system

Abstract

An object of the system according to the embodiment is to consistently perform lyrics writing / composition, voice training, and production based on personal information.SOLUTION: A system includes an information collection part, an analysis part, a lyric composition part, a voice training part, a production part, and a debut support part. The information collection unit collects information on at least one of a user's daily conversation, SNS, and diary. The analysis unit analyzes the information collected by the information collection unit. The lyrics composer performs lyrics composition based on the information analyzed by the analyzer. The voice training unit performs voice training based on the lyrics and the melody generated by the lyrics composing unit. The producing unit produces the performance of the user trained by the voice training unit. The debut supporting unit supports debut of the user produced by the producing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to consistently perform songwriting, composing, voice training, and production based on individual information.

[0005] The system according to the embodiment aims to provide a consistent service of songwriting, composing, voice training, and producing based on personal information. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, a songwriting unit, a voice training unit, a production unit, and a debut support unit. The information collection unit collects at least one piece of information from users' daily conversations, social media, and diaries. The analysis unit analyzes the information collected by the information collection unit. The songwriting unit writes and composes music based on the information analyzed by the analysis unit. The voice training unit provides voice training based on the lyrics and melody generated by the songwriting unit. The production unit produces performances for users trained by the voice training unit. The debut support unit supports the debut of users produced by the production unit. [Effects of the Invention]

[0007] The system according to the embodiment can consistently perform songwriting, composing, voice training, and producing based on personal information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The training program according to an embodiment of the present invention is a training program in which a generation AI writes lyrics and composes music based on information such as a user's daily conversations, SNS, and diary, and then the generation AI provides voice training and production, enabling the user to debut as a popular idol. As a result, the training program allows the user to gain the skills and experience needed to debut as a popular idol with the support of the AI.

[0029] The training program according to the embodiment includes an information collection unit, an analysis unit, a songwriting and composing unit, a voice training unit, a production unit, and a debut support unit. The information collection unit collects at least one of the following information: a user's daily conversations, social media posts, and diary entries. For example, it can collect audio data and text data. The information collection unit can also automatically collect social media posts and acquire digital diary entries. The analysis unit analyzes the information collected by the information collection unit. For example, it can analyze the user's emotions and interests using text analysis technology. The analysis unit can also analyze the user's emotional fluctuation patterns using an emotion analysis algorithm. The analysis unit can also identify topics of interest to the user using topic modeling technology. The songwriting and composing unit composes songs based on the information analyzed by the analysis unit. For example, the generation AI generates lyrics that reflect keywords and emotions obtained from the user's daily conversations and social media posts. The generation AI can also compose melodies based on the user's emotions. The generation AI can also select a musical genre based on the user's interests and compose music that matches that genre. The voice training department provides voice training based on the lyrics and melodies created by the songwriting department. For example, the generation AI analyzes the user's vocal characteristics and vocal habits and, based on that, suggests a training menu for vocal practice and improving singing technique. The generation AI can also generate an appropriate voice training plan based on the user's voice recording data. The generation AI can also adjust the training menu according to changes in the user's voice. The production department produces performances for users trained by the voice training department. For example, the generation AI analyzes the user's singing and dance performances and suggests areas for improvement and strengthening. The generation AI can also suggest costumes, makeup, stage direction, etc. that match the user's image. The generation AI can also generate a production plan based on the user's performance data. The debut support department supports the debut of users produced by the production department.For example, the generation AI can propose schedule management and promotion strategies for debut. The generation AI can also evaluate the user's performance and provide advice on acquiring the skills and experience necessary for debut. The generation AI can also generate a debut support plan based on information about the user's debut. As a result, the training program according to the embodiment allows the user to acquire the skills and experience necessary for debuting as a popular idol with the support of the AI. For example, the user can prepare for debut by writing lyrics and composing music based on information obtained from daily conversations and social media posts, and improving their performance through voice training and production.

