system

Abstract

The system according to the embodiment aims to accurately estimate the feelings of animals and translate them into words to provide them. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an estimation unit, and a provision unit. The collection unit collects data on the behavior, facial expressions, voice, and body movements of animals. The analysis unit analyzes the data collected by the collection unit. The estimation unit estimates the animal's feelings based on the data analyzed by the analysis unit. The provision unit converts the animal's feelings estimated by the estimation unit into words and provides them.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to accurately understand an animal's feelings, and there were challenges in improving communication.

[0005] The system according to the embodiment aims to accurately estimate the feelings of animals and translate them into words to provide them. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an estimation unit, and a provision unit. The collection unit collects data on the behavior, facial expressions, voice, and body movements of the animal. The analysis unit analyzes the data collected by the collection unit. The estimation unit estimates the animal's feelings based on the data analyzed by the analysis unit. The provision unit converts the animal's feelings estimated by the estimation unit into words and provides them. [Effects of the Invention]

[0007] The system according to the embodiment can accurately estimate the feelings of animals and convert them into words to provide the information. [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 animal emotion estimation system according to an embodiment of the present invention collects information such as animal behavior, facial expressions, voice, body movements, and poses, analyzes it, and converts the animal's feelings into words. This system is extremely useful for owners and animal caregivers who have difficulty grasping an animal's physical condition or who struggle to communicate with animals that do not speak. Understanding an animal's facial expressions, body movements, and voices allows us to predict their emotions, enabling early detection and prevention of illness and stress reduction. Furthermore, improving communication with pets can improve their quality of life and foster better relationships. This approach can also be utilized in zoos and veterinary clinics, providing a better environment for animals. For example, a "collection unit" is required to collect data such as animal behavior, facial expressions, voice, and body movements. Next, an "analysis unit" is required to analyze the collected data, followed by an "estimation unit" to estimate the animal's feelings based on the analysis results. Finally, a "providing unit" is required to translate the estimated animal's feelings into words and provide them. The collection unit collects data using sensors and cameras, the analysis unit analyzes the collected data, the estimation unit estimates the animal's feelings based on the analysis results, and the provision unit converts the estimated feelings into words and provides them. It is also important to clarify how each unit works together. For example, it clearly describes the flow of operations, such as the collection unit sending the collected data to the analysis unit, the analysis unit sending the analysis results to the estimation unit, and the estimation unit sending the estimation results to the provision unit. This allows the animal emotion estimation system to convert the animal's feelings into words and provide them.

[0029] An animal emotion estimation system according to an embodiment includes a collection unit, an analysis unit, an estimation unit, and a provision unit. The collection unit collects data on the behavior, facial expressions, voice, and body movements of an animal. The collection unit collects data using, for example, sensors or cameras. Examples of sensors include temperature sensors and acceleration sensors. Examples of cameras include still image cameras and video cameras. The analysis unit analyzes the collected data and extracts information for estimating the animal's feelings. The analysis unit uses, for example, image analysis or voice analysis. The estimation unit estimates the animal's feelings based on the analyzed data. The estimation unit uses, for example, a machine learning model or a statistical method. The provision unit converts the estimated animal's feelings into words and provides them. The provision unit uses, for example, natural language generation technology or template-based conversion. This enables the animal emotion estimation system according to an embodiment to convert the animal's feelings into words and provide them.

[0030] The collection unit can collect data on the animal's behavior, facial expressions, voice, and body movements using a sensor or a camera. The collection unit collects the animal's behavior data using, for example, a temperature sensor or an acceleration sensor. For example, a temperature sensor measures the animal's body temperature, and an acceleration sensor detects the animal's movements. The collection unit can also record the animal's facial expressions and body movements using a still camera or a video camera. For example, a still camera captures the animal's facial expressions, and a video camera continuously records the animal's movements. In this way, by using a sensor or a camera, data such as the animal's behavior, facial expressions, voice, and body movements can be accurately collected.

[0031] The analysis unit can analyze the collected data and extract information for estimating the animal's feelings. The analysis unit can, for example, use image analysis technology to analyze the animal's facial expression data. For example, the image analysis technology detects the animal's facial features and analyzes changes in facial expression. The analysis unit can also analyze the animal's cry data using audio analysis technology. For example, the audio analysis technology analyzes the frequency and volume of the animal's cry to detect changes in emotion. Furthermore, the analysis unit can analyze the animal's body movement data and extract behavioral patterns. For example, the speed and direction of the animal's movement can be analyzed to identify specific behavioral patterns. In this way, by analyzing the collected data, information for estimating the animal's feelings can be extracted.

[0032] The estimation unit can estimate the animal's feelings based on the analyzed data. The estimation unit estimates the animal's feelings using, for example, a machine learning model. For example, the machine learning model learns the collected data and identifies patterns for estimating the animal's feelings. The estimation unit can also estimate the animal's feelings using statistical methods. For example, the statistical methods analyze the distribution of the collected data and construct a probabilistic model for estimating the animal's feelings. Furthermore, the estimation unit can estimate the animal's feelings based on the animal's behavioral patterns and changes in facial expressions. For example, if the animal repeats a specific behavior, the estimation unit estimates the animal's feelings based on that behavior. This allows the animal's feelings to be accurately estimated based on the analyzed data.

[0033] The providing unit can convert the estimated animal's feelings into words and provide them. The providing unit can convert the animal's feelings into words using, for example, natural language generation technology. For example, natural language generation technology generates appropriate words based on the estimated animal's feelings. The providing unit can also convert the animal's feelings into words using template-based conversion. For example, template-based conversion converts the animal's feelings into words based on a predefined template. Furthermore, when converting the animal's feelings into words, the providing unit can select appropriate expressions depending on the animal's state. For example, if the animal is excited, a concise and easy-to-understand expression is used, and if the animal is relaxed, an expression including a detailed explanation is used. In this way, by converting the estimated animal's feelings into words and providing them, it is possible to make the animal's feelings easier to understand.

[0034] When converting the animal's feelings into words, the providing unit can select an expression that corresponds to the animal's state. The providing unit selects an appropriate expression based on, for example, the animal's emotion score. For example, if the animal is excited, a concise and easy-to-understand expression is used. Furthermore, if the animal is relaxed, the providing unit can also use an expression that includes a detailed explanation. Furthermore, if the animal is feeling stressed, the providing unit can also use an expression that helps to reduce stress. For example, if the animal is feeling stressed, the providing unit selects an expression that includes advice for relaxing. In this way, by selecting an appropriate expression that corresponds to the animal's state, the animal's feelings can be more accurately conveyed.

[0035] The collection unit can estimate the emotion of the animal and adjust the timing of data collection based on the estimated emotion of the animal. The collection unit adjusts the timing of data collection based on, for example, the emotion score of the animal. For example, if the animal is excited, the collection frequency is increased to collect detailed data. The collection unit can also reduce the collection frequency when the animal is relaxed to reduce the burden of data collection. Furthermore, the collection unit can adjust the collection timing to reduce the stress when the animal is feeling stressed. For example, if the animal is feeling stressed, the collection unit reduces the collection frequency to reduce the stress of the animal. In this way, by adjusting the timing of data collection based on the emotion of the animal, more appropriate data can be collected.

