system

Abstract

The system according to the embodiment aims to analyze the intentions and emotions of infants, caregivers, and animals, and to promote mutual communication. [Solution] A system according to an embodiment includes a voice input unit, a voice analysis unit, and a display unit. The voice input unit captures voice. The voice analysis unit analyzes the voice captured by the voice input unit and identifies intentions and emotions. The display unit displays the emotions identified by the voice analysis unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to accurately grasp the intentions and emotions of infants, caregivers, and animals, and there was a problem that mutual communication was not sufficient.

[0005] The system according to the embodiment aims to analyze the intentions and emotions of infants, caregivers, and animals, and to promote mutual communication. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice input unit, a voice analysis unit, and a display unit. The voice input unit captures voice. The voice analysis unit analyzes the voice captured by the voice input unit and identifies intentions and emotions. The display unit displays the emotions identified by the voice analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the intentions and emotions of infants, caregivers, and animals, and promote mutual communication. [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 communication system according to an embodiment of the present invention captures the voices of infants, caregivers, and animals (pets), analyzes their intentions and emotions, and enables mutual communication. This communication system captures the voices of infants, caregivers, or animals, analyzes their intentions and emotions, and provides the results to a user. For example, a voice input unit captures the voice of an infant, caregiver, or animal. A voice analysis unit then analyzes the captured voice to identify the intentions and emotions. For example, if an infant is crying, the crying pattern is analyzed to identify emotions such as hunger, sleepiness, or pain. Similarly, the voices of caregivers and animals are analyzed to identify their intentions and emotions. The analyzed intentions and emotions are displayed on a display unit and provided to the user. For example, if an infant is crying because they are hungry, the word "hunger" is displayed, allowing the user to take appropriate action based on this information. Furthermore, if a caregiver is in pain, the word "pain" is displayed, allowing appropriate care to be provided. Similarly, in the case of animals, their intentions and emotions are displayed, allowing the user to respond appropriately. This system enables mutual communication with infants, people who are unable to speak due to illness and caregiving, and animals, and allows appropriate responses to be taken. This communication system enables mutual communication with infants, people who are unable to speak due to illness and caregiving, and animals, and allows appropriate responses to be taken.

[0029] A communication system according to an embodiment includes a voice input unit, a voice analysis unit, and a display unit. The voice input unit captures voices from infants, people with impaired speech due to illness, or animals. The voice input unit can capture voices using, for example, a microphone or a voice recognition device. The voice input unit can also accept voice input from a smartphone or other device. For example, voices can be captured using a smartphone's microphone and transmitted to the system via Bluetooth (registered trademark) or Wi-Fi. The voice input unit also has a function for filtering environmental sounds and removing noise. For example, noise canceling technology can be used to filter ambient noise in real time to obtain clear voices. Furthermore, the voice input unit can integrate data from multiple voice input devices to improve analysis accuracy. For example, audio data from multiple microphones can be integrated to improve analysis accuracy. The voice analysis unit uses a generative AI to analyze the captured voice and identify the intention and emotion at that time. The generative AI analyzes the voice using a deep learning model or a natural language processing model. For example, the generative AI can analyze voice characteristics such as tone, pitch, and rhythm to identify the intention and emotion. The voice analysis unit can improve the accuracy of the analysis by storing the analysis results in the cloud and comparing them with past data. For example, the analysis results can be stored using a cloud storage service, and security can be ensured using data encryption technology. The display unit visually displays the analysis results and also provides audio feedback. The display unit can visually display the analysis results using a display or LED. For example, if an infant is crying because they are hungry, it will display "Hungry." The display unit can also provide audio feedback of the analysis results using a speaker or voice synthesis technology. For example, it can provide audio feedback such as "I'm hungry." This allows the communication system according to the embodiment to realize mutual communication with infants, people who are caring for their sick, and animals, and to take appropriate measures.

[0030] The audio input unit can accept audio input from a smartphone or other device. For example, the audio input unit can capture audio using a microphone on the smartphone and transmit it to the system via Bluetooth or Wi-Fi. For example, the audio input unit can capture audio using a microphone on the smartphone and transmit it to the system via Bluetooth or Wi-Fi. The audio input unit can also accept audio input from other devices. For example, it can accept audio input from a smart speaker or a wearable device. This enables flexible audio input by accepting audio input from a smartphone or other device. Some or all of the above-described processing in the audio input unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio input unit can input audio data captured by a smartphone microphone to a generation AI and have the generation AI analyze the audio data.

[0031] The voice analysis unit uses a generative AI to analyze voices and learn the characteristics of the voices of infants, people requiring care due to illness, and animals. The voice analysis unit uses a generative AI to analyze the captured voice and identify the intentions and emotions at that time. The generative AI analyzes voices using a deep learning model or a natural language processing model. For example, the generative AI analyzes voice characteristics such as tone, pitch, and rhythm to identify the intentions and emotions. The generative AI can learn the characteristics of the voices of infants, people requiring care due to illness, and animals. For example, the generative AI learns the crying patterns of infants and identifies emotions such as hunger, sleepiness, and pain. Similarly, the generative AI can learn the voices of people requiring care due to illness and animals and identify their intentions and emotions. In this way, the use of generative AI improves the accuracy of voice analysis and can learn the characteristics of the voices of infants, people requiring care due to illness, and animals. Some or all of the above-mentioned processing in the voice analysis unit may be performed using AI, for example, or without AI. For example, the voice analysis unit can input the captured voice data into the generation AI and have the generation AI analyze the voice data.

