System

Abstract

An object of a system according to an embodiment is to translate a cry of an animal into human language and support communication with the animal.SOLUTION: A system includes a voice collection unit, an analysis unit, and a translation unit. The sound collection unit collects a cry of an animal. The analysis part analyzes the cries of the animals collected by the sound collection part. The translation unit translates the cry of the animal into human words based on the voice data analyzed 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 been unable to translate animal sounds into human language, making it difficult to communicate with animals.

[0005] The system according to the embodiment aims to translate animal sounds into human language and support communication with animals. [Means for solving the problem]

[0006] The system according to the embodiment includes a sound collection unit, an analysis unit, and a translation unit. The sound collection unit collects animal sounds. The analysis unit analyzes the animal sounds collected by the sound collection unit. The translation unit translates the animal sounds into human language based on the sound data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can translate animal sounds into human language and support communication with animals. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 support system according to an embodiment of the present invention is a system that translates animal cries into human language and supports communication with animals. As a result, the conversation support system can translate animal cries into human language and support communication with animals.

[0029] A conversation support system according to an embodiment includes a sound collection unit, an analysis unit, and a translation unit. The sound collection unit collects animal sounds. For example, the sound collection unit collects animal sounds using a microphone. The sound collection unit can also collect animal sounds using a recording device. The sound collection unit can also collect animal sounds using a smartphone microphone. The analysis unit analyzes the animal sounds collected by the sound collection unit. For example, the analysis unit analyzes the animal sounds using audio signal processing technology. The analysis unit can also analyze the animal sounds using a machine learning algorithm. The analysis unit can also identify characteristics of the animal sounds, such as frequency, volume, and rhythm. The translation unit translates the animal sounds into human language based on the audio data analyzed by the analysis unit. For example, the translation unit translates the animal sounds into human language using a generation AI (e.g., a text generation AI or a multimodal generation AI). The translation unit can also convert the meaning of the animal sounds into human language. The translation unit can also convert animal cries that have meanings such as "I'm hungry" or "I want to play" into human words. This allows the conversation support system according to the embodiment to translate animal cries into human words and support communication with animals. For example, the user can understand what the animal is trying to convey. The user can also understand the animal's feelings and requests. The user can also smoothly communicate with animals.

[0030] The audio collection unit can incorporate filtering technology that automatically removes environmental sounds and background noise when collecting animal sounds. For example, when collecting animal sounds, the audio collection unit incorporates filtering technology that analyzes the surrounding environmental sounds and background noise in real time and removes noise components. For example, wind noise and the cries of other animals are removed. The audio collection unit also uses filtering technology that emphasizes specific frequency bands and reduces noise when collecting animal sounds. For example, the frequency band of animal sounds is emphasized to suppress background noise. The audio collection unit also develops an algorithm that automatically detects environmental sounds and background noise and removes noise components in real time. For example, the audio collection unit separates animal sounds from noise to obtain pure animal sound data. This allows for the acquisition of purer audio data.

[0031] The analysis unit can analyze the meaning of an animal's cries based on the animal's characteristic data. For example, the analysis unit adds past health checkup results and medical history to the animal's characteristic data and builds a system that analyzes the meaning of the animal's cries based on its health condition. For example, the analysis unit identifies the meaning of the cries based on medical history data. The analysis unit also integrates health checkup results and medical history data with the animal's characteristic data and analyzes the cries based on its health condition. For example, it analyzes cries related to specific diseases. The analysis unit also monitors the animal's health condition in real time and develops a system that analyzes the meaning of the cries based on that data. For example, it identifies the meaning of the cries depending on changes in the animal's health condition. This makes it possible to analyze the meaning of the animal's cries based on the animal's characteristic data.

[0032] The translation unit can automatically translate the translation results into different languages, making it possible to accommodate international users. The translation unit, for example, builds a system that automatically translates the translation results into different languages, making it possible to accommodate international users. For example, it translates into multiple languages ​​such as English, French, and Chinese. The translation unit also uses an automatic translation algorithm to convert the translation results into different languages ​​and provide them to international users. For example, it displays the translation results on a multilingual platform. The translation unit also develops a system that automatically translates the translation results into different languages, making it possible to accommodate international users. For example, it displays the translation results in a multilingual application. This makes it possible to accommodate international users.

