System

Abstract

An object of a system according to an embodiment is to provide an individualized practice method or training plan for improving a user's singing skill.SOLUTION: A system according to an embodiment includes a singing voice analysis unit, a practice method suggestion unit, and a training plan providing unit. The singing voice analysis section analyzes the user's singing voice. The practice method proposing section proposes a practice method based on a result of the analysis by the singing voice analyzing section. A training plan provider collects and analyzes the user's singing data and provides an individually customized training plan.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 techniques have had the problem of making it difficult to provide users with personalized practice methods and training plans to improve their singing ability.

[0005] The system according to the embodiment aims to provide a user with a personalized practice method or training plan to improve their singing ability. [Means for solving the problem]

[0006] The system according to the embodiment includes a singing voice analysis unit, a practice method suggestion unit, and a training plan providing unit. The singing voice analysis unit analyzes the user's singing voice. The practice method suggestion unit suggests a practice method based on the results of the analysis by the singing voice analysis unit. The training plan providing unit collects and analyzes the user's singing data and provides an individually customized training plan. [Effects of the Invention]

[0007] The system according to the embodiment can provide a user with a personalized practice method or training plan to improve their singing ability. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A singing ability improvement support system according to an embodiment of the present invention is a system that uses a generative AI to improve a user's singing ability. This system analyzes the user's singing voice, identifies areas for improvement, suggests practice methods, and provides an individually customized training plan. This allows the singing ability improvement support system to efficiently improve the user's singing ability.

[0029] A singing ability improvement support system according to an embodiment includes a singing voice analysis unit, a practice method suggestion unit, and a training plan provision unit. The singing voice analysis unit analyzes the user's singing voice. For example, the singing voice analysis unit uses a generation AI to evaluate factors such as pitch, rhythm, pronunciation, and expressiveness, and identifies areas that need improvement. The generation AI receives audio data sung by the user as input and performs analysis based on the audio data. For example, it provides specific feedback such as, "Your pitch is unstable. You're particularly off-key in the chorus." The practice method suggestion unit proposes practice methods based on the results of the analysis by the singing voice analysis unit. For example, if the pitch is unstable, the generation AI suggests scale practice to stabilize the pitch or repeated practice of a specific phrase. The generation AI receives prompts based on the analysis results as input and generates practice methods based on the prompts. For example, it provides specific practice methods such as, "To stabilize your pitch, try singing the Do-Re-Mi-Fa-So-La-Si-Do scale slowly." The training plan provision unit collects and analyzes the user's singing data and provides individually customized training plans. For example, the generation AI suggests which parts of the song a user should focus on practicing based on their past practice data and progress. The generation AI receives the user's singing data and progress as input and generates a training plan based on that data. For example, it may provide a specific training plan such as, "Your pitch has improved, but your rhythm is still unstable. Let's focus on practicing rhythm for the next week." This allows the singing ability improvement support system according to the embodiment to efficiently improve the user's singing ability.

[0030] The singing voice analysis unit can evaluate the user's vocal quality and the health of their vocal cords and suggest vocal cord care methods. In the singing voice analysis unit, for example, the generation AI analyzes the user's vocal quality and evaluates the health of their vocal cords. For example, if the voice is hoarse, it will suggest vocal cord care methods. It also analyzes the health of the user's vocal cords and suggests vocal cord care methods. For example, it will suggest stretching methods to reduce vocal cord fatigue. It also evaluates the health of the vocal cords based on the results of the voice quality analysis and suggests care methods. For example, it will suggest hydration methods to prevent the vocal cords from drying out. This allows the user to improve their singing ability while maintaining the health of their vocal cords.

[0031] The singing voice analysis unit can analyze the user's breathing patterns while singing and point out areas for improvement in their breathing technique. For example, the singing voice analysis unit uses a generation AI to analyze the user's breathing patterns while singing and point out areas for improvement in their breathing technique. For example, it provides advice on adjusting the timing of breaths. It also points out areas for improvement in their breathing technique based on the results of the breathing pattern analysis. For example, it suggests ways to practice abdominal breathing. It also analyzes the user's breathing patterns in real time and points out areas for improvement in their breathing technique. For example, it suggests breathing techniques to prevent shortness of breath. By improving the user's breathing technique, more stable singing becomes possible.

[0032] The practice method suggestion unit can provide a reasonable practice plan by taking into account the user's physical condition and the condition of their vocal cords. For example, the generation AI of the practice method suggestion unit provides a reasonable practice plan by taking into account the user's physical condition and the condition of their vocal cords. For example, it may suggest exercises to reduce vocal cord fatigue. It may also analyze the user's physical condition and provide a reasonable practice plan. For example, it may suggest lighter exercises if the user is not feeling well. It may also analyze the condition of the vocal cords and provide a reasonable practice plan. For example, it may suggest exercises to prevent the vocal cords from drying out. In this way, by providing a practice plan that takes into account the user's physical condition and the condition of their vocal cords, the user can continue practicing without strain.

