System

Abstract

An object of a system according to an embodiment is to estimate and display an animal's feelings and emotions.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, and a display unit. The collection unit collects a behavior or a voice of the animal. The analysis unit analyzes the data collected by the collection unit and estimates the thoughts and feelings of the animal. The display unit displays the thoughts and feelings estimated by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to accurately understand the thoughts and emotions of animals and communicate with them.

[0005] The system according to the embodiment aims to estimate and display the thoughts and emotions of animals. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a display unit. The collection unit collects the behavior or sounds of the animal. The analysis unit analyzes the data collected by the collection unit and estimates the thoughts and emotions of the animal. The display unit displays the thoughts and emotions estimated by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can estimate and display the thoughts and emotions of animals. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A conversation tool according to an embodiment of the present invention is a system that collects, analyzes, and displays animal behavior and sounds. This system realizes communication between humans and animals by collecting, analyzing, and displaying animal behavior and sounds. For example, the conversation tool collects animal behavior and sounds using sensors, and AI analyzes the data. The analysis results are then displayed in text or audio, making it easier for humans to understand the animal's thoughts and emotions. This allows for a deeper understanding of the animal's thoughts and emotions and enables communication. As a result, the conversation tool collects, analyzes, and displays the animal's behavior and sounds, enabling communication between humans and animals. For example, knowing what a pet is feeling can enable more appropriate care to be provided. Furthermore, in interactions with wild animals and animal conservation activities, AI can analyze the animal's condition, enabling more effective information sharing and care to be provided.

[0029] A conversation tool according to an embodiment includes a collection unit, an analysis unit, and a display unit. The collection unit collects animal behavior or sound. The animal behavior or sound includes, but is not limited to, cries, movements, and specific gestures. For example, the collection unit collects animal cries using a microphone. The collection unit can also collect animal movements using a camera. The collection unit can also collect specific animal gestures using an acceleration sensor. For example, the collection unit collects dog barks using a microphone. The collection unit can also collect cat movements using a camera. The collection unit can also collect specific bird gestures using an acceleration sensor. The analysis unit analyzes the data collected by the collection unit and infers the thoughts and emotions of the animal. The inference is performed using, for example, a machine learning algorithm or a statistical method, but is not limited to, these examples. For example, the analysis unit infers the animal's thoughts and emotions using a machine learning algorithm. The analysis unit can also infer the animal's thoughts and emotions using a statistical method. The analysis unit can also perform data preprocessing and feature extraction to infer the thoughts and emotions of animals. For example, the analysis unit can use a machine learning algorithm to infer the thoughts and emotions of dogs. The analysis unit can also use statistical techniques to infer the thoughts and emotions of cats. The analysis unit can also perform data preprocessing and feature extraction to infer the thoughts and emotions of birds. The display unit displays the thoughts and emotions estimated by the analysis unit. The display may be, for example, text or audio, but is not limited to these examples. For example, the display unit displays the analysis results in text. The display unit can also display the analysis results in audio. The display unit can also display the analysis results using a screen display or voice synthesis. For example, the display unit displays the analysis results in text on a screen. The display unit can also display the analysis results in audio and play it back using voice synthesis. The display unit can also display the analysis results in text on a screen. As a result, the conversation tool according to the embodiment can collect, analyze, and display the behaviors and sounds of animals, thereby enabling communication between humans and animals.

[0030] The collection unit can collect animal behavior or sounds using a sensor. Examples of sensors include, but are not limited to, a microphone, a camera, an acceleration sensor, etc. For example, the collection unit can collect animal sounds using a microphone. The collection unit can also collect animal movements using a camera. The collection unit can also collect specific animal gestures using an acceleration sensor. For example, the collection unit can collect dog barks using a microphone. The collection unit can also collect cat movements using a camera. The collection unit can also collect specific bird gestures using an acceleration sensor. In this way, accurate data can be obtained by collecting animal behavior and sounds using sensors. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input audio data acquired by a microphone to a generation AI and have the generation AI analyze the audio data.

[0031] The analysis unit can analyze the collected data and infer the thoughts and emotions of the animal. The analysis can be performed by, for example, data preprocessing, feature extraction, application of an algorithm, etc., but is not limited to these examples. For example, the analysis unit can preprocess the data and convert the collected data into a format that is easy to analyze. The analysis unit can also extract features and extract important information from the data. The analysis unit can also apply a machine learning algorithm to infer the thoughts and emotions of the animal. For example, the analysis unit can preprocess the data and convert the collected data into a format that is easy to analyze. The analysis unit can also extract features and extract important information from the data. The analysis unit can also apply a machine learning algorithm to infer the thoughts and emotions of the animal. In this way, the thoughts and emotions of the animal can be inferred by analyzing the collected data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI infer the thoughts and emotions of the animal.

[0032] The display unit can display the analysis results in text or audio. Display can be performed, for example, using a screen display or voice synthesis, but is not limited to these examples. For example, the display unit can display the analysis results in text on a screen. The display unit can also display the analysis results in audio and play them back using voice synthesis. The display unit can also display the analysis results in text on a screen. For example, the display unit can display the analysis results in text on a screen. The display unit can also display the analysis results in audio and play them back using voice synthesis. The display unit can also display the analysis results in text on a screen. Displaying the analysis results in text or audio makes it easier for people to understand the thoughts and emotions of animals. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can convert the analysis results into text or audio using a generation AI and display it.

[0033] The analysis unit can use a different analysis method for each type of animal. Examples of different analysis methods include, but are not limited to, voice analysis for dogs and behavioral analysis for cats. For example, the analysis unit performs voice analysis for dogs. The analysis unit can also perform behavioral analysis for cats. The analysis unit can also perform environmental analysis for birds. For example, the analysis unit performs voice analysis for dogs. The analysis unit can also perform behavioral analysis for cats. The analysis unit can also perform environmental analysis for birds. Using different analysis methods for each type of animal enables more accurate analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input different analysis algorithms for each type of animal into the generation AI and cause the generation AI to perform the analysis.

[0034] The display unit can display specific application examples in animal protection activities. Specific application examples include, but are not limited to, examples of use in a shelter and monitoring of wild animals. The display unit can display, for example, an example of use in a shelter. The display unit can also display an example of monitoring wild animals. The display unit can also display specific application examples in animal protection activities. For example, the display unit can display an example of use in a shelter. The display unit can also display an example of monitoring wild animals. The display unit can also display specific application examples in animal protection activities. In this way, by displaying specific application examples in animal protection activities, protection activities can be carried out more effectively. Some or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can display specific application examples in animal protection activities using generation AI.

[0035] The collection unit can estimate the emotion of the animal and adjust the timing of collecting behavior and sound based on the estimated emotion of the animal. The adjustment of the collection timing is performed, for example, based on the behavioral pattern of the animal, but is not limited to such an example. For example, if a dog is excited, the collection unit shortens the collection timing and collects data more frequently. Furthermore, if a cat is relaxed, the collection unit can lengthen the collection timing and reduce the frequency of data collection. Furthermore, if a bird is alert, the collection unit can adjust the collection timing to avoid stressing the bird. For example, if a dog is excited, the collection unit shortens the collection timing and collects data more frequently. Furthermore, if a cat is relaxed, the collection unit can lengthen the collection timing and reduce the frequency of data collection. Furthermore, if a bird is alert, the collection unit can adjust the collection timing to avoid stressing the bird. Thus, adjusting the collection timing based on the emotion of the animal enables more appropriate data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input animal emotion data into the generation AI and have the generation AI adjust the collection timing.

