system

Abstract

The system according to the embodiment aims to provide voice training in an easy and scientific manner. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user feedback. The analysis unit analyzes the voice data collected by the collection unit. The generation unit generates a training plan based on the analysis results obtained by the analysis unit. The provision unit provides the training plan generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, voice training was difficult and not easy to do.

[0005] The system according to the embodiment aims to provide voice training in an easy and scientific manner. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user feedback. The analysis unit analyzes the voice data collected by the collection unit. The generation unit generates a training plan based on the analysis results obtained by the analysis unit. The provision unit provides the training plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows for easy and scientific voice training. [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 pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example 1) A voice training system according to an embodiment of the present invention utilizes various sensor technologies and a generation AI to collect and analyze a user's voice, generate a training plan, and provide it to the user. This voice training system uses various sensors to collect the user's voice, which is then analyzed by a generation AI to identify the user's vocal characteristics. Based on the analysis results, the generation AI then generates and provides an optimal training plan for the user. For example, the voice training system uses a microphone and vibration sensor to collect the user's voice. This allows for the collection of detailed data such as pitch, intensity, and duration. The generation AI then analyzes the collected voice data. Based on the collected data, the generation AI identifies the user's vocal characteristics. For example, it analyzes fluctuations in pitch and intensity, vocal duration, and other factors to understand the user's vocal patterns. Based on the analysis results, the generation AI then generates an optimal training plan for the user. The generation AI considers the user's vocal characteristics and proposes effective training methods. For example, this may include training to strengthen specific vocal ranges or training to extend vocal duration. Finally, the generated training plan is provided to the user. By following the provided plan, users can effectively progress through voice training at their own pace. This lowers the barrier to voice training, making it easier for more people to take part in voice training. The voice training system collects and analyzes the user's voice, generates and provides a training plan, enabling effective voice training.

[0029] A voice training system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects a user's voice. Examples of the user's voice include, but are not limited to, speaking, singing, and shouting. The collection unit collects data on the user's voice using, for example, a microphone or a vibration sensor. Examples of the microphone include a condenser microphone or a dynamic microphone. Examples of the vibration sensor include an acceleration sensor or a piezoelectric sensor. The analysis unit identifies characteristics of the user's vocalization based on the collected data. Examples of the vocalization characteristics include, but are not limited to, pitch, volume, and tone quality. The analysis unit analyzes, for example, fluctuations in pitch and intensity, duration of vocalization, and the like based on the collected data to understand the user's vocalization patterns. The generation unit generates a training plan based on the user's vocalization characteristics, including exercises to strengthen a specific vocal range and extending vocalization duration. The training plan may include, but is not limited to, exercises to strengthen a specific vocal range or training to extend vocal duration. The providing unit provides the generated training plan to the user. For example, by providing the generated training plan to the user, the providing unit allows the user to train at their own pace. In this way, the voice training system according to the embodiment collects and analyzes the user's voice, generates and provides a training plan, thereby enabling effective voice training.

[0030] The collection unit can collect user voice data using a microphone or a vibration sensor. The collection unit collects user voice data using, for example, a microphone. For example, a condenser microphone or a dynamic microphone is used as the microphone. A condenser microphone has high sensitivity and a wide frequency band, making it suitable for collecting detailed voice data. A dynamic microphone is highly durable and suitable for collecting stable voice data even in noisy environments. The collection unit can also collect user voice data using a vibration sensor. For example, an acceleration sensor or a piezoelectric sensor is used as the vibration sensor. An acceleration sensor detects vibrations and converts them into voice data. A piezoelectric sensor detects changes in pressure and converts them into voice data. In this way, the collection unit can collect detailed voice data by using a microphone or a vibration sensor. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data acquired by the microphone or vibration sensor into a generation AI and have the generation AI analyze the voice data.

[0031] The analysis unit can identify the user's vocal characteristics based on the collected data. The analysis unit, for example, analyzes the pitch and intensity fluctuations, vocal duration, etc. based on the collected data to understand the user's vocal patterns. Vocal characteristics include, but are not limited to, pitch range, volume, and sound quality. For example, the analysis unit measures pitch to analyze the pitch range and identify the range of the user's vocalization. For example, the analysis unit measures sound intensity to analyze the volume and identify the strength and weakness of the user's vocalization. For example, the analysis unit measures sound duration and fluctuations to identify the user's vocal pattern. This allows the analysis unit to identify vocal characteristics based on the collected data and generate an optimal training plan for the user. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input the collected voice data into a generation AI and have the generation AI identify the vocal characteristics.