[0033] The training plan providing unit can provide a reasonable training plan by taking into account the user's physical condition and the condition of their vocal cords. For example, the training plan providing unit provides a reasonable training plan by using a generation AI to consider the user's physical condition and the condition of their vocal cords. For example, it may propose a plan to reduce vocal cord fatigue. It may also analyze the user's physical condition and provide a reasonable training plan. For example, it may propose a lighter plan if the user is not feeling well. It may also analyze the condition of the vocal cords and provide a reasonable training plan. For example, it may propose a plan to prevent the vocal cords from drying out. In this way, by providing a training plan that takes into account the user's physical condition and the condition of their vocal cords, the user can continue practicing without straining themselves.

[0034] The training plan providing unit can customize the plan according to the user's singing style and preferences. For example, the training plan providing unit uses a generation AI to provide a training plan according to the user's singing style and preferences. For example, a pop training plan is proposed for a user who likes pop music. The unit also analyzes the singing style and provides a training plan according to the user's preferences. For example, a jazz training plan is proposed for a user who likes jazz. The unit also analyzes the user's preferences in real time and provides a training plan according to the singing style. For example, a classical training plan is proposed for a user who likes classical music. This allows for more effective practice by providing a training plan according to the user's singing style and preferences.

[0035] The training plan providing unit can provide training plans corresponding to different music genres, supporting the improvement of singing ability for each genre. For example, the training plan providing unit provides training plans corresponding to different music genres using a generation AI, supporting the improvement of singing ability for each genre. For example, it provides training plans for pop and classical music. It also provides training plans corresponding to different genres, supporting the improvement of singing ability for each genre. For example, it provides training plans for rock and jazz. It also provides training plans for different genres according to the user's preferences, supporting the improvement of singing ability for each genre. For example, it provides training plans for enka and R&B. In this way, it supports the improvement of singing ability for each genre by providing training plans corresponding to different music genres.

[0036] The singing voice analysis unit can analyze the user's singing data in real time and provide instant feedback. In the singing voice analysis unit, for example, a generation AI analyzes the user's singing data in real time and provides instant feedback. For example, it points out any deviations in pitch while singing. It also analyzes the singing data in real time and provides instant feedback. For example, it points out any deviations in rhythm on the spot. It also analyzes the user's singing data in real time and provides instant feedback. For example, it points out areas for improvement in pronunciation on the spot. By providing feedback in real time, the user can instantly understand areas for improvement and reflect them in their practice.

[0037] The singing voice analysis unit can remove background noise when analyzing audio data and evaluate only the pure singing voice. For example, the singing voice analysis unit can filter out environmental sounds. Furthermore, the singing voice analysis unit can remove background noise when analyzing audio data and evaluate only the pure singing voice. For example, the singing voice analysis unit can apply a filter that removes noise. Furthermore, the singing voice analysis unit can remove background noise when analyzing the user's audio data and evaluate only the pure singing voice. For example, the singing voice analysis unit can use noise canceling technology. By removing background noise, the singing voice analysis unit can evaluate only the pure singing voice and provide accurate feedback.

[0038] The singing voice analysis unit can compare the analysis results of the singing data with the user's past data to evaluate the progress. For example, the generation AI in the singing voice analysis unit compares the analysis results of the singing data with the user's past data to evaluate the progress. For example, it evaluates the degree of improvement in pitch. It also compares the analysis results of the singing data with the past data to evaluate the progress. For example, it evaluates the stability of rhythm. It also compares the user's singing data with the past data to evaluate the progress. For example, it evaluates the degree of improvement in pronunciation. In this way, by comparing with the user's past data, it is possible to accurately evaluate the progress and provide appropriate feedback.

[0039] The singing voice analysis unit can compare the analysis results of the voice data with other users and provide a relative evaluation. For example, the generation AI in the singing voice analysis unit compares the analysis results of the voice data with other users and provides a relative evaluation. For example, comparison with users of the same age. The analysis results of the voice data can also be compared with other users and provide a relative evaluation. For example, comparison with singers of the same genre. The user's voice data can also be compared with other users and provide a relative evaluation. For example, comparison with singers of the same level. In this way, by comparing with other users, the user's singing ability can be evaluated relatively and areas for improvement can be identified.

[0040] The singing voice analysis unit can analyze singing data in different languages ​​and provide feedback in multiple languages. For example, the generation AI in the singing voice analysis unit analyzes singing data in different languages ​​and provides feedback in multiple languages. For example, it analyzes singing in English and Japanese. It also analyzes singing data in different languages ​​and provides feedback in multiple languages. For example, it analyzes singing in Spanish and French. It also analyzes the user's singing data in different languages ​​and provides feedback in multiple languages. For example, it analyzes singing in Chinese and Korean. This makes it possible to analyze singing data in different languages ​​and provide feedback in multiple languages, thereby meeting the diverse needs of users.

