System

Abstract

An object of a system according to an embodiment is to enable a hearing-impaired person to naturally communicate with confidence.SOLUTION: A system according to an embodiment includes an analysis unit and a navigation unit. The analysis unit analyzes the movement of the mouth or the tongue in real time. The navigation part navigates the optimum pronunciation method by voice and vision based on the data analyzed by the analysis part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not providing sufficient speech support to enable hearing-impaired people to communicate confidently and naturally.

[0005] The system according to the embodiment aims to enable people with hearing impairments to communicate confidently and naturally. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit and a navigation unit. The analysis unit analyzes mouth or tongue movements in real time. The navigation unit provides audio and visual guidance to find the optimal pronunciation method based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] Systems according to embodiments can enable hearing impaired people to communicate confidently and naturally. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​navigation system according to an embodiment of the present invention is a system that helps hearing-impaired people communicate more naturally and confidently. This system analyzes the movements of the mouth and tongue during speech in real time, and the AI ​​guides the user through the optimal pronunciation method using audio and visual aids. This allows hearing-impaired people to communicate more naturally and confidently.

[0029] An AI navigation system according to an embodiment includes an analysis unit and a navigation unit. The analysis unit analyzes mouth and tongue movements in real time. For example, a camera or sensor can be used to capture the user's mouth and tongue movements, and the generation AI analyzes those movements. When the user attempts to pronounce "hello," the analysis unit analyzes the mouth shape and tongue position of the generation AI to identify the movements required for correct pronunciation. The navigation unit provides audio and visual guidance for the optimal pronunciation method based on the data analyzed by the analysis unit. For example, the generation AI may provide audio instructions such as "Lift your tongue a little higher" while simultaneously displaying a diagram on the screen showing the correct tongue position. When the user pronounces "thank you," the navigation unit evaluates the user's pronunciation and provides feedback such as "your pronunciation is accurate." After the user completes a week of practice, the navigation unit provides a report stating, "Your pronunciation has improved significantly over the past week." This enables hearing-impaired individuals to communicate more naturally and confidently. For example, this improves pronunciation in everyday conversations and work situations, thereby improving the quality of communication. It is also expected that practicing pronunciation will increase self-esteem and provide more opportunities for social participation.

[0030] The analysis unit can identify movement patterns optimized for individual users based on the user's past speech data. For example, the analysis unit uses a generation AI to collect the user's past speech data and analyze mouth and tongue movement patterns. For example, the analysis unit identifies the mouth shape and tongue position when pronouncing a specific sound based on audio data the user has produced in the past. The analysis unit also develops an algorithm to identify movement patterns optimized for individual users based on the user's speech data. For example, the generation AI learns the user's pronunciation characteristics and suggests the optimal pronunciation method. This makes it possible to provide pronunciation assistance optimized for the user.

[0031] The analysis unit can simultaneously analyze the user's overall facial expression and provide feedback to improve the naturalness of speech. For example, the analysis unit uses the generation AI to capture the user's overall facial expression and analyze it in conjunction with the movement of the mouth and tongue. For example, it can identify the shape of the mouth and the position of the tongue when the user pronounces words while smiling, thereby supporting natural speech. The analysis unit also provides feedback to improve the naturalness of speech based on the user's facial expression data. For example, the generation AI can analyze the user's facial expression and provide specific advice such as, "Please pronounce words with a more smiling face." This improves the naturalness of speech.

[0032] The analysis unit can simultaneously capture sign language movements and support the coordination of sign language and speech. For example, the generation AI captures the user's sign language movements and analyzes them in conjunction with mouth and tongue movements. For example, it simultaneously analyzes the hand movements and mouth shape when a user says "hello" in sign language. The analysis unit also develops algorithms to support the coordination of sign language and speech. For example, the generation AI analyzes sign language movements and speech movements to support the coordination. This improves the coordination of sign language and speech.

[0033] The analysis unit can learn pronunciation patterns of different languages ​​and provide multilingual speech assistance. For example, the generation AI learns pronunciation patterns of different languages ​​and suggests the optimal pronunciation method to the user. For example, the analysis unit analyzes pronunciation patterns of English, Japanese, French, etc. and provides appropriate feedback to the user. The analysis unit also develops algorithms for learning pronunciation patterns of different languages. For example, the generation AI builds a database for providing multilingual pronunciation assistance and suggests the optimal pronunciation method to the user. This makes multilingual speech assistance possible.