[0032] The display unit can visually display the analysis results and also provide audio feedback. The display unit can visually display the analysis results using a display or LED display. For example, if an infant is hungry and crying, it displays "hungry." The display unit can also provide audio feedback of the analysis results using a speaker or voice synthesis technology. For example, it can provide audio feedback such as "I'm hungry." This not only visually displays the analysis results but also provides audio feedback, thereby diversifying the information provided to the user. Some or all of the above-mentioned 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 analysis results to a generation AI and have the generation AI perform visual display and audio feedback.

[0033] The voice analysis unit can improve the accuracy of the analysis by storing the analysis results in the cloud and comparing them with past data. The voice analysis unit can improve the accuracy of the analysis by storing the analysis results in the cloud and comparing them with past data. For example, the analysis results are stored using a cloud storage service and security is ensured using data encryption technology. The voice analysis unit can improve the accuracy of the analysis by comparing them with past data. For example, the voice analysis unit compares past analysis results with current analysis results and improves the analysis algorithm. In this way, the analysis results are stored in the cloud and compared with past data, thereby improving the accuracy of the analysis. Some or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit can improve the accuracy of the analysis by storing the analysis results in the cloud and having a generating AI compare them with past data.

[0034] The audio input unit can filter environmental sounds and remove noise when inputting audio. The audio input unit can filter environmental sounds and remove noise when inputting audio. For example, the audio input unit can filter ambient noise in real time to obtain clear audio. The audio input unit can also remove noise in a specific frequency band and emphasize only the target audio. Furthermore, the audio input unit can reduce background noise and make the target audio clearer. This allows clear audio to be obtained by filtering environmental sounds and removing noise. Some or all of the above-described processing in the audio input unit may be performed using, or without, AI, for example. For example, the audio input unit can input the obtained audio data to a generation AI and have the generation AI perform noise removal.

[0035] The voice input unit can integrate data from multiple voice input devices during voice input to improve analysis accuracy. The voice input unit can integrate data from multiple voice input devices during voice input to improve analysis accuracy. For example, the voice input unit can integrate voice data from multiple microphones to improve analysis accuracy. The voice input unit can also integrate voice data from a smartphone and a smart speaker to perform more accurate analysis. Furthermore, the voice input unit can integrate voice data from a wearable device and a fixed device to improve analysis accuracy. In this way, by integrating data from multiple voice input devices, analysis accuracy is improved. Some or all of the above-described processing in the voice input unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice input unit can input voice data acquired from multiple devices to a generation AI and have the generation AI perform data integration and analysis.

[0036] The voice input unit can prioritize acquisition of highly relevant voices based on the user's geographical location information when inputting voice. The voice input unit can prioritize acquisition of highly relevant voices based on the user's geographical location information when inputting voice. For example, when the user is in a specific location, voices related to that location can be prioritized. Furthermore, when the user is moving, voices related to that location can be prioritized. Furthermore, when the user is in a specific area, voices related to that area can be prioritized. This enables more appropriate voice input by prioritizing acquisition of highly relevant voices based on the user's geographical location information. Some or all of the above-described processing in the voice input unit may be performed using, or without, AI. For example, the voice input unit can input the user's geographical location information to a generation AI and cause the generation AI to acquire highly relevant voices.

[0037] The voice input unit can analyze the user's social media activity and acquire related voices when voice is input. The voice input unit can analyze the user's social media activity and acquire related voices when voice is input. For example, the voice input unit analyzes the user's social media posts and prioritizes acquiring related voices. The voice input unit can also acquire highly relevant voices based on the user's social media activity history. Furthermore, the voice input unit can analyze the posts of the user's followers and friends on social media and acquire related voices. In this way, related voices can be acquired by analyzing the user's social media activity. Some or all of the above-described processing in the voice input unit may be performed using AI, for example, or may be performed without using AI. For example, the voice input unit can input the user's social media data to a generation AI and cause the generation AI to acquire related voices.

[0038] During voice analysis, the voice analysis unit can analyze voice features in detail to identify intentions and emotions. During voice analysis, the voice analysis unit can analyze voice features in detail to identify intentions and emotions. For example, the voice analysis unit analyzes the frequency characteristics of the voice to identify emotions. The voice analysis unit can also analyze the rhythm and tempo of the voice to identify intentions. Furthermore, the voice analysis unit can analyze the strength and intonation of the voice to identify emotions. In this way, by analyzing voice features in detail, intentions and emotions can be accurately identified. Some or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit can input voice data to a generation AI to have the voice features analyzed in detail.

[0039] The voice analysis unit can apply different analysis algorithms depending on the voice category during voice analysis. The voice analysis unit can apply different analysis algorithms depending on the voice category during voice analysis. For example, the voice analysis unit applies a specific algorithm to the voice of an infant to analyze it. The voice analysis unit can also apply a different algorithm to the voice of a person who is unable to speak due to caregiving for an illness to analyze it. Furthermore, the voice analysis unit can apply a dedicated algorithm to the voice of an animal to analyze it. In this way, by applying different analysis algorithms depending on the voice category, the analysis accuracy is improved. Some or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit can input voice data to a generation AI and apply an analysis algorithm depending on the category to the generation AI.