[0041] The practice method suggestion unit can provide practice method suggestions in the form of video tutorials and interactive lessons. For example, the practice method suggestion unit has a generation AI provide practice method suggestions in the form of video tutorials. For example, it provides a video for pitch practice. Also, it provides practice method suggestions in the form of interactive lessons. For example, it provides an interactive lesson for rhythm practice. Also, it provides practice methods in the form of video tutorials and interactive lessons. For example, it provides videos and interactive lessons for pronunciation practice. In this way, by providing practice methods in the form of video tutorials and interactive lessons, the user can practice more effectively.

[0042] The practice method suggestion unit can suggest practice methods corresponding to different music genres and support the improvement of singing ability for each genre. For example, the generation AI in the practice method suggestion unit suggests practice methods corresponding to different music genres and supports the improvement of singing ability for each genre. For example, it suggests practice methods for pop and classical music. It also suggests practice methods corresponding to different genres and supports the improvement of singing ability for each genre. For example, it suggests practice methods for rock and jazz. It also suggests practice methods for different genres according to the user's preferences and supports the improvement of singing ability for each genre. For example, it suggests practice methods for enka and R&B. In this way, by suggesting practice methods corresponding to different music genres, it supports the improvement of singing ability for each genre.

[0043] The training plan providing unit can provide the training plan in the form of a video tutorial and an interactive lesson. For example, the generation AI in the training plan providing unit provides the training plan in the form of a video tutorial. For example, a video for pitch practice is provided. Also, the training plan is provided in the form of an interactive lesson. For example, an interactive lesson for rhythm practice is provided. Also, the training plan is provided in the form of a video tutorial or an interactive lesson. For example, a video and an interactive lesson for pronunciation practice are provided. In this way, by providing the training plan in the form of a video tutorial or an interactive lesson, the user can practice more effectively.

[0044] The training plan providing unit can provide training plans corresponding to different music genres, supporting the improvement of singing ability for each genre. For example, the training plan providing unit provides training plans corresponding to different music genres using a generation AI, supporting the improvement of singing ability for each genre. For example, it provides training plans for pop and classical music. It also provides training plans corresponding to different genres, supporting the improvement of singing ability for each genre. For example, it provides training plans for rock and jazz. It also provides training plans for different genres according to the user's preferences, supporting the improvement of singing ability for each genre. For example, it provides training plans for enka and R&B. In this way, it supports the improvement of singing ability for each genre by providing training plans corresponding to different music genres.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The singing voice analysis unit can analyze the user's vocal range and suggest ways to expand the range. For example, the generation AI can analyze the user's vocal range and suggest practice methods to expand the range. For example, it can provide vocal exercises to produce high notes or practice methods to stabilize low notes. It can also analyze the user's vocal range in real time and suggest ways to expand the range. For example, if it is difficult to produce a particular range, it can suggest practice methods to strengthen that range. This allows the user to expand their vocal range, enabling them to sing more diversely.

[0047] The practice method suggestion unit can analyze the user's posture while singing and point out areas for improvement. For example, the generation AI analyzes the user's posture and provides advice on how to sing with correct posture. For example, it provides specific advice such as straightening the spine and relaxing the shoulders. It also analyzes the user's posture in real time and points out areas for improvement. For example, if posture deteriorates after singing for a long period of time, it provides advice on how to correct it on the spot. This allows singing with correct posture to be more stable.

[0048] The training plan provider takes into account the user's lifestyle habits and can suggest ways to improve them to contribute to improving singing ability. For example, the generation AI analyzes the user's lifestyle habits and suggests improvements to sleep, diet, exercise, etc. For example, it provides specific advice such as getting enough sleep and eating foods that are good for the vocal cords. It also analyzes the user's lifestyle habits in real time and suggests ways to improve them. For example, it suggests the best time to hydrate to prevent a dry throat. This can support the improvement of singing ability by improving lifestyle habits.

[0049] The singing voice analysis unit can analyze the user's facial expressions while singing and point out areas for improvement. For example, the generation AI can analyze the user's facial expressions and provide advice to help them express their emotions more effectively. For example, it can provide specific advice such as smiling when singing or being conscious of eyebrow movements. It can also analyze the user's facial expressions in real time and point out areas for improvement. For example, if the user's facial expression becomes stiff during a particular phrase, it can provide advice on how to correct it on the spot. This allows for richer facial expressions, enabling more emotional singing.