[0036] The collection unit can analyze the animal's past behavioral data and select the most appropriate sensor placement. For example, the collection unit places sensors in areas where the animal is most active based on the past behavioral data. For example, by placing sensors in places where the animal frequently passes through, more data can be collected. The collection unit can also place sensors in areas where the animal is active during specific times of the day based on the past behavioral data. For example, if the animal is most active in the morning, the collection unit places sensors during that time of day. Furthermore, the collection unit can analyze the past behavioral data and place sensors in areas where the animal is likely to feel stressed. For example, if the animal feels stressed in a specific place, the collection unit places sensors in that place and collects data. In this way, the optimal sensor placement can be selected by analyzing the animal's past behavioral data.

[0037] The collection unit can customize the collection method based on the type of animal and individual differences when collecting data. For example, the collection unit uses different collection methods for dogs and cats. For example, for dogs, an acceleration sensor is used to collect active behavior, and for cats, a camera is used to collect static behavior. The collection unit can also change the collection method for young animals and old animals. For example, for young animals, play behavior is collected, and for old animals, resting behavior is collected. Furthermore, the collection unit can adjust the collection method for healthy animals and sick animals. For example, for healthy animals, normal behavior is collected, and for sick animals, abnormal behavior is collected. This allows more appropriate data to be collected by customizing the collection method based on the type of animal and individual differences.

[0038] The collection unit can estimate the emotion of the animal and determine the priority of data to be collected based on the estimated emotion of the animal. The collection unit determines the priority of data to be collected based on, for example, the emotion score of the animal. For example, if the animal is excited, it prioritizes collecting behavioral data. Furthermore, if the animal is relaxed, the collection unit can also prioritize collecting audio data. Furthermore, if the animal is feeling stressed, the collection unit can also prioritize collecting facial expression data. For example, if the animal is feeling stressed, the collection unit prioritizes collecting facial expression data to identify the cause of the stress. In this way, by determining the priority of data to be collected based on the emotion of the animal, it is possible to prioritize collecting more important data.

[0039] When collecting data, the collection unit can prioritize collecting highly relevant data based on the animal's environmental information. For example, when the animal is outdoors, the collection unit prioritizes collecting environmental sounds. For example, when the animal is active outdoors, the collection unit collects surrounding environmental sounds and associates them with the animal's behavior. The collection unit can also prioritize collecting temperature and humidity data when the animal is indoors. For example, when the animal is spending time indoors, the collection unit collects the indoor temperature and humidity to evaluate the animal's comfort. Furthermore, when the animal is in a specific location, the collection unit can prioritize collecting environmental data for that location. For example, when the animal spends a long time in a specific area, the collection unit collects environmental data for that area and associates it with the animal's behavior. In this way, by preferentially collecting highly relevant data based on the animal's environmental information, more useful data can be collected.

[0040] The collection unit can monitor the animal's health condition during data collection and increase the collection frequency if an abnormality is detected. For example, the collection unit can increase the collection frequency if the animal's body temperature is abnormally high. For example, if the animal's body temperature is higher than normal, the collection unit can collect body temperature data frequently to identify the cause of the abnormality. The collection unit can also increase the collection frequency if the animal's heart rate is abnormally low. For example, if the animal's heart rate is lower than normal, the collection unit can collect heart rate data frequently to identify the cause of the abnormality. Furthermore, the collection unit can increase the collection frequency if the animal's behavior is abnormal. For example, if the animal exhibits unusual behavior, the collection unit can collect behavioral data frequently to identify the cause of the abnormality. In this way, the animal's health condition can be monitored and the collection frequency can be increased if an abnormality is detected, thereby making it possible to detect the abnormality early.

[0041] The analysis unit can estimate the emotion of the animal and adjust the analysis algorithm based on the estimated emotion of the animal. The analysis unit adjusts the analysis algorithm based on, for example, the emotion score of the animal. For example, if the animal is excited, an algorithm that performs a quick analysis is used. Alternatively, if the animal is relaxed, the analysis unit can use an algorithm that performs a detailed analysis. Furthermore, if the animal is feeling stressed, the analysis unit can use an analysis algorithm specialized for stress reduction. For example, if the animal is feeling stressed, the analysis unit uses an algorithm that identifies the cause of the stress and suggests measures to reduce the stress. In this way, by adjusting the analysis algorithm based on the emotion of the animal, more accurate analysis can be performed.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit performs a detailed analysis on important data. For example, data related to the health condition of an animal is analyzed in detail to help detect abnormalities early. The analysis unit can also perform a simplified analysis on data of low importance. For example, data on the normal behavior of an animal is analyzed in a simplified manner to check for abnormalities. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the collected data. For example, data related to the health condition of an animal is analyzed with priority to help detect abnormalities early. In this way, efficient analysis can be performed by adjusting the level of detail of the analysis based on the importance of the collected data.

[0043] During analysis, the analysis unit can apply different analysis methods depending on the animal's behavioral pattern. For example, if the animal is actively moving, the analysis unit applies a dynamic analysis method. For example, if the animal is running, the speed and direction of the movement are analyzed. The analysis unit can also apply a static analysis method if the animal is stationary. For example, if the animal is resting, the analysis unit analyzes its posture and facial expression. Furthermore, if the animal repeats a specific behavior, the analysis unit can apply an analysis method specialized for that behavior. For example, if the animal moves in a specific pattern, the pattern is analyzed and the meaning of the behavior is inferred. This allows for more appropriate analysis by applying different analysis methods depending on the animal's behavioral pattern.

[0044] The analysis unit can estimate the animal's emotions and adjust the display method of the analysis results based on the estimated animal's emotions. The analysis unit can adjust the display method of the analysis results based on, for example, the animal's emotion score. For example, if the animal is excited, it provides a visually easy-to-understand display method. Furthermore, if the animal is relaxed, it can also provide a display method including detailed information. Furthermore, if the animal is feeling stressed, the analysis unit can highlight and display information that is useful for stress reduction. For example, if the animal is feeling stressed, the analysis unit identifies the cause of the stress and displays information suggesting measures to reduce the stress. In this way, by adjusting the display method of the analysis results based on the animal's emotions, it is possible to provide a more understandable display.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the animal's living environment. For example, if the animal is outdoors, the analysis unit prioritizes analyzing environmental data. For example, if the animal is active outdoors, the analysis unit analyzes surrounding environmental data and associates it with the animal's behavior. The analysis unit can also prioritize analyzing behavioral data if the animal is indoors. For example, if the animal is spending time indoors, the analysis unit analyzes indoor behavioral data and evaluates the animal's comfort. Furthermore, if the animal is in a specific location, the analysis unit can prioritize analyzing data for that location. For example, if the animal spends a long time in a specific area, the analysis unit analyzes data for that area and associates it with the animal's behavior. In this way, by determining the priority of analysis based on the animal's living environment, more important data can be prioritized for analysis.

[0046] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to animals. The analysis unit performs the analysis by referring to, for example, the latest research on animal behavior. For example, the analysis algorithm is adjusted based on the latest research on animal behavior patterns. The analysis unit can also perform the analysis by referring to literature on animal health conditions. For example, the algorithm for detecting abnormalities in health conditions is adjusted based on the latest research on animal health conditions. Furthermore, the analysis unit can also perform the analysis by referring to literature on animal emotions. For example, the algorithm for detecting changes in emotions is adjusted based on the latest research on animal emotions. In this way, by referring to literature related to animals, the accuracy of the analysis can be improved.

