, builds a system that integrates data from different animal species to identify common behavioral patterns. For example, it analyzes behavioral data from dogs and cats to reveal common behavioral patterns. The analysis unit also analyzes data from different animal species to develop an algorithm that identifies common emotional tendencies. For example, it integrates data from dogs and birds to analyze common emotional tendencies. The analysis unit also develops a system that integrates data from different animal species to reveal behavioral patterns and emotional tendencies. For example, it analyzes data from cats and birds to identify common behavioral patterns. This makes it possible to integrate data from different animal species to reveal common behavioral patterns and emotional tendencies.

[0097] The analysis unit uses generative AI to analyze data on an animal's behavior, facial expressions, voice, body movements, and poses, and can simultaneously verbalize not only its emotions but also its health condition and stress level. For example, the analysis unit uses generative AI to analyze animal behavior data and build a system that simultaneously verbalizes not only its emotions but also its health condition and stress level. For example, it converts the data into words such as "happy," "healthy," and "no stress." The analysis unit also analyzes animal behavior data and develops an algorithm that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "anxious," "unwell," and "stressed." Furthermore, the analysis unit uses generative AI to analyze the animal's behavioral data and physiological data and develops a system that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "excited," "healthy," and "no stress." This makes it possible to use generative AI to simultaneously verbalize not only an animal's emotions but also its health condition and stress level.

[0098] The analysis unit uses the emotion estimation function to analyze data on the animal's behavior, facial expressions, voice, body movements, and poses, and monitors changes in emotion in real time and can instantly verbalize them. The analysis unit, for example, uses the emotion estimation function to analyze animal behavior data in real time and build a system that instantly verbalizes changes in emotion. For example, the emotion an animal feels when it barks is analyzed in real time and converted into words such as "anxiety." The analysis unit also develops an algorithm that monitors animal behavior data in real time and instantly verbalizes changes in emotion. For example, the emotion an animal feels when it wags its tail is analyzed and converted into words such as "happy." The analysis unit also uses the emotion estimation function to analyze animal behavior data and emotion data in real time and develop a system that instantly verbalizes changes in emotion. For example, the emotion an animal feels when it jumps is analyzed and converted into words such as "excited." In this way, the emotion estimation function can be used to monitor changes in animal emotion in real time and instantly verbalize them.

[0099] The analysis unit can integrate data from different animal species to reveal common behavioral patterns and emotional tendencies. The analysis unit, for example, builds a system that integrates data from different animal species to identify common behavioral patterns. For example, it analyzes behavioral data from dogs and cats to reveal common behavioral patterns. The analysis unit also analyzes data from different animal species to develop an algorithm that identifies common emotional tendencies. For example, it integrates data from dogs and birds to analyze common emotional tendencies. The analysis unit also develops a system that integrates data from different animal species to reveal behavioral patterns and emotional tendencies. For example, it analyzes data from cats and birds to identify common behavioral patterns. This makes it possible to integrate data from different animal species to reveal common behavioral patterns and emotional tendencies.

[0100] The analysis unit uses generative AI to analyze data on an animal's behavior, facial expressions, voice, body movements, and poses, and can simultaneously verbalize not only its emotions but also its health condition and stress level. For example, the analysis unit uses generative AI to analyze animal behavior data and build a system that simultaneously verbalizes not only its emotions but also its health condition and stress level. For example, it converts the data into words such as "happy," "healthy," and "no stress." The analysis unit also analyzes animal behavior data and develops an algorithm that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "anxious," "unwell," and "stressed." Furthermore, the analysis unit uses generative AI to analyze the animal's behavioral data and physiological data and develops a system that simultaneously verbalizes its emotions, health condition, and stress level. For example, it converts the data into words such as "excited," "healthy," and "no stress." This makes it possible to use generative AI to simultaneously verbalize not only an animal's emotions but also its health condition and stress level.