[0032] The generation unit can generate a training plan for practicing to improve a specific range or for extending vocal duration based on the user's vocal characteristics. The generation unit, for example, considers the user's vocal characteristics and suggests exercises for improving a specific range. Specific ranges include, but are not limited to, high, mid, and low ranges. For example, the generation unit suggests training to repeatedly sing a specific scale as an exercise for improving a high range. For example, the generation unit suggests training to continuously sing a specific scale as an exercise for improving a mid-range. For example, the generation unit suggests training to sing a low note for a long period of time as an exercise for improving a low range. The generation unit can also generate a training plan for extending vocal duration. For example, vocal duration includes, but is not limited to, measurements in seconds and target duration values. For example, the generation unit suggests training to sing for a long period of time as an exercise for extending vocal duration. In this way, the generation unit generates a training plan that considers the user's vocal characteristics, thereby enabling effective voice training. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit may input the user's vocal characteristics into the generation AI and cause the generation AI to generate a training plan.

[0033] The providing unit can provide the generated training plan to the user. For example, the providing unit provides the generated training plan to the user. For example, by providing the generated training plan to the user, the providing unit allows the user to train at their own pace. For example, the providing unit provides the generated training plan to the user through a web application or a mobile application. The web application or mobile application is designed to allow the user to easily access and execute the training plan. The providing unit can also provide the generated training plan to the user through email or a messaging app. In this way, by providing the generated training plan to the user, the user can train at their own pace. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the generated training plan into AI and cause the AI ​​to execute a method of providing the plan to the user.

[0034] The collection unit can analyze the user's past voice data and select the optimal collection method. For example, if the user has collected a lot of high-pitched voices in the past, the collection unit selects a collection method specialized for high-pitched voices. For example, if the user has collected a lot of low-pitched voices in the past, the collection unit selects a collection method specialized for low-pitched voices. For example, if the user has collected voices with a certain rhythm in the past, the collection unit selects a collection method that matches that rhythm. In this way, the collection unit can select the optimal collection method for the user by analyzing the past voice data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past voice data into a generation AI and cause the generation AI to select the optimal collection method.

[0035] The collection unit can filter the user's current environmental sounds to remove noise when collecting voices. For example, when the user collects voices outdoors, the collection unit removes noise by filtering out wind noise and car sounds. For example, when the user collects voices indoors, the collection unit removes noise by filtering out sounds from air conditioners and home appliances. For example, when the user collects voices in a public place, the collection unit removes noise by filtering out voices of people talking around the user. In this way, the collection unit can collect clearer voice data by filtering environmental sounds and removing noise. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input environmental sound data to a generation AI and cause the generation AI to perform noise removal processing.

[0036] When collecting voices, the collection unit can prioritize collecting highly relevant voices by taking into account the user's geographical location information. For example, if the user is in a park, the collection unit prioritizes collecting voices suitable for a natural environment. For example, if the user is in an office, the collection unit prioritizes collecting voices suitable for a business setting. For example, if the user is at home, the collection unit prioritizes collecting voices suitable for a relaxed environment. In this way, the collection unit can prioritize collecting highly relevant voices by taking into account the geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant voices.

[0037] When collecting comments, the collection unit can analyze the user's social media activities and collect related comments. For example, if the user posts many singing videos on social media, the collection unit prioritizes collecting singing-related comments. For example, if the user posts many business-related videos on social media, the collection unit prioritizes collecting business-related comments. For example, if the user engages in many creative activities on social media, the collection unit prioritizes collecting creative comments. This allows the collection unit to effectively collect related comments by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input the user's social media activity data into the generation AI and cause the generation AI to collect related comments.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the voice. For example, the analysis unit performs a detailed analysis on important voice data. For example, the analysis unit performs a basic analysis on general voice data. For example, the analysis unit performs an analysis tailored to a specific purpose on voice data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the voice, enabling efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input voice importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the voice category. For example, the analysis unit applies an analysis algorithm for pitch and rhythm to a singing voice. For example, the analysis unit applies an analysis algorithm for pronunciation and intonation to a business voice. For example, the analysis unit applies an analysis algorithm for sound texture and expressiveness to a creative voice. In this way, the analysis unit can perform more accurate analysis by applying an analysis algorithm according to the voice category. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input voice category data into the generation AI and have the generation AI apply the analysis algorithm.