[0047] The estimation unit can estimate the emotion of the animal and adjust the estimation algorithm based on the estimated emotion of the animal. The estimation unit adjusts the estimation algorithm based on, for example, the emotion score of the animal. For example, if the animal is excited, an algorithm that makes a quick estimation is used. Also, if the animal is relaxed, the estimation unit can use an algorithm that makes a detailed estimation. Furthermore, if the animal is stressed, the estimation unit can use an estimation algorithm specialized for stress reduction. For example, if the animal is stressed, the estimation unit uses an algorithm that identifies the cause of the stress and suggests measures to reduce the stress. In this way, by adjusting the estimation algorithm based on the emotion of the animal, more accurate estimation can be performed.

[0048] The estimation unit can adjust the level of detail of the estimation based on the importance of the analysis data during estimation. For example, the estimation unit performs detailed estimation for important data. For example, data related to the health condition of an animal can be estimated in detail to help detect abnormalities early. The estimation unit can also perform simplified estimation for data with low importance. For example, data on the normal behavior of an animal can be estimated in a simplified manner to check for abnormalities. Furthermore, the estimation unit can determine the priority of the estimation based on the importance of the analysis data. For example, data related to the health condition of an animal can be estimated with priority to help detect abnormalities early. In this way, efficient estimation can be performed by adjusting the level of detail of the estimation based on the importance of the analysis data.

[0049] The estimation unit can apply different estimation methods depending on the behavioral pattern of the animal when making an estimation. For example, if the animal is actively moving, the estimation unit applies a dynamic estimation method. For example, if the animal is running, estimation is made based on the speed and direction of the movement. The estimation unit can also apply a static estimation method if the animal is stationary. For example, if the animal is resting, estimation is made based on the animal's posture and facial expression. Furthermore, if the animal is repeating a specific behavior, the estimation unit can apply an estimation method specialized for that behavior. For example, if the animal is moving in a specific pattern, estimation is made based on that pattern. In this way, by applying different estimation methods depending on the behavioral pattern of the animal, more appropriate estimation can be made.

[0050] The estimation unit can estimate the emotion of the animal and adjust the display method of the estimation result based on the estimated emotion of the animal. The estimation unit can adjust the display method of the estimation result based on, for example, the emotion score of the animal. For example, if the animal is excited, it provides a visually easy-to-understand display method. Furthermore, if the animal is relaxed, it can also provide a display method including detailed information. Furthermore, if the animal is feeling stressed, the estimation unit can highlight and display information that is useful for stress reduction. For example, if the animal is feeling stressed, the estimation unit identifies the cause of the stress and displays information suggesting measures to reduce the stress. In this way, by adjusting the display method of the estimation result based on the emotion of the animal, it is possible to provide a more understandable display.

[0051] During estimation, the estimation unit can determine a priority of estimation based on the animal's living environment. For example, when the animal is outdoors, the estimation unit prioritizes estimation of environmental data. For example, when the animal is active outdoors, estimation is performed based on surrounding environmental data. The estimation unit can also prioritize estimation of behavioral data when the animal is indoors. For example, when the animal is spending time indoors, estimation is performed based on indoor behavioral data. Furthermore, when the animal is in a specific location, the estimation unit can also prioritize estimation of data for that location. For example, if the animal spends a long time in a specific area, estimation is performed based on data for that area. In this way, by determining the priority of estimation based on the animal's living environment, more important data can be prioritized for estimation.

[0052] The estimation unit can improve the accuracy of the estimation by referring to literature related to animals during estimation. The estimation unit performs estimation by referring to, for example, the latest research on animal behavior. For example, the estimation algorithm is adjusted based on the latest research on animal behavior patterns. The estimation unit can also perform estimation by referring to literature on animal health conditions. For example, the algorithm for detecting abnormalities in health conditions is adjusted based on the latest research on animal health conditions. Furthermore, the estimation unit can perform estimation by referring to literature on animal emotions. For example, the algorithm for detecting changes in emotions is adjusted based on the latest research on animal emotions. In this way, by referring to literature related to animals, the accuracy of the estimation can be improved.

[0053] The providing unit can estimate the emotion of the animal and adjust the way words are expressed based on the estimated emotion of the animal. The providing unit adjusts the way words are expressed based on, for example, the emotion score of the animal. For example, if the animal is excited, a concise and easy-to-understand expression is used. Also, if the animal is relaxed, the providing unit can use an expression including a detailed explanation. Furthermore, if the animal is stressed, the providing unit can use an expression that helps reduce stress. For example, if the animal is stressed, the providing unit selects an expression including advice for relaxing. In this way, by adjusting the way words are expressed based on the emotion of the animal, more appropriate expressions can be used.

[0054] The providing unit can adjust the level of detail of the provided information based on the importance of the inference result when providing the information. For example, the providing unit provides a detailed explanation for an important inference result. For example, an inference result regarding an animal's health condition is explained in detail to help detect abnormalities early. The providing unit can also provide a simplified explanation for an inference result with low importance. For example, an inference result regarding an animal's normal behavior is explained in simple terms to check for abnormalities. Furthermore, the providing unit can determine the priority of the provision based on the importance of the inference result. For example, an inference result regarding an animal's health condition is preferentially provided to help detect abnormalities early. In this way, by adjusting the level of detail of the provision based on the importance of the inference result, efficient information provision can be achieved.

[0055] The providing unit can apply different representation methods depending on the type of animal and individual differences when providing information. The providing unit uses different representation methods for dogs and cats, for example. For example, for dogs, information on active behavior is provided, and for cats, information on sedentary behavior is provided. The providing unit can also use different representation methods for young animals and old animals. For example, for young animals, information on play is provided, and for old animals, information on rest is provided. Furthermore, the providing unit can use different representation methods for healthy animals and sick animals. For example, for healthy animals, information on normal behavior is provided, and for sick animals, information on abnormal behavior is provided. In this way, by applying different representation methods depending on the type of animal and individual differences, more appropriate information can be provided.

[0056] The providing unit can estimate the emotion of the animal and determine the priority of information to be provided based on the estimated emotion of the animal. The providing unit determines the priority of information to be provided based on, for example, the emotion score of the animal. For example, if the animal is excited, information about the animal's behavior is provided preferentially. Furthermore, if the animal is relaxed, the providing unit can also provide information about the animal's health condition preferentially. Furthermore, if the animal is feeling stressed, the providing unit can also provide information useful for stress reduction preferentially. For example, if the animal is feeling stressed, the providing unit provides information including advice on how to relax preferentially. In this way, by determining the priority of information to be provided based on the emotion of the animal, more important information can be provided preferentially.

[0057] When providing information, the providing unit can determine the priority of provision based on the animal's living environment. For example, if the animal is outdoors, the providing unit can provide information about the environment preferentially. For example, if the animal is active outdoors, the providing unit can provide information about the surrounding environment. Furthermore, if the animal is indoors, the providing unit can provide information about the animal's behavior preferentially. For example, if the animal is spending time indoors, the providing unit can provide information about the animal's indoor behavior. Furthermore, if the animal is in a specific location, the providing unit can provide information related to that location preferentially. For example, if the animal spends a long time in a specific area, the providing unit can provide information related to that area. In this way, by determining the priority of provision based on the animal's living environment, more important information can be provided preferentially.