[0050] The singing voice analysis unit can analyze the strength of the user's voice while singing and point out areas for improvement in dynamics. For example, the generation AI can analyze the strength of the user's voice and provide advice on how to use dynamics effectively. For example, it can provide specific advice such as making the voice stronger in the chorus or softer in the ballad section. The system can also analyze the strength of the user's voice in real time and point out areas for improvement in dynamics. For example, if the voice becomes weak in a particular phrase, it can provide advice on how to correct it on the spot. This allows for more expressive singing by using dynamics effectively.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The singing voice analysis unit analyzes the user's singing voice. For example, the singing voice analysis unit uses the generation AI to evaluate elements such as pitch, rhythm, pronunciation, and expressiveness, and identifies areas that need improvement. The generation AI receives the user's singing voice data as input and performs analysis based on that voice data. For example, it provides specific feedback such as, "Your pitch is unstable. You're particularly off-key in the chorus." Step 2: The practice method suggestion unit suggests practice methods based on the results of the analysis by the singing voice analysis unit. For example, if the pitch is unstable, the generation AI will suggest scale practice to stabilize the pitch or a method of repeatedly practicing a specific phrase. The generation AI receives prompts based on the analysis results as input and generates practice methods based on those prompts. For example, it will suggest a specific practice method such as, "To stabilize the pitch, try singing the Do-Re-Mi-Fa-So-La-Si scale slowly." Step 3: The training plan provider collects and analyzes the user's singing data and provides an individually customized training plan. For example, the generation AI suggests which parts of the song the user should focus on practicing based on their past practice data and progress. The generation AI receives the user's singing data and progress as input and generates a training plan based on that data. For example, it might provide a specific training plan such as, "Your pitch has improved, but your rhythm is still unstable. Let's focus on practicing rhythm for the next week."

[0053] (Example 2) A singing ability improvement support system according to an embodiment of the present invention is a system that uses a generative AI to improve a user's singing ability. This system analyzes the user's singing voice, identifies areas for improvement, suggests practice methods, and provides an individually customized training plan. This allows the singing ability improvement support system to efficiently improve the user's singing ability.

[0054] A singing ability improvement support system according to an embodiment includes a singing voice analysis unit, a practice method suggestion unit, and a training plan provision unit. The singing voice analysis unit analyzes the user's singing voice. For example, the singing voice analysis unit uses a generation AI to evaluate factors such as pitch, rhythm, pronunciation, and expressiveness, and identifies areas that need improvement. The generation AI receives audio data sung by the user as input and performs analysis based on the audio data. For example, it provides specific feedback such as, "Your pitch is unstable. You're particularly off-key in the chorus." The practice method suggestion unit proposes practice methods based on the results of the analysis by the singing voice analysis unit. For example, if the pitch is unstable, the generation AI suggests scale practice to stabilize the pitch or repeated practice of a specific phrase. The generation AI receives prompts based on the analysis results as input and generates practice methods based on the prompts. For example, it provides specific practice methods such as, "To stabilize your pitch, try singing the Do-Re-Mi-Fa-So-La-Si-Do scale slowly." The training plan provision unit collects and analyzes the user's singing data and provides individually customized training plans. For example, the generation AI suggests which parts of the song a user should focus on practicing based on their past practice data and progress. The generation AI receives the user's singing data and progress as input and generates a training plan based on that data. For example, it may provide a specific training plan such as, "Your pitch has improved, but your rhythm is still unstable. Let's focus on practicing rhythm for the next week." This allows the singing ability improvement support system according to the embodiment to efficiently improve the user's singing ability.

[0055] The singing voice analysis unit can estimate the user's emotional state and provide feedback according to the emotion. For example, the singing voice analysis unit uses a generation AI to estimate the user's emotional state while singing and provide feedback according to the emotion. For example, if the user is nervous, advice is given to relax. The singing voice analysis unit also analyzes the emotions while singing and provides feedback according to the emotion. For example, if the user is singing a sad song, advice is given to put more emotion into the song. The singing voice analysis unit also analyzes the user's emotional state in real time and provides feedback according to the emotion. For example, if the user is feeling a strong sense of joy, advice is given to maintain that emotion. This allows for more effective practice by providing feedback according to the user's emotions.

[0056] The singing voice analysis unit can evaluate the user's vocal quality and the health of their vocal cords and suggest vocal cord care methods. In the singing voice analysis unit, for example, the generation AI analyzes the user's vocal quality and evaluates the health of their vocal cords. For example, if the voice is hoarse, it will suggest vocal cord care methods. It also analyzes the health of the user's vocal cords and suggests vocal cord care methods. For example, it will suggest stretching methods to reduce vocal cord fatigue. It also evaluates the health of the vocal cords based on the results of the voice quality analysis and suggests care methods. For example, it will suggest hydration methods to prevent the vocal cords from drying out. This allows the user to improve their singing ability while maintaining the health of their vocal cords.