[0040] During audio analysis, the audio analysis unit can determine the analysis priority based on the time of audio submission. During audio analysis, the audio analysis unit can determine the analysis priority based on the time of audio submission. For example, the audio analysis unit prioritizes analysis of the most recent audio data. The audio analysis unit can also postpone analysis of audio data that was submitted earlier. Furthermore, the audio analysis unit can adjust the order of analysis based on the time of submission. This enables efficient analysis by determining the analysis priority based on the time of audio submission. Some or all of the above-mentioned processing in the audio analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the audio analysis unit can input the time of audio data submission to the generation AI and have the generation AI determine the analysis priority.

[0041] The audio analysis unit can adjust the order of analysis based on the relevance of the audio during audio analysis. The audio analysis unit can adjust the order of analysis based on the relevance of the audio during audio analysis. For example, the audio analysis unit prioritizes analysis of highly relevant audio data. The audio analysis unit can also postpone analysis of less relevant audio data. Furthermore, the audio analysis unit can adjust the order of analysis based on the relevance. As a result, adjusting the order of analysis based on the relevance of the audio enables efficient analysis. Some or all of the above-described processing in the audio analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the audio analysis unit can input the relevance of the audio data to a generation AI and have the generation AI adjust the order of analysis.

[0042] The display unit can adjust the level of detail of the display based on the importance of the analysis result when displaying the analysis result. The display unit can adjust the level of detail of the display based on the importance of the analysis result when displaying the analysis result. For example, the display unit can display analysis results with high importance in detail. The display unit can also display analysis results with low importance in a concise manner. Furthermore, the display unit can adjust the level of detail of the display based on the importance. As a result, by adjusting the level of detail of the display based on the importance of the analysis result, it is possible to provide information that is important to the user 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 importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the display.

[0043] The display unit can apply different display methods depending on the category of the analysis results when displaying the results. The display unit can apply different display methods depending on the category of the analysis results when displaying the results. For example, the display unit can display the analysis results of infants and young children in a user-friendly design. The display unit can also display the analysis results of people who are unable to continue caregiving due to illness in a simple, highly visible design. Furthermore, the display unit can display the analysis results of animals in a design that corresponds to the type of animal. This allows for a display that is easy for users to understand by applying different display methods depending on the category of the analysis results. 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 category of the analysis results into the generation AI and apply different display methods to the generation AI.

[0044] The display unit can determine the display priority based on the submission time of the analysis results when displaying the results. The display unit can determine the display priority based on the submission time of the analysis results when displaying the results. For example, the display unit can prioritize displaying the most recent analysis results. The display unit can also postpone analysis results that were submitted earlier. Furthermore, the display unit can adjust the display order based on the submission time. This allows the most recent information to be provided preferentially by determining the display priority based on the submission time of the analysis results. Some or all of the above-mentioned 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 submission time of the analysis results to the generation AI and have the generation AI determine the display priority.

[0045] The display unit can adjust the display order based on the relevance of the analysis results when displaying them. The display unit can adjust the display order based on the relevance of the analysis results when displaying them. For example, the display unit can prioritize displaying highly relevant analysis results. The display unit can also postpone analysis results with low relevance. Furthermore, the display unit can adjust the display order based on the relevance. As a result, by adjusting the display order based on the relevance of the analysis results, it is possible to provide information with high relevance to the user preferentially. Some or all of the above-mentioned 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 of the analysis results to a generation AI and have the generation AI adjust the display order.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] In addition to analyzing audio data, the audio analysis unit can also identify the source of the audio. For example, it can identify which room a crying baby is coming from and notify the user. It can also identify the location of the voice of a sick person who is caring for them, encouraging a quick response. It can also identify the location of an animal's voice and determine the location of a pet. By identifying the source of the audio, the user can take quick and appropriate action.

[0048] The voice input unit can acquire biometric information of the user when inputting voice and use it for voice analysis. For example, it can acquire the user's heart rate and body temperature to improve the accuracy of voice analysis. It can also measure the user's stress level and reflect it in the results of voice analysis. It can also monitor the user's sleep state and adjust the timing of voice analysis. This makes it possible to utilize the user's biometric information to perform more accurate voice analysis.

[0049] The display unit can be customized to suit the user's preferences when displaying analysis results. For example, if the user prefers visual information, the analysis results can be displayed using graphs or icons. If the user prefers audio feedback, the analysis results can be provided by audio. Furthermore, if the user prefers text information, the analysis results can be displayed in detailed text. This allows customization according to the user's preferences, making the system easier to use.

[0050] In addition to analyzing audio data, the audio analysis unit can analyze background sounds and provide environmental information. For example, it can analyze the background sounds of an infant crying to estimate the temperature and humidity of the room. It can also analyze the background sounds of a sick person who is caring for them and evaluate the ambient noise level. It can also analyze the background sounds of animals' voices to understand the environment around pets. By analyzing the background sounds of audio, users can obtain environmental information and take more appropriate measures.

[0051] In addition to analyzing audio data, the audio analysis unit can identify the speaker of the audio. For example, it can identify the crying of an infant and analyze the emotion of that particular infant. It can also identify the voice of a sick person who is unable to speak due to caregiving and provide individualized care. It can also identify the voice of an animal and analyze the emotion of a particular pet. This allows for more personalized care by identifying the speaker of the audio.