[0036] The collection unit can analyze the animal's past behavioral history and select the optimal collection method. The selection of the optimal collection method is performed, for example, based on past data, but is not limited to such an example. For example, if a dog was active during a specific time period in the past, the collection unit collects data during that time period. Furthermore, if a cat often meowed at a specific location in the past, the collection unit can collect data at that location. Furthermore, if a bird reacted to a specific sound in the past, the collection unit can collect data using that sound. For example, if a dog was active during a specific time period in the past, the collection unit collects data during that time period. Furthermore, if a cat often meowed at a specific location in the past, the collection unit can collect data at that location. Furthermore, if a bird reacted to a specific sound in the past, the collection unit can collect data using that sound. In this way, the optimal collection method can be selected by analyzing the animal's past behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past behavioral data of animals into the generation AI and have the generation AI select the optimal collection method.

[0037] When collecting behaviors and sounds, the collection unit can filter the data based on the animal's current health condition and environment. Filtering can be performed, for example, based on health evaluation criteria and environmental conditions, but is not limited to these examples. For example, if a dog is not in good health, the collection unit can filter the collected data to prevent stress. If a cat is in a noisy environment, the collection unit can filter the collected data to remove noise. If a bird is in a cold environment, the collection unit can filter the collected data to consider the influence of the environment. For example, if a dog is not in good health, the collection unit can filter the collected data to prevent stress. If a cat is in a noisy environment, the collection unit can filter the collected data to remove noise. If a bird is in a cold environment, the collection unit can filter the collected data to consider the influence of the environment. Thus, filtering based on the animal's health condition and environment enables more accurate data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input animal health and environmental data into the generation AI and have the generation AI perform filtering.

[0038] When collecting behavior and sound, the collection unit can select the optimal collection means depending on the type of animal and individual differences. The selection of the optimal collection means is performed, for example, based on the type of animal and individual differences, but is not limited to such examples. For example, in the case of a dog, the collection unit collects both sound and behavior. In addition, in the case of a cat, the collection unit can mainly collect behavior and auxiliary collect sound. In addition, in the case of a bird, the collection unit can mainly collect sound and auxiliary collect behavior. For example, in the case of a dog, the collection unit collects both sound and behavior. In addition, in the case of a cat, the collection unit can mainly collect behavior and auxiliary collect sound. In addition, in the case of a bird, the collection unit can mainly collect sound and auxiliary collect behavior. This enables more appropriate data collection by selecting the optimal collection means depending on the type of animal and individual differences. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause the generation AI to select the collection means based on the type of animal and individual differences.

[0039] 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 determination of the priority of data is performed, for example, based on the intensity of the emotion or the importance of the data, but is not limited to such examples. For example, the collection unit can prioritize collecting voice data when a dog is excited. The collection unit can also prioritize collecting behavioral data when a cat is relaxed. The collection unit can also prioritize collecting environmental data when a bird is alert. For example, the collection unit can prioritize collecting voice data when a dog is excited. The collection unit can also prioritize collecting behavioral data when a cat is relaxed. The collection unit can also prioritize collecting environmental data when a bird is alert. Thus, by determining the priority of data to be collected based on the emotion of the animal, more important data can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the emotion data of the animal to the generation AI and cause the generation AI to determine the priority of the data.

[0040] When collecting behaviors and sounds, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the animals. Consideration of the geographical location information can be performed, for example, based on the use of GPS data or the analysis of location information, but is not limited to these examples. For example, if a dog is in a park, the collection unit can prioritize collecting behavioral data in the park. Furthermore, if a cat is inside a house, the collection unit can also prioritize collecting voice data inside the house. Furthermore, if a bird is in a forest, the collection unit can also prioritize collecting environmental data in the forest. For example, if a dog is in a park, the collection unit can prioritize collecting behavioral data in the park. Furthermore, if a cat is inside a house, the collection unit can also prioritize collecting voice data inside the house. Furthermore, if a bird is in a forest, the collection unit can also prioritize collecting environmental data in the forest. In this way, highly relevant data can be prioritized by taking into account the geographical location information of the animals. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without using AI. For example, the collection unit can input the geographic location information of animals into the generation AI and cause the generation AI to prioritize the collection of highly relevant data.

[0041] The collection unit can analyze the social behavior of animals and collect related data when collecting behaviors and sounds. Analysis of social behavior is performed based on, for example, group behavior and interactions with other animals, but is not limited to such examples. For example, the collection unit collects behavioral data when a dog is playing with other dogs. The collection unit can also collect audio data when a cat is communicating with other cats. The collection unit can also collect environmental data when birds are flying in a flock. For example, the collection unit collects behavioral data when a dog is playing with other dogs. The collection unit can also collect audio data when a cat is communicating with other cats. The collection unit can also collect environmental data when birds are flying in a flock. In this way, related data can be collected by analyzing the social behavior of animals. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the social behavior data of animals to the generation AI and cause the generation AI to collect related data.

[0042] When collecting behaviors and sounds, the collection unit can customize the collection method by reflecting past feedback from the animal. Reflecting the feedback can be performed, for example, based on evaluations of past data or user opinions, but is not limited to these examples. For example, if a dog reacted to a specific sound in the past, the collection unit can collect data using the sound. Furthermore, if a cat exhibited a specific behavior in the past, the collection unit can collect data based on the behavior. Furthermore, if a bird was active in a specific environment in the past, the collection unit can collect data in that environment. For example, if a dog reacted to a specific sound in the past, the collection unit can collect data using the sound. Furthermore, if a cat exhibited a specific behavior in the past, the collection unit can collect data based on the behavior. Furthermore, if a bird was active in a specific environment in the past, the collection unit can collect data in that environment. In this way, the collection method can be customized by reflecting past feedback from the animal. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input past feedback data from the animal into the generation AI and cause the generation AI to customize the collection method.

[0043] The analysis unit can estimate the emotion of the animal and adjust the way the analysis is presented based on the estimated emotion of the animal. The adjustment of the way the presentation is presented can be, for example, based on a change in the presentation depending on the intensity of the emotion, but is not limited to such an example. For example, the analysis unit can visually emphasize the analysis results when a dog is excited. The analysis unit can also display the analysis results calmly when a cat is relaxed. The analysis unit can also display the analysis results in detail when a bird is alert. For example, the analysis unit can visually emphasize the analysis results when a dog is excited. The analysis unit can also display the analysis results calmly when a cat is relaxed. The analysis unit can also display the analysis results in detail when a bird is alert. By adjusting the way the analysis is presented based on the emotion of the animal, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the emotion data of the animal into the generation AI and cause the generation AI to adjust the way the analysis is presented.

[0044] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of behavior or sound. The adjustment of the level of detail is performed, for example, based on changing the depth of analysis according to the importance, but is not limited to such an example. For example, if a dog's bark is important, the analysis unit can perform a detailed analysis of the sound data. Furthermore, if a cat's behavior is important, the analysis unit can also perform a detailed analysis of the behavioral data. Furthermore, if a bird's environmental data is important, the analysis unit can also perform a detailed analysis of the environmental data. For example, if a dog's bark is important, the analysis unit can perform a detailed analysis of the sound data. Furthermore, if a cat's behavior is important, the analysis unit can also perform a detailed analysis of the behavioral data. Furthermore, if a bird's environmental data is important, the analysis unit can also perform a detailed analysis of the environmental data. In this way, by adjusting the level of detail of the analysis based on the importance of behavior or sound, more important data can be analyzed in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input behavioral and voice importance data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the type of animal and individual differences during analysis. The application of different analysis algorithms can be based on, for example, an algorithm for dogs, an algorithm for cats, etc., but is not limited to these examples. For example, the analysis unit can apply a specific bark analysis algorithm to dogs. The analysis unit can also apply a specific behavior analysis algorithm to cats. The analysis unit can also apply a specific environmental analysis algorithm to birds. For example, the analysis unit can apply a specific bark analysis algorithm to dogs. The analysis unit can also apply a specific behavior analysis algorithm to cats. The analysis unit can also apply a specific environmental analysis algorithm to birds. This enables more accurate analysis by applying different analysis algorithms depending on the type of animal and individual differences. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input an analysis algorithm based on the type of animal and individual differences into the generation AI and have the generation AI perform the analysis.