[0040] During analysis, the analysis unit can determine the priority of analysis based on the time when the voice was collected. For example, the analysis unit prioritizes analysis of recently collected voice data. For example, the analysis unit prioritizes analysis of voice data collected during a specific event or training session. For example, the analysis unit prioritizes analysis of voice data collected within a period specified by the user. This enables efficient analysis by the analysis unit determining the priority of analysis based on the time when the voice was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input data on the time when the voice was collected into the generation AI and have the generation AI determine the analysis priority.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the voices. For example, the analysis unit analyzes voice data of the same category together. For example, the analysis unit prioritizes analysis of voice data related to a specific training goal. For example, the analysis unit prioritizes analysis of highly relevant voice data based on the user's past training history. This allows the analysis unit to adjust the order of analysis based on the relevance of the voices, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input voice relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0042] When generating a training plan, the generation unit can analyze the user's past training history and select an optimal plan. The generation unit, for example, generates a plan for the next step based on the results of the user's past training. The generation unit, for example, generates a plan for improvement by taking into account training that the user was weak at in the past. The generation unit, for example, generates a plan to strengthen training that the user was good at in the past. In this way, the generation unit can provide the user with an optimal training plan by analyzing the user's past training history. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's past training history data into the generation AI and have the generation AI select an optimal plan.

[0043] When generating a training plan, the generation unit can customize the plan based on the user's current lifestyle. For example, if the user is busy, the generation unit generates a short but effective training plan. For example, if the user has time, the generation unit generates a detailed and time-consuming training plan. For example, if the user is preparing for a specific event, the generation unit generates a training plan tailored to the event. In this way, the generation unit can provide the user with an optimal training plan by customizing the plan based on the user's current lifestyle. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input the user's lifestyle data into the generation AI and have the generation AI customize the plan.

[0044] When generating a training plan, the generation unit can select an optimal plan by taking into account the user's geographical location information. For example, if the user is at home, the generation unit generates a training plan that can be done at home. For example, if the user is in the office, the generation unit generates a training plan that can be done at the office. For example, if the user is traveling, the generation unit generates a training plan that can be done at the travel destination. In this way, the generation unit can provide an optimal training plan for the user by taking into account the geographical location information. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and cause the generation AI to select an optimal plan.

[0045] When generating a training plan, the generation unit can analyze the user's social media activity and propose a plan. For example, if the user posts many singing videos on social media, the generation unit generates a training plan related to singing. For example, if the user posts many business-related videos on social media, the generation unit generates a training plan related to business scenes. For example, if the user engages in many creative activities on social media, the generation unit generates a creative training plan. In this way, the generation unit can provide an optimal training plan for the user by analyzing social media activity. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's social media activity data into the generation AI and have the generation AI execute the plan proposal.

[0046] When providing a training plan, the providing unit can select the optimal provision method by referring to the user's past training history. For example, if the user has preferred audio guides in the past, the providing unit provides the plan using audio guides. For example, if the user has preferred text guides in the past, the providing unit provides the plan using text guides. For example, if the user has preferred video guides in the past, the providing unit provides the plan using video guides. This allows the providing unit to select the optimal provision method for the user by referring to the past training history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past training history data into the generation AI and cause the generation AI to select the optimal provision method.

[0047] When providing a training plan, the providing unit can select the optimal provision method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit provides the plan in a display method that matches the screen size. For example, if the user is using a tablet, the providing unit provides the plan in a display method that is optimized for a large screen. For example, if the user is using a smartwatch, the providing unit provides the plan in a display method that is simple and highly visible. This allows the providing unit to select the optimal provision method for the user by taking into account the device information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal provision method.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] When collecting the user's voice data, the collection unit can monitor the user's health condition and adjust the collection method according to the health condition. For example, if the user has a cold, the collection unit reduces the frequency of collecting voice data to reduce the burden on the user. Also, if the user is tired, the collection unit collects data for a short period of time, taking into consideration the user's physical condition. Furthermore, if the user is in good health, the collection unit can apply the normal collection method and collect detailed data. This allows the collection unit to achieve flexible data collection according to the user's health condition.

[0050] When analyzing the user's voice data, the analysis unit can compare it with the user's past vocal data to evaluate progress. For example, if the user previously struggled with a particular range, the analysis unit can evaluate the improvement in that range. Also, if the user previously trained to extend the duration of vocalizations, the analysis unit can compare the current duration with the past data to evaluate progress. Furthermore, if the user previously adjusted the volume, the analysis unit can evaluate the stability of the current volume. This allows the analysis unit to specifically evaluate the effectiveness of the user's training and provide feedback.

[0051] The generation unit can incorporate game elements into the training plan based on the user's vocal characteristics. For example, practice to strengthen a specific vocal range can be provided in a game format, allowing the user to train while having fun. Training to extend vocal duration can be provided in a time attack format, allowing the user to take on a challenge. Furthermore, training to adjust volume can be implemented using a point system, allowing the user to feel a sense of accomplishment. In this way, the generation unit can increase the user's motivation and provide effective training.

[0052] When providing the generated training plan, the providing unit can customize it according to the user's learning style. For example, a user who prefers visual learning can be provided with a plan that makes extensive use of visual guides. A user who prefers auditory learning can be provided with a plan that mainly uses audio guides. Furthermore, a user who prefers experiential learning can be provided with a plan that includes many opportunities to actually practice out loud. This allows the providing unit to provide the optimal training plan according to the user's learning style.