[0034] The navigation unit can detect subtle differences in the user's pronunciation and provide more precise feedback. For example, the generation AI in the navigation unit detects subtle differences in the user's pronunciation and provides precise feedback. For example, the generation AI analyzes the subtle differences in sounds when the user says "hello" and points out specific areas for improvement. The navigation unit also develops algorithms to detect subtle differences in the user's pronunciation. For example, the generation AI analyzes differences in phonemes and pronunciation timing and provides precise feedback. This makes it possible to detect subtle differences in the user's pronunciation and provide precise feedback.

[0035] The navigation unit can track the user's pronunciation progress in real time and dynamically adjust the individual practice plan. For example, the generation AI in the navigation unit tracks the user's pronunciation progress in real time and dynamically adjusts the individual practice plan. For example, if the user has difficulty pronouncing a particular sound, the navigation unit provides a practice plan specialized for that sound. The navigation unit also develops an algorithm for tracking the user's pronunciation progress. For example, the generation AI analyzes the user's pronunciation accuracy and the number of practice sessions and dynamically adjusts the practice plan. This allows the user's pronunciation progress to be tracked in real time and the practice plan to be dynamically adjusted.

[0036] The navigation unit can provide a function for recording a user's pronunciation and playing it back later for self-evaluation. The navigation unit provides a function for, for example, a generation AI to record a user's pronunciation and playing it back later for self-evaluation. For example, a user can record the sound of saying "hello" and play it back later for self-evaluation. The navigation unit also develops an algorithm for recording a user's pronunciation. For example, the generation AI can analyze the recorded data and provide feedback for self-evaluation. This can provide a function for a user to perform self-evaluation.

[0037] The navigation unit can provide a function for comparing a user's pronunciation with other users and conducting mutual evaluation. The navigation unit, for example, provides a function for the generation AI to compare a user's pronunciation with other users and conduct mutual evaluation. For example, the user's pronunciation of "hello" is compared with other users and a mutual evaluation is conducted. The navigation unit also develops an algorithm for comparing users' pronunciation. For example, the generation AI analyzes the pronunciation data of other users and provides feedback for mutual evaluation. This allows a function for the user to compare with other users and conduct mutual evaluation.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The navigation unit can detect subtle differences in the user's pronunciation and provide more precise feedback. For example, the generation AI can detect subtle differences in the user's pronunciation and provide precise feedback. For example, it can analyze the subtle differences in sounds when the user says "hello" and point out specific areas for improvement. The navigation unit also develops algorithms to detect subtle differences in the user's pronunciation. For example, the generation AI can analyze differences in phonemes and pronunciation timing and provide precise feedback. This allows it to detect subtle differences in the user's pronunciation and provide precise feedback.

[0040] The navigation unit can track the user's pronunciation progress in real time and dynamically adjust the individual practice plan. For example, the generation AI tracks the user's pronunciation progress in real time and dynamically adjusts the individual practice plan. For example, if the user has difficulty pronouncing a particular sound, it will provide a practice plan specialized for that sound. The navigation unit also develops an algorithm for tracking the user's pronunciation progress. For example, the generation AI analyzes the user's pronunciation accuracy and the number of practice sessions and dynamically adjusts the practice plan. This allows the user's pronunciation progress to be tracked in real time and the practice plan to be dynamically adjusted.

[0041] The navigation unit can provide a function to record the user's pronunciation and play it back later for self-evaluation. For example, a function is provided in which the generation AI records the user's pronunciation and plays it back later for self-evaluation. For example, a function is provided in which the user records the sound of saying "hello" and plays it back later for self-evaluation. The navigation unit also develops an algorithm for recording the user's pronunciation. For example, the generation AI analyzes the recorded data and provides feedback for self-evaluation. This can provide a function for the user to perform self-evaluation.