[0057] The singing voice analysis unit can analyze the user's breathing patterns while singing and point out areas for improvement in their breathing technique. For example, the singing voice analysis unit uses a generation AI to analyze the user's breathing patterns while singing and point out areas for improvement in their breathing technique. For example, it provides advice on adjusting the timing of breaths. It also points out areas for improvement in their breathing technique based on the results of the breathing pattern analysis. For example, it suggests ways to practice abdominal breathing. It also analyzes the user's breathing patterns in real time and points out areas for improvement in their breathing technique. For example, it suggests breathing techniques to prevent shortness of breath. By improving the user's breathing technique, more stable singing becomes possible.

[0058] The practice method suggestion unit can estimate the user's emotional state and suggest a practice method that suits the emotion. For example, the practice method suggestion unit uses a generation AI to estimate the user's emotional state and suggest a practice method that suits the emotion. For example, if motivation is low, start with easy practice. The practice method suggestion unit can also analyze the emotional state and suggest a practice method that suits the emotion. For example, if the user is nervous, it can suggest practice to relax. The practice method suggestion unit can also analyze the user's emotions in real time and suggest a practice method that suits the emotion. For example, if the user is feeling a strong sense of joy, it can suggest practice to maintain that emotion. This allows for more effective practice by suggesting a practice method that suits the user's emotion.

[0059] The practice method suggestion unit can provide a reasonable practice plan by taking into account the user's physical condition and the condition of their vocal cords. For example, the generation AI of the practice method suggestion unit provides a reasonable practice plan by taking into account the user's physical condition and the condition of their vocal cords. For example, it may suggest exercises to reduce vocal cord fatigue. It may also analyze the user's physical condition and provide a reasonable practice plan. For example, it may suggest lighter exercises if the user is not feeling well. It may also analyze the condition of the vocal cords and provide a reasonable practice plan. For example, it may suggest exercises to prevent the vocal cords from drying out. In this way, by providing a practice plan that takes into account the user's physical condition and the condition of their vocal cords, the user can continue practicing without strain.

[0060] The training plan providing unit can estimate the user's emotional state and provide a training plan that corresponds to the emotion. For example, the training plan providing unit uses a generation AI to estimate the user's emotional state and provide a training plan that corresponds to the emotion. For example, if motivation is low, a plan that is likely to give a sense of accomplishment is proposed. The unit also analyzes the emotional state and provides a training plan that corresponds to the emotion. For example, if the user is feeling stressed, a plan to relax is proposed. The unit also analyzes the user's emotions in real time and provides a training plan that corresponds to the emotion. For example, if the emotion of joy is strong, a plan to maintain that emotion is proposed. This allows for more effective practice by providing a training plan that corresponds to the user's emotions.

[0061] The training plan providing unit can provide a reasonable training plan by taking into account the user's physical condition and the condition of their vocal cords. For example, the training plan providing unit provides a reasonable training plan by using a generation AI to consider the user's physical condition and the condition of their vocal cords. For example, it may propose a plan to reduce vocal cord fatigue. It may also analyze the user's physical condition and provide a reasonable training plan. For example, it may propose a lighter plan if the user is not feeling well. It may also analyze the condition of the vocal cords and provide a reasonable training plan. For example, it may propose a plan to prevent the vocal cords from drying out. In this way, by providing a training plan that takes into account the user's physical condition and the condition of their vocal cords, the user can continue practicing without straining themselves.

[0062] The training plan providing unit can customize the plan according to the user's singing style and preferences. For example, the training plan providing unit uses a generation AI to provide a training plan according to the user's singing style and preferences. For example, a pop training plan is proposed for a user who likes pop music. The unit also analyzes the singing style and provides a training plan according to the user's preferences. For example, a jazz training plan is proposed for a user who likes jazz. The unit also analyzes the user's preferences in real time and provides a training plan according to the singing style. For example, a classical training plan is proposed for a user who likes classical music. This allows for more effective practice by providing a training plan according to the user's singing style and preferences.

[0063] The training plan providing unit can provide training plans corresponding to different music genres, supporting the improvement of singing ability for each genre. For example, the training plan providing unit provides training plans corresponding to different music genres using a generation AI, supporting the improvement of singing ability for each genre. For example, it provides training plans for pop and classical music. It also provides training plans corresponding to different genres, supporting the improvement of singing ability for each genre. For example, it provides training plans for rock and jazz. It also provides training plans for different genres according to the user's preferences, supporting the improvement of singing ability for each genre. For example, it provides training plans for enka and R&B. In this way, it supports the improvement of singing ability for each genre by providing training plans corresponding to different music genres.

[0064] The training plan providing unit can use the emotion estimation function to provide a training plan that corresponds to the user's emotions. For example, the generation AI in the training plan providing unit uses the emotion estimation function to provide a training plan that corresponds to the user's emotions. For example, if motivation is low, a plan that is likely to give a sense of accomplishment is suggested. The emotion estimation function is also used to provide a training plan that corresponds to the user's emotions. For example, if the user is feeling stressed, a plan for relaxation is suggested. The user's emotions are also analyzed in real time to provide a training plan that corresponds to the emotions. For example, if the emotion of joy is strong, a plan to maintain that emotion is suggested. In this way, by providing a training plan that corresponds to the user's emotions, more effective practice is possible.