[0046] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of the animal. Referencing past analysis results can be performed, for example, using a database or analyzing past trends, but is not limited to these examples. For example, the analysis unit can improve the current analysis accuracy by referring to past analysis results of a dog's bark. The analysis unit can also improve the current analysis accuracy by referring to past analysis results of a cat's behavior. The analysis unit can also improve the current analysis accuracy by referring to past analysis results of a bird's environment. For example, the analysis unit can improve the current analysis accuracy by referring to past analysis results of a dog's bark. The analysis unit can also improve the current analysis accuracy by referring to past analysis results of a cat's behavior. The analysis unit can also improve the current analysis accuracy by referring to past analysis results of a bird's environment. In this way, the analysis accuracy is improved by referring to past analysis results of the animal. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input past analysis result data of the animal into the generation AI and cause the generation AI to improve the analysis accuracy.

[0047] The analysis unit can estimate the emotion of the animal and adjust the length of the analysis based on the estimated emotion of the animal. The adjustment of the length of the analysis is performed, for example, by changing the analysis time according to the intensity of the emotion, but is not limited to such an example. For example, if a dog is excited, the analysis unit can perform a short analysis. Furthermore, if a cat is relaxed, the analysis unit can perform a long, detailed analysis. Furthermore, if a bird is alert, the analysis unit can perform a quick analysis. For example, if a dog is excited, the analysis unit can perform a short analysis. Furthermore, if a cat is relaxed, the analysis unit can perform a long, detailed analysis. Furthermore, if a bird is alert, the analysis unit can perform a quick analysis. This allows for more appropriate analysis by adjusting the length of the analysis based on the emotion of the animal. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the emotion data of the animal into the generation AI and cause the generation AI to adjust the length of the analysis.

[0048] During analysis, the analysis unit can determine the priority of analysis based on the time when the behaviors and sounds were collected. The priority determination is performed, for example, based on setting the priority based on the time of collection, but is not limited to such an example. For example, the analysis unit may prioritize analysis immediately after a dog's bark is collected. The analysis unit may also prioritize analysis immediately after a cat's behavior is collected. The analysis unit may also prioritize analysis immediately after a bird's environmental data is collected. For example, the analysis unit may prioritize analysis immediately after a dog's bark is collected. The analysis unit may also prioritize analysis immediately after a cat's behavior is collected. The analysis unit may also prioritize analysis immediately after a bird's environmental data is collected. By determining the priority of analysis based on the time when the behaviors and sounds were collected, more important data can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit may input data on the time when the behaviors and sounds were collected into the generation AI and cause the generation AI to determine the analysis priority.

[0049] During analysis, the analysis unit can adjust the order of analysis based on the relevance of behaviors and sounds. Adjustment of the order is performed, for example, based on a change in the order based on the relevance, but is not limited to such an example. For example, if a dog's bark is related to the behavior, the analysis unit may prioritize analyzing the bark. Furthermore, if a cat's behavior is related to the environment, the analysis unit may prioritize analyzing the behavior. Furthermore, if a bird's sound is related to the environment, the analysis unit may prioritize analyzing the sound. For example, if a dog's bark is related to the behavior, the analysis unit may prioritize analyzing the bark. Furthermore, if a cat's behavior is related to the environment, the analysis unit may prioritize analyzing the behavior. Furthermore, if a bird's sound is related to the environment, the analysis unit may prioritize analyzing the sound. Thus, by adjusting the order of analysis based on the relevance of behaviors and sounds, more relevant data can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input behavioral and voice association data into the generation AI and have the generation AI adjust the order of analysis.

[0050] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the animal's health condition and environment. Adjustment of the use of technical terms is performed, for example, based on changes in terms according to the animal's health condition, but is not limited to such examples. For example, if a dog's health condition is poor, the analysis unit can display the analysis results while avoiding technical terms. Furthermore, if a cat is in a noisy environment, the analysis unit can display the analysis results while avoiding technical terms. Furthermore, if a bird is in a cold environment, the analysis unit can display the analysis results while avoiding technical terms. For example, if a dog's health condition is poor, the analysis unit can display the analysis results while avoiding technical terms. Furthermore, if a cat is in a noisy environment, the analysis unit can display the analysis results while avoiding technical terms. Furthermore, if a bird is in a cold environment, the analysis unit can display the analysis results while avoiding technical terms. By adjusting the use of technical terms in the analysis according to the animal's health condition and environment, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input animal health and environmental data into the generation AI and have the generation AI adjust the use of technical terms.

[0051] The display unit can estimate the emotion of the animal and adjust the display method based on the estimated emotion of the animal. The adjustment of the display method is performed, for example, by changing the display method according to the intensity of the emotion, but is not limited to such an example. For example, the display unit can provide a visually emphasized display method when a dog is excited. The display unit can also provide a calm display method when a cat is relaxed. The display unit can also provide a detailed display method when a bird is alert. For example, the display unit can provide a visually emphasized display method when a dog is excited. The display unit can also provide a calm display method when a cat is relaxed. The display unit can also provide a detailed display method when a bird is alert. This allows for a more appropriate display by adjusting the display method based on the emotion of the animal. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or without AI. For example, the display unit can input emotion data of the animal to a generation AI and cause the generation AI to adjust the display method.

[0052] The display unit can adjust the level of detail of the display based on the importance of the analysis results during display. Adjustment of the level of detail of the display is performed, for example, based on a change in the depth of the display according to the importance, but is not limited to this example. For example, the display unit can display detailed analysis results when a dog's bark is important. The display unit can also display detailed analysis results when a cat's behavior is important. The display unit can also display detailed analysis results when bird environmental data is important. For example, the display unit can display detailed analysis results when a dog's bark is important. The display unit can also display detailed analysis results when a cat's behavior is important. The display unit can also display detailed analysis results when bird environmental data is important. By adjusting the level of detail of the display based on the importance of the analysis results, more important information can be displayed in detail. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or without AI. For example, the display unit can input importance data of the analysis results to the generation AI and cause the generation AI to adjust the level of detail of the display.

[0053] The display unit can apply different display methods depending on the type of animal and individual differences when displaying. The application of different display methods can be based on, for example, a display method for dogs, a display method for cats, etc., but is not limited to such examples. For example, the display unit can provide a visually emphasized display method for dogs. The display unit can also provide a gentle display method for cats. The display unit can also provide a detailed display method for birds. For example, the display unit can provide a visually emphasized display method for dogs. The display unit can also provide a gentle display method for cats. The display unit can also provide a detailed display method for birds. This enables more appropriate display by applying different display methods depending on the type of animal and individual differences. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input a display method based on the type of animal and individual differences into the generation AI and have the generation AI execute the display.

[0054] The display unit can improve the accuracy of the display by referring to past display results of the animals when displaying the images. Referring to past display results may be performed, for example, using a database or analyzing past trends, but is not limited to these examples. For example, the display unit can improve the current display accuracy by referring to past display results of dogs. The display unit can also improve the current display accuracy by referring to past display results of cats. The display unit can also improve the current display accuracy by referring to past display results of birds. For example, the display unit can improve the current display accuracy by referring to past display results of dogs. The display unit can also improve the current display accuracy by referring to past display results of cats. The display unit can also improve the current display accuracy by referring to past display results of birds. In this way, the accuracy of the display is improved by referring to past display results of animals. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input past display result data of animals to the generation AI and cause the generation AI to improve the display accuracy.