[0042] The navigation unit can provide a function for comparing a user's pronunciation with other users and conducting mutual evaluations. For example, the generation AI can provide a function for comparing a user's pronunciation with other users and conducting mutual evaluations. For example, the user's pronunciation of "hello" can be compared with other users and a mutual evaluation can be conducted. The navigation unit can also develop an algorithm for comparing users' pronunciations. For example, the generation AI can analyze the pronunciation data of other users and provide feedback for mutual evaluations. This can provide a function for a user to compare with other users and conduct mutual evaluations.

[0043] The navigation unit can receive community-based feedback by recording the user's pronunciation and sharing it with other users. For example, the generation AI provides a function to record the user's pronunciation and share it with other users. For example, the user can share the audio of themselves pronouncing "thank you" with the community and receive feedback from other users. The navigation unit also develops an algorithm for receiving community-based feedback. For example, the generation AI analyzes the feedback of other users and provides appropriate advice to the user. This allows the user to receive community-based feedback.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The analysis unit analyzes mouth and tongue movements in real time. For example, a camera or sensor can be used to capture the user's mouth and tongue movements, and the generation AI analyzes those movements. When the user tries to pronounce "hello," the generation AI analyzes the shape of the mouth and the position of the tongue to identify the movements required for correct pronunciation. Step 2: The navigation unit provides audio and visual guidance to find the optimal pronunciation method based on the data analyzed by the analysis unit. For example, the generation AI may give audio instructions such as "Lift your tongue a little higher" while simultaneously displaying a diagram on the screen showing the correct tongue position. When the user pronounces "thank you," the generation AI evaluates the pronunciation and provides feedback such as "your pronunciation is accurate." After the user has completed a week of practice, the generation AI provides a report stating, "your pronunciation has improved significantly over the past week."

[0046] (Example 2) The AI ​​navigation system according to an embodiment of the present invention is a system that helps hearing-impaired people communicate more naturally and confidently. This system analyzes the movements of the mouth and tongue during speech in real time, and the AI ​​guides the user through the optimal pronunciation method using audio and visual aids. This allows hearing-impaired people to communicate more naturally and confidently.

[0047] An AI navigation system according to an embodiment includes an analysis unit and a navigation unit. The analysis unit analyzes mouth and tongue movements in real time. For example, a camera or sensor can be used to capture the user's mouth and tongue movements, and the generation AI analyzes those movements. When the user attempts to pronounce "hello," the analysis unit analyzes the mouth shape and tongue position of the generation AI to identify the movements required for correct pronunciation. The navigation unit provides audio and visual guidance for the optimal pronunciation method based on the data analyzed by the analysis unit. For example, the generation AI may provide audio instructions such as "Lift your tongue a little higher" while simultaneously displaying a diagram on the screen showing the correct tongue position. When the user pronounces "thank you," the navigation unit evaluates the user's pronunciation and provides feedback such as "your pronunciation is accurate." After the user completes a week of practice, the navigation unit provides a report stating, "Your pronunciation has improved significantly over the past week." This enables hearing-impaired individuals to communicate more naturally and confidently. For example, this improves pronunciation in everyday conversations and work situations, thereby improving the quality of communication. It is also expected that practicing pronunciation will increase self-esteem and provide more opportunities for social participation.

[0048] The analysis unit can identify movement patterns optimized for individual users based on the user's past speech data. For example, the analysis unit uses a generation AI to collect the user's past speech data and analyze mouth and tongue movement patterns. For example, the analysis unit identifies the mouth shape and tongue position when pronouncing a specific sound based on audio data the user has produced in the past. The analysis unit also develops an algorithm to identify movement patterns optimized for individual users based on the user's speech data. For example, the generation AI learns the user's pronunciation characteristics and suggests the optimal pronunciation method. This makes it possible to provide pronunciation assistance optimized for the user.

[0049] The analysis unit can simultaneously analyze the user's overall facial expression and provide feedback to improve the naturalness of speech. For example, the analysis unit uses the generation AI to capture the user's overall facial expression and analyze it in conjunction with the movement of the mouth and tongue. For example, it can identify the shape of the mouth and the position of the tongue when the user pronounces words while smiling, thereby supporting natural speech. The analysis unit also provides feedback to improve the naturalness of speech based on the user's facial expression data. For example, the generation AI can analyze the user's facial expression and provide specific advice such as, "Please pronounce words with a more smiling face." This improves the naturalness of speech.