[0065] The singing voice analysis unit can analyze the user's singing data in real time and provide instant feedback. In the singing voice analysis unit, for example, a generation AI analyzes the user's singing data in real time and provides instant feedback. For example, it points out any deviations in pitch while singing. It also analyzes the singing data in real time and provides instant feedback. For example, it points out any deviations in rhythm on the spot. It also analyzes the user's singing data in real time and provides instant feedback. For example, it points out areas for improvement in pronunciation on the spot. By providing feedback in real time, the user can instantly understand areas for improvement and reflect them in their practice.

[0066] The singing voice analysis unit can remove background noise when analyzing audio data and evaluate only the pure singing voice. For example, the singing voice analysis unit can filter out environmental sounds. Furthermore, the singing voice analysis unit can remove background noise when analyzing audio data and evaluate only the pure singing voice. For example, the singing voice analysis unit can apply a filter that removes noise. Furthermore, the singing voice analysis unit can remove background noise when analyzing the user's audio data and evaluate only the pure singing voice. For example, the singing voice analysis unit can use noise canceling technology. By removing background noise, the singing voice analysis unit can evaluate only the pure singing voice and provide accurate feedback.

[0067] The singing voice analysis unit can compare the analysis results of the singing data with the user's past data to evaluate the progress. For example, the generation AI in the singing voice analysis unit compares the analysis results of the singing data with the user's past data to evaluate the progress. For example, it evaluates the degree of improvement in pitch. It also compares the analysis results of the singing data with the past data to evaluate the progress. For example, it evaluates the stability of rhythm. It also compares the user's singing data with the past data to evaluate the progress. For example, it evaluates the degree of improvement in pronunciation. In this way, by comparing with the user's past data, it is possible to accurately evaluate the progress and provide appropriate feedback.

[0068] The singing voice analysis unit can compare the analysis results of the voice data with other users and provide a relative evaluation. For example, the generation AI in the singing voice analysis unit compares the analysis results of the voice data with other users and provides a relative evaluation. For example, comparison with users of the same age. The analysis results of the voice data can also be compared with other users and provide a relative evaluation. For example, comparison with singers of the same genre. The user's voice data can also be compared with other users and provide a relative evaluation. For example, comparison with singers of the same level. In this way, by comparing with other users, the user's singing ability can be evaluated relatively and areas for improvement can be identified.

[0069] The singing voice analysis unit can analyze singing data in different languages ​​and provide feedback in multiple languages. For example, the generation AI in the singing voice analysis unit analyzes singing data in different languages ​​and provides feedback in multiple languages. For example, it analyzes singing in English and Japanese. It also analyzes singing data in different languages ​​and provides feedback in multiple languages. For example, it analyzes singing in Spanish and French. It also analyzes the user's singing data in different languages ​​and provides feedback in multiple languages. For example, it analyzes singing in Chinese and Korean. This makes it possible to analyze singing data in different languages ​​and provide feedback in multiple languages, thereby meeting the diverse needs of users.

[0070] The singing voice analysis unit can use the emotion estimation function to provide analysis results according to the user's emotions. For example, the generation AI in the singing voice analysis unit uses the emotion estimation function to provide analysis results according to the user's emotions. For example, in the case of sadness, advice is given on how to express the emotion. Also, using the emotion estimation function, analysis results are provided according to the user's emotions. For example, in the case of joy, advice is given on how to maintain that emotion. Also, the user's emotions are analyzed and analysis results according to the emotion are provided. For example, in the case of anger, advice is given on how to express that emotion. In this way, by providing analysis results according to the user's emotions, more effective feedback can be provided.

[0071] The practice method suggestion unit can provide practice method suggestions in the form of video tutorials and interactive lessons. For example, the practice method suggestion unit has a generation AI provide practice method suggestions in the form of video tutorials. For example, it provides a video for pitch practice. Also, it provides practice method suggestions in the form of interactive lessons. For example, it provides an interactive lesson for rhythm practice. Also, it provides practice methods in the form of video tutorials and interactive lessons. For example, it provides videos and interactive lessons for pronunciation practice. In this way, by providing practice methods in the form of video tutorials and interactive lessons, the user can practice more effectively.

[0072] The practice method suggestion unit can suggest practice methods corresponding to different music genres and support the improvement of singing ability for each genre. For example, the generation AI in the practice method suggestion unit suggests practice methods corresponding to different music genres and supports the improvement of singing ability for each genre. For example, it suggests practice methods for pop and classical music. It also suggests practice methods corresponding to different genres and supports the improvement of singing ability for each genre. For example, it suggests practice methods for rock and jazz. It also suggests practice methods for different genres according to the user's preferences and supports the improvement of singing ability for each genre. For example, it suggests practice methods for enka and R&B. In this way, by suggesting practice methods corresponding to different music genres, it supports the improvement of singing ability for each genre.