[0052] In addition to analyzing voice data, the voice analysis unit can automatically translate the language of the voice. For example, it can analyze the crying of an infant and provide emotional expressions in different languages. It can also analyze the voice of a sick person who is unable to speak due to caregiving and provide care instructions in different languages. It can also analyze the voice of an animal and provide emotional expressions of a pet in different languages. This allows it to automatically translate the language of the voice, making it possible to accommodate users who speak different languages.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The audio input unit captures the voice of an infant, a person unable to speak due to illness, or an animal. The audio input unit can capture audio using, for example, a microphone or a voice recognition device. The audio input unit can also accept audio input from a smartphone or other device. For example, audio can be captured using a smartphone's microphone and transmitted to the system via Bluetooth or Wi-Fi. The audio input unit also has a function for filtering environmental sounds and removing noise. For example, noise canceling technology can be used to filter ambient noise in real time to obtain clear audio. Furthermore, the audio input unit can integrate data from multiple audio input devices to improve analysis accuracy. For example, audio data from multiple microphones can be integrated to improve analysis accuracy. Step 2: The voice analysis unit uses the generative AI to analyze the captured voice and identify the intention and emotion at that time. The generative AI analyzes the voice using a deep learning model or a natural language processing model. For example, the generative AI analyzes features such as tone, pitch, and rhythm of the voice to identify the intention and emotion. The voice analysis unit saves the analysis results in the cloud and can improve the accuracy of the analysis by comparing them with past data. For example, the analysis results can be saved using a cloud storage service, and security can be ensured using data encryption technology. Step 3: The display unit visually displays the analysis results and also provides audio feedback. The display unit can visually display the analysis results using a display or LED indicator. For example, if an infant is crying because they are hungry, it will display "hungry." The display unit can also provide audio feedback of the analysis results using a speaker or voice synthesis technology. For example, it will provide audio feedback such as "I'm hungry."

[0055] (Example 2) A communication system according to an embodiment of the present invention captures the voices of infants, caregivers, and animals (pets), analyzes their intentions and emotions, and enables mutual communication. This communication system captures the voices of infants, caregivers, or animals, analyzes their intentions and emotions, and provides the results to a user. For example, a voice input unit captures the voice of an infant, caregiver, or animal. A voice analysis unit then analyzes the captured voice to identify the intentions and emotions. For example, if an infant is crying, the crying pattern is analyzed to identify emotions such as hunger, sleepiness, or pain. Similarly, the voices of caregivers and animals are analyzed to identify their intentions and emotions. The analyzed intentions and emotions are displayed on a display unit and provided to the user. For example, if an infant is crying because they are hungry, the word "hunger" is displayed, allowing the user to take appropriate action based on this information. Furthermore, if a caregiver is in pain, the word "pain" is displayed, allowing appropriate care to be provided. Similarly, in the case of animals, their intentions and emotions are displayed, allowing the user to respond appropriately. This system enables mutual communication with infants, people who are unable to speak due to illness and caregiving, and animals, and allows appropriate responses to be taken. This communication system enables mutual communication with infants, people who are unable to speak due to illness and caregiving, and animals, and allows appropriate responses to be taken.

[0056] A communication system according to an embodiment includes an audio input unit, an audio analysis unit, and a display unit. The audio input unit captures the voices of infants, people with impaired speech due to illness, or animals. The audio input unit can capture audio using, for example, a microphone or a voice recognition device. The audio input unit can also accept audio input from a smartphone or other device. For example, audio can be captured using a smartphone microphone and transmitted to the system via Bluetooth or Wi-Fi. The audio input unit also has a function for filtering environmental sounds and removing noise. For example, noise canceling technology can be used to filter ambient noise in real time to obtain clear audio. Furthermore, the audio input unit can integrate data from multiple audio input devices to improve analysis accuracy. For example, audio data from multiple microphones can be integrated to improve analysis accuracy. The audio analysis unit uses a generative AI to analyze the captured audio and identify the intention and emotion at that time. The generative AI analyzes the audio using a deep learning model or a natural language processing model. For example, the generative AI can analyze features such as tone, pitch, and rhythm of the audio to identify the intention and emotion. The voice analysis unit can improve the accuracy of the analysis by storing the analysis results in the cloud and comparing them with past data. For example, the analysis results can be stored using a cloud storage service, and security can be ensured using data encryption technology. The display unit visually displays the analysis results and also provides audio feedback. The display unit can visually display the analysis results using a display or LED. For example, if an infant is crying because they are hungry, it will display "Hungry." The display unit can also provide audio feedback of the analysis results using a speaker or voice synthesis technology. For example, it can provide audio feedback such as "I'm hungry." This allows the communication system according to the embodiment to realize mutual communication with infants, people who are caring for their sick, and animals, and to take appropriate measures.

[0057] The audio input unit can accept audio input from a smartphone or other device. For example, the audio input unit can capture audio using a microphone on the smartphone and transmit it to the system via Bluetooth or Wi-Fi. For example, the audio input unit can capture audio using a microphone on the smartphone and transmit it to the system via Bluetooth or Wi-Fi. The audio input unit can also accept audio input from other devices. For example, it can accept audio input from a smart speaker or a wearable device. This enables flexible audio input by accepting audio input from a smartphone or other device. Some or all of the above-described processing in the audio input unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio input unit can input audio data captured by a smartphone microphone to a generation AI and have the generation AI analyze the audio data.