[0053] When providing the generated training plan, the provider can collect user feedback and adjust the plan in real time. For example, if the user finds a particular training difficult, the provider can lower the difficulty of that training. Alternatively, if the user finds a particular training too easy, the provider can increase the difficulty of that training. Furthermore, if the user is interested in a particular training, the provider can strengthen that training. This allows the provider to flexibly adjust the training plan based on the user's feedback and provide effective training.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The collection unit collects the user's voice. The user's voice may include talking, singing, shouting, etc. The collection unit collects data on the user's voice using a microphone and a vibration sensor. The microphone may be a condenser microphone or a dynamic microphone, and the vibration sensor may be an acceleration sensor or a piezoelectric sensor. Step 2: The analysis unit identifies the user's vocal characteristics based on the collected data. Vocal characteristics include pitch, volume, and tone quality. The analysis unit analyzes the collected data to determine the user's vocal patterns, such as fluctuations in pitch and intensity, and the duration of vocalization. Step 3: The generation unit generates a training plan for practicing to strengthen a specific range of vocal sounds and for extending vocal duration based on the user's vocal characteristics. The training plan includes exercises for strengthening a specific range of vocal sounds and training for extending vocal duration. Step 4: The provider provides the generated training plan to the user, allowing the user to train at their own pace.

[0056] (Example 2) A voice training system according to an embodiment of the present invention utilizes various sensor technologies and a generation AI to collect and analyze a user's voice, generate a training plan, and provide it to the user. This voice training system uses various sensors to collect the user's voice, which is then analyzed by a generation AI to identify the user's vocal characteristics. Based on the analysis results, the generation AI then generates and provides an optimal training plan for the user. For example, the voice training system uses a microphone and vibration sensor to collect the user's voice. This allows for the collection of detailed data such as pitch, intensity, and duration. The generation AI then analyzes the collected voice data. Based on the collected data, the generation AI identifies the user's vocal characteristics. For example, it analyzes fluctuations in pitch and intensity, vocal duration, and other factors to understand the user's vocal patterns. Based on the analysis results, the generation AI then generates an optimal training plan for the user. The generation AI considers the user's vocal characteristics and proposes effective training methods. For example, this may include training to strengthen specific vocal ranges or training to extend vocal duration. Finally, the generated training plan is provided to the user. By following the provided plan, users can effectively progress through voice training at their own pace. This lowers the barrier to voice training, making it easier for more people to take part in voice training. The voice training system collects and analyzes the user's voice, generates and provides a training plan, enabling effective voice training.

[0057] A voice training system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects a user's voice. Examples of the user's voice include, but are not limited to, speaking, singing, and shouting. The collection unit collects data on the user's voice using, for example, a microphone or a vibration sensor. Examples of the microphone include a condenser microphone or a dynamic microphone. Examples of the vibration sensor include an acceleration sensor or a piezoelectric sensor. The analysis unit identifies characteristics of the user's vocalization based on the collected data. Examples of the vocalization characteristics include, but are not limited to, pitch, volume, and tone quality. The analysis unit analyzes, for example, fluctuations in pitch and intensity, duration of vocalization, and the like based on the collected data to understand the user's vocalization patterns. The generation unit generates a training plan based on the user's vocalization characteristics, including exercises to strengthen a specific vocal range and extending vocalization duration. The training plan may include, but is not limited to, exercises to strengthen a specific vocal range or training to extend vocal duration. The providing unit provides the generated training plan to the user. For example, by providing the generated training plan to the user, the providing unit allows the user to train at their own pace. In this way, the voice training system according to the embodiment collects and analyzes the user's voice, generates and provides a training plan, thereby enabling effective voice training.

[0058] The collection unit can collect user voice data using a microphone or a vibration sensor. The collection unit collects user voice data using, for example, a microphone. For example, a condenser microphone or a dynamic microphone is used as the microphone. A condenser microphone has high sensitivity and a wide frequency band, making it suitable for collecting detailed voice data. A dynamic microphone is highly durable and suitable for collecting stable voice data even in noisy environments. The collection unit can also collect user voice data using a vibration sensor. For example, an acceleration sensor or a piezoelectric sensor is used as the vibration sensor. An acceleration sensor detects vibrations and converts them into voice data. A piezoelectric sensor detects changes in pressure and converts them into voice data. In this way, the collection unit can collect detailed voice data by using a microphone or a vibration sensor. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data acquired by the microphone or vibration sensor into a generation AI and have the generation AI analyze the voice data.