[0073] The practice method suggestion unit can use the emotion estimation function to suggest practice methods that correspond to the user's emotions. For example, the generation AI in the practice method suggestion unit uses the emotion estimation function to suggest practice methods that correspond to the user's emotions. For example, if motivation is low, start with easy practice. The emotion estimation function can also be used to suggest practice methods that correspond to the user's emotions. For example, if the user is nervous, it can suggest practice to relax. The unit can also analyze the user's emotions in real time and suggest practice methods that correspond to the emotions. For example, if the user is feeling a strong sense of joy, it can suggest practice to maintain that emotion. This allows for more effective practice by suggesting practice methods that correspond to the user's emotions.

[0074] The training plan providing unit can provide the training plan in the form of a video tutorial and an interactive lesson. For example, the generation AI in the training plan providing unit provides the training plan in the form of a video tutorial. For example, a video for pitch practice is provided. Also, the training plan is provided in the form of an interactive lesson. For example, an interactive lesson for rhythm practice is provided. Also, the training plan is provided in the form of a video tutorial or an interactive lesson. For example, a video and an interactive lesson for pronunciation practice are provided. In this way, by providing the training plan in the form of a video tutorial or an interactive lesson, the user can practice more effectively.

[0075] The training plan providing unit can provide training plans corresponding to different music genres, supporting the improvement of singing ability for each genre. For example, the training plan providing unit provides training plans corresponding to different music genres using a generation AI, supporting the improvement of singing ability for each genre. For example, it provides training plans for pop and classical music. It also provides training plans corresponding to different genres, supporting the improvement of singing ability for each genre. For example, it provides training plans for rock and jazz. It also provides training plans for different genres according to the user's preferences, supporting the improvement of singing ability for each genre. For example, it provides training plans for enka and R&B. In this way, it supports the improvement of singing ability for each genre by providing training plans corresponding to different music genres.

[0076] The training plan providing unit can use the emotion estimation function to provide a training plan that corresponds to the user's emotions. For example, the generation AI in the training plan providing unit uses the emotion estimation function to provide a training plan that corresponds to the user's emotions. For example, if motivation is low, a plan that is likely to give a sense of accomplishment is suggested. The emotion estimation function is also used to provide a training plan that corresponds to the user's emotions. For example, if the user is feeling stressed, a plan for relaxation is suggested. The user's emotions are also analyzed in real time to provide a training plan that corresponds to the emotions. For example, if the emotion of joy is strong, a plan to maintain that emotion is suggested. In this way, by providing a training plan that corresponds to the user's emotions, more effective practice is possible.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The singing voice analysis unit can analyze the user's vocal range and suggest ways to expand the range. For example, the generation AI can analyze the user's vocal range and suggest practice methods to expand the range. For example, it can provide vocal exercises to produce high notes or practice methods to stabilize low notes. It can also analyze the user's vocal range in real time and suggest ways to expand the range. For example, if it is difficult to produce a particular range, it can suggest practice methods to strengthen that range. This allows the user to expand their vocal range, enabling them to sing more diversely.

[0079] The practice method suggestion unit can analyze the user's posture while singing and point out areas for improvement. For example, the generation AI analyzes the user's posture and provides advice on how to sing with correct posture. For example, it provides specific advice such as straightening the spine and relaxing the shoulders. It also analyzes the user's posture in real time and points out areas for improvement. For example, if posture deteriorates after singing for a long period of time, it provides advice on how to correct it on the spot. This allows singing with correct posture to be more stable.

[0080] The training plan provider takes into account the user's lifestyle habits and can suggest ways to improve them to contribute to improving singing ability. For example, the generation AI analyzes the user's lifestyle habits and suggests improvements to sleep, diet, exercise, etc. For example, it provides specific advice such as getting enough sleep and eating foods that are good for the vocal cords. It also analyzes the user's lifestyle habits in real time and suggests ways to improve them. For example, it suggests the best time to hydrate to prevent a dry throat. This can support the improvement of singing ability by improving lifestyle habits.

[0081] The singing voice analysis unit can analyze the user's facial expressions while singing and point out areas for improvement. For example, the generation AI can analyze the user's facial expressions and provide advice to help them express their emotions more effectively. For example, it can provide specific advice such as smiling when singing or being conscious of eyebrow movements. It can also analyze the user's facial expressions in real time and point out areas for improvement. For example, if the user's facial expression becomes stiff during a particular phrase, it can provide advice on how to correct it on the spot. This allows for richer facial expressions, enabling more emotional singing.