[0030] The information collection unit can analyze the user's daily rhythm and activity patterns and suggest optimal training and rest times. The information collection unit, for example, analyzes data collected from the user's smartphone or wearable device to understand the user's daily rhythm and activity patterns. For example, it suggests optimal training times based on sleep time and exercise volume. The information collection unit also collects data from the user's calendar or schedule app to analyze daily activity patterns. For example, it adjusts training times to match work or school schedules. The information collection unit also analyzes the user's activity patterns over a long period of time to identify optimal times of day to maximize the effectiveness of training. For example, it suggests training during times when energy levels are high. This makes it possible to provide training and rest that are tailored to the user's daily rhythm.

[0031] The information collection unit analyzes a user's past music preferences and playlists to suggest songwriting and composition directions that match their individual musical style. For example, the information collection unit analyzes the user's music streaming service history to understand trends in songs and playlists listened to in the past. For example, it suggests songwriting and composition directions based on a user's preference for songs from a particular genre or artist. The information collection unit also analyzes playlists created by the user to extract characteristics such as the tempo, key, and lyric theme of the songs. For example, it composes melodies that match the user's preferred tempo and key. The information collection unit also analyzes a user's music preferences over a long period of time to understand changes and trends in preferences. For example, it suggests the latest musical styles that match recent preferences. This allows it to provide songwriting and composition directions based on the user's music preferences.

[0032] The analysis unit can also analyze the social media posts of the user's friends and followers, and utilize information obtained from the user's social network. For example, the analysis unit can analyze the social media posts of the user's friends and followers to identify common interests. For example, the analysis unit can propose a training plan tailored to the user based on music and event information posted by friends. The analysis unit can also analyze the user's social network to understand relationships with friends and followers. For example, the analysis unit can adjust a training plan based on events and activities in which close friends participate. The analysis unit can also analyze emotion estimation data of the user's friends and followers to propose a training plan that takes social influence into account. For example, the analysis unit can encourage friends to participate in activities that generate positive emotions. This makes it possible to utilize information obtained from the user's social network.

[0033] The songwriting unit can analyze the tone and rhythmic sense of the user's voice and generate a melody line that is optimal for that. For example, the songwriting unit analyzes the tone of the user's voice and generates a melody line that matches that tone. For example, if the user's voice is strong in the high range, the songwriting unit composes a melody that makes use of that high range. The songwriting unit can also analyze the user's sense of rhythm and generate a melody line that is optimal for that rhythmic sense. For example, the songwriting unit can compose a melody that incorporates a rhythmic pattern that the user is good at. The songwriting unit can also analyze the characteristics of the user's voice and generate a melody line that makes use of those characteristics. For example, the songwriting unit can compose a melody that makes use of the resonance of the user's voice. In this way, a melody line based on the tone and rhythmic sense of the user's voice can be generated.

[0034] The songwriting unit can analyze the user's past musical works and performances of cover songs to compose lyrics and music that fit the user's individual musical style. For example, the songwriting unit can analyze the user's past musical works and compose lyrics and music that fit their style and theme. For example, if their past works were rock, the unit can compose a new song in the same style. The songwriting unit can also analyze the performance of the user's cover songs and compose lyrics and music that fit that performance. For example, it can compose an original song that incorporates the characteristics of the cover song. The songwriting unit can also compose lyrics and music that fit the user's individual musical style based on the performance data of the user's past musical works and cover songs. For example, it can compose a song that reflects the user's specialty genre or theme. This makes it possible to compose lyrics and music based on the user's past musical works and performances of cover songs.

[0035] The voice training unit can analyze the health of the user's vocal cords and suggest optimal vocal training and rest. The voice training unit, for example, analyzes the health of the user's vocal cords and suggests optimal vocal training. For example, it measures the fatigue level of the vocal cords and sets an appropriate rest period. The voice training unit also monitors the health of the user's vocal cords over a long period of time and evaluates the effectiveness of vocal training. For example, it adjusts a training menu according to the recovery status of the vocal cords. The voice training unit also analyzes the health of the user's vocal cords and suggests vocal training and rest to protect and promote recovery of the vocal cords. For example, it teaches vocal techniques that do not put strain on the vocal cords. This makes it possible to provide vocal training and rest based on the health of the user's vocal cords.