[0055] The display unit can estimate the emotion of the animal and adjust the length of the display based on the estimated emotion. Adjustment of the length of the display can be performed, for example, by changing the display time according to the intensity of the emotion, but is not limited to this example. For example, if the dog is excited, the display unit can display a short, to-the-point message. Furthermore, if the cat is relaxed, the display unit can display a longer message with detailed explanations. Furthermore, if the bird is wary, the display unit can display a quick, concise message. For example, if the dog is excited, the display unit can display a short, to-the-point message. Furthermore, if the cat is relaxed, the display unit can display a longer message with detailed explanations. Furthermore, if the bird is wary, the display unit can display a quick, concise message. Adjusting the length of the display based on the emotion of the animal enables a more appropriate display. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input the emotion data of the animal into a generation AI and have the generation AI adjust the length of the display.

[0056] The display unit can determine the display priority based on the collection time of the analysis results when displaying the analysis results. The priority determination is performed, for example, based on a priority setting based on the collection time, but is not limited to such an example. For example, the display unit prioritizes display immediately after a dog's bark is collected. The display unit can also prioritize display immediately after a cat's behavior is collected. The display unit can also prioritize display immediately after a bird's environmental data is collected. For example, the display unit prioritizes display immediately after a dog's bark is collected. The display unit can also prioritize display immediately after a cat's behavior is collected. The display unit can also prioritize display immediately after a bird's environmental data is collected. In this way, by determining the display priority based on the collection time of the analysis results, more important information can be displayed preferentially. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data on the collection time of the analysis results to the generation AI and cause the generation AI to determine the display priority.

[0057] The display unit can adjust the display order based on the relevance of the analysis results when displaying the results. The adjustment of the order is performed, for example, based on a change in the order based on the relevance, but is not limited to such an example. For example, if a dog's bark and its behavior are related, the display unit can prioritize displaying the bark. Furthermore, if a cat's behavior and its environment are related, the display unit can prioritize displaying the behavior. Furthermore, if a bird's sound and its environment are related, the display unit can prioritize displaying the sound. For example, if a dog's bark and its behavior are related, the display unit can prioritize displaying the bark. Furthermore, if a cat's behavior and its environment are related, the display unit can prioritize displaying the behavior. Furthermore, if a bird's sound and its environment are related, the display unit can prioritize displaying the sound. In this way, by adjusting the display order based on the relevance of the analysis results, more relevant information can be displayed preferentially. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the relevance data of the analysis results to the generation AI and cause the generation AI to adjust the display order.

[0058] The display unit can display specific application examples in animal protection activities. Specific application examples include, but are not limited to, examples of use in a shelter and monitoring of wild animals. The display unit can display, for example, an example of use in a shelter. The display unit can also display an example of monitoring wild animals. The display unit can also display specific application examples in animal protection activities. For example, the display unit can display an example of use in a shelter. The display unit can also display an example of monitoring wild animals. The display unit can also display specific application examples in animal protection activities. In this way, by displaying specific application examples in animal protection activities, protection activities can be carried out more effectively. Some or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can display specific application examples in animal protection activities using generation AI.

[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] When collecting the behavior and sounds of an animal, the collection unit can also simultaneously collect biological information such as the animal's body temperature and heart rate. For example, when collecting the barking of a dog, the collection unit can measure the body temperature of the dog. Also, when collecting the movements of a cat, the collection unit can measure the cat's heart rate. Also, when collecting a specific gesture of a bird, the collection unit can measure the bird's breathing rate. In this way, by simultaneously collecting the behavior and sounds of an animal and biological information, more detailed data can be obtained.

[0061] The analysis unit can refer to the animal's past health records and medical data when analyzing the collected data. For example, the analysis unit can refer to past health checkup results when analyzing a dog's bark. The analysis unit can also refer to the animal's past medical history when analyzing a cat's behavior. The analysis unit can also refer to past treatment records when analyzing a bird's specific gestures. This allows for more accurate analysis by referring to the animal's past health records and medical data.

[0062] When displaying the analysis results, the display unit can display advice and suggestions based on the animal's behavior and sound. For example, the display unit can suggest appropriate care methods to the owner based on the analysis results of a dog's bark. The display unit can also provide the owner with advice on how to play with the cat based on the analysis results of the cat's behavior. The display unit can also suggest ways to improve the bird's environment to the owner based on the analysis results of a specific bird gesture. In this way, by displaying advice and suggestions based on the analysis results, the owner can take more appropriate measures.

[0063] When collecting animal behavior and sounds, the collection unit can simultaneously collect environmental data such as ambient sounds, temperature, and humidity. For example, the collection unit records ambient sounds when collecting dog barks. The collection unit can also measure ambient temperature when collecting cat movements. The collection unit can also measure ambient humidity when collecting specific bird gestures. This allows for more detailed data to be obtained by simultaneously collecting animal behavior and sounds and environmental data.

[0064] When analyzing the collected data, the analysis unit can learn animal behavior and sound patterns and detect abnormal behavior and sound. For example, the analysis unit can learn dog bark patterns and detect abnormal barks. The analysis unit can also learn cat behavior patterns and detect abnormal movements. The analysis unit can also learn specific bird gesture patterns and detect abnormal gestures. In this way, by learning animal behavior and sound patterns, abnormal behavior and sound can be detected early.

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

[0066] Step 1: The collection unit collects animal behavior or sound. The animal behavior or sound includes, for example, cries, movements, specific gestures, etc. The collection unit can collect animal cries using a microphone, collect animal movements using a camera, and collect specific animal gestures using an acceleration sensor. Step 2: The analysis unit analyzes the data collected by the collection unit and infers the animal's thoughts and emotions. This is done using machine learning algorithms and statistical methods. The analysis unit can also infer the animal's thoughts and emotions by preprocessing the data and extracting features. Step 3: The display unit displays the thoughts and emotions estimated by the analysis unit. The display is performed as text or audio. The display unit displays the analysis results as text on a screen. The display unit can also display the analysis results as audio and play it back using speech synthesis.

[0067] (Example 2) A conversation tool according to an embodiment of the present invention is a system that collects, analyzes, and displays animal behavior and sounds. This system realizes communication between humans and animals by collecting, analyzing, and displaying animal behavior and sounds. For example, the conversation tool collects animal behavior and sounds using sensors, and AI analyzes the data. The analysis results are then displayed in text or audio, making it easier for humans to understand the animal's thoughts and emotions. This allows for a deeper understanding of the animal's thoughts and emotions and enables communication. As a result, the conversation tool collects, analyzes, and displays the animal's behavior and sounds, enabling communication between humans and animals. For example, knowing what a pet is feeling can enable more appropriate care to be provided. Furthermore, in interactions with wild animals and animal conservation activities, AI can analyze the animal's condition, enabling more effective information sharing and care to be provided.