[0058] The voice analysis unit uses a generative AI to analyze voices and learn the characteristics of the voices of infants, people requiring care due to illness, and animals. The voice analysis unit uses a generative AI to analyze the captured voice and identify the intentions and emotions at that time. The generative AI analyzes voices using a deep learning model or a natural language processing model. For example, the generative AI analyzes voice characteristics such as tone, pitch, and rhythm to identify the intentions and emotions. The generative AI can learn the characteristics of the voices of infants, people requiring care due to illness, and animals. For example, the generative AI learns the crying patterns of infants and identifies emotions such as hunger, sleepiness, and pain. Similarly, the generative AI can learn the voices of people requiring care due to illness and animals and identify their intentions and emotions. In this way, the use of generative AI improves the accuracy of voice analysis and can learn the characteristics of the voices of infants, people requiring care due to illness, and animals. Some or all of the above-mentioned processing in the voice analysis unit may be performed using AI, for example, or without AI. For example, the voice analysis unit can input the captured voice data into the generation AI and have the generation AI analyze the voice data.

[0059] The display unit can visually display the analysis results and also provide audio feedback. The display unit can visually display the analysis results using a display or LED display. For example, if an infant is hungry and crying, it displays "hungry." The display unit can also provide audio feedback of the analysis results using a speaker or voice synthesis technology. For example, it can provide audio feedback such as "I'm hungry." This not only visually displays the analysis results but also provides audio feedback, thereby diversifying the information provided to the user. Some or all of the above-mentioned 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 analysis results to a generation AI and have the generation AI perform visual display and audio feedback.

[0060] The voice analysis unit can improve the accuracy of the analysis by storing the analysis results in the cloud and comparing them with past data. The voice analysis unit can improve the accuracy of the analysis by storing the analysis results in the cloud and comparing them with past data. For example, the analysis results are stored using a cloud storage service and security is ensured using data encryption technology. The voice analysis unit can improve the accuracy of the analysis by comparing them with past data. For example, the voice analysis unit compares past analysis results with current analysis results and improves the analysis algorithm. In this way, the analysis results are stored in the cloud and compared with past data, thereby improving the accuracy of the analysis. Some or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit can improve the accuracy of the analysis by storing the analysis results in the cloud and having a generating AI compare them with past data.

[0061] The voice input unit can estimate the user's emotion and adjust the timing of voice input based on the estimated emotion. The voice input unit can estimate the user's emotion and adjust the timing of voice input based on the estimated emotion. For example, if the user is stressed, the timing of voice input can be delayed to capture voice in a relaxed state. Also, if the user is relaxed, the timing of voice input can be advanced to capture voice quickly. Furthermore, if the user is in a hurry, the timing of voice input can be optimized to capture voice efficiently. This enables more appropriate voice input by adjusting the timing of voice input according to the user's emotion. Some or all of the above-mentioned processing in the voice input unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice input unit can input user emotion data to a generation AI and have the generation AI adjust the timing of voice input.

[0062] The audio input unit can filter environmental sounds and remove noise when inputting audio. The audio input unit can filter environmental sounds and remove noise when inputting audio. For example, the audio input unit can filter ambient noise in real time to obtain clear audio. The audio input unit can also remove noise in a specific frequency band and emphasize only the target audio. Furthermore, the audio input unit can reduce background noise and make the target audio clearer. This allows clear audio to be obtained by filtering environmental sounds and removing noise. Some or all of the above-described processing in the audio input unit may be performed using, or without, AI, for example. For example, the audio input unit can input the obtained audio data to a generation AI and have the generation AI perform noise removal.

[0063] The voice input unit can integrate data from multiple voice input devices during voice input to improve analysis accuracy. The voice input unit can integrate data from multiple voice input devices during voice input to improve analysis accuracy. For example, the voice input unit can integrate voice data from multiple microphones to improve analysis accuracy. The voice input unit can also integrate voice data from a smartphone and a smart speaker to perform more accurate analysis. Furthermore, the voice input unit can integrate voice data from a wearable device and a fixed device to improve analysis accuracy. In this way, by integrating data from multiple voice input devices, analysis accuracy is improved. Some or all of the above-described processing in the voice input unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice input unit can input voice data acquired from multiple devices to a generation AI and have the generation AI perform data integration and analysis.

[0064] The voice input unit can estimate the user's emotions and determine the priority of voice inputs based on the estimated emotions. The voice input unit can estimate the user's emotions and determine the priority of voice inputs based on the estimated emotions. For example, if the user is nervous, the priority of voice inputs can be lowered and voices can be captured in a relaxed state. Also, if the user is relaxed, the priority of voice inputs can be increased and voices can be captured quickly. Furthermore, if the user is in a hurry, the priority of voice inputs can be optimized and voices can be captured efficiently. This enables more appropriate voice input by determining the priority of voice inputs according to the user's emotions. Some or all of the above-described processing in the voice input unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice input unit can input user emotion data to a generation AI and have the generation AI determine the priority of voice inputs.