[0050] The analysis unit can use the emotion estimation function to estimate the user's emotional state in real time and provide pronunciation advice according to the emotion. For example, the analysis unit uses the emotion estimation function to analyze the user's emotional state in real time and provide pronunciation advice. For example, if the user is nervous, it provides specific advice on how to relax. The analysis unit also develops an algorithm for providing pronunciation advice based on the user's emotional state. For example, the generation AI analyzes the user's emotional state and provides advice such as "Take a deep breath and relax." This makes it possible to provide pronunciation assistance according to the user's emotions.

[0051] The analysis unit can simultaneously capture sign language movements and support the coordination of sign language and speech. For example, the generation AI captures the user's sign language movements and analyzes them in conjunction with mouth and tongue movements. For example, it simultaneously analyzes the hand movements and mouth shape when a user says "hello" in sign language. The analysis unit also develops algorithms to support the coordination of sign language and speech. For example, the generation AI analyzes sign language movements and speech movements to support the coordination. This improves the coordination of sign language and speech.

[0052] The analysis unit can learn pronunciation patterns of different languages ​​and provide multilingual speech assistance. For example, the generation AI learns pronunciation patterns of different languages ​​and suggests the optimal pronunciation method to the user. For example, the analysis unit analyzes pronunciation patterns of English, Japanese, French, etc. and provides appropriate feedback to the user. The analysis unit also develops algorithms for learning pronunciation patterns of different languages. For example, the generation AI builds a database for providing multilingual pronunciation assistance and suggests the optimal pronunciation method to the user. This makes multilingual speech assistance possible.

[0053] The analysis unit can use the emotion estimation function to provide positive feedback to increase the user's motivation when practicing speaking. For example, the analysis unit uses the emotion estimation function to analyze the user's emotional state and provide positive feedback to increase the user's motivation for speaking practice. For example, if the user feels tired during practice, an encouraging message is displayed. The analysis unit also develops an algorithm to provide feedback to increase motivation based on the user's emotional state. For example, a generation AI analyzes the user's emotional state and provides positive feedback such as "great pronunciation." This can increase the user's motivation.

[0054] The navigation unit can detect subtle differences in the user's pronunciation and provide more precise feedback. For example, the generation AI in the navigation unit detects subtle differences in the user's pronunciation and provides precise feedback. For example, the generation AI analyzes the subtle differences in sounds when the user says "hello" and points out specific areas for improvement. The navigation unit also develops algorithms to detect subtle differences in the user's pronunciation. For example, the generation AI analyzes differences in phonemes and pronunciation timing and provides precise feedback. This makes it possible to detect subtle differences in the user's pronunciation and provide precise feedback.

[0055] The navigation unit can track the user's pronunciation progress in real time and dynamically adjust the individual practice plan. For example, the generation AI in the navigation unit tracks the user's pronunciation progress in real time and dynamically adjusts the individual practice plan. For example, if the user has difficulty pronouncing a particular sound, the navigation unit provides a practice plan specialized for that sound. The navigation unit also develops an algorithm for tracking the user's pronunciation progress. For example, the generation AI analyzes the user's pronunciation accuracy and the number of practice sessions and dynamically adjusts the practice plan. This allows the user's pronunciation progress to be tracked in real time and the practice plan to be dynamically adjusted.

[0056] The navigation unit can use the emotion estimation function to provide pronunciation advice according to the user's emotional state, thereby reducing stress. The navigation unit, for example, uses the emotion estimation function to analyze the user's emotional state and provide pronunciation advice. For example, if the user is nervous, it provides specific advice on how to relax. The navigation unit also develops an algorithm for providing advice on how to reduce stress based on the user's emotional state. For example, a generation AI analyzes the user's emotional state and provides advice such as "Take a deep breath and relax." This allows the user to receive pronunciation advice according to their emotional state, thereby reducing stress.

[0057] The navigation unit can provide a function for recording a user's pronunciation and playing it back later for self-evaluation. The navigation unit provides a function for, for example, a generation AI to record a user's pronunciation and playing it back later for self-evaluation. For example, a user can record the sound of saying "hello" and play it back later for self-evaluation. The navigation unit also develops an algorithm for recording a user's pronunciation. For example, the generation AI can analyze the recorded data and provide feedback for self-evaluation. This can provide a function for a user to perform self-evaluation.