[0033] The translation unit can provide the user with the translation results visually or audibly, thereby facilitating understanding through sight and hearing. The translation unit, for example, builds a system that provides the user with the translation results visually or audibly. For example, the translation results are displayed not only as text but also as sound or images. The translation unit also provides the user with the translation results visually or audibly, thereby facilitating understanding through sight and hearing. For example, the translation results are displayed as animations or sound. The translation unit also develops a system that provides the user with the translation results visually or audibly, thereby facilitating understanding through sight and hearing. For example, the translation results are displayed in an interactive format. This can facilitate understanding through sight and hearing.

[0034] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0035] The conversation support system can also be equipped with a health monitoring unit that monitors the health of animals. For example, the health monitoring unit collects biological data such as the animal's body temperature, heart rate, and respiratory rate in real time and provides it to the analysis unit. The health monitoring unit can also monitor the animal's activity level and sleep patterns and issue an alert if an abnormality is detected. Furthermore, the health monitoring unit can also build a system that evaluates the animal's health based on the collected data and notifies a veterinarian as necessary. This allows for more effective animal health management.

[0036] The conversation support system can further include a behavior monitoring unit that monitors the behavior of animals. For example, the behavior monitoring unit can monitor the animal's movements in real time using cameras and sensors to detect abnormal behavior. The behavior monitoring unit can also analyze the animal's behavioral patterns and detect early signs of stress or anxiety. Furthermore, the behavior monitoring unit can also build a system that accumulates animal behavior data and tracks long-term behavioral changes. This makes it possible to take appropriate measures based on the animal's behavior.

[0037] The conversation support system can further include a preference learning unit that learns the animal's preferences. For example, the preference learning unit records the animal's reactions to specific sounds or foods and learns the animal's preferences based on that data. The preference learning unit can also make suggestions that take the animal's preferences into consideration. For example, it can play the animal's favorite music or provide the animal's favorite food. The preference learning unit can also build a system that accumulates the animal's preference data and tracks changes in preferences over time. This allows for the provision of more personalized services based on the animal's preferences.

[0038] The conversation support system can further include a social relationship analysis unit that analyzes the social relationships of animals. For example, the social relationship analysis unit can analyze how animals interact with other animals and humans and evaluate their social relationships. The social relationship analysis unit can also analyze the social behavior patterns of animals and detect early signs of isolation or stress. Furthermore, the social relationship analysis unit can build a system that accumulates data on the social relationships of animals and tracks long-term changes in those relationships. This can enable more effective management of the social health of animals.

[0039] The conversation support system can further include a learning evaluation unit that evaluates the learning ability of the animal. For example, the learning evaluation unit can evaluate how quickly the animal learns new tricks or commands and adjust the learning program based on the data. The learning evaluation unit can also monitor the animal's learning progress in real time and provide feedback as needed. Furthermore, the learning evaluation unit can build a system that accumulates the animal's learning data and tracks changes in learning ability over the long term. This enables effective training based on the animal's learning ability.

[0040] The processing flow of the first embodiment will be briefly explained below.

[0041] Step 1: The sound collection unit collects animal sounds. For example, the sound collection unit collects animal sounds using a microphone, a recording device, or a microphone on a smartphone. Step 2: The analysis unit analyzes the animal sounds collected by the sound collection unit. For example, the analysis unit may use audio signal processing technology or machine learning algorithms to analyze the animal sounds and identify characteristics such as frequency, volume, and rhythm. Step 3: The translation unit translates the animal sounds into human words based on the audio data analyzed by the analysis unit. For example, the translation unit uses generative AI (text generation AI or multimodal generation AI) to convert the animal sounds into human words such as "I'm hungry" or "I want to play."