[0068] A conversation tool according to an embodiment includes a collection unit, an analysis unit, and a display unit. The collection unit collects animal behavior or sound. The animal behavior or sound includes, but is not limited to, cries, movements, and specific gestures. For example, the collection unit collects animal cries using a microphone. The collection unit can also collect animal movements using a camera. The collection unit can also collect specific animal gestures using an acceleration sensor. For example, the collection unit collects dog barks using a microphone. The collection unit can also collect cat movements using a camera. The collection unit can also collect specific bird gestures using an acceleration sensor. The analysis unit analyzes the data collected by the collection unit and infers the thoughts and emotions of the animal. The inference is performed using, for example, a machine learning algorithm or a statistical method, but is not limited to, these examples. For example, the analysis unit infers the animal's thoughts and emotions using a machine learning algorithm. The analysis unit can also infer the animal's thoughts and emotions using a statistical method. The analysis unit can also perform data preprocessing and feature extraction to infer the thoughts and emotions of animals. For example, the analysis unit can use a machine learning algorithm to infer the thoughts and emotions of dogs. The analysis unit can also use statistical techniques to infer the thoughts and emotions of cats. The analysis unit can also perform data preprocessing and feature extraction to infer the thoughts and emotions of birds. The display unit displays the thoughts and emotions estimated by the analysis unit. The display may be, for example, text or audio, but is not limited to these examples. For example, the display unit displays the analysis results in text. The display unit can also display the analysis results in audio. The display unit can also display the analysis results using a screen display or voice synthesis. For example, the display unit displays the analysis results in text on a screen. The display unit can also display the analysis results in audio and play it back using voice synthesis. The display unit can also display the analysis results in text on a screen. As a result, the conversation tool according to the embodiment can collect, analyze, and display the behaviors and sounds of animals, thereby enabling communication between humans and animals.

[0069] The collection unit can collect animal behavior or sounds using a sensor. Examples of sensors include, but are not limited to, a microphone, a camera, an acceleration sensor, etc. For example, the collection unit can collect animal sounds using a microphone. The collection unit can also collect animal movements using a camera. The collection unit can also collect specific animal gestures using an acceleration sensor. For example, the collection unit can collect dog barks using a microphone. The collection unit can also collect cat movements using a camera. The collection unit can also collect specific bird gestures using an acceleration sensor. In this way, accurate data can be obtained by collecting animal behavior and sounds using sensors. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input audio data acquired by a microphone to a generation AI and have the generation AI analyze the audio data.

[0070] The analysis unit can analyze the collected data and infer the thoughts and emotions of the animal. The analysis can be performed by, for example, data preprocessing, feature extraction, application of an algorithm, etc., but is not limited to these examples. For example, the analysis unit can preprocess the data and convert the collected data into a format that is easy to analyze. The analysis unit can also extract features and extract important information from the data. The analysis unit can also apply a machine learning algorithm to infer the thoughts and emotions of the animal. For example, the analysis unit can preprocess the data and convert the collected data into a format that is easy to analyze. The analysis unit can also extract features and extract important information from the data. The analysis unit can also apply a machine learning algorithm to infer the thoughts and emotions of the animal. In this way, the thoughts and emotions of the animal can be inferred by analyzing the collected data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI infer the thoughts and emotions of the animal.

[0071] The display unit can display the analysis results in text or audio. Display can be performed, for example, using a screen display or voice synthesis, but is not limited to these examples. For example, the display unit can display the analysis results in text on a screen. The display unit can also display the analysis results in audio and play them back using voice synthesis. The display unit can also display the analysis results in text on a screen. For example, the display unit can display the analysis results in text on a screen. The display unit can also display the analysis results in audio and play them back using voice synthesis. The display unit can also display the analysis results in text on a screen. Displaying the analysis results in text or audio makes it easier for people to understand the thoughts and emotions of animals. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can convert the analysis results into text or audio using a generation AI and display it.

[0072] The analysis unit can use a different analysis method for each type of animal. Examples of different analysis methods include, but are not limited to, voice analysis for dogs and behavioral analysis for cats. For example, the analysis unit performs voice analysis for dogs. The analysis unit can also perform behavioral analysis for cats. The analysis unit can also perform environmental analysis for birds. For example, the analysis unit performs voice analysis for dogs. The analysis unit can also perform behavioral analysis for cats. The analysis unit can also perform environmental analysis for birds. Using different analysis methods for each type of animal enables more accurate analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input different analysis algorithms for each type of animal into the generation AI and cause the generation AI to perform the analysis.

[0073] The display unit can display specific application examples in animal protection activities. Specific application examples include, but are not limited to, examples of use in a shelter and monitoring of wild animals. The display unit can display, for example, an example of use in a shelter. The display unit can also display an example of monitoring wild animals. The display unit can also display specific application examples in animal protection activities. For example, the display unit can display an example of use in a shelter. The display unit can also display an example of monitoring wild animals. The display unit can also display specific application examples in animal protection activities. In this way, by displaying specific application examples in animal protection activities, protection activities can be carried out more effectively. Some or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can display specific application examples in animal protection activities using generation AI.

[0074] The collection unit can estimate the emotion of the animal and adjust the timing of collecting behavior and sound based on the estimated emotion of the animal. The adjustment of the collection timing is performed, for example, based on the behavioral pattern of the animal, but is not limited to such an example. For example, if a dog is excited, the collection unit shortens the collection timing and collects data more frequently. Furthermore, if a cat is relaxed, the collection unit can lengthen the collection timing and reduce the frequency of data collection. Furthermore, if a bird is alert, the collection unit can adjust the collection timing to avoid stressing the bird. For example, if a dog is excited, the collection unit shortens the collection timing and collects data more frequently. Furthermore, if a cat is relaxed, the collection unit can lengthen the collection timing and reduce the frequency of data collection. Furthermore, if a bird is alert, the collection unit can adjust the collection timing to avoid stressing the bird. Thus, adjusting the collection timing based on the emotion of the animal enables more appropriate data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input animal emotion data into the generation AI and have the generation AI adjust the collection timing.

[0075] The collection unit can analyze the animal's past behavioral history and select the optimal collection method. The selection of the optimal collection method is performed, for example, based on past data, but is not limited to such an example. For example, if a dog was active during a specific time period in the past, the collection unit collects data during that time period. Furthermore, if a cat often meowed at a specific location in the past, the collection unit can collect data at that location. Furthermore, if a bird reacted to a specific sound in the past, the collection unit can collect data using that sound. For example, if a dog was active during a specific time period in the past, the collection unit collects data during that time period. Furthermore, if a cat often meowed at a specific location in the past, the collection unit can collect data at that location. Furthermore, if a bird reacted to a specific sound in the past, the collection unit can collect data using that sound. In this way, the optimal collection method can be selected by analyzing the animal's past behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past behavioral data of animals into the generation AI and have the generation AI select the optimal collection method.

[0076] When collecting behaviors and sounds, the collection unit can filter the data based on the animal's current health condition and environment. Filtering can be performed, for example, based on health evaluation criteria and environmental conditions, but is not limited to these examples. For example, if a dog is not in good health, the collection unit can filter the collected data to prevent stress. If a cat is in a noisy environment, the collection unit can filter the collected data to remove noise. If a bird is in a cold environment, the collection unit can filter the collected data to consider the influence of the environment. For example, if a dog is not in good health, the collection unit can filter the collected data to prevent stress. If a cat is in a noisy environment, the collection unit can filter the collected data to remove noise. If a bird is in a cold environment, the collection unit can filter the collected data to consider the influence of the environment. Thus, filtering based on the animal's health condition and environment enables more accurate data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input animal health and environmental data into the generation AI and have the generation AI perform filtering.

[0077] When collecting behavior and sound, the collection unit can select the optimal collection means depending on the type of animal and individual differences. The selection of the optimal collection means is performed, for example, based on the type of animal and individual differences, but is not limited to such examples. For example, in the case of a dog, the collection unit collects both sound and behavior. In addition, in the case of a cat, the collection unit can mainly collect behavior and auxiliary collect sound. In addition, in the case of a bird, the collection unit can mainly collect sound and auxiliary collect behavior. For example, in the case of a dog, the collection unit collects both sound and behavior. In addition, in the case of a cat, the collection unit can mainly collect behavior and auxiliary collect sound. In addition, in the case of a bird, the collection unit can mainly collect sound and auxiliary collect behavior. This enables more appropriate data collection by selecting the optimal collection means depending on the type of animal and individual differences. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause the generation AI to select the collection means based on the type of animal and individual differences.