[0058] The navigation unit can provide a function for comparing a user's pronunciation with other users and conducting mutual evaluation. The navigation unit, for example, provides a function for the generation AI to compare a user's pronunciation with other users and conduct mutual evaluation. For example, the user's pronunciation of "hello" is compared with other users and a mutual evaluation is conducted. The navigation unit also develops an algorithm for comparing users' pronunciation. For example, the generation AI analyzes the pronunciation data of other users and provides feedback for mutual evaluation. This allows a function for the user to compare with other users and conduct mutual evaluation.

[0059] The navigation unit can use the emotion estimation function to monitor the user's emotional state when practicing pronunciation and provide advice to maximize the effectiveness of the practice. For example, the navigation unit can use the emotion estimation function to monitor the user's emotional state and provide advice to maximize the effectiveness of the practice. For example, if the user feels tired during practice, the navigation unit can advise the user to take a break. The navigation unit can also develop an algorithm to provide advice to maximize the effectiveness of the practice based on the user's emotional state. For example, the generation AI can analyze the user's emotional state and provide advice such as "Take a short break and then resume." This makes it possible to monitor the user's emotional state and provide advice to maximize the effectiveness of the practice.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The navigation unit can detect subtle differences in the user's pronunciation and provide more precise feedback. For example, the generation AI can detect subtle differences in the user's pronunciation and provide precise feedback. For example, it can analyze the subtle differences in sounds when the user says "hello" and point out specific areas for improvement. The navigation unit also develops algorithms to detect subtle differences in the user's pronunciation. For example, the generation AI can analyze differences in phonemes and pronunciation timing and provide precise feedback. This allows it to detect subtle differences in the user's pronunciation and provide precise feedback.

[0062] The navigation unit can track the user's pronunciation progress in real time and dynamically adjust the individual practice plan. For example, the generation AI tracks the user's pronunciation progress in real time and dynamically adjusts the individual practice plan. For example, if the user has difficulty pronouncing a particular sound, it will provide a practice plan specialized for that sound. The navigation unit also develops an algorithm for tracking the user's pronunciation progress. For example, the generation AI analyzes the user's pronunciation accuracy and the number of practice sessions and dynamically adjusts the practice plan. This allows the user's pronunciation progress to be tracked in real time and the practice plan to be dynamically adjusted.

[0063] The navigation unit can provide a function to record the user's pronunciation and play it back later for self-evaluation. For example, a function is provided in which the generation AI records the user's pronunciation and plays it back later for self-evaluation. For example, a function is provided in which the user records the sound of saying "hello" and plays it back later for self-evaluation. The navigation unit also develops an algorithm for recording the user's pronunciation. For example, the generation AI analyzes the recorded data and provides feedback for self-evaluation. This can provide a function for the user to perform self-evaluation.

[0064] The navigation unit can provide a function for comparing a user's pronunciation with other users and conducting mutual evaluations. For example, the generation AI can provide a function for comparing a user's pronunciation with other users and conducting mutual evaluations. For example, the user's pronunciation of "hello" can be compared with other users and a mutual evaluation can be conducted. The navigation unit can also develop an algorithm for comparing users' pronunciations. For example, the generation AI can analyze the pronunciation data of other users and provide feedback for mutual evaluations. This can provide a function for a user to compare with other users and conduct mutual evaluations.

[0065] The navigation unit can receive community-based feedback by recording the user's pronunciation and sharing it with other users. For example, the generation AI provides a function to record the user's pronunciation and share it with other users. For example, the user can share the audio of themselves pronouncing "thank you" with the community and receive feedback from other users. The navigation unit also develops an algorithm for receiving community-based feedback. For example, the generation AI analyzes the feedback of other users and provides appropriate advice to the user. This allows the user to receive community-based feedback.

[0066] The navigation unit can use the emotion estimation function to provide pronunciation advice according to the user's emotional state, thereby reducing stress. For example, the emotion estimation function can be used to analyze the user's emotional state and provide pronunciation advice. For example, if the user is nervous, specific advice on how to relax can be provided. The navigation unit can also develop an algorithm to provide advice on how to reduce stress based on the user's emotional state. For example, the generation AI can analyze the user's emotional state and provide advice such as "take a deep breath and relax." This can provide pronunciation advice according to the user's emotional state, thereby reducing stress.