[0036] The voice training unit can analyze the characteristics of the user's voice and provide training that suits the user's individual vocal technique and singing style. For example, the voice training unit analyzes the characteristics of the user's voice and provides vocal technique that suits those characteristics. For example, if the user's voice is strong in the low range, the voice training unit will suggest vocal training that makes use of that range. The voice training unit also analyzes the user's singing style and provides a training menu that suits that style. For example, if the user is good at jazz, the voice training unit will suggest vocal training specialized for jazz. The voice training unit also analyzes the user's vocal characteristics over a long period of time and provides a training menu that suits changes in the voice. For example, the training content can be adjusted according to the growth and changes in the voice. This makes it possible to provide training that suits the user's vocal technique and singing style based on the user's vocal characteristics.

[0037] The voice training unit can analyze the user's physical movements and posture and suggest training to improve factors that affect vocalization. The voice training unit, for example, analyzes the user's physical movements and posture and identifies factors that affect vocalization. For example, it provides guidance on improving posture and breathing techniques. The voice training unit also monitors the user's physical movements and posture over a long period of time and suggests optimal posture and movements for vocalization. For example, it analyzes posture during vocalization using a camera and provides feedback in real time. The voice training unit also analyzes the user's physical movements and posture and provides a training menu to improve factors that affect vocalization. For example, it suggests vocal exercises that incorporate stretching and muscle training. This makes it possible to provide vocal improvement training based on the user's physical movements and posture.

[0038] The voice training unit can analyze the user's diet and lifestyle habits and provide advice to improve voice quality. The voice training unit, for example, analyzes the user's diet and lifestyle habits and provides advice to improve voice quality. For example, it can suggest foods and nutrients that are good for the vocal cords. The voice training unit can also monitor the user's lifestyle habits over a long period of time and provide specific advice to improve voice quality. For example, it can suggest sleep time and stress management methods. The voice training unit can also analyze the user's diet and lifestyle habits and provide customized advice to improve voice quality. For example, it can suggest individual nutritional plans and exercise plans. This makes it possible to provide advice to improve voice quality based on the user's diet and lifestyle habits.

[0039] The production department can analyze the user's performance data and propose optimal stage direction and costumes. The production department, for example, analyzes the user's performance data and proposes optimal stage direction. For example, it sets lighting and sound settings to match the user's movements and facial expressions. The production department also monitors the user's performance data over a long period of time and evaluates the effectiveness of the stage direction. For example, it adjusts the direction based on the audience's reaction and the quality of the performance. The production department also analyzes the user's performance data and proposes optimal costumes. For example, it selects designs and materials that match the user's body type and movements. This makes it possible to propose stage direction and costumes based on the user's performance data.

[0040] The production department can analyze the preferences of the user's fan base and provide a production plan based on that. The production department, for example, analyzes the preferences of the user's fan base and provides a production plan based on that. For example, it incorporates songs and performance styles that fans like. The production department also monitors the preferences of the user's fan base over a long period of time and evaluates the effectiveness of the production plan. For example, it adjusts the plan based on fans' reactions and feedback. The production department also analyzes the preferences of the user's fan base and suggests content and events that fans want. For example, it plans events that fans want to attend and merchandise that fans want to purchase. This makes it possible to provide a production plan based on the preferences of the user's fan base.

[0041] The production department can analyze a user's performance from different perspectives and suggest multiple areas for improvement. For example, the production department can analyze a user's performance from different perspectives and suggest multiple areas for improvement. For example, it can evaluate each element individually, such as singing ability, dancing, and facial expressions. The production department can also monitor a user's performance over a long period of time and evaluate the effectiveness of the areas for improvement. For example, it can evaluate based on the quality of the performance after the improvements and the audience's reaction. The production department can also analyze a user's performance from different perspectives and provide a comprehensive improvement plan. For example, it can propose a plan that includes improving singing ability, dancing technique, and improving stage direction. This allows the production department to analyze a user's performance from multiple angles and suggest areas for improvement.