[0058] When providing information, the providing unit can improve the accuracy of the information provided by referring to literature related to animals. The providing unit, for example, provides information by referring to the latest research on animal behavior. For example, information is provided based on the latest research on animal behavior patterns. The providing unit can also provide information by referring to literature on animal health conditions. For example, information on health conditions is provided based on the latest research on animal health conditions. The providing unit can also provide information by referring to literature on animal emotions. For example, information on emotions is provided based on the latest research on animal emotions. In this way, by referring to literature related to animals, the accuracy of the information provided can be improved.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The animal emotion estimation system may further include a health management unit that monitors the animal's health. The health management unit collects physiological data, such as the animal's body temperature, heart rate, and respiratory rate, and transmits this data to the analysis unit. The analysis unit evaluates the animal's health based on the collected physiological data and notifies the estimation unit if an abnormality is detected. The estimation unit adjusts the animal's emotion estimation results based on the notification from the health management unit and transmits them to the providing unit. The providing unit provides information about the animal's health to the owner and, if necessary, recommends that the owner visit a veterinarian. For example, if the animal's body temperature is abnormally high, the providing unit can display a message such as, "The body temperature is high. We recommend that you visit a veterinarian." If the heart rate is abnormally low, the providing unit can display a warning such as, "The heart rate is decreasing. Please consult a specialist immediately." This allows the animal's health to be monitored in real time, allowing for rapid response if an abnormality is detected.

[0061] The animal emotion estimation system may further include a history management unit that records the animal's behavioral history. The history management unit stores the data sent from the collection unit over a long period of time and tracks changes in the animal's behavioral patterns and emotions. The analysis unit analyzes the animal's behavioral patterns and emotional changes based on the data stored in the history management unit and notifies the estimation unit if an abnormality is detected. The estimation unit adjusts the animal's emotion estimation results based on the notification from the history management unit and sends them to the provision unit. The provision unit can provide appropriate advice to the owner based on the animal's behavioral history. For example, if the animal is prone to stress during a certain time of day, the provision unit can display advice such as, "The animal is prone to stress during this time of day, so please provide a quiet environment." Furthermore, if the animal repeatedly exhibits a certain behavior, the provision unit can display a warning such as, "This behavior may be a sign of stress. Please observe carefully." This allows for more appropriate advice to be provided based on the animal's behavioral history.

[0062] The animal emotion estimation system may further include an environmental monitoring unit that collects environmental data about the animal. The environmental monitoring unit collects environmental data about the animal, such as the temperature, humidity, and noise level, and transmits this data to the analysis unit. The analysis unit evaluates the impact on the animal's emotions based on the collected environmental data and notifies the estimation unit. The estimation unit adjusts the animal's emotion estimation result based on the notification from the environmental monitoring unit and transmits it to the providing unit. The providing unit can provide appropriate advice to the owner based on the animal's environmental data. For example, if the room temperature is too high, the providing unit can display advice such as "The room temperature is too high. Please provide a cool environment." If the noise level is high, the providing unit can display a warning such as "The noise level is high. Please provide a quiet environment." This allows for more appropriate advice to be provided based on the animal's environmental data.

[0063] The animal emotion estimation system may further include a diet management unit that manages the animal's dietary data. The diet management unit records the animal's dietary content and intake amount and transmits this data to the analysis unit. The analysis unit evaluates the impact on the animal's health condition and emotions based on the collected dietary data and notifies the estimation unit. The estimation unit adjusts the animal's emotion estimation result based on the notification from the diet management unit and transmits it to the provision unit. The provision unit can provide appropriate advice to the owner based on the animal's dietary data. For example, if the animal feels stressed after eating a certain food, the provision unit can display advice such as "This food may stress the animal. Try a different food." If the animal is not eating enough, the provision unit can display a warning such as "The animal is not eating enough. Please increase the amount of food." This allows for more appropriate advice to be provided based on the animal's dietary data.

[0064] The animal emotion estimation system may further include an exercise management unit that manages the animal's exercise data. The exercise management unit records the animal's exercise amount and exercise patterns and transmits this data to the analysis unit. The analysis unit evaluates the impact on the animal's health condition and emotions based on the collected exercise data and notifies the estimation unit. The estimation unit adjusts the animal's emotion estimation result based on the notification from the exercise management unit and transmits it to the provision unit. The provision unit can provide appropriate advice to the owner based on the animal's exercise data. For example, if the animal is feeling stressed due to lack of exercise, the provision unit can display advice such as "The animal is not exercising enough. Please let the animal exercise more." If the animal is exercising too much, the provision unit can display a warning such as "The animal is exercising too much. Please let the animal rest." This allows for more appropriate advice to be provided based on the animal's exercise data.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects data on the animal's behavior, facial expressions, voice, and body movements. The collection unit collects data using sensors and cameras. Sensors include temperature sensors and acceleration sensors, and cameras include still cameras and video cameras. Step 2: The analysis unit analyzes the collected data and extracts information to estimate the animal's feelings. The analysis unit uses methods such as image analysis and audio analysis. Step 3: The estimation unit estimates the animal's feelings based on the analyzed data. The estimation unit uses machine learning models and statistical methods. Step 4: The provider converts the estimated animal's feelings into words and provides them. The provider uses natural language generation technology or template-based conversion.

[0067] (Example 2) The animal emotion estimation system according to an embodiment of the present invention collects information such as animal behavior, facial expressions, voice, body movements, and poses, analyzes it, and converts the animal's feelings into words. This system is extremely useful for owners and animal caregivers who have difficulty grasping an animal's physical condition or who struggle to communicate with animals that do not speak. Understanding an animal's facial expressions, body movements, and voices allows us to predict their emotions, enabling early detection and prevention of illness and stress reduction. Furthermore, improving communication with pets can improve their quality of life and foster better relationships. This approach can also be utilized in zoos and veterinary clinics, providing a better environment for animals. For example, a "collection unit" is required to collect data such as animal behavior, facial expressions, voice, and body movements. Next, an "analysis unit" is required to analyze the collected data, followed by an "estimation unit" to estimate the animal's feelings based on the analysis results. Finally, a "providing unit" is required to translate the estimated animal's feelings into words and provide them. The collection unit collects data using sensors and cameras, the analysis unit analyzes the collected data, the estimation unit estimates the animal's feelings based on the analysis results, and the provision unit converts the estimated feelings into words and provides them. It is also important to clarify how each unit works together. For example, it clearly describes the flow of operations, such as the collection unit sending the collected data to the analysis unit, the analysis unit sending the analysis results to the estimation unit, and the estimation unit sending the estimation results to the provision unit. This allows the animal emotion estimation system to convert the animal's feelings into words and provide them.

[0068] An animal emotion estimation system according to an embodiment includes a collection unit, an analysis unit, an estimation unit, and a provision unit. The collection unit collects data on the behavior, facial expressions, voice, and body movements of an animal. The collection unit collects data using, for example, sensors or cameras. Examples of sensors include temperature sensors and acceleration sensors. Examples of cameras include still image cameras and video cameras. The analysis unit analyzes the collected data and extracts information for estimating the animal's feelings. The analysis unit uses, for example, image analysis or voice analysis. The estimation unit estimates the animal's feelings based on the analyzed data. The estimation unit uses, for example, a machine learning model or a statistical method. The provision unit converts the estimated animal's feelings into words and provides them. The provision unit uses, for example, natural language generation technology or template-based conversion. This enables the animal emotion estimation system according to an embodiment to convert the animal's feelings into words and provide them.

[0069] The collection unit can collect data on the animal's behavior, facial expressions, voice, and body movements using a sensor or a camera. The collection unit collects the animal's behavior data using, for example, a temperature sensor or an acceleration sensor. For example, a temperature sensor measures the animal's body temperature, and an acceleration sensor detects the animal's movements. The collection unit can also record the animal's facial expressions and body movements using a still camera or a video camera. For example, a still camera captures the animal's facial expressions, and a video camera continuously records the animal's movements. In this way, by using a sensor or a camera, data such as the animal's behavior, facial expressions, voice, and body movements can be accurately collected.