[0042] (Example 2) A conversation support system according to an embodiment of the present invention is a system that translates animal cries into human language and supports communication with animals. As a result, the conversation support system can translate animal cries into human language and support communication with animals.

[0043] A conversation support system according to an embodiment includes a sound collection unit, an analysis unit, and a translation unit. The sound collection unit collects animal sounds. For example, the sound collection unit collects animal sounds using a microphone. The sound collection unit can also collect animal sounds using a recording device. The sound collection unit can also collect animal sounds using a smartphone microphone. The analysis unit analyzes the animal sounds collected by the sound collection unit. For example, the analysis unit analyzes the animal sounds using audio signal processing technology. The analysis unit can also analyze the animal sounds using a machine learning algorithm. The analysis unit can also identify characteristics of the animal sounds, such as frequency, volume, and rhythm. The translation unit translates the animal sounds into human language based on the audio data analyzed by the analysis unit. For example, the translation unit translates the animal sounds into human language using a generation AI (e.g., a text generation AI or a multimodal generation AI). The translation unit can also convert the meaning of the animal sounds into human language. The translation unit can also convert animal cries that have meanings such as "I'm hungry" or "I want to play" into human words. This allows the conversation support system according to the embodiment to translate animal cries into human words and support communication with animals. For example, the user can understand what the animal is trying to convey. The user can also understand the animal's feelings and requests. The user can also smoothly communicate with animals.

[0044] The audio collection unit can incorporate filtering technology that automatically removes environmental sounds and background noise when collecting animal sounds. For example, when collecting animal sounds, the audio collection unit incorporates filtering technology that analyzes the surrounding environmental sounds and background noise in real time and removes noise components. For example, wind noise and the cries of other animals are removed. The audio collection unit also uses filtering technology that emphasizes specific frequency bands and reduces noise when collecting animal sounds. For example, the frequency band of animal sounds is emphasized to suppress background noise. The audio collection unit also develops an algorithm that automatically detects environmental sounds and background noise and removes noise components in real time. For example, the audio collection unit separates animal sounds from noise to obtain pure animal sound data. This allows for the acquisition of purer audio data.

[0045] The analysis unit can estimate the emotional state of an animal in real time and classify audio data based on that emotion. For example, the analysis unit analyzes the emotional state of an animal in real time when collecting animal cries and calculates an emotion score. For example, emotions such as joy, anger, and sadness are classified. The analysis unit also analyzes the animal's facial expressions and behavior simultaneously with the animal's cries to build a system that estimates the emotional state. For example, the analysis unit analyzes the animal's facial expressions and movements using a camera or sensor. The analysis unit also uses an emotion estimation algorithm to classify the animal's cry data based on the emotional state and store the data in a database. For example, cry data with a high emotion score is preferentially stored. This makes it possible to classify audio data based on the animal's emotion.

[0046] The analysis unit can analyze the meaning of an animal's cries based on the animal's characteristic data. For example, the analysis unit adds past health checkup results and medical history to the animal's characteristic data and builds a system that analyzes the meaning of the animal's cries based on its health condition. For example, the analysis unit identifies the meaning of the cries based on medical history data. The analysis unit also integrates health checkup results and medical history data with the animal's characteristic data and analyzes the cries based on its health condition. For example, it analyzes cries related to specific diseases. The analysis unit also monitors the animal's health condition in real time and develops a system that analyzes the meaning of the cries based on that data. For example, it identifies the meaning of the cries depending on changes in the animal's health condition. This makes it possible to analyze the meaning of the animal's cries based on the animal's characteristic data.

[0047] The translation unit can provide emotion-based translation results based on the emotional state. For example, when translating animal sounds, the translation unit builds a system that analyzes the emotional state of an animal in real time and provides translation results based on that emotion. For example, if the animal is happy, a positive translation result is provided. The translation unit also develops an algorithm that estimates the emotional state of an animal and generates translation results based on that emotion. For example, if the animal is feeling anxious, an appropriate translation result is provided. The translation unit also develops a system that translates the animal's sound based on the emotion estimation data and provides translation results based on the emotion. For example, if the animal is excited, a translation result that reflects that emotion is provided. This makes it possible to provide translation results based on the animal's emotion.