[0101] The analysis unit uses the emotion estimation function to analyze data on the animal's behavior, facial expressions, voice, body movements, and poses, and monitors changes in emotion in real time and can instantly verbalize them. The analysis unit, for example, uses the emotion estimation function to analyze animal behavior data in real time and build a system that instantly verbalizes changes in emotion. For example, the emotion an animal feels when it barks is analyzed in real time and converted into words such as "anxiety." The analysis unit also develops an algorithm that monitors animal behavior data in real time and instantly verbalizes changes in emotion. For example, the emotion an animal feels when it wags its tail is analyzed and converted into words such as "happy." The analysis unit also uses the emotion estimation function to analyze animal behavior data and emotion data in real time and develop a system that instantly verbalizes changes in emotion. For example, the emotion an animal feels when it jumps is analyzed and converted into words such as "excited." In this way, the emotion estimation function can be used to monitor changes in animal emotion in real time and instantly verbalize them.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The information collection unit collects information on the animal's behavior, facial expressions, voice, body movements, and pose. For example, a camera may be used to capture the animal's facial expressions and a microphone may be used to collect the animal's voice. The information collection unit may also use an acceleration sensor to detect the animal's body movements. Furthermore, the information collection unit may also use a posture sensor to detect the animal's pose. The analysis unit analyzes the information collected by the information collection unit and converts the animal's feelings into words. For example, a generative AI (such as a text generation AI or a multimodal generation AI) may be used to analyze data on the animal's behavior, facial expressions, voice, body movements, and pose, and convert the animal's feelings into words. The analysis unit may also use an emotion analysis algorithm to estimate the animal's emotions and convert them into words. Furthermore, the analysis unit may also use natural language generation technology to convert the animal's feelings into appropriate words. The notification unit notifies the user of the words converted by the analysis unit. For example, the animal's feelings can be conveyed by voice using a voice notification. The animal's feelings can also be displayed in text using a text notification. The animal's feelings can also be displayed visually using a visual notification. This allows the system that converts animal feelings into words to collect information on the animal's behavior, facial expressions, voice, body movements, and poses, and analyzes this information to convert the animal's feelings into words.

[0104] For example, when collecting data on an animal's behavior, facial expressions, voice, body movements, and poses, the information collection unit incorporates body temperature sensors and heart rate sensors into the collar or bracelet. This allows the animal's physiological data to be collected simultaneously and analyzed together with the behavioral data. The information collection unit also builds a system that monitors the animal's body temperature and heart rate in real time and links this with the behavioral data. For example, it analyzes changes in the animal's heart rate when the animal is excited, which helps to estimate its emotions. Furthermore, to collect physiological data, the information collection unit develops sensors that come into direct contact with the animal's skin and incorporates them into the collar or bracelet. This allows the animal's body temperature and heart rate to be accurately measured and integrated with the behavioral data for analysis. This allows for simultaneous collection of physiological data such as the animal's body temperature and heart rate, enabling a more detailed understanding of the animal's condition.

[0105] The information collection unit, for example, builds a system that equips drones with cameras and microphones and monitors the behavior of animals over a wide area in real time. This allows for efficient collection of animal behavior data. The information collection unit also uses drones to monitor animal behavior from the sky and collect behavior data. For example, it observes the behavior of a group of wild animals and analyzes the data. Furthermore, the information collection unit equips drones with acceleration sensors and records the movements of animals in detail. This allows for analysis of animal behavior patterns and helps in estimating emotions. This makes it possible to use drones to monitor the behavior of animals over a wide area in real time and collect data.

[0106] The analysis unit, for example, uses an emotion estimation algorithm to build a system that estimates the emotion an animal is feeling when it behaves in a specific way in real time. For example, it analyzes the emotion an animal feels when it wags its tail and collects the data. The analysis unit also analyzes animal behavioral data and develops a system that displays the emotion estimation results in real time. For example, it estimates the emotion an animal feels when it barks and collects the data. The analysis unit also uses an emotion estimation function to build a system that simultaneously collects animal behavioral data and emotion data. For example, it analyzes the emotion an animal feels when it jumps and collects the data. This makes it possible to monitor changes in an animal's emotion in real time and instantly verbalize them.

[0107] For example, the information collection unit may install sensors to collect environmental data in addition to collars or bracelets to collect animal behavioral data. For example, data such as temperature, humidity, and light intensity may be collected. The information collection unit may also install environmental sensors in the animal's living space to build a system that simultaneously collects both animal behavioral data and environmental data. For example, it may analyze the environmental conditions when an animal exhibits a specific behavior. Furthermore, the information collection unit may link wearable devices with environmental sensors to develop a system that integrates and analyzes animal behavioral data and environmental data. For example, it may identify the environmental factors that cause an animal to exhibit a specific behavior. This allows for more detailed analysis by simultaneously collecting animal behavioral data and environmental data.

[0108] The information collection unit develops sensors specialized for each animal species, such as for dogs, cats, and birds, to improve the accuracy of data collection. For example, a sensor for dogs analyzes barks, and a sensor for cats analyzes meows. The information collection unit also incorporates sensors specialized for each animal species into collars or bracelets to build a system that collects animal behavior data with high accuracy. For example, it develops a sensor that analyzes bird flight patterns. Furthermore, the information collection unit adjusts the sensitivity of the sensor and analysis algorithms to accommodate different animal species, improving the accuracy of data collection. For example, it develops an acceleration sensor specialized for cat movements. In this way, by developing sensors specialized for each animal species, the accuracy of data collection can be improved.