[0070] The analysis unit can analyze the collected data and extract information for estimating the animal's feelings. The analysis unit can, for example, use image analysis technology to analyze the animal's facial expression data. For example, the image analysis technology detects the animal's facial features and analyzes changes in facial expression. The analysis unit can also analyze the animal's cry data using audio analysis technology. For example, the audio analysis technology analyzes the frequency and volume of the animal's cry to detect changes in emotion. Furthermore, the analysis unit can analyze the animal's body movement data and extract behavioral patterns. For example, the speed and direction of the animal's movement can be analyzed to identify specific behavioral patterns. In this way, by analyzing the collected data, information for estimating the animal's feelings can be extracted.

[0071] The estimation unit can estimate the animal's feelings based on the analyzed data. The estimation unit estimates the animal's feelings using, for example, a machine learning model. For example, the machine learning model learns the collected data and identifies patterns for estimating the animal's feelings. The estimation unit can also estimate the animal's feelings using statistical methods. For example, the statistical methods analyze the distribution of the collected data and construct a probabilistic model for estimating the animal's feelings. Furthermore, the estimation unit can estimate the animal's feelings based on the animal's behavioral patterns and changes in facial expressions. For example, if the animal repeats a specific behavior, the estimation unit estimates the animal's feelings based on that behavior. This allows the animal's feelings to be accurately estimated based on the analyzed data.

[0072] The providing unit can convert the estimated animal's feelings into words and provide them. The providing unit can convert the animal's feelings into words using, for example, natural language generation technology. For example, natural language generation technology generates appropriate words based on the estimated animal's feelings. The providing unit can also convert the animal's feelings into words using template-based conversion. For example, template-based conversion converts the animal's feelings into words based on a predefined template. Furthermore, when converting the animal's feelings into words, the providing unit can select appropriate expressions depending on the animal's state. For example, if the animal is excited, a concise and easy-to-understand expression is used, and if the animal is relaxed, an expression including a detailed explanation is used. In this way, by converting the estimated animal's feelings into words and providing them, it is possible to make the animal's feelings easier to understand.

[0073] When converting the animal's feelings into words, the providing unit can select an expression that corresponds to the animal's state. The providing unit selects an appropriate expression based on, for example, the animal's emotion score. For example, if the animal is excited, a concise and easy-to-understand expression is used. Furthermore, if the animal is relaxed, the providing unit can also use an expression that includes a detailed explanation. Furthermore, if the animal is feeling stressed, the providing unit can also use an expression that helps to reduce stress. For example, if the animal is feeling stressed, the providing unit selects an expression that includes advice for relaxing. In this way, by selecting an appropriate expression that corresponds to the animal's state, the animal's feelings can be more accurately conveyed.

[0074] The collection unit can estimate the emotion of the animal and adjust the timing of data collection based on the estimated emotion of the animal. The collection unit adjusts the timing of data collection based on, for example, the emotion score of the animal. For example, if the animal is excited, the collection frequency is increased to collect detailed data. The collection unit can also reduce the collection frequency when the animal is relaxed to reduce the burden of data collection. Furthermore, the collection unit can adjust the collection timing to reduce the stress when the animal is feeling stressed. For example, if the animal is feeling stressed, the collection unit reduces the collection frequency to reduce the stress of the animal. In this way, by adjusting the timing of data collection based on the emotion of the animal, more appropriate data can be collected.

[0075] The collection unit can analyze the animal's past behavioral data and select the most appropriate sensor placement. For example, the collection unit places sensors in areas where the animal is most active based on the past behavioral data. For example, by placing sensors in places where the animal frequently passes through, more data can be collected. The collection unit can also place sensors in areas where the animal is active during specific times of the day based on the past behavioral data. For example, if the animal is most active in the morning, the collection unit places sensors during that time of day. Furthermore, the collection unit can analyze the past behavioral data and place sensors in areas where the animal is likely to feel stressed. For example, if the animal feels stressed in a specific place, the collection unit places sensors in that place and collects data. In this way, the optimal sensor placement can be selected by analyzing the animal's past behavioral data.

[0076] The collection unit can customize the collection method based on the type of animal and individual differences when collecting data. For example, the collection unit uses different collection methods for dogs and cats. For example, for dogs, an acceleration sensor is used to collect active behavior, and for cats, a camera is used to collect static behavior. The collection unit can also change the collection method for young animals and old animals. For example, for young animals, play behavior is collected, and for old animals, resting behavior is collected. Furthermore, the collection unit can adjust the collection method for healthy animals and sick animals. For example, for healthy animals, normal behavior is collected, and for sick animals, abnormal behavior is collected. This allows more appropriate data to be collected by customizing the collection method based on the type of animal and individual differences.

[0077] The collection unit can estimate the emotion of the animal and determine the priority of data to be collected based on the estimated emotion of the animal. The collection unit determines the priority of data to be collected based on, for example, the emotion score of the animal. For example, if the animal is excited, it prioritizes collecting behavioral data. Furthermore, if the animal is relaxed, the collection unit can also prioritize collecting audio data. Furthermore, if the animal is feeling stressed, the collection unit can also prioritize collecting facial expression data. For example, if the animal is feeling stressed, the collection unit prioritizes collecting facial expression data to identify the cause of the stress. In this way, by determining the priority of data to be collected based on the emotion of the animal, it is possible to prioritize collecting more important data.

[0078] When collecting data, the collection unit can prioritize collecting highly relevant data based on the animal's environmental information. For example, when the animal is outdoors, the collection unit prioritizes collecting environmental sounds. For example, when the animal is active outdoors, the collection unit collects surrounding environmental sounds and associates them with the animal's behavior. The collection unit can also prioritize collecting temperature and humidity data when the animal is indoors. For example, when the animal is spending time indoors, the collection unit collects the indoor temperature and humidity to evaluate the animal's comfort. Furthermore, when the animal is in a specific location, the collection unit can prioritize collecting environmental data for that location. For example, when the animal spends a long time in a specific area, the collection unit collects environmental data for that area and associates it with the animal's behavior. In this way, by preferentially collecting highly relevant data based on the animal's environmental information, more useful data can be collected.

[0079] The collection unit can monitor the animal's health condition during data collection and increase the collection frequency if an abnormality is detected. For example, the collection unit can increase the collection frequency if the animal's body temperature is abnormally high. For example, if the animal's body temperature is higher than normal, the collection unit can collect body temperature data frequently to identify the cause of the abnormality. The collection unit can also increase the collection frequency if the animal's heart rate is abnormally low. For example, if the animal's heart rate is lower than normal, the collection unit can collect heart rate data frequently to identify the cause of the abnormality. Furthermore, the collection unit can increase the collection frequency if the animal's behavior is abnormal. For example, if the animal exhibits unusual behavior, the collection unit can collect behavioral data frequently to identify the cause of the abnormality. In this way, the animal's health condition can be monitored and the collection frequency can be increased if an abnormality is detected, thereby making it possible to detect the abnormality early.