[0059] The analysis unit can identify the user's vocal characteristics based on the collected data. The analysis unit, for example, analyzes the pitch and intensity fluctuations, vocal duration, etc. based on the collected data to understand the user's vocal patterns. Vocal characteristics include, but are not limited to, pitch range, volume, and sound quality. For example, the analysis unit measures pitch to analyze the pitch range and identify the range of the user's vocalization. For example, the analysis unit measures sound intensity to analyze the volume and identify the strength and weakness of the user's vocalization. For example, the analysis unit measures sound duration and fluctuations to identify the user's vocal pattern. This allows the analysis unit to identify vocal characteristics based on the collected data and generate an optimal training plan for the user. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input the collected voice data into a generation AI and have the generation AI identify the vocal characteristics.

[0060] The generation unit can generate a training plan for practicing to improve a specific range or for extending vocal duration based on the user's vocal characteristics. The generation unit, for example, considers the user's vocal characteristics and suggests exercises for improving a specific range. Specific ranges include, but are not limited to, high, mid, and low ranges. For example, the generation unit suggests training to repeatedly sing a specific scale as an exercise for improving a high range. For example, the generation unit suggests training to continuously sing a specific scale as an exercise for improving a mid-range. For example, the generation unit suggests training to sing a low note for a long period of time as an exercise for improving a low range. The generation unit can also generate a training plan for extending vocal duration. For example, vocal duration includes, but is not limited to, measurements in seconds and target duration values. For example, the generation unit suggests training to sing for a long period of time as an exercise for extending vocal duration. In this way, the generation unit generates a training plan that considers the user's vocal characteristics, thereby enabling effective voice training. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit may input the user's vocal characteristics into the generation AI and cause the generation AI to generate a training plan.

[0061] The providing unit can provide the generated training plan to the user. For example, the providing unit provides the generated training plan to the user. For example, by providing the generated training plan to the user, the providing unit allows the user to train at their own pace. For example, the providing unit provides the generated training plan to the user through a web application or a mobile application. The web application or mobile application is designed to allow the user to easily access and execute the training plan. The providing unit can also provide the generated training plan to the user through email or a messaging app. In this way, by providing the generated training plan to the user, the user can train at their own pace. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the generated training plan into AI and cause the AI ​​to execute a method of providing the plan to the user.

[0062] The collection unit can estimate the user's emotions and adjust the timing of voice collection based on the estimated user emotions. For example, when the user is relaxed, the collection unit randomly sets the timing of voice collection to encourage natural speech. For example, when the user is nervous, the collection unit allows the user time to relax before collecting voice. For example, when the user is concentrating, the collection unit collects voice multiple times in a short period of time to maintain concentration. In this way, the collection unit can collect more natural speech data by adjusting the timing of voice collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, for example, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of voice collection.

[0063] The collection unit can analyze the user's past voice data and select the optimal collection method. For example, if the user has collected a lot of high-pitched voices in the past, the collection unit selects a collection method specialized for high-pitched voices. For example, if the user has collected a lot of low-pitched voices in the past, the collection unit selects a collection method specialized for low-pitched voices. For example, if the user has collected voices with a certain rhythm in the past, the collection unit selects a collection method that matches that rhythm. In this way, the collection unit can select the optimal collection method for the user by analyzing the past voice data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past voice data into a generation AI and cause the generation AI to select the optimal collection method.

[0064] The collection unit can filter the user's current environmental sounds to remove noise when collecting voices. For example, when the user collects voices outdoors, the collection unit removes noise by filtering out wind noise and car sounds. For example, when the user collects voices indoors, the collection unit removes noise by filtering out sounds from air conditioners and home appliances. For example, when the user collects voices in a public place, the collection unit removes noise by filtering out voices of people talking around the user. In this way, the collection unit can collect clearer voice data by filtering environmental sounds and removing noise. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input environmental sound data to a generation AI and cause the generation AI to perform noise removal processing.

[0065] The collection unit can estimate the user's emotions and determine the priority of voices to be collected based on the estimated user's emotions. For example, when the user is relaxed, the collection unit prioritizes collecting natural utterances. For example, when the user is tense, the collection unit prioritizes collecting utterances for relaxation. For example, when the user is concentrating, the collection unit prioritizes collecting utterances for maintaining concentration. This allows the collection unit to prioritize the voices to be collected according to the user's emotions, enabling more effective data collection. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of voices to be collected.

[0066] When collecting voices, the collection unit can prioritize collecting highly relevant voices by taking into account the user's geographical location information. For example, if the user is in a park, the collection unit prioritizes collecting voices suitable for a natural environment. For example, if the user is in an office, the collection unit prioritizes collecting voices suitable for a business setting. For example, if the user is at home, the collection unit prioritizes collecting voices suitable for a relaxed environment. In this way, the collection unit can prioritize collecting highly relevant voices by taking into account the geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant voices.