[0042] The production department can analyze the user's cultural background and regional trends and provide a production plan based on that. For example, the production department can analyze the user's cultural background and provide a production plan rooted in that culture. For example, if the user is familiar with Japanese culture, the production department can suggest Japanese-style stage productions and costumes. The production department can also analyze the user's regional trends and provide a production plan that reflects those trends. For example, if the user is from Latin America, the production department can suggest stage productions that incorporate elements of Latin music. The production department can also analyze the user's cultural background and regional trends and provide a production plan based on that. For example, if the user is interested in traditional African music, the production department can suggest stage productions that incorporate African rhythms. This makes it possible to provide a production plan based on the user's cultural background and regional trends.

[0043] The debut support unit can analyze the user's skills and experience and propose optimal debut timing and promotion strategies. The debut support unit, for example, analyzes the user's skills and experience and proposes optimal debut timing. For example, it recommends debuting when the user's singing ability or performance quality reaches a certain level. The debut support unit also proposes optimal promotion strategies based on the user's past experience and track record. For example, it provides promotion plans tailored to the user's specialty genres and styles. The debut support unit also monitors the user's skills and experience over a long period of time and evaluates growth toward debut. For example, it adjusts the debut timing according to improvement in skills and accumulation of experience. This makes it possible to provide debut timing and promotion strategies based on the user's skills and experience.

[0044] The debut support unit can analyze the user's performance data and provide specific advice for improving skills toward a debut. The debut support unit, for example, analyzes the user's performance data and provides specific advice for improving skills toward a debut. For example, it proposes a training menu for improving singing ability or dance technique. The debut support unit also monitors the user's performance data over a long period of time and evaluates the effectiveness of skill improvement. For example, it proposes the next step based on the results of training. The debut support unit also analyzes the user's performance data and provides customized advice for improving skills toward a debut. For example, it proposes a training plan that takes individual weaknesses and strengths into consideration. This makes it possible to provide specific advice for improving skills based on the user's performance data.

[0045] The debut support unit can analyze the user's schedule for debut from different perspectives and propose multiple debut scenarios. For example, the debut support unit analyzes the user's schedule for debut and proposes multiple debut scenarios. For example, it provides a scenario that combines different timing and promotion strategies. The debut support unit also monitors the user's schedule for debut over a long period of time and proposes an optimal debut scenario. For example, it adjusts the scenario according to skill improvements and market trends. The debut support unit also analyzes the user's schedule for debut from different perspectives and proposes a comprehensive debut scenario. For example, it provides a scenario that takes the user's strengths and weaknesses into consideration. In this way, the user's schedule for debut can be analyzed from multiple angles and multiple debut scenarios can be proposed.

[0046] The debut support unit can analyze the user's cultural background and regional market trends and provide a debut support plan based on that. For example, the debut support unit can analyze the user's cultural background and provide a debut support plan rooted in that culture. For example, if the user is familiar with Japanese culture, the debut support unit can propose a promotion strategy specialized for the Japanese market. The debut support unit can also analyze the market trends in the user's region and provide a debut support plan that reflects those trends. For example, if the user is from Latin America, the debut support unit can propose a promotion strategy specialized for the Latin American market. The debut support unit can also analyze the user's cultural background and regional market trends and provide a debut support plan based on those trends. For example, if the user is interested in traditional African music, the debut support unit can propose a promotion strategy specialized for the African market. This makes it possible to provide a debut support plan based on the user's cultural background and regional market trends.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The analysis unit can analyze the user's diet and lifestyle habits and provide advice to improve voice quality. For example, it can analyze the user's diet and suggest ingredients and nutrients that are good for the vocal cords. It can also monitor the user's lifestyle habits over a long period of time and provide specific advice to improve voice quality. For example, it can suggest sleep time and stress management methods. It can also analyze the user's diet and lifestyle habits and provide customized advice to improve voice quality. For example, it can suggest individual nutritional plans and exercise plans. This makes it possible to provide advice to improve voice quality based on the user's diet and lifestyle habits.

[0049] The songwriting unit can analyze the tone and rhythmic sense of the user's voice and generate a melody line that is optimal for that. For example, it can analyze the tone of the user's voice and generate a melody line that matches that tone. For example, if the user's voice is strong in the high range, it can compose a melody that makes use of that range. It can also analyze the user's sense of rhythm and generate a melody line that is optimal for that rhythmic sense. For example, it can compose a melody that incorporates the rhythmic patterns that the user is good at. It can also analyze the characteristics of the user's voice and generate a melody line that makes use of those characteristics. For example, it can compose a melody that makes use of the resonance of the user's voice. In this way, it is possible to generate a melody line based on the tone and rhythmic sense of the user's voice.