[0080] The analysis unit can estimate the emotion of the animal and adjust the analysis algorithm based on the estimated emotion of the animal. The analysis unit adjusts the analysis algorithm based on, for example, the emotion score of the animal. For example, if the animal is excited, an algorithm that performs a quick analysis is used. Alternatively, if the animal is relaxed, the analysis unit can use an algorithm that performs a detailed analysis. Furthermore, if the animal is feeling stressed, the analysis unit can use an analysis algorithm specialized for stress reduction. For example, if the animal is feeling stressed, the analysis unit uses an algorithm that identifies the cause of the stress and suggests measures to reduce the stress. In this way, by adjusting the analysis algorithm based on the emotion of the animal, more accurate analysis can be performed.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit performs a detailed analysis on important data. For example, data related to the health condition of an animal is analyzed in detail to help detect abnormalities early. The analysis unit can also perform a simplified analysis on data of low importance. For example, data on the normal behavior of an animal is analyzed in a simplified manner to check for abnormalities. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the collected data. For example, data related to the health condition of an animal is analyzed with priority to help detect abnormalities early. In this way, efficient analysis can be performed by adjusting the level of detail of the analysis based on the importance of the collected data.

[0082] During analysis, the analysis unit can apply different analysis methods depending on the animal's behavioral pattern. For example, if the animal is actively moving, the analysis unit applies a dynamic analysis method. For example, if the animal is running, the speed and direction of the movement are analyzed. The analysis unit can also apply a static analysis method if the animal is stationary. For example, if the animal is resting, the analysis unit analyzes its posture and facial expression. Furthermore, if the animal repeats a specific behavior, the analysis unit can apply an analysis method specialized for that behavior. For example, if the animal moves in a specific pattern, the pattern is analyzed and the meaning of the behavior is inferred. This allows for more appropriate analysis by applying different analysis methods depending on the animal's behavioral pattern.

[0083] The analysis unit can estimate the animal's emotions and adjust the display method of the analysis results based on the estimated animal's emotions. The analysis unit can adjust the display method of the analysis results based on, for example, the animal's emotion score. For example, if the animal is excited, it provides a visually easy-to-understand display method. Furthermore, if the animal is relaxed, it can also provide a display method including detailed information. Furthermore, if the animal is feeling stressed, the analysis unit can highlight and display information that is useful for stress reduction. For example, if the animal is feeling stressed, the analysis unit identifies the cause of the stress and displays information suggesting measures to reduce the stress. In this way, by adjusting the display method of the analysis results based on the animal's emotions, it is possible to provide a more understandable display.

[0084] During analysis, the analysis unit can determine the priority of analysis based on the animal's living environment. For example, if the animal is outdoors, the analysis unit prioritizes analyzing environmental data. For example, if the animal is active outdoors, the analysis unit analyzes surrounding environmental data and associates it with the animal's behavior. The analysis unit can also prioritize analyzing behavioral data if the animal is indoors. For example, if the animal is spending time indoors, the analysis unit analyzes indoor behavioral data and evaluates the animal's comfort. Furthermore, if the animal is in a specific location, the analysis unit can prioritize analyzing data for that location. For example, if the animal spends a long time in a specific area, the analysis unit analyzes data for that area and associates it with the animal's behavior. In this way, by determining the priority of analysis based on the animal's living environment, more important data can be prioritized for analysis.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to animals. The analysis unit performs the analysis by referring to, for example, the latest research on animal behavior. For example, the analysis algorithm is adjusted based on the latest research on animal behavior patterns. The analysis unit can also perform the analysis by referring to literature on animal health conditions. For example, the algorithm for detecting abnormalities in health conditions is adjusted based on the latest research on animal health conditions. Furthermore, the analysis unit can also perform the analysis by referring to literature on animal emotions. For example, the algorithm for detecting changes in emotions is adjusted based on the latest research on animal emotions. In this way, by referring to literature related to animals, the accuracy of the analysis can be improved.

[0086] The estimation unit can estimate the emotion of the animal and adjust the estimation algorithm based on the estimated emotion of the animal. The estimation unit adjusts the estimation algorithm based on, for example, the emotion score of the animal. For example, if the animal is excited, an algorithm that makes a quick estimation is used. Also, if the animal is relaxed, the estimation unit can use an algorithm that makes a detailed estimation. Furthermore, if the animal is stressed, the estimation unit can use an estimation algorithm specialized for stress reduction. For example, if the animal is stressed, the estimation unit uses an algorithm that identifies the cause of the stress and suggests measures to reduce the stress. In this way, by adjusting the estimation algorithm based on the emotion of the animal, more accurate estimation can be performed.

[0087] The estimation unit can adjust the level of detail of the estimation based on the importance of the analysis data during estimation. For example, the estimation unit performs detailed estimation for important data. For example, data related to the health condition of an animal can be estimated in detail to help detect abnormalities early. The estimation unit can also perform simplified estimation for data with low importance. For example, data on the normal behavior of an animal can be estimated in a simplified manner to check for abnormalities. Furthermore, the estimation unit can determine the priority of the estimation based on the importance of the analysis data. For example, data related to the health condition of an animal can be estimated with priority to help detect abnormalities early. In this way, efficient estimation can be performed by adjusting the level of detail of the estimation based on the importance of the analysis data.

[0088] The estimation unit can apply different estimation methods depending on the behavioral pattern of the animal when making an estimation. For example, if the animal is actively moving, the estimation unit applies a dynamic estimation method. For example, if the animal is running, estimation is made based on the speed and direction of the movement. The estimation unit can also apply a static estimation method if the animal is stationary. For example, if the animal is resting, estimation is made based on the animal's posture and facial expression. Furthermore, if the animal is repeating a specific behavior, the estimation unit can apply an estimation method specialized for that behavior. For example, if the animal is moving in a specific pattern, estimation is made based on that pattern. In this way, by applying different estimation methods depending on the behavioral pattern of the animal, more appropriate estimation can be made.

[0089] The estimation unit can estimate the emotion of the animal and adjust the display method of the estimation result based on the estimated emotion of the animal. The estimation unit can adjust the display method of the estimation result based on, for example, the emotion score of the animal. For example, if the animal is excited, it provides a visually easy-to-understand display method. Furthermore, if the animal is relaxed, it can also provide a display method including detailed information. Furthermore, if the animal is feeling stressed, the estimation unit can highlight and display information that is useful for stress reduction. For example, if the animal is feeling stressed, the estimation unit identifies the cause of the stress and displays information suggesting measures to reduce the stress. In this way, by adjusting the display method of the estimation result based on the emotion of the animal, it is possible to provide a more understandable display.

[0090] During estimation, the estimation unit can determine a priority of estimation based on the animal's living environment. For example, when the animal is outdoors, the estimation unit prioritizes estimation of environmental data. For example, when the animal is active outdoors, estimation is performed based on surrounding environmental data. The estimation unit can also prioritize estimation of behavioral data when the animal is indoors. For example, when the animal is spending time indoors, estimation is performed based on indoor behavioral data. Furthermore, when the animal is in a specific location, the estimation unit can also prioritize estimation of data for that location. For example, if the animal spends a long time in a specific area, estimation is performed based on data for that area. In this way, by determining the priority of estimation based on the animal's living environment, more important data can be prioritized for estimation.

[0091] The estimation unit can improve the accuracy of the estimation by referring to literature related to animals during estimation. The estimation unit performs estimation by referring to, for example, the latest research on animal behavior. For example, the estimation algorithm is adjusted based on the latest research on animal behavior patterns. The estimation unit can also perform estimation by referring to literature on animal health conditions. For example, the algorithm for detecting abnormalities in health conditions is adjusted based on the latest research on animal health conditions. Furthermore, the estimation unit can perform estimation by referring to literature on animal emotions. For example, the algorithm for detecting changes in emotions is adjusted based on the latest research on animal emotions. In this way, by referring to literature related to animals, the accuracy of the estimation can be improved.