[0067] When collecting comments, the collection unit can analyze the user's social media activities and collect related comments. For example, if the user posts many singing videos on social media, the collection unit prioritizes collecting singing-related comments. For example, if the user posts many business-related videos on social media, the collection unit prioritizes collecting business-related comments. For example, if the user engages in many creative activities on social media, the collection unit prioritizes collecting creative comments. This allows the collection unit to effectively collect related comments by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input the user's social media activity data into the generation AI and cause the generation AI to collect related comments.

[0068] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, when the user is relaxed, the analysis unit displays the analysis results in a visually easy-to-understand manner. For example, when the user is nervous, the analysis unit displays the analysis results in a simple manner. For example, when the user is concentrating, the analysis unit provides detailed analysis results. This allows the analysis unit to provide more understandable analysis results by adjusting the way the analysis is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0069] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the voice. For example, the analysis unit performs a detailed analysis on important voice data. For example, the analysis unit performs a basic analysis on general voice data. For example, the analysis unit performs an analysis tailored to a specific purpose on voice data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the voice, enabling efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input voice importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0070] During analysis, the analysis unit can apply different analysis algorithms depending on the voice category. For example, the analysis unit applies an analysis algorithm for pitch and rhythm to a singing voice. For example, the analysis unit applies an analysis algorithm for pronunciation and intonation to a business voice. For example, the analysis unit applies an analysis algorithm for sound texture and expressiveness to a creative voice. In this way, the analysis unit can perform more accurate analysis by applying an analysis algorithm according to the voice category. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input voice category data into the generation AI and have the generation AI apply the analysis algorithm.

[0071] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis and provides a longer report. For example, if the user is nervous, the analysis unit performs a concise analysis and provides a shorter report. For example, if the user is concentrating, the analysis unit performs a detailed analysis as needed and provides a report of an appropriate length. This allows the analysis unit to adjust the length of the analysis according to the user's emotions and provide an analysis result of an appropriate length. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0072] During analysis, the analysis unit can determine the priority of analysis based on the time when the voice was collected. For example, the analysis unit prioritizes analysis of recently collected voice data. For example, the analysis unit prioritizes analysis of voice data collected during a specific event or training session. For example, the analysis unit prioritizes analysis of voice data collected within a period specified by the user. This enables efficient analysis by the analysis unit determining the priority of analysis based on the time when the voice was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input data on the time when the voice was collected into the generation AI and have the generation AI determine the analysis priority.

[0073] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the voices. For example, the analysis unit analyzes voice data of the same category together. For example, the analysis unit prioritizes analysis of voice data related to a specific training goal. For example, the analysis unit prioritizes analysis of highly relevant voice data based on the user's past training history. This allows the analysis unit to adjust the order of analysis based on the relevance of the voices, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input voice relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0074] The generation unit can estimate the user's emotions and adjust the training plan generation method based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a training plan that can be performed in a relaxed state. For example, if the user is tense, the generation unit generates a training plan to relieve tension. For example, if the user is concentrating, the generation unit generates a training plan that makes use of concentration. In this way, the generation unit can provide a more effective training plan by adjusting the training plan generation method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the training plan generation method.

[0075] When generating a training plan, the generation unit can analyze the user's past training history and select an optimal plan. The generation unit, for example, generates a plan for the next step based on the results of the user's past training. The generation unit, for example, generates a plan for improvement by taking into account training that the user was weak at in the past. The generation unit, for example, generates a plan to strengthen training that the user was good at in the past. In this way, the generation unit can provide the user with an optimal training plan by analyzing the user's past training history. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's past training history data into the generation AI and have the generation AI select an optimal plan.

[0076] When generating a training plan, the generation unit can customize the plan based on the user's current lifestyle. For example, if the user is busy, the generation unit generates a short but effective training plan. For example, if the user has time, the generation unit generates a detailed and time-consuming training plan. For example, if the user is preparing for a specific event, the generation unit generates a training plan tailored to the event. In this way, the generation unit can provide the user with an optimal training plan by customizing the plan based on the user's current lifestyle. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input the user's lifestyle data into the generation AI and have the generation AI customize the plan.

[0077] The generation unit can estimate the user's emotions and prioritize training plans based on the estimated user's emotions. For example, if the user is relaxed, the generation unit prioritizes training that can be done in a relaxed state. For example, if the user is tense, the generation unit prioritizes training to relieve tension. For example, if the user is concentrating, the generation unit prioritizes training that makes use of concentration. This allows the generation unit to prioritize training plans according to the user's emotions, enabling more effective training. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI determine the priorities of the training plans.