[0050] The production department can analyze the preferences of a user's fan base and provide a production plan based on that. For example, it can analyze the preferences of a user's fan base and provide a production plan based on that. For example, it can incorporate songs and performance styles that fans like. It can also monitor the preferences of a user's fan base over a long period of time and evaluate the effectiveness of the production plan. For example, it can adjust the plan based on fans' reactions and feedback. It can also analyze the preferences of a user's fan base and suggest content and events that fans want. For example, it can plan events that fans want to attend and merchandise that fans want to purchase. This makes it possible to provide a production plan based on the preferences of a user's fan base.

[0051] The voice training unit can analyze the user's physical movements and posture and suggest training to improve factors that affect vocalization. For example, it can analyze the user's physical movements and posture and identify factors that affect vocalization. For example, it can provide guidance on improving posture and breathing techniques. It can also monitor the user's physical movements and posture over a long period of time and suggest optimal posture and movements for vocalization. For example, it can analyze the user's posture during vocalization using a camera and provide feedback in real time. It can also analyze the user's physical movements and posture and provide a training menu to improve factors that affect vocalization. For example, it can suggest vocal exercises that incorporate stretching and strength training. This makes it possible to provide vocal improvement training based on the user's physical movements and posture.

[0052] The debut support unit can analyze the user's skills and experience and propose optimal debut timing and promotion strategies. For example, it can analyze the user's skills and experience and propose optimal debut timing. For example, it can recommend debuting when the user's singing ability or performance quality reaches a certain level. It can also propose optimal promotion strategies based on the user's past experience and track record. For example, it can provide promotion plans tailored to the user's specialty genres and styles. It can also monitor the user's skills and experience over a long period of time and evaluate growth toward debut. For example, it can adjust the debut timing according to improvements in skills and accumulated experience. This makes it possible to provide debut timing and promotion strategies based on the user's skills and experience.

[0053] The production department can analyze the user's cultural background and regional trends and provide a production plan based on that. For example, the production department can analyze the user's cultural background and provide a production plan rooted in that culture. For example, if the user is familiar with Japanese culture, the production department can suggest Japanese-style stage productions and costumes. The production department can also analyze the user's regional trends and provide a production plan that reflects those trends. For example, if the user is from Latin America, the production department can suggest stage productions that incorporate elements of Latin music. The production department can also analyze the user's cultural background and regional trends and provide a production plan based on those analysis results. For example, if the user is interested in traditional African music, the production department can suggest stage productions that incorporate African rhythms. This makes it possible to provide a production plan based on the user's cultural background and regional trends.

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

[0055] Step 1: The information collection unit collects at least one of the following information: daily conversations, social media, and diary entries of the user. For example, audio data and text data can be collected. The information collection unit can also automatically collect social media posts and acquire digital diary entries. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit uses text analysis technology to analyze the user's emotions and interests. The analysis unit can also use a sentiment analysis algorithm to analyze the user's emotional fluctuation patterns. The analysis unit can also use topic modeling technology to identify topics of interest to the user. Step 3: The songwriting unit composes lyrics and music based on the information analyzed by the analysis unit. For example, the generation AI generates lyrics that reflect keywords and emotions obtained from the user's daily conversations and social media posts. The generation AI can also compose melodies based on the user's emotions. The generation AI can also select a musical genre based on the user's interests and compose music that suits that genre. Step 4: The voice training department performs voice training based on the lyrics and melody generated by the songwriting department. For example, the generation AI analyzes the user's vocal characteristics and vocal habits, and based on that, suggests a training menu for vocal practice and improving singing technique. The generation AI can also generate an appropriate voice training plan based on recording data of the user's voice. The generation AI can also adjust the training menu according to changes in the user's voice. Step 5: The production department produces the user's performance trained by the voice training department. For example, the generation AI analyzes the user's singing and dancing performance and suggests areas for improvement and strengthening. The generation AI can also suggest costumes, makeup, and stage direction that match the user's image. The generation AI can also generate a production plan based on the user's performance data. Step 6: The debut support department supports the debut of the user produced by the production department. For example, the generation AI proposes schedule management and promotion strategies for the debut. The generation AI can also evaluate the user's performance and provide advice on how to acquire the skills and experience necessary for the debut. The generation AI can also generate a debut support plan based on information about the user's debut.