[0092] The providing unit can estimate the emotion of the animal and adjust the way words are expressed based on the estimated emotion of the animal. The providing unit adjusts the way words are expressed based on, for example, the emotion score of the animal. For example, if the animal is excited, a concise and easy-to-understand expression is used. Also, if the animal is relaxed, the providing unit can use an expression including a detailed explanation. Furthermore, if the animal is stressed, the providing unit can use an expression that helps reduce stress. For example, if the animal is stressed, the providing unit selects an expression including advice for relaxing. In this way, by adjusting the way words are expressed based on the emotion of the animal, more appropriate expressions can be used.

[0093] The providing unit can adjust the level of detail of the provided information based on the importance of the inference result when providing the information. For example, the providing unit provides a detailed explanation for an important inference result. For example, an inference result regarding an animal's health condition is explained in detail to help detect abnormalities early. The providing unit can also provide a simplified explanation for an inference result with low importance. For example, an inference result regarding an animal's normal behavior is explained in simple terms to check for abnormalities. Furthermore, the providing unit can determine the priority of the provision based on the importance of the inference result. For example, an inference result regarding an animal's health condition is preferentially provided to help detect abnormalities early. In this way, by adjusting the level of detail of the provision based on the importance of the inference result, efficient information provision can be achieved.

[0094] The providing unit can apply different representation methods depending on the type of animal and individual differences when providing information. The providing unit uses different representation methods for dogs and cats, for example. For example, for dogs, information on active behavior is provided, and for cats, information on sedentary behavior is provided. The providing unit can also use different representation methods for young animals and old animals. For example, for young animals, information on play is provided, and for old animals, information on rest is provided. Furthermore, the providing unit can use different representation methods for healthy animals and sick animals. For example, for healthy animals, information on normal behavior is provided, and for sick animals, information on abnormal behavior is provided. In this way, by applying different representation methods depending on the type of animal and individual differences, more appropriate information can be provided.

[0095] The providing unit can estimate the emotion of the animal and determine the priority of information to be provided based on the estimated emotion of the animal. The providing unit determines the priority of information to be provided based on, for example, the emotion score of the animal. For example, if the animal is excited, information about the animal's behavior is provided preferentially. Furthermore, if the animal is relaxed, the providing unit can also provide information about the animal's health condition preferentially. Furthermore, if the animal is feeling stressed, the providing unit can also provide information useful for stress reduction preferentially. For example, if the animal is feeling stressed, the providing unit provides information including advice on how to relax preferentially. In this way, by determining the priority of information to be provided based on the emotion of the animal, more important information can be provided preferentially.

[0096] When providing information, the providing unit can determine the priority of provision based on the animal's living environment. For example, if the animal is outdoors, the providing unit can provide information about the environment preferentially. For example, if the animal is active outdoors, the providing unit can provide information about the surrounding environment. Furthermore, if the animal is indoors, the providing unit can provide information about the animal's behavior preferentially. For example, if the animal is spending time indoors, the providing unit can provide information about the animal's indoor behavior. Furthermore, if the animal is in a specific location, the providing unit can provide information related to that location preferentially. For example, if the animal spends a long time in a specific area, the providing unit can provide information related to that area. In this way, by determining the priority of provision based on the animal's living environment, more important information can be provided preferentially.

[0097] When providing information, the providing unit can improve the accuracy of the information provided by referring to literature related to animals. The providing unit, for example, provides information by referring to the latest research on animal behavior. For example, information is provided based on the latest research on animal behavior patterns. The providing unit can also provide information by referring to literature on animal health conditions. For example, information on health conditions is provided based on the latest research on animal health conditions. The providing unit can also provide information by referring to literature on animal emotions. For example, information on emotions is provided based on the latest research on animal emotions. In this way, by referring to literature related to animals, the accuracy of the information provided can be improved. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, estimation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data on the behavior, facial expressions, voice, and body movements of the animal using the camera 42 and sensors of the smart device 14. The analysis unit analyzes the data collected, for example, by the specific processing unit 290 of the data processing device 12, and extracts information for estimating the animal's feelings. For example, the estimation unit estimates the animal's feelings based on the data analyzed by the specific processing unit 290 of the data processing device 12. For example, the provision unit converts the animal's feelings estimated by the control unit 46A of the smart device 14 into words and provides them. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, estimation unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data on the behavior, facial expressions, voice, and body movements of the animal using the camera 42 and sensors of the smart glasses 214. The analysis unit, for example, analyzes the data collected by the specific processing unit 290 of the data processing device 12 and extracts information for estimating the animal's feelings. For example, the estimation unit estimates the animal's feelings based on the data analyzed by the specific processing unit 290 of the data processing device 12. For example, the provision unit converts the animal's feelings estimated by the control unit 46A of the smart glasses 214 into words and provides them. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, estimation unit, and provision unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects data on the behavior, facial expressions, voice, and body movements of the animal using the camera 42 and sensors of the headset-type terminal 314. The analysis unit analyzes the data collected, for example, by the specific processing unit 290 of the data processing device 12, and extracts information for estimating the animal's feelings. For example, the estimation unit estimates the animal's feelings based on the data analyzed by the specific processing unit 290 of the data processing device 12. For example, the provision unit converts the animal's feelings estimated by the control unit 46A of the headset-type terminal 314 into words and provides the words. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, estimation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data on the animal's behavior, facial expressions, voice, and body movements using the camera 42 and sensors of the robot 414. The analysis unit analyzes the data collected, for example, by the specific processing unit 290 of the data processing device 12, and extracts information for estimating the animal's feelings. For example, the estimation unit estimates the animal's feelings based on the data analyzed by the specific processing unit 290 of the data processing device 12. For example, the provision unit converts the animal's feelings estimated by the control unit 46A of the robot 414 into words and provides the words.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The animal emotion estimation system may further include a health management unit that monitors the animal's health. The health management unit collects physiological data, such as the animal's body temperature, heart rate, and respiratory rate, and transmits this data to the analysis unit. The analysis unit evaluates the animal's health based on the collected physiological data and notifies the estimation unit if an abnormality is detected. The estimation unit adjusts the animal's emotion estimation results based on the notification from the health management unit and transmits them to the providing unit. The providing unit provides information about the animal's health to the owner and, if necessary, recommends that the owner visit a veterinarian. For example, if the animal's body temperature is abnormally high, the providing unit can display a message such as, "The body temperature is high. We recommend that you visit a veterinarian." If the heart rate is abnormally low, the providing unit can display a warning such as, "The heart rate is decreasing. Please consult a specialist immediately." This allows the animal's health to be monitored in real time, allowing for rapid response if an abnormality is detected.

[0100] The animal emotion estimation system may further include a history management unit that records the animal's behavioral history. The history management unit stores the data sent from the collection unit over a long period of time and tracks changes in the animal's behavioral patterns and emotions. The analysis unit analyzes the animal's behavioral patterns and emotional changes based on the data stored in the history management unit and notifies the estimation unit if an abnormality is detected. The estimation unit adjusts the animal's emotion estimation results based on the notification from the history management unit and sends them to the provision unit. The provision unit can provide appropriate advice to the owner based on the animal's behavioral history. For example, if the animal is prone to stress during a certain time of day, the provision unit can display advice such as, "The animal is prone to stress during this time of day, so please provide a quiet environment." Furthermore, if the animal repeatedly exhibits a certain behavior, the provision unit can display a warning such as, "This behavior may be a sign of stress. Please observe carefully." This allows for more appropriate advice to be provided based on the animal's behavioral history.