[0065] The voice input unit can prioritize acquisition of highly relevant voices based on the user's geographical location information when inputting voice. The voice input unit can prioritize acquisition of highly relevant voices based on the user's geographical location information when inputting voice. For example, when the user is in a specific location, voices related to that location can be prioritized. Furthermore, when the user is moving, voices related to that location can be prioritized. Furthermore, when the user is in a specific area, voices related to that area can be prioritized. This enables more appropriate voice input by prioritizing acquisition of highly relevant voices based on the user's geographical location information. Some or all of the above-described processing in the voice input unit may be performed using, or without, AI. For example, the voice input unit can input the user's geographical location information to a generation AI and cause the generation AI to acquire highly relevant voices.

[0066] The voice input unit can analyze the user's social media activity and acquire related voices when voice is input. The voice input unit can analyze the user's social media activity and acquire related voices when voice is input. For example, the voice input unit analyzes the user's social media posts and prioritizes acquiring related voices. The voice input unit can also acquire highly relevant voices based on the user's social media activity history. Furthermore, the voice input unit can analyze the posts of the user's followers and friends on social media and acquire related voices. In this way, related voices can be acquired by analyzing the user's social media activity. Some or all of the above-described processing in the voice input unit may be performed using AI, for example, or may be performed without using AI. For example, the voice input unit can input the user's social media data to a generation AI and cause the generation AI to acquire related voices.

[0067] The voice analysis unit can estimate the user's emotions and adjust the voice analysis presentation method based on the estimated emotions. The voice analysis unit can estimate the user's emotions and adjust the voice analysis presentation method based on the estimated emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is nervous, concise and to the point analysis results can be provided. Furthermore, if the user is in a hurry, analysis results can be provided quickly. This allows for adjusting the voice analysis presentation method according to the user's emotions to provide more appropriate analysis results. Some or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit can input the user's emotion data into a generation AI and have the generation AI adjust the voice analysis presentation method.

[0068] During voice analysis, the voice analysis unit can analyze voice features in detail to identify intentions and emotions. During voice analysis, the voice analysis unit can analyze voice features in detail to identify intentions and emotions. For example, the voice analysis unit analyzes the frequency characteristics of the voice to identify emotions. The voice analysis unit can also analyze the rhythm and tempo of the voice to identify intentions. Furthermore, the voice analysis unit can analyze the strength and intonation of the voice to identify emotions. In this way, by analyzing voice features in detail, intentions and emotions can be accurately identified. Some or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit can input voice data to a generation AI to have the voice features analyzed in detail.

[0069] The voice analysis unit can apply different analysis algorithms depending on the voice category during voice analysis. The voice analysis unit can apply different analysis algorithms depending on the voice category during voice analysis. For example, the voice analysis unit applies a specific algorithm to the voice of an infant to analyze it. The voice analysis unit can also apply a different algorithm to the voice of a person who is unable to speak due to caregiving for an illness to analyze it. Furthermore, the voice analysis unit can apply a dedicated algorithm to the voice of an animal to analyze it. In this way, by applying different analysis algorithms depending on the voice category, the analysis accuracy is improved. Some or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit can input voice data to a generation AI and apply an analysis algorithm depending on the category to the generation AI.

[0070] The voice analysis unit can estimate the user's emotions and adjust the length of the voice analysis based on the estimated emotions. The voice analysis unit can estimate the user's emotions and adjust the length of the voice analysis based on the estimated emotions. For example, if the user is relaxed, a detailed analysis can be performed. If the user is nervous, a brief analysis can be performed. Furthermore, if the user is in a hurry, the analysis can be performed quickly. This allows for adjusting the length of the voice analysis according to the user's emotions to provide more appropriate analysis results. Some or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit can input the user's emotion data into a generation AI and have the generation AI adjust the length of the voice analysis.

[0071] During audio analysis, the audio analysis unit can determine the analysis priority based on the time of audio submission. During audio analysis, the audio analysis unit can determine the analysis priority based on the time of audio submission. For example, the audio analysis unit prioritizes analysis of the most recent audio data. The audio analysis unit can also postpone analysis of audio data that was submitted earlier. Furthermore, the audio analysis unit can adjust the order of analysis based on the time of submission. This enables efficient analysis by determining the analysis priority based on the time of audio submission. Some or all of the above-mentioned processing in the audio analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the audio analysis unit can input the time of audio data submission to the generation AI and have the generation AI determine the analysis priority.

[0072] The audio analysis unit can adjust the order of analysis based on the relevance of the audio during audio analysis. The audio analysis unit can adjust the order of analysis based on the relevance of the audio during audio analysis. For example, the audio analysis unit prioritizes analysis of highly relevant audio data. The audio analysis unit can also postpone analysis of less relevant audio data. Furthermore, the audio analysis unit can adjust the order of analysis based on the relevance. As a result, adjusting the order of analysis based on the relevance of the audio enables efficient analysis. Some or all of the above-described processing in the audio analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the audio analysis unit can input the relevance of the audio data to a generation AI and have the generation AI adjust the order of analysis.

[0073] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to provide more appropriate information by adjusting the display method according to the user's emotions. Some or all of the above-mentioned 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 user's emotion data into a generation AI and have the generation AI adjust the display method.