[0056] (Example 2) The training program according to an embodiment of the present invention is a training program in which a generation AI writes lyrics and composes music based on information such as a user's daily conversations, SNS, and diary, and then the generation AI provides voice training and production, enabling the user to debut as a popular idol. As a result, the training program allows the user to gain the skills and experience needed to debut as a popular idol with the support of the AI.

[0057] The training program according to the embodiment includes an information collection unit, an analysis unit, a songwriting and composing unit, a voice training unit, a production unit, and a debut support unit. The information collection unit collects at least one of the following information: a user's daily conversations, social media posts, and diary entries. For example, it can collect audio data and text data. The information collection unit can also automatically collect social media posts and acquire digital diary entries. The analysis unit analyzes the information collected by the information collection unit. For example, it can analyze the user's emotions and interests using text analysis technology. The analysis unit can also analyze the user's emotional fluctuation patterns using an emotion analysis algorithm. The analysis unit can also identify topics of interest to the user using topic modeling technology. The songwriting and composing unit composes songs based on the information analyzed by the analysis unit. For example, the generation AI generates lyrics that reflect keywords and emotions obtained from the user's daily conversations and social media posts. The generation AI can also compose melodies based on the user's emotions. The generation AI can also select a musical genre based on the user's interests and compose music that matches that genre. The voice training department provides voice training based on the lyrics and melodies created by the songwriting department. For example, the generation AI analyzes the user's vocal characteristics and vocal habits and, based on that, suggests a training menu for vocal practice and improving singing technique. The generation AI can also generate an appropriate voice training plan based on the user's voice recording data. The generation AI can also adjust the training menu according to changes in the user's voice. The production department produces performances for users trained by the voice training department. For example, the generation AI analyzes the user's singing and dance performances and suggests areas for improvement and strengthening. The generation AI can also suggest costumes, makeup, stage direction, etc. that match the user's image. The generation AI can also generate a production plan based on the user's performance data. The debut support department supports the debut of users produced by the production department.For example, the generation AI can propose schedule management and promotion strategies for debut. The generation AI can also evaluate the user's performance and provide advice on acquiring the skills and experience necessary for debut. The generation AI can also generate a debut support plan based on information about the user's debut. As a result, the training program according to the embodiment allows the user to acquire the skills and experience necessary for debuting as a popular idol with the support of the AI. For example, the user can prepare for debut by writing lyrics and composing music based on information obtained from daily conversations and social media posts, and improving their performance through voice training and production.

[0058] The information collection unit can analyze emotions in the user's daily conversations or social media posts and generate an individual training plan based on the emotional fluctuation patterns. For example, the information collection unit collects the user's daily conversations and social media posts in real time and analyzes the emotional fluctuation patterns using the emotion estimation function. For example, it identifies when the user feels joy or sadness and adjusts the training plan based on that. The information collection unit also uses the emotion estimation function to analyze the user's emotional fluctuation patterns over a long period of time to understand the periodicity and trends of emotions. For example, it identifies trends in emotional fluctuations on specific days of the week or during specific time periods and proposes a training plan tailored to those trends. The information collection unit also analyzes the emotional fluctuation patterns based on the user's emotion estimation data and detects signs of stress and fatigue. For example, it identifies when emotions tend to become negative and provides a training plan that encourages relaxation and rest during those times. This makes it possible to provide a training plan based on the user's emotions.