[0101] The animal emotion estimation system may further include an environmental monitoring unit that collects environmental data about the animal. The environmental monitoring unit collects environmental data about the animal, such as the temperature, humidity, and noise level, and transmits this data to the analysis unit. The analysis unit evaluates the impact on the animal's emotions based on the collected environmental data and notifies the estimation unit. The estimation unit adjusts the animal's emotion estimation result based on the notification from the environmental monitoring unit and transmits it to the providing unit. The providing unit can provide appropriate advice to the owner based on the animal's environmental data. For example, if the room temperature is too high, the providing unit can display advice such as "The room temperature is too high. Please provide a cool environment." If the noise level is high, the providing unit can display a warning such as "The noise level is high. Please provide a quiet environment." This allows for more appropriate advice to be provided based on the animal's environmental data.

[0102] The animal emotion estimation system may further include a diet management unit that manages the animal's dietary data. The diet management unit records the animal's dietary content and intake amount and transmits this data to the analysis unit. The analysis unit evaluates the impact on the animal's health condition and emotions based on the collected dietary data and notifies the estimation unit. The estimation unit adjusts the animal's emotion estimation result based on the notification from the diet management unit and transmits it to the provision unit. The provision unit can provide appropriate advice to the owner based on the animal's dietary data. For example, if the animal feels stressed after eating a certain food, the provision unit can display advice such as "This food may stress the animal. Try a different food." If the animal is not eating enough, the provision unit can display a warning such as "The animal is not eating enough. Please increase the amount of food." This allows for more appropriate advice to be provided based on the animal's dietary data.

[0103] The animal emotion estimation system may further include an exercise management unit that manages the animal's exercise data. The exercise management unit records the animal's exercise amount and exercise patterns and transmits this data to the analysis unit. The analysis unit evaluates the impact on the animal's health condition and emotions based on the collected exercise data and notifies the estimation unit. The estimation unit adjusts the animal's emotion estimation result based on the notification from the exercise management unit and transmits it to the provision unit. The provision unit can provide appropriate advice to the owner based on the animal's exercise data. For example, if the animal is feeling stressed due to lack of exercise, the provision unit can display advice such as "The animal is not exercising enough. Please let the animal exercise more." If the animal is exercising too much, the provision unit can display a warning such as "The animal is exercising too much. Please let the animal rest." This allows for more appropriate advice to be provided based on the animal's exercise data.

[0104] The animal emotion estimation system may further include a behavior guide unit that estimates the animal's emotion and guides the owner's behavior based on the estimated animal emotion. The behavior guide unit provides specific behavioral instructions to the owner based on the emotion estimation result transmitted from the estimation unit. For example, if the animal is stressed, the behavior guide unit may display an instruction such as "The animal is stressed. Please move it to a quiet place." If the animal is relaxed, the behavior guide unit may provide advice such as "The animal is relaxed. Please maintain the current environment." If the animal is excited, the behavior guide unit may provide a specific behavioral instruction such as "The animal is excited. Please provide a toy to calm it down." This allows the owner to take appropriate actions based on the animal's emotion, reduce the animal's stress, and build a better relationship with the animal.

[0105] The animal emotion estimation system can further include a music providing unit that estimates the emotion of the animal and plays appropriate music based on the estimated emotion of the animal. The music providing unit selects and plays music that corresponds to the emotion of the animal based on the emotion estimation result transmitted from the estimation unit. For example, if the animal is feeling stressed, the music providing unit can play music that has a relaxing effect. Also, if the animal is excited, it can play gentle music to calm the animal. Furthermore, if the animal is relaxed, it can play soothing music to maintain that state. In this way, by providing appropriate music based on the emotion of the animal, it is possible to reduce the stress of the animal and help it relax.

[0106] The animal emotion estimation system can further include a play suggestion unit that estimates the emotion of the animal and suggests appropriate play based on the estimated emotion of the animal. The play suggestion unit suggests play that corresponds to the emotion of the animal based on the emotion estimation result transmitted from the estimation unit. For example, if the animal is feeling stressed, the play suggestion unit can suggest play that helps relieve stress. Also, if the animal is excited, the play suggestion unit can suggest play that helps the animal release energy. Furthermore, if the animal is relaxed, the play suggestion unit can suggest gentle play to maintain that state. In this way, by suggesting appropriate play based on the emotion of the animal, the animal's stress can be reduced and it can be relaxed.

[0107] The animal emotion estimation system can further include a meal suggestion unit that estimates the emotion of the animal and suggests an appropriate meal based on the estimated emotion of the animal. The meal suggestion unit suggests a meal according to the emotion of the animal based on the emotion estimation result transmitted from the estimation unit. For example, if the animal is feeling stressed, the meal suggestion unit can suggest a meal that helps reduce stress. Also, if the animal is excited, the meal suggestion unit can suggest a meal that will calm the animal. Furthermore, if the animal is relaxed, the meal suggestion unit can suggest a meal that will maintain that state. In this way, by suggesting an appropriate meal based on the emotion of the animal, the animal's stress can be reduced and it can be relaxed.

[0108] The animal emotion estimation system can further include a care suggestion unit that estimates the emotion of the animal and suggests appropriate care based on the estimated emotion of the animal. The care suggestion unit suggests care according to the emotion of the animal based on the emotion estimation result transmitted from the estimation unit. For example, if the animal is feeling stressed, the care suggestion unit can suggest care that helps reduce stress. Also, if the animal is excited, the care suggestion unit can suggest care to calm the animal. Furthermore, if the animal is relaxed, the care suggestion unit can suggest care to maintain that state. In this way, by suggesting appropriate care based on the emotion of the animal, the stress of the animal can be reduced and it can be relaxed.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The collection unit collects data on the animal's behavior, facial expressions, voice, and body movements. The collection unit collects data using sensors and cameras. Sensors include temperature sensors and acceleration sensors, and cameras include still cameras and video cameras. Step 2: The analysis unit analyzes the collected data and extracts information to estimate the animal's feelings. The analysis unit uses methods such as image analysis and audio analysis. Step 3: The estimation unit estimates the animal's feelings based on the analyzed data. The estimation unit uses machine learning models and statistical methods. Step 4: The provider converts the estimated animal's feelings into words and provides them. The provider uses natural language generation technology or template-based conversion.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0159] 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.

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[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.

[0182] [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data on animal behavior, facial expressions, sounds, and body movements; an analysis unit that analyzes the data collected by the collection unit; an estimation unit that estimates the animal's feelings based on the data analyzed by the analysis unit; a providing unit that converts the feeling of the animal estimated by the estimating unit into words and provides the words. A system characterized by:

2. The collecting unit Use sensors or cameras to collect data on animal behavior, facial expressions, sounds, and body movements 2. The system of claim 1.

3. The analysis unit Analyzing the collected data and extracting information to estimate the animal's feelings 2. The system of claim 1.

4. The estimation unit Inferring the animal's feelings based on the analyzed data 2. The system of claim 1.

5. The providing unit Translate the estimated animal's feelings into words and provide them 2. The system of claim 1.

6. The providing unit When translating an animal's feelings into words, choose expressions that suit the animal's state.

2. The system of claim 1.

7. The collecting unit Inferring the animal's emotions and adjusting the timing of data collection based on the inferred emotions 2. The system of claim 1.

8. The collecting unit Analyzing past animal behavior data and selecting the most suitable sensor placement 2. The system of claim 1.

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