[0074] The display unit can adjust the level of detail of the display based on the importance of the analysis result when displaying the analysis result. The display unit can adjust the level of detail of the display based on the importance of the analysis result when displaying the analysis result. For example, the display unit can display analysis results with high importance in detail. The display unit can also display analysis results with low importance in a concise manner. Furthermore, the display unit can adjust the level of detail of the display based on the importance. As a result, by adjusting the level of detail of the display based on the importance of the analysis result, it is possible to provide information that is important to the user 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 importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the display.

[0075] The display unit can apply different display methods depending on the category of the analysis results when displaying the results. The display unit can apply different display methods depending on the category of the analysis results when displaying the results. For example, the display unit can display the analysis results of infants and young children in a user-friendly design. The display unit can also display the analysis results of people who are unable to continue caregiving due to illness in a simple, highly visible design. Furthermore, the display unit can display the analysis results of animals in a design that corresponds to the type of animal. This allows for a display that is easy for users to understand by applying different display methods depending on the category of the analysis results. 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 category of the analysis results into the generation AI and apply different display methods to the generation AI.

[0076] The display unit can estimate the user's emotion and adjust the display length based on the estimated emotion. The display unit can estimate the user's emotion and adjust the display length based on the estimated emotion. For example, if the user is relaxed, detailed information can be displayed. If the user is nervous, a concise display can be displayed. Furthermore, if the user is in a hurry, a quick display can be displayed. This makes it possible to provide more appropriate information by adjusting the display length according to the user's emotion. 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 user's emotion data into a generation AI and have the generation AI adjust the display length.

[0077] The display unit can determine the display priority based on the submission time of the analysis results when displaying the results. The display unit can determine the display priority based on the submission time of the analysis results when displaying the results. For example, the display unit can prioritize displaying the most recent analysis results. The display unit can also postpone analysis results that were submitted earlier. Furthermore, the display unit can adjust the display order based on the submission time. This allows the most recent information to be provided preferentially by determining the display priority based on the submission time of the analysis results. Some or all of the above-mentioned 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 submission time of the analysis results to the generation AI and have the generation AI determine the display priority.

[0078] The display unit can adjust the display order based on the relevance of the analysis results when displaying them. The display unit can adjust the display order based on the relevance of the analysis results when displaying them. For example, the display unit can prioritize displaying highly relevant analysis results. The display unit can also postpone analysis results with low relevance. Furthermore, the display unit can adjust the display order based on the relevance. As a result, by adjusting the display order based on the relevance of the analysis results, it is possible to provide information with high relevance to the user preferentially. Some or all of the above-mentioned 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 of the analysis results to a generation AI and have the generation AI adjust the display order. === Hard Collateral 1-1 === Each of the multiple elements, including the voice input unit, voice analysis unit, and display unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the voice input unit can capture the voices of infants, people who are unable to speak due to illness or care, or animals using the microphone 38B of the smart device 14. The voice analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes voices using a generative AI to identify the intentions and emotions at that time. The display unit can provide visual and audio feedback of the analysis results using, for example, the display 40A and speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the voice input unit, voice 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 voice input unit can capture the voices of infants, people who are unable to speak due to illness or care, or animals using the microphone 238 of the smart glasses 214. The voice analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes voices using a generative AI to identify the intentions and emotions at that time. The display unit can provide visual and audio feedback of the analysis results using, for example, the display and speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned voice input unit, voice analysis unit, and display unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the voice input unit can capture the voices of infants, people who are unable to speak due to illness or care, or animals using the microphone 238 of the headset-type terminal 314. The voice analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes voices using a generation AI to identify the intentions and emotions at that time. The display unit can provide visual and audio feedback of the analysis results using, for example, the display 343 and speaker 240 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the voice input unit, voice 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 voice input unit can capture the voices of infants, people who are unable to speak due to illness or care, or animals using the microphone 238 of the robot 414. The voice analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes voices using a generative AI to identify the intentions and emotions at that time. The display unit can provide visual and audio feedback of the analysis results using, for example, the display and speaker 240 of the robot 414.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] In addition to analyzing audio data, the audio analysis unit can also identify the source of the audio. For example, it can identify which room a crying baby is coming from and notify the user. It can also identify the location of the voice of a sick person who is caring for them, encouraging a quick response. It can also identify the location of an animal's voice and determine the location of a pet. By identifying the source of the audio, the user can take quick and appropriate action.

[0081] The voice input unit can acquire biometric information of the user when inputting voice and use it for voice analysis. For example, it can acquire the user's heart rate and body temperature to improve the accuracy of voice analysis. It can also measure the user's stress level and reflect it in the results of voice analysis. It can also monitor the user's sleep state and adjust the timing of voice analysis. This makes it possible to utilize the user's biometric information to perform more accurate voice analysis.

[0082] In addition to analyzing voice data, the voice analysis unit can evaluate the emotional intensity of the voice. For example, it can evaluate the intensity of an infant's cry to determine the level of urgency. It can also evaluate the intensity of the voice of a sick person who is unable to provide care and estimate the level of pain. It can also evaluate the intensity of an animal's voice to estimate the stress level. By evaluating the emotional intensity of the voice, the user can take more appropriate action.