[0048] The translation unit can automatically translate the translation results into different languages, making it possible to accommodate international users. The translation unit, for example, builds a system that automatically translates the translation results into different languages, making it possible to accommodate international users. For example, it translates into multiple languages ​​such as English, French, and Chinese. The translation unit also uses an automatic translation algorithm to convert the translation results into different languages ​​and provide them to international users. For example, it displays the translation results on a multilingual platform. The translation unit also develops a system that automatically translates the translation results into different languages, making it possible to accommodate international users. For example, it displays the translation results in a multilingual application. This makes it possible to accommodate international users.

[0049] The translation unit can provide the user with the translation results visually or audibly, thereby facilitating understanding through sight and hearing. The translation unit, for example, builds a system that provides the user with the translation results visually or audibly. For example, the translation results are displayed not only as text but also as sound or images. The translation unit also provides the user with the translation results visually or audibly, thereby facilitating understanding through sight and hearing. For example, the translation results are displayed as animations or sound. The translation unit also develops a system that provides the user with the translation results visually or audibly, thereby facilitating understanding through sight and hearing. For example, the translation results are displayed in an interactive format. This can facilitate understanding through sight and hearing.

[0050] The translation unit uses the emotion estimation function to add emotional nuances to the translation results, thereby enabling natural conversation. The translation unit, for example, uses the emotion estimation function to build a system that adds emotional nuances to the translation results. For example, it provides translation results that reflect the emotional state of animals. The translation unit also develops an algorithm that adds emotional nuances to the translation results, thereby enabling more natural conversation. For example, it generates translation results that reflect the emotions of animals. The translation unit also develops a system that adds emotional nuances to the translation results based on the emotion estimation data, thereby enabling more natural conversation. For example, it provides translation results that reflect the emotional state of animals. This enables more natural conversation.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The conversation support system can also be equipped with a health monitoring unit that monitors the health of animals. For example, the health monitoring unit collects biological data such as the animal's body temperature, heart rate, and respiratory rate in real time and provides it to the analysis unit. The health monitoring unit can also monitor the animal's activity level and sleep patterns and issue an alert if an abnormality is detected. Furthermore, the health monitoring unit can also build a system that evaluates the animal's health based on the collected data and notifies a veterinarian as necessary. This allows for more effective animal health management.

[0053] The conversation support system can further include a behavior monitoring unit that monitors the behavior of animals. For example, the behavior monitoring unit can monitor the animal's movements in real time using cameras and sensors to detect abnormal behavior. The behavior monitoring unit can also analyze the animal's behavioral patterns and detect early signs of stress or anxiety. Furthermore, the behavior monitoring unit can also build a system that accumulates animal behavior data and tracks long-term behavioral changes. This makes it possible to take appropriate measures based on the animal's behavior.

[0054] The conversation support system can further include a preference learning unit that learns the animal's preferences. For example, the preference learning unit records the animal's reactions to specific sounds or foods and learns the animal's preferences based on that data. The preference learning unit can also make suggestions that take the animal's preferences into consideration. For example, it can play the animal's favorite music or provide the animal's favorite food. The preference learning unit can also build a system that accumulates the animal's preference data and tracks changes in preferences over time. This allows for the provision of more personalized services based on the animal's preferences.

[0055] The conversation support system can further include a social relationship analysis unit that analyzes the social relationships of animals. For example, the social relationship analysis unit can analyze how animals interact with other animals and humans and evaluate their social relationships. The social relationship analysis unit can also analyze the social behavior patterns of animals and detect early signs of isolation or stress. Furthermore, the social relationship analysis unit can build a system that accumulates data on the social relationships of animals and tracks long-term changes in those relationships. This can enable more effective management of the social health of animals.