[0067] The navigation unit can use the emotion estimation function to monitor the user's emotional state when practicing pronunciation and provide advice to maximize the effectiveness of the practice. For example, the emotion estimation function can be used to monitor the user's emotional state and provide advice to maximize the effectiveness of the practice. For example, if the user feels tired during practice, the navigation unit can advise the user to take a break. The navigation unit can also develop an algorithm to provide advice to maximize the effectiveness of the practice based on the user's emotional state. For example, the generation AI can analyze the user's emotional state and provide advice such as "Take a short break and then resume." This makes it possible to monitor the user's emotional state and provide advice to maximize the effectiveness of the practice.

[0068] The navigation unit can use the emotion estimation function to provide positive feedback to increase the user's motivation when practicing pronunciation. For example, the emotion estimation function can be used to analyze the user's emotional state and provide positive feedback to increase the user's motivation for pronunciation practice. For example, if the user feels tired while practicing, an encouraging message can be displayed. The navigation unit can also develop an algorithm to provide feedback to increase motivation based on the user's emotional state. For example, a generation AI can analyze the user's emotional state and provide positive feedback such as "great pronunciation." This can increase the user's motivation.

[0069] The navigation unit can use the emotion estimation function to provide pronunciation advice according to the user's emotional state, maximizing the effectiveness of practice. For example, the emotion estimation function can be used to analyze the user's emotional state and provide pronunciation advice. For example, if the user feels nervous during practice, specific advice on how to relax can be provided. The navigation unit can also develop an algorithm to provide advice to maximize the effectiveness of practice based on the user's emotional state. For example, the generation AI can analyze the user's emotional state and provide advice such as "take a deep breath and relax." This allows the system to provide pronunciation advice according to the user's emotional state, maximizing the effectiveness of practice.

[0070] The navigation unit can use the emotion estimation function to provide pronunciation advice according to the user's emotional state, maximizing the effectiveness of practice. For example, the emotion estimation function can be used to analyze the user's emotional state and provide pronunciation advice. For example, if the user feels nervous during practice, specific advice on how to relax can be provided. The navigation unit can also develop an algorithm to provide advice to maximize the effectiveness of practice based on the user's emotional state. For example, the generation AI can analyze the user's emotional state and provide advice such as "take a deep breath and relax." This allows the system to provide pronunciation advice according to the user's emotional state, maximizing the effectiveness of practice.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The analysis unit analyzes mouth and tongue movements in real time. For example, a camera or sensor can be used to capture the user's mouth and tongue movements, and the generation AI analyzes those movements. When the user tries to pronounce "hello," the generation AI analyzes the shape of the mouth and the position of the tongue to identify the movements required for correct pronunciation. Step 2: The navigation unit provides audio and visual guidance to find the optimal pronunciation method based on the data analyzed by the analysis unit. For example, the generation AI may give audio instructions such as "Lift your tongue a little higher" while simultaneously displaying a diagram on the screen showing the correct tongue position. When the user pronounces "thank you," the generation AI evaluates the pronunciation and provides feedback such as "your pronunciation is accurate." After the user has completed a week of practice, the generation AI provides a report stating, "your pronunciation has improved significantly over the past week."

[0073] 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.

[0074] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0086] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0087] 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.

[0088] 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.

[0089] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0102] 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.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0140] 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 analysis unit that analyzes mouth or tongue movements in real time; and a navigation unit that provides audio and visual guidance on the optimal pronunciation method based on the data analyzed by the analysis unit. A system characterized by:

2. The analysis unit The system simultaneously analyzes the user's overall facial expression and provides feedback to improve the naturalness of speech.

2. The system of claim 1.

3. The analysis unit Simultaneously captures sign language movements to support the coordination of sign language and speech.

2. The system of claim 1.

4. The navigation unit Detect subtle differences in your pronunciation to provide more precise feedback 2. The system of claim 1.

5. The analysis unit Estimates the user's emotional state in real time and provides pronunciation advice according to the emotion.

2. The system of claim 1.

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