[0109] For example, when analyzing collected data, the analysis unit compares it with the animal's past behavioral data to identify long-term behavioral patterns. For example, it analyzes behavioral changes based on data from the past few months. The analysis unit also uses the animal's past data to build a system for long-term monitoring of emotional changes. For example, it analyzes changes in the frequency with which the animal exhibits a specific behavior. Furthermore, the analysis unit compares past data with current data to develop an algorithm that identifies changes in the animal's behavioral patterns and emotions. For example, it analyzes changes in emotions when the animal exhibits a specific behavior. This makes it possible to analyze long-term behavioral patterns and emotional changes by comparing it with the animal's past data.

[0110] The analysis unit, for example, uses generative AI to develop an analytical model that takes into account individual differences in animals and analyzes behavioral data. For example, the analytical model is optimized based on the behavioral patterns of each individual animal. The analysis unit also builds a system that takes into account individual differences in animals and uses individually optimized analytical models. For example, it develops analytical models that correspond to the age and sex of the animal. Furthermore, the analysis unit uses generative AI to develop a system that automatically generates analytical models that reflect individual differences in animals. For example, it generates individually optimized models based on the animal's past data. This enables more accurate analysis by taking into account individual differences in animals and using individually optimized analytical models.

[0111] The analysis unit, for example, uses an emotion estimation function to analyze animal behavioral data in real time and build a system that instantly verbalizes changes in emotion. For example, the emotion an animal feels when it barks is analyzed in real time and converted into words such as "anxiety." The analysis unit also develops an algorithm that monitors animal behavioral data in real time and instantly verbalizes changes in emotion. For example, the emotion an animal feels when it wags its tail is analyzed and converted into words such as "happy." The analysis unit also uses the emotion estimation function to analyze animal behavioral data and emotion data in real time and develop a system that instantly verbalizes changes in emotion. For example, the emotion an animal feels when it jumps is analyzed and converted into words such as "excited." In this way, the emotion estimation function can be used to monitor changes in animals' emotions in real time and instantly verbalize them.

[0112] The analysis unit, for example, builds a system that integrates data from different animal species and identifies common behavioral patterns. For example, it analyzes behavioral data from dogs and cats to identify common behavioral patterns. The analysis unit also analyzes data from different animal species and develops an algorithm that identifies common emotional tendencies. For example, it integrates data from dogs and birds to analyze common emotional tendencies. The analysis unit also develops a system that integrates data from different animal species and identifies behavioral patterns and emotional tendencies. For example, it analyzes data from cats and birds to identify common behavioral patterns. This makes it possible to integrate data from different animal species and identify common behavioral patterns and emotional tendencies.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The collection unit collects information on the animal's behavior, facial expressions, voice, body movements, and pose. For example, a camera can be used to capture the animal's facial expressions and a microphone can be used to collect the animal's voice. It is also possible to use an acceleration sensor to detect the animal's body movements and a posture sensor to detect the animal's pose. Step 2: The analysis unit analyzes the information collected by the collection unit and converts the animal's feelings into words. For example, it uses generative AI (text generation AI or multimodal generation AI) to analyze data on the animal's behavior, facial expressions, voice, body movements, and poses, and converts the animal's feelings into words. It can also use emotion analysis algorithms to estimate the animal's emotions and convert them into words. It can also use natural language generation technology to convert the animal's feelings into appropriate words. Step 3: The notification unit notifies the user of the words converted by the analysis unit. For example, the animal's feelings can be conveyed by voice using a voice notification, or the animal's feelings can be displayed in text using a text notification, or the animal's feelings can be displayed visually using a visual notification.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

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

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

[0143] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

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

[0145] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0147] 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.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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. [Explanation of symbols]

[0182] 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 information collection unit that collects information on the animal's behavior, facial expression, voice, body movement, and pose; an analysis unit that analyzes the information collected by the information collection unit and converts the animal's feelings into words; a notification unit that notifies the user of the words converted by the analysis unit. A system characterized by:

2. The information collecting unit Physiological data of the animal, such as body temperature or heart rate, is also collected simultaneously.

2. The system of claim 1.

3. The information collecting unit Using drones to monitor and collect data on animal behavior over a wide area in real time 2. The system of claim 1.

4. The analysis unit Analyzes data on the animal's behavior, facial expressions, voice, body movements, and poses, monitors changes in emotions in real time, and instantly verbalizes them.

2. The system of claim 1.

5. The information collecting unit In addition to wearable devices, environmental sensors will be installed to simultaneously collect data on the surrounding environment.

2. The system of claim 1.

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