[0078] 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 determination of the priority of data is performed, for example, based on the intensity of the emotion or the importance of the data, but is not limited to such examples. For example, the collection unit can prioritize collecting voice data when a dog is excited. The collection unit can also prioritize collecting behavioral data when a cat is relaxed. The collection unit can also prioritize collecting environmental data when a bird is alert. For example, the collection unit can prioritize collecting voice data when a dog is excited. The collection unit can also prioritize collecting behavioral data when a cat is relaxed. The collection unit can also prioritize collecting environmental data when a bird is alert. Thus, by determining the priority of data to be collected based on the emotion of the animal, more important data can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the emotion data of the animal to the generation AI and cause the generation AI to determine the priority of the data.

[0079] When collecting behaviors and sounds, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the animals. Consideration of the geographical location information can be performed, for example, based on the use of GPS data or the analysis of location information, but is not limited to these examples. For example, if a dog is in a park, the collection unit can prioritize collecting behavioral data in the park. Furthermore, if a cat is inside a house, the collection unit can also prioritize collecting voice data inside the house. Furthermore, if a bird is in a forest, the collection unit can also prioritize collecting environmental data in the forest. For example, if a dog is in a park, the collection unit can prioritize collecting behavioral data in the park. Furthermore, if a cat is inside a house, the collection unit can also prioritize collecting voice data inside the house. Furthermore, if a bird is in a forest, the collection unit can also prioritize collecting environmental data in the forest. In this way, highly relevant data can be prioritized by taking into account the geographical location information of the animals. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without using AI. For example, the collection unit can input the geographic location information of animals into the generation AI and cause the generation AI to prioritize the collection of highly relevant data.

[0080] The collection unit can analyze the social behavior of animals and collect related data when collecting behaviors and sounds. Analysis of social behavior is performed based on, for example, group behavior and interactions with other animals, but is not limited to such examples. For example, the collection unit collects behavioral data when a dog is playing with other dogs. The collection unit can also collect audio data when a cat is communicating with other cats. The collection unit can also collect environmental data when birds are flying in a flock. For example, the collection unit collects behavioral data when a dog is playing with other dogs. The collection unit can also collect audio data when a cat is communicating with other cats. The collection unit can also collect environmental data when birds are flying in a flock. In this way, related data can be collected by analyzing the social behavior of animals. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the social behavior data of animals to the generation AI and cause the generation AI to collect related data.

[0081] When collecting behaviors and sounds, the collection unit can customize the collection method by reflecting past feedback from the animal. Reflecting the feedback can be performed, for example, based on evaluations of past data or user opinions, but is not limited to these examples. For example, if a dog reacted to a specific sound in the past, the collection unit can collect data using the sound. Furthermore, if a cat exhibited a specific behavior in the past, the collection unit can collect data based on the behavior. Furthermore, if a bird was active in a specific environment in the past, the collection unit can collect data in that environment. For example, if a dog reacted to a specific sound in the past, the collection unit can collect data using the sound. Furthermore, if a cat exhibited a specific behavior in the past, the collection unit can collect data based on the behavior. Furthermore, if a bird was active in a specific environment in the past, the collection unit can collect data in that environment. In this way, the collection method can be customized by reflecting past feedback from the animal. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input past feedback data from the animal into the generation AI and cause the generation AI to customize the collection method.

[0082] The analysis unit can estimate the emotion of the animal and adjust the way the analysis is presented based on the estimated emotion of the animal. The adjustment of the way the presentation is presented can be, for example, based on a change in the presentation depending on the intensity of the emotion, but is not limited to such an example. For example, the analysis unit can visually emphasize the analysis results when a dog is excited. The analysis unit can also display the analysis results calmly when a cat is relaxed. The analysis unit can also display the analysis results in detail when a bird is alert. For example, the analysis unit can visually emphasize the analysis results when a dog is excited. The analysis unit can also display the analysis results calmly when a cat is relaxed. The analysis unit can also display the analysis results in detail when a bird is alert. By adjusting the way the analysis is presented based on the emotion of the animal, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the emotion data of the animal into the generation AI and cause the generation AI to adjust the way the analysis is presented.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of behavior or sound. The adjustment of the level of detail is performed, for example, based on changing the depth of analysis according to the importance, but is not limited to such an example. For example, if a dog's bark is important, the analysis unit can perform a detailed analysis of the sound data. Furthermore, if a cat's behavior is important, the analysis unit can also perform a detailed analysis of the behavioral data. Furthermore, if a bird's environmental data is important, the analysis unit can also perform a detailed analysis of the environmental data. For example, if a dog's bark is important, the analysis unit can perform a detailed analysis of the sound data. Furthermore, if a cat's behavior is important, the analysis unit can also perform a detailed analysis of the behavioral data. Furthermore, if a bird's environmental data is important, the analysis unit can also perform a detailed analysis of the environmental data. In this way, by adjusting the level of detail of the analysis based on the importance of behavior or sound, more important data can be analyzed in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input behavioral and voice importance data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the type of animal and individual differences during analysis. The application of different analysis algorithms can be based on, for example, an algorithm for dogs, an algorithm for cats, etc., but is not limited to these examples. For example, the analysis unit can apply a specific bark analysis algorithm to dogs. The analysis unit can also apply a specific behavior analysis algorithm to cats. The analysis unit can also apply a specific environmental analysis algorithm to birds. For example, the analysis unit can apply a specific bark analysis algorithm to dogs. The analysis unit can also apply a specific behavior analysis algorithm to cats. The analysis unit can also apply a specific environmental analysis algorithm to birds. This enables more accurate analysis by applying different analysis algorithms depending on the type of animal and individual differences. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input an analysis algorithm based on the type of animal and individual differences into the generation AI and have the generation AI perform the analysis.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of the animal. Referencing past analysis results can be performed, for example, using a database or analyzing past trends, but is not limited to these examples. For example, the analysis unit can improve the current analysis accuracy by referring to past analysis results of a dog's bark. The analysis unit can also improve the current analysis accuracy by referring to past analysis results of a cat's behavior. The analysis unit can also improve the current analysis accuracy by referring to past analysis results of a bird's environment. For example, the analysis unit can improve the current analysis accuracy by referring to past analysis results of a dog's bark. The analysis unit can also improve the current analysis accuracy by referring to past analysis results of a cat's behavior. The analysis unit can also improve the current analysis accuracy by referring to past analysis results of a bird's environment. In this way, the analysis accuracy is improved by referring to past analysis results of the animal. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input past analysis result data of the animal into the generation AI and cause the generation AI to improve the analysis accuracy.

[0086] The analysis unit can estimate the emotion of the animal and adjust the length of the analysis based on the estimated emotion of the animal. The adjustment of the length of the analysis is performed, for example, by changing the analysis time according to the intensity of the emotion, but is not limited to such an example. For example, if a dog is excited, the analysis unit can perform a short analysis. Furthermore, if a cat is relaxed, the analysis unit can perform a long, detailed analysis. Furthermore, if a bird is alert, the analysis unit can perform a quick analysis. For example, if a dog is excited, the analysis unit can perform a short analysis. Furthermore, if a cat is relaxed, the analysis unit can perform a long, detailed analysis. Furthermore, if a bird is alert, the analysis unit can perform a quick analysis. This allows for more appropriate analysis by adjusting the length of the analysis based on the emotion of the animal. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the emotion data of the animal into the generation AI and cause the generation AI to adjust the length of the analysis.