[0056] The conversation support system can further include a learning evaluation unit that evaluates the learning ability of the animal. For example, the learning evaluation unit can evaluate how quickly the animal learns new tricks or commands and adjust the learning program based on the data. The learning evaluation unit can also monitor the animal's learning progress in real time and provide feedback as needed. Furthermore, the learning evaluation unit can build a system that accumulates the animal's learning data and tracks changes in learning ability over the long term. This enables effective training based on the animal's learning ability.

[0057] The conversation support system can further include a music providing unit that estimates the emotional state of the animal and plays appropriate music based on that emotion. For example, if the animal is relaxed, calm music can be played. If the animal is excited, energetic music can be played. Furthermore, the music providing unit can be configured to monitor the emotional state of the animal in real time and automatically switch music according to changes. This makes it possible to provide music that matches the emotional state of the animal.

[0058] The conversation support system can further include a play suggestion unit that estimates the emotional state of the animal and suggests appropriate games based on that emotion. For example, if the animal is bored, it can suggest a new toy or way to play. Also, if the animal is feeling stressed, it can suggest a game that will help the animal relax. Furthermore, the play suggestion unit can be configured to monitor the animal's emotional state in real time and automatically update the suggested games in response to changes. This makes it possible to suggest games that suit the animal's emotional state.

[0059] The conversation support system can further include a meal suggestion unit that estimates the emotional state of the animal and suggests an appropriate meal based on that emotion. For example, if the animal is feeling stressed, it can suggest a meal that has a relaxing effect. Also, if the animal is feeling listless, it can suggest a nutritious meal. Furthermore, the meal suggestion unit can be configured to monitor the emotional state of the animal in real time and automatically update the meal suggestions according to changes. This makes it possible to suggest meals according to the emotional state of the animal.

[0060] The conversation support system can further include an environment setting unit that estimates the emotional state of the animal and sets appropriate environmental settings based on that emotion. For example, if the animal is relaxed, the lighting can be softened. If the animal is excited, the environmental sounds can be adjusted. Furthermore, the environment setting unit can be configured to monitor the emotional state of the animal in real time and automatically adjust the environmental settings according to changes. This makes it possible to set the environment according to the emotional state of the animal.

[0061] The conversation support system can further include a care suggestion unit that estimates the emotional state of the animal and suggests appropriate care based on that emotion. For example, if the animal is feeling anxious, it can suggest a relaxing massage. Or, if the animal is tired, it can suggest care that encourages rest. Furthermore, the care suggestion unit can be configured to monitor the emotional state of the animal in real time and automatically update the care suggestions according to changes. This makes it possible to suggest care that suits the emotional state of the animal.

[0062] The processing flow of the second embodiment will be briefly explained below.

[0063] Step 1: The sound collection unit collects animal sounds. For example, the sound collection unit collects animal sounds using a microphone, a recording device, or a microphone on a smartphone. Step 2: The analysis unit analyzes the animal sounds collected by the sound collection unit. For example, the analysis unit may use audio signal processing technology or machine learning algorithms to analyze the animal sounds and identify characteristics such as frequency, volume, and rhythm. Step 3: The translation unit translates the animal sounds into human words based on the audio data analyzed by the analysis unit. For example, the translation unit uses generative AI (text generation AI or multimodal generation AI) to convert the animal sounds into human words such as "I'm hungry" or "I want to play."

[0064] 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.

[0065] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0066] 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.

[0067] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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).

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0077] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0092] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0098] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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."

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0131] 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 sound collection unit that collects animal sounds; an analysis unit that analyzes the animal sounds collected by the sound collection unit; a translation unit that translates animal cries into human language based on the audio data analyzed by the analysis unit. A system characterized by:

2. The sound collection unit When collecting the animal sounds, filtering technology will be introduced to automatically remove environmental and background noise.

2. The system of claim 1.

3. The analysis unit Estimating the emotional state of an animal in real time and classifying the audio data based on the emotion.

2. The system of claim 1.

4. The translation unit Automatically translate the results into different languages ​​to accommodate international users 2. The system of claim 1.

5. The translation unit Provides emotion-based translation results based on emotional state 2. The system of claim 1.

Images