[0083] The display unit can be customized to suit the user's preferences when displaying analysis results. For example, if the user prefers visual information, the analysis results can be displayed using graphs or icons. If the user prefers audio feedback, the analysis results can be provided by audio. Furthermore, if the user prefers text information, the analysis results can be displayed in detailed text. This allows customization according to the user's preferences, making the system easier to use.

[0084] In addition to analyzing audio data, the audio analysis unit can analyze background sounds and provide environmental information. For example, it can analyze the background sounds of an infant crying to estimate the temperature and humidity of the room. It can also analyze the background sounds of a sick person who is caring for them and evaluate the ambient noise level. It can also analyze the background sounds of animals' voices to understand the environment around pets. By analyzing the background sounds of audio, users can obtain environmental information and take more appropriate measures.

[0085] The voice input unit can estimate the user's emotions and filter the voice input based on the estimated emotions. For example, if the user is feeling stressed, noise cancellation can be strengthened to obtain clear voice. If the user is relaxed, voice can be obtained while preserving natural environmental sounds. Furthermore, if the user is in a hurry, only important voice can be obtained preferentially. Thus, by filtering the voice input according to the user's emotions, more appropriate voice input is possible.

[0086] In addition to analyzing audio data, the audio analysis unit can identify the speaker of the audio. For example, it can identify the crying of an infant and analyze the emotion of that particular infant. It can also identify the voice of a sick person who is unable to speak due to caregiving and provide individualized care. It can also identify the voice of an animal and analyze the emotion of a particular pet. This allows for more personalized care by identifying the speaker of the audio.

[0087] When displaying the analysis results, the display unit can estimate the user's emotions and adjust the display color tone based on the estimated emotions. For example, if the user is nervous, the display can be in a subdued color tone. If the user is relaxed, the display can be in a bright color tone. Furthermore, if the user is in a hurry, the display can be in a highly visible color tone. This allows for more appropriate information to be provided by adjusting the display color tone according to the user's emotions.

[0088] In addition to analyzing voice data, the voice analysis unit can automatically translate the language of the voice. For example, it can analyze the crying of an infant and provide emotional expressions in different languages. It can also analyze the voice of a sick person who is unable to speak due to caregiving and provide care instructions in different languages. It can also analyze the voice of an animal and provide emotional expressions of a pet in different languages. This allows it to automatically translate the language of the voice, making it possible to accommodate users who speak different languages.

[0089] When displaying the analysis results, the display unit can estimate the user's emotions and adjust the font size of the display based on the estimated emotions. For example, if the user is nervous, the display can be in a large font size. On the other hand, if the user is relaxed, the display can be in a standard font size. Furthermore, if the user is in a hurry, the font size can be adjusted to emphasize the main points. In this way, by adjusting the font size of the display according to the user's emotions, more appropriate information can be provided.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The audio input unit captures the voice of an infant, a person unable to speak due to illness, or an animal. The audio input unit can capture audio using, for example, a microphone or a voice recognition device. The audio input unit can also accept audio input from a smartphone or other device. For example, audio can be captured using a smartphone's microphone and transmitted to the system via Bluetooth or Wi-Fi. The audio input unit also has a function for filtering environmental sounds and removing noise. For example, noise canceling technology can be used to filter ambient noise in real time to obtain clear audio. Furthermore, the audio input unit can integrate data from multiple audio input devices to improve analysis accuracy. For example, audio data from multiple microphones can be integrated to improve analysis accuracy. Step 2: The voice analysis unit uses the generative AI to analyze the captured voice and identify the intention and emotion at that time. The generative AI analyzes the voice using a deep learning model or a natural language processing model. For example, the generative AI analyzes features such as tone, pitch, and rhythm of the voice to identify the intention and emotion. The voice analysis unit saves the analysis results in the cloud and can improve the accuracy of the analysis by comparing them with past data. For example, the analysis results can be saved using a cloud storage service, and security can be ensured using data encryption technology. Step 3: The display unit visually displays the analysis results and also provides audio feedback. The display unit can visually display the analysis results using a display or LED indicator. For example, if an infant is crying because they are hungry, it will display "hungry." The display unit can also provide audio feedback of the analysis results using a speaker or voice synthesis technology. For example, it will provide audio feedback such as "I'm hungry."

[0092] 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0094] 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.

[0095] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] [Explanation of symbols]

[0164] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an audio input unit for capturing audio; a voice analysis unit that analyzes the voice captured by the voice input unit and identifies intentions and emotions; a display unit that displays the emotion identified by the voice analysis unit; Equipped with A system characterized by:

2. The voice input unit Accepts voice input from a smartphone or other device 2. The system of claim 1.

3. The voice analysis unit Analyzes voices using generative AI to learn the characteristics of the voices of infants, people requiring care, and animals.

2. The system of claim 1.

4. The display unit The analysis results are displayed visually and audio feedback is also provided.

2. The system of claim 1.

5. The voice analysis unit Analysis results are saved in the cloud and compared with past data to improve analysis accuracy.

2. The system of claim 1.

6. The voice input unit Estimate the user's emotions and adjust the timing of voice input based on the estimated emotions.

2. The system of claim 1.

7. The voice input unit When inputting voice, it filters out ambient sounds and removes noise.

2. The system of claim 1.

8. The voice input unit When inputting voice data, data from multiple voice input devices is integrated to improve analysis accuracy.

2. The system of claim 1.

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