[0087] During analysis, the analysis unit can determine the priority of analysis based on the time when the behaviors and sounds were collected. The priority determination is performed, for example, based on setting the priority based on the time of collection, but is not limited to such an example. For example, the analysis unit may prioritize analysis immediately after a dog's bark is collected. The analysis unit may also prioritize analysis immediately after a cat's behavior is collected. The analysis unit may also prioritize analysis immediately after a bird's environmental data is collected. For example, the analysis unit may prioritize analysis immediately after a dog's bark is collected. The analysis unit may also prioritize analysis immediately after a cat's behavior is collected. The analysis unit may also prioritize analysis immediately after a bird's environmental data is collected. By determining the priority of analysis based on the time when the behaviors and sounds were collected, more important data can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit may input data on the time when the behaviors and sounds were collected into the generation AI and cause the generation AI to determine the analysis priority.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of behaviors and sounds. Adjustment of the order is performed, for example, based on a change in the order based on the relevance, but is not limited to such an example. For example, if a dog's bark is related to the behavior, the analysis unit may prioritize analyzing the bark. Furthermore, if a cat's behavior is related to the environment, the analysis unit may prioritize analyzing the behavior. Furthermore, if a bird's sound is related to the environment, the analysis unit may prioritize analyzing the sound. For example, if a dog's bark is related to the behavior, the analysis unit may prioritize analyzing the bark. Furthermore, if a cat's behavior is related to the environment, the analysis unit may prioritize analyzing the behavior. Furthermore, if a bird's sound is related to the environment, the analysis unit may prioritize analyzing the sound. Thus, by adjusting the order of analysis based on the relevance of behaviors and sounds, more relevant data can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input behavioral and voice association data into the generation AI and have the generation AI adjust the order of analysis.

[0089] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the animal's health condition and environment. Adjustment of the use of technical terms is performed, for example, based on changes in terms according to the animal's health condition, but is not limited to such examples. For example, if a dog's health condition is poor, the analysis unit can display the analysis results while avoiding technical terms. Furthermore, if a cat is in a noisy environment, the analysis unit can display the analysis results while avoiding technical terms. Furthermore, if a bird is in a cold environment, the analysis unit can display the analysis results while avoiding technical terms. For example, if a dog's health condition is poor, the analysis unit can display the analysis results while avoiding technical terms. Furthermore, if a cat is in a noisy environment, the analysis unit can display the analysis results while avoiding technical terms. Furthermore, if a bird is in a cold environment, the analysis unit can display the analysis results while avoiding technical terms. By adjusting the use of technical terms in the analysis according to the animal's health condition and environment, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input animal health and environmental data into the generation AI and have the generation AI adjust the use of technical terms.

[0090] The display unit can estimate the emotion of the animal and adjust the display method based on the estimated emotion of the animal. The adjustment of the display method is performed, for example, by changing the display method according to the intensity of the emotion, but is not limited to such an example. For example, the display unit can provide a visually emphasized display method when a dog is excited. The display unit can also provide a calm display method when a cat is relaxed. The display unit can also provide a detailed display method when a bird is alert. For example, the display unit can provide a visually emphasized display method when a dog is excited. The display unit can also provide a calm display method when a cat is relaxed. The display unit can also provide a detailed display method when a bird is alert. This allows for a more appropriate display by adjusting the display method based on the emotion of the animal. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or without AI. For example, the display unit can input emotion data of the animal to a generation AI and cause the generation AI to adjust the display method.

[0091] The display unit can adjust the level of detail of the display based on the importance of the analysis results during display. Adjustment of the level of detail of the display is performed, for example, based on a change in the depth of the display according to the importance, but is not limited to this example. For example, the display unit can display detailed analysis results when a dog's bark is important. The display unit can also display detailed analysis results when a cat's behavior is important. The display unit can also display detailed analysis results when bird environmental data is important. For example, the display unit can display detailed analysis results when a dog's bark is important. The display unit can also display detailed analysis results when a cat's behavior is important. The display unit can also display detailed analysis results when bird environmental data is important. By adjusting the level of detail of the display based on the importance of the analysis results, more important information can be displayed in detail. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or without AI. For example, the display unit can input importance data of the analysis results to the generation AI and cause the generation AI to adjust the level of detail of the display.

[0092] The display unit can apply different display methods depending on the type of animal and individual differences when displaying. The application of different display methods can be based on, for example, a display method for dogs, a display method for cats, etc., but is not limited to such examples. For example, the display unit can provide a visually emphasized display method for dogs. The display unit can also provide a gentle display method for cats. The display unit can also provide a detailed display method for birds. For example, the display unit can provide a visually emphasized display method for dogs. The display unit can also provide a gentle display method for cats. The display unit can also provide a detailed display method for birds. This enables more appropriate display by applying different display methods depending on the type of animal and individual differences. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input a display method based on the type of animal and individual differences into the generation AI and have the generation AI execute the display.

[0093] The display unit can improve the accuracy of the display by referring to past display results of the animals when displaying the images. Referring to past display results may be performed, for example, using a database or analyzing past trends, but is not limited to these examples. For example, the display unit can improve the current display accuracy by referring to past display results of dogs. The display unit can also improve the current display accuracy by referring to past display results of cats. The display unit can also improve the current display accuracy by referring to past display results of birds. For example, the display unit can improve the current display accuracy by referring to past display results of dogs. The display unit can also improve the current display accuracy by referring to past display results of cats. The display unit can also improve the current display accuracy by referring to past display results of birds. In this way, the accuracy of the display is improved by referring to past display results of animals. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input past display result data of animals to the generation AI and cause the generation AI to improve the display accuracy.

[0094] The display unit can estimate the emotion of the animal and adjust the length of the display based on the estimated emotion. Adjustment of the length of the display can be performed, for example, by changing the display time according to the intensity of the emotion, but is not limited to this example. For example, if the dog is excited, the display unit can display a short, to-the-point message. Furthermore, if the cat is relaxed, the display unit can display a longer message with detailed explanations. Furthermore, if the bird is wary, the display unit can display a quick, concise message. For example, if the dog is excited, the display unit can display a short, to-the-point message. Furthermore, if the cat is relaxed, the display unit can display a longer message with detailed explanations. Furthermore, if the bird is wary, the display unit can display a quick, concise message. Adjusting the length of the display based on the emotion of the animal enables a more appropriate display. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input the emotion data of the animal into a generation AI and have the generation AI adjust the length of the display.

[0095] The display unit can determine the display priority based on the collection time of the analysis results when displaying the analysis results. The priority determination is performed, for example, based on a priority setting based on the collection time, but is not limited to such an example. For example, the display unit prioritizes display immediately after a dog's bark is collected. The display unit can also prioritize display immediately after a cat's behavior is collected. The display unit can also prioritize display immediately after a bird's environmental data is collected. For example, the display unit prioritizes display immediately after a dog's bark is collected. The display unit can also prioritize display immediately after a cat's behavior is collected. The display unit can also prioritize display immediately after a bird's environmental data is collected. In this way, by determining the display priority based on the collection time of the analysis results, more important information can be displayed preferentially. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data on the collection time of the analysis results to the generation AI and cause the generation AI to determine the display priority.

[0096] The display unit can adjust the display order based on the relevance of the analysis results when displaying the results. The adjustment of the order is performed, for example, based on a change in the order based on the relevance, but is not limited to such an example. For example, if a dog's bark and its behavior are related, the display unit can prioritize displaying the bark. Furthermore, if a cat's behavior and its environment are related, the display unit can prioritize displaying the behavior. Furthermore, if a bird's sound and its environment are related, the display unit can prioritize displaying the sound. For example, if a dog's bark and its behavior are related, the display unit can prioritize displaying the bark. Furthermore, if a cat's behavior and its environment are related, the display unit can prioritize displaying the behavior. Furthermore, if a bird's sound and its environment are related, the display unit can prioritize displaying the sound. In this way, by adjusting the display order based on the relevance of the analysis results, more relevant information can be displayed preferentially. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the relevance data of the analysis results to the generation AI and cause the generation AI to adjust the display order.