[0078] When generating a training plan, the generation unit can select an optimal plan by taking into account the user's geographical location information. For example, if the user is at home, the generation unit generates a training plan that can be done at home. For example, if the user is in the office, the generation unit generates a training plan that can be done at the office. For example, if the user is traveling, the generation unit generates a training plan that can be done at the travel destination. In this way, the generation unit can provide an optimal training plan for the user by taking into account the geographical location information. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and cause the generation AI to select an optimal plan.

[0079] When generating a training plan, the generation unit can analyze the user's social media activity and propose a plan. For example, if the user posts many singing videos on social media, the generation unit generates a training plan related to singing. For example, if the user posts many business-related videos on social media, the generation unit generates a training plan related to business scenes. For example, if the user engages in many creative activities on social media, the generation unit generates a creative training plan. In this way, the generation unit can provide an optimal training plan for the user by analyzing social media activity. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's social media activity data into the generation AI and have the generation AI execute the plan proposal.

[0080] The providing unit can estimate the user's emotions and adjust the method of providing the training plan based on the estimated user's emotions. For example, if the user is relaxed, the providing unit provides the plan in a calm voice so that the user can receive it in a relaxed state. For example, if the user is tense, the providing unit provides the plan in a calm voice to relieve tension. For example, if the user is concentrating, the providing unit provides the plan in a clear and concise voice to maintain concentration. This allows the providing unit to adjust the method of providing the training plan according to the user's emotions, enabling more effective training. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the method of providing the plan.

[0081] When providing a training plan, the providing unit can select the optimal provision method by referring to the user's past training history. For example, if the user has preferred audio guides in the past, the providing unit provides the plan using audio guides. For example, if the user has preferred text guides in the past, the providing unit provides the plan using text guides. For example, if the user has preferred video guides in the past, the providing unit provides the plan using video guides. This allows the providing unit to select the optimal provision method for the user by referring to the past training history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past training history data into the generation AI and cause the generation AI to select the optimal provision method.

[0082] The providing unit can estimate the user's emotions and adjust the order in which training plans are provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit first provides training that can be done in a relaxed state. For example, if the user is tense, the providing unit first provides training to relieve tension. For example, if the user is concentrating, the providing unit first provides training that makes use of concentration. In this way, the providing unit adjusts the order in which training plans are provided according to the user's emotions, enabling more effective training. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the provision order.

[0083] When providing a training plan, the providing unit can select the optimal provision method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit provides the plan in a display method that matches the screen size. For example, if the user is using a tablet, the providing unit provides the plan in a display method that is optimized for a large screen. For example, if the user is using a smartwatch, the providing unit provides the plan in a display method that is simple and highly visible. This allows the providing unit to select the optimal provision method for the user by taking into account the device information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal provision method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's voice using a microphone or vibration sensor of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and identifies characteristics of the user's vocalization based on the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a training plan based on the characteristics of the user's vocalization. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated training plan to the user. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's voice using a microphone or vibration sensor of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and identifies the characteristics of the user's voice based on the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a training plan based on the characteristics of the user's voice. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated training plan to the user. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the user's voice using a microphone or vibration sensor of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and identifies the characteristics of the user's voice based on the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a training plan based on the characteristics of the user's voice. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the generated training plan to the user. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's voice using a microphone or vibration sensor of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and identifies the characteristics of the user's vocalization based on the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a training plan based on the characteristics of the user's vocalization. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the generated training plan to the user.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] When collecting the user's voice data, the collection unit can monitor the user's health condition and adjust the collection method according to the health condition. For example, if the user has a cold, the collection unit reduces the frequency of collecting voice data to reduce the burden on the user. Also, if the user is tired, the collection unit collects data for a short period of time, taking into consideration the user's physical condition. Furthermore, if the user is in good health, the collection unit can apply the normal collection method and collect detailed data. This allows the collection unit to achieve flexible data collection according to the user's health condition.

[0086] When analyzing the user's voice data, the analysis unit can compare it with the user's past vocal data to evaluate progress. For example, if the user previously struggled with a particular range, the analysis unit can evaluate the improvement in that range. Also, if the user previously trained to extend the duration of vocalizations, the analysis unit can compare the current duration with the past data to evaluate progress. Furthermore, if the user previously adjusted the volume, the analysis unit can evaluate the stability of the current volume. This allows the analysis unit to specifically evaluate the effectiveness of the user's training and provide feedback.

[0087] The generation unit can incorporate game elements into the training plan based on the user's vocal characteristics. For example, practice to strengthen a specific vocal range can be provided in a game format, allowing the user to train while having fun. Training to extend vocal duration can be provided in a time attack format, allowing the user to take on a challenge. Furthermore, training to adjust volume can be implemented using a point system, allowing the user to feel a sense of accomplishment. In this way, the generation unit can increase the user's motivation and provide effective training.