[0082] The practice method suggestion unit can estimate the user's emotions while singing and suggest ways to improve singing technique according to the emotion. For example, the generation AI can estimate the user's emotions and suggest techniques to express emotions more effectively. For example, it can provide specific advice such as the tone of voice to express sadness or the volume of voice to express joy. It can also analyze the user's emotions in real time and suggest ways to improve singing technique according to the emotion. For example, it can suggest how to raise the voice to express anger. This allows the user to express emotions more effectively and improve their singing expressiveness.

[0083] The training plan provider can estimate the user's emotional state and suggest methods to maintain motivation according to the emotion. For example, the generation AI can estimate the user's emotion and suggest methods to maintain motivation. For example, if motivation is low, it can set short-term goals that will give a sense of accomplishment. It can also analyze the emotional state in real time and suggest methods to maintain motivation. For example, if the user is feeling stressed, it can suggest taking a break to relax. This makes it easier to continue practicing by suggesting methods to maintain motivation according to the user's emotion.

[0084] The singing voice analysis unit can estimate the user's emotions while singing and suggest ways to improve the singing style according to the emotion. For example, the generation AI can estimate the user's emotions and suggest a singing style that will express the emotion more effectively. For example, it can provide specific advice such as singing at a slow tempo to express sadness or singing at a fast tempo to express joy. It can also analyze the user's emotions in real time and suggest ways to improve the singing style according to the emotion. For example, it can suggest a powerful singing style to express anger. This allows the user to express emotions more effectively, improving their singing expressiveness.

[0085] The practice method suggestion unit can estimate the user's emotional state and suggest a refreshing method according to the emotion. For example, the generation AI can estimate the user's emotion and suggest a refreshing method. For example, if motivation is low, it can suggest a short break to change the mood. It can also analyze the emotional state in real time and suggest a refreshing method. For example, if you are feeling stressed, it can suggest taking deep breaths to relax. This can improve the efficiency of practice by suggesting a refreshing method according to the emotion.

[0086] The training plan providing unit can estimate the user's emotional state and provide feedback according to the emotion. For example, the generation AI estimates the user's emotion and provides feedback according to the emotion. For example, if motivation is low, it can provide words of encouragement. It can also analyze the emotional state in real time and provide feedback according to the emotion. For example, if the user is feeling stressed, it can provide advice on how to relax. In this way, providing feedback according to the emotion makes it easier for the user to maintain motivation.

[0087] The singing voice analysis unit can analyze the strength of the user's voice while singing and point out areas for improvement in dynamics. For example, the generation AI can analyze the strength of the user's voice and provide advice on how to use dynamics effectively. For example, it can provide specific advice such as making the voice stronger in the chorus or softer in the ballad section. The system can also analyze the strength of the user's voice in real time and point out areas for improvement in dynamics. For example, if the voice becomes weak in a particular phrase, it can provide advice on how to correct it on the spot. This allows for more expressive singing by using dynamics effectively.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The singing voice analysis unit analyzes the user's singing voice. For example, the singing voice analysis unit uses the generation AI to evaluate elements such as pitch, rhythm, pronunciation, and expressiveness, and identifies areas that need improvement. The generation AI receives the user's singing voice data as input and performs analysis based on that voice data. For example, it provides specific feedback such as, "Your pitch is unstable. You're particularly off-key in the chorus." Step 2: The practice method suggestion unit suggests practice methods based on the results of the analysis by the singing voice analysis unit. For example, if the pitch is unstable, the generation AI will suggest scale practice to stabilize the pitch or a method of repeatedly practicing a specific phrase. The generation AI receives prompts based on the analysis results as input and generates practice methods based on those prompts. For example, it will suggest a specific practice method such as, "To stabilize the pitch, try singing the Do-Re-Mi-Fa-So-La-Si scale slowly." Step 3: The training plan provider collects and analyzes the user's singing data and provides an individually customized training plan. For example, the generation AI suggests which parts of the song the user should focus on practicing based on their past practice data and progress. The generation AI receives the user's singing data and progress as input and generates a training plan based on that data. For example, it might provide a specific training plan such as, "Your pitch has improved, but your rhythm is still unstable. Let's focus on practicing rhythm for the next week."

[0090] 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.

[0091] 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.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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]

[0157] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a singing voice analysis unit that analyzes the user's singing voice; a practice method suggestion unit that suggests a practice method based on the analysis result by the singing voice analysis unit; a training plan providing unit that collects and analyzes the user's singing data and provides an individually customized training plan. A system characterized by:

2. The singing voice analysis unit Analyze the user's singing data in real time and provide immediate feedback 2. The system of claim 1.

3. The practice method suggestion unit Estimate the emotional state of the user and suggest a practice method according to the emotion.

2. The system of claim 1.

4. The training plan providing unit Estimating the emotional state of the user and providing a training plan according to the emotion.

2. The system of claim 1.

5. The singing voice analysis unit Estimating the emotional state of the user and providing feedback according to the emotion 2. The system of claim 1.

Images