[0097] The display unit can display specific application examples in animal protection activities. Specific application examples include, but are not limited to, examples of use in a shelter and monitoring of wild animals. The display unit can display, for example, an example of use in a shelter. The display unit can also display an example of monitoring wild animals. The display unit can also display specific application examples in animal protection activities. For example, the display unit can display an example of use in a shelter. The display unit can also display an example of monitoring wild animals. The display unit can also display specific application examples in animal protection activities. In this way, by displaying specific application examples in animal protection activities, protection activities can be carried out more effectively. Some or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can display specific application examples in animal protection activities using generation AI. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and display 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 animal sounds and movements using the microphone 38B and camera 42 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and infers the thoughts and emotions of the animal. The display unit displays the analysis results in text or audio using the display 40A and speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and display 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 animal sounds and movements using the microphone 238 and camera 42 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and infers the thoughts and emotions of the animal. The display unit displays the analysis results by voice using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and display 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 animal sounds and movements using the microphone 238 and camera 42 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and infers the thoughts and emotions of the animal. The display unit displays the analysis results in text or audio using the display 343 and speaker 240 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and display 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 the animal's cries and movements using the microphone 238 and camera 42 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to infer the animal's thoughts and emotions. The display unit displays the analysis results audibly and visually using the speaker 240 and LEDs in the eyes of the robot 414.

[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] When collecting the behavior and sounds of an animal, the collection unit can also simultaneously collect biological information such as the animal's body temperature and heart rate. For example, when collecting the barking of a dog, the collection unit can measure the body temperature of the dog. Also, when collecting the movements of a cat, the collection unit can measure the cat's heart rate. Also, when collecting a specific gesture of a bird, the collection unit can measure the bird's breathing rate. In this way, by simultaneously collecting the behavior and sounds of an animal and biological information, more detailed data can be obtained.

[0100] The analysis unit can refer to the animal's past health records and medical data when analyzing the collected data. For example, the analysis unit can refer to past health checkup results when analyzing a dog's bark. The analysis unit can also refer to the animal's past medical history when analyzing a cat's behavior. The analysis unit can also refer to past treatment records when analyzing a bird's specific gestures. This allows for more accurate analysis by referring to the animal's past health records and medical data.

[0101] When displaying the analysis results, the display unit can display advice and suggestions based on the animal's behavior and sound. For example, the display unit can suggest appropriate care methods to the owner based on the analysis results of a dog's bark. The display unit can also provide the owner with advice on how to play with the cat based on the analysis results of the cat's behavior. The display unit can also suggest ways to improve the bird's environment to the owner based on the analysis results of a specific bird gesture. In this way, by displaying advice and suggestions based on the analysis results, the owner can take more appropriate measures.

[0102] When collecting animal behavior and sounds, the collection unit can simultaneously collect environmental data such as ambient sounds, temperature, and humidity. For example, the collection unit records ambient sounds when collecting dog barks. The collection unit can also measure ambient temperature when collecting cat movements. The collection unit can also measure ambient humidity when collecting specific bird gestures. This allows for more detailed data to be obtained by simultaneously collecting animal behavior and sounds and environmental data.

[0103] When analyzing the collected data, the analysis unit can learn animal behavior and sound patterns and detect abnormal behavior and sound. For example, the analysis unit can learn dog bark patterns and detect abnormal barks. The analysis unit can also learn cat behavior patterns and detect abnormal movements. The analysis unit can also learn specific bird gesture patterns and detect abnormal gestures. In this way, by learning animal behavior and sound patterns, abnormal behavior and sound can be detected early.

[0104] The analysis unit can estimate the animal's emotion and evaluate the animal's stress level based on the estimated emotion. For example, the analysis unit can analyze a dog's bark and evaluate whether the dog is feeling stressed. The analysis unit can also analyze a cat's behavior and evaluate the cat's stress level. The analysis unit can also analyze a bird's specific gestures and evaluate the bird's stress level. As a result, by evaluating the stress level based on the animal's emotion, owners can more accurately understand the health condition of their pet.

[0105] When displaying the analysis results, the display unit can display encouraging or comforting messages for the owner based on the animal's emotions. For example, if a dog is feeling stressed, the display unit can display an encouraging message for the owner. Also, if a cat is relaxed, the display unit can display a comforting message for the owner. Also, if a bird is wary, the display unit can display a reassuring message for the owner. In this way, by displaying messages for the owner based on the animal's emotions, the owner can more easily understand the animal's condition.

[0106] The collection unit can estimate the emotion of the animal and select the type of data to collect based on the estimated emotion of the animal. For example, if a dog is excited, the collection unit can prioritize collecting audio data. Also, if a cat is relaxed, the collection unit can prioritize collecting behavioral data. Also, if a bird is alert, the collection unit can prioritize collecting environmental data. In this way, by selecting the type of data to collect based on the emotion of the animal, more appropriate data collection is possible.

[0107] The analysis unit can estimate the emotion of the animal and adjust the accuracy of the analysis based on the estimated emotion of the animal. For example, if a dog is excited, the analysis unit performs a detailed analysis. If a cat is relaxed, the analysis unit can also perform a simple analysis. If a bird is alert, the analysis unit can also perform a quick analysis. In this way, adjusting the accuracy of the analysis based on the emotion of the animal enables more appropriate analysis.

[0108] When displaying the analysis results, the display unit can determine the priority of the information to be displayed based on the emotion of the animal. For example, if a dog is excited, the display unit can prioritize displaying the analysis results of the voice data. Also, if a cat is relaxed, the display unit can prioritize displaying the analysis results of the behavior data. Also, if a bird is alert, the display unit can prioritize displaying the analysis results of the environmental data. In this way, by determining the priority of the information to be displayed based on the emotion of the animal, more important information can be displayed preferentially.

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

[0110] Step 1: The collection unit collects animal behavior or sound. The animal behavior or sound includes, for example, cries, movements, specific gestures, etc. The collection unit can collect animal cries using a microphone, collect animal movements using a camera, and collect specific animal gestures using an acceleration sensor. Step 2: The analysis unit analyzes the data collected by the collection unit and infers the animal's thoughts and emotions. This is done using machine learning algorithms and statistical methods. The analysis unit can also infer the animal's thoughts and emotions by preprocessing the data and extracting features. Step 3: The display unit displays the thoughts and emotions estimated by the analysis unit. The display is performed as text or audio. The display unit displays the analysis results as text on a screen. The display unit can also display the analysis results as audio and play it back using speech synthesis.

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

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

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

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

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

[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 for collecting animal behavior or sound; an analysis unit that analyzes the data collected by the collection unit and estimates the thoughts and feelings of the animal; a display unit that displays the thoughts and feelings estimated by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Collect animal behavior or sounds with sensors 2. The system of claim 1.

3. The analysis unit Analyzing the collected data to infer the thoughts and emotions of animals 2. The system of claim 1.

4. The display unit Display analysis results in text or audio 2. The system of claim 1.

5. The analysis unit Use different analysis methods for different animal species 2. The system of claim 1.

6. The display unit Show specific examples of applications in animal protection activities 2. The system of claim 1.

7. The collecting unit Estimate the emotion of the animal and adjust the timing of collecting behavior and sound based on the estimated emotion of the animal.

2. The system of claim 1.

8. The collecting unit Analyzing the animal's past behavioral history and selecting the most appropriate collection method 2. The system of claim 1.

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