[0088] When providing the generated training plan, the providing unit can customize it according to the user's learning style. For example, a user who prefers visual learning can be provided with a plan that makes extensive use of visual guides. A user who prefers auditory learning can be provided with a plan that mainly uses audio guides. Furthermore, a user who prefers experiential learning can be provided with a plan that includes many opportunities to actually practice out loud. This allows the providing unit to provide the optimal training plan according to the user's learning style.

[0089] When providing the generated training plan, the provider can collect user feedback and adjust the plan in real time. For example, if the user finds a particular training difficult, the provider can lower the difficulty of that training. Alternatively, if the user finds a particular training too easy, the provider can increase the difficulty of that training. Furthermore, if the user is interested in a particular training, the provider can strengthen that training. This allows the provider to flexibly adjust the training plan based on the user's feedback and provide effective training.

[0090] The collection unit can estimate the user's emotions and adjust the voice collection environment based on the estimated user emotions. For example, if the user is relaxed, the collection unit collects voice in a quiet environment. If the user is tense, the collection unit collects voice while playing relaxing music. Furthermore, if the user is concentrating, the collection unit can collect voice by creating an environment that enhances concentration. In this way, the collection unit can provide an optimal collection environment according to the user's emotions and collect more natural voice data.

[0091] The analysis unit can estimate the user's emotions and adjust the method of providing feedback on the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis results can be provided as positive feedback. If the user is tense, the analysis results can be provided along with encouraging words. Furthermore, if the user is concentrating, the analysis results can be provided along with specific suggestions for improvement. In this way, the analysis unit can provide feedback according to the user's emotions and maintain the user's motivation.

[0092] The generation unit can estimate the user's emotions and adjust the contents of the training plan based on the estimated user's emotions. For example, if the user is relaxed, a plan including many exercises that can be performed in a relaxed state is generated. If the user is tense, a plan including many exercises to relieve tension is generated. Furthermore, if the user is concentrating, a plan including many exercises that make use of concentration can be generated. This allows the generation unit to provide an optimal training plan according to the user's emotions.

[0093] The provision unit can estimate the user's emotions and adjust the timing of providing the training plan based on the estimated user's emotions. For example, if the user is relaxed, the provision timing is set so that the user can start training in a relaxed state. If the user is tense, the training is provided after allowing time for the user to relieve tension. Furthermore, if the user is concentrating, the training can be provided in a short period of time to maintain concentration. This allows the provision unit to set the optimal provision timing according to the user's emotions and provide effective training.

[0094] The providing unit can estimate the user's emotions and adjust the format of the training plan based on the estimated user's emotions. For example, if the user is relaxed, the plan can be provided in a gentle voice so that the user can receive the plan in a relaxed state. If the user is tense, the plan can be provided in a calm voice to relieve tension. Furthermore, if the user is concentrating, the plan can be provided in a clear and concise voice to maintain concentration. This allows the providing unit to set the optimal format of provision according to the user's emotions and provide effective training.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The collection unit collects the user's voice. The user's voice may include talking, singing, shouting, etc. The collection unit collects data on the user's voice using a microphone and a vibration sensor. The microphone may be a condenser microphone or a dynamic microphone, and the vibration sensor may be an acceleration sensor or a piezoelectric sensor. Step 2: The analysis unit identifies the user's vocal characteristics based on the collected data. Vocal characteristics include pitch, volume, and tone quality. The analysis unit analyzes the collected data to determine the user's vocal patterns, such as fluctuations in pitch and intensity, and the duration of vocalization. Step 3: The generation unit generates a training plan for practicing to strengthen a specific range of vocal sounds and for extending vocal duration based on the user's vocal characteristics. The training plan includes exercises for strengthening a specific range of vocal sounds and training for extending vocal duration. Step 4: The provider provides the generated training plan to the user, allowing the user to train at their own pace.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0099] 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.

[0100] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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, in order to avoid confusion and to 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.

[0167] 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.

[0168] [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user feedback; an analysis unit that analyzes the voice data collected by the collection unit; a generation unit that generates a training plan based on the analysis results obtained by the analysis unit; a providing unit that provides the training plan generated by the generating unit. A system characterized by:

2. The collecting unit Collecting user voice data using a microphone or vibration sensor 2. The system of claim 1.

3. The analysis unit Identify the characteristics of the user's speech based on the collected data 2. The system of claim 1.

4. The generation unit Generates training plans based on the user's vocal characteristics, including exercises to strengthen specific ranges and extend vocal duration.

2. The system of claim 1.

5. The providing unit Provide the generated training plan to the user 2. The system of claim 1.

6. The collecting unit The system estimates the user's emotions and adjusts the timing of voice collection based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit Analyze past user feedback data and select the optimal collection method 2. The system of claim 1.

8. The collecting unit Filtering the user's current ambient sounds to remove noise when collecting voice 2. The system of claim 1.

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