[0059] The information collection unit can analyze the user's daily rhythm and activity patterns and suggest optimal training and rest times. The information collection unit, for example, analyzes data collected from the user's smartphone or wearable device to understand the user's daily rhythm and activity patterns. For example, it suggests optimal training times based on sleep time and exercise volume. The information collection unit also collects data from the user's calendar or schedule app to analyze daily activity patterns. For example, it adjusts training times to match work or school schedules. The information collection unit also analyzes the user's activity patterns over a long period of time to identify optimal times of day to maximize the effectiveness of training. For example, it suggests training during times when energy levels are high. This makes it possible to provide training and rest that are tailored to the user's daily rhythm.

[0060] The information collection unit analyzes a user's past music preferences and playlists to suggest songwriting and composition directions that match their individual musical style. For example, the information collection unit analyzes the user's music streaming service history to understand trends in songs and playlists listened to in the past. For example, it suggests songwriting and composition directions based on a user's preference for songs from a particular genre or artist. The information collection unit also analyzes playlists created by the user to extract characteristics such as the tempo, key, and lyric theme of the songs. For example, it composes melodies that match the user's preferred tempo and key. The information collection unit also analyzes a user's music preferences over a long period of time to understand changes and trends in preferences. For example, it suggests the latest musical styles that match recent preferences. This allows it to provide songwriting and composition directions based on the user's music preferences.

[0061] The analysis unit can analyze a user's visual data and infer emotions or interests from their facial expressions or posture. For example, the analysis unit analyzes photos and videos posted by the user on a social networking site and infers emotions using facial expression recognition technology. For example, it detects smiling or surprised expressions and adjusts a training plan based on those emotions. The analysis unit also analyzes the user's visual data and infers interests and concerns from their posture and movements. For example, it identifies topics that interest the user based on their posture during specific activities or locations. The analysis unit also analyzes the user's visual data over a long period of time to understand patterns of change in emotions and interests. For example, it suggests a training plan based on emotional fluctuations related to specific seasons or events. This makes it possible to infer emotions and interests based on the user's visual data.

[0062] The analysis unit can also analyze the social media posts of the user's friends and followers, and utilize information obtained from the user's social network. For example, the analysis unit can analyze the social media posts of the user's friends and followers to identify common interests. For example, the analysis unit can propose a training plan tailored to the user based on music and event information posted by friends. The analysis unit can also analyze the user's social network to understand relationships with friends and followers. For example, the analysis unit can adjust a training plan based on events and activities in which close friends participate. The analysis unit can also analyze emotion estimation data of the user's friends and followers to propose a training plan that takes social influence into account. For example, the analysis unit can encourage friends to participate in activities that generate positive emotions. This makes it possible to utilize information obtained from the user's social network.

[0063] The analysis unit can use the emotion estimation function to suggest training and production plans tailored to periods when the user is feeling specific emotions. For example, the analysis unit can use the emotion estimation function to identify periods when the user is feeling specific emotions and suggest training plans tailored to those periods. For example, the analysis unit can conduct intensive training when the user is feeling positive emotions. The analysis unit can also suggest production plans tailored to periods when the user is feeling specific emotions based on the user's emotion estimation data. For example, the analysis unit can provide a plan to improve performance when the user is feeling confident. The analysis unit can also use the emotion estimation function to suggest training plans that encourage relaxation and rest when the user is feeling negative emotions. For example, the analysis unit can incorporate relaxation during periods of high stress. This makes it possible to provide training and production plans tailored to the user's emotions.

[0064] The songwriting unit uses the emotion estimation function to generate lyrics and melodies based on the user's emotions, thereby enabling musical expression according to the intensity of the emotions. The songwriting unit, for example, uses the emotion estimation function to generate lyrics based on the user's emotions. For example, it generates positive lyrics when the user is feeling happy and sentimental lyrics when the user is feeling sad. The songwriting unit also composes a melody according to the intensity of the emotions based on the user's emotion estimation data. For example, it composes a dynamic melody to express strong emotions and a quiet melody to express calm emotions. The songwriting unit also uses the emotion estimation function to create musical expression that matches the user's emotional fluctuations. For example, it speeds up the tempo in parts where emotions are intense and slows down the tempo in parts where emotions are calm. This allows the songwriting unit to generate lyrics and melodies based on the user's emotions, enabling musical expression according to the intensity of the emotions.

[0065] The songwriting unit can analyze the tone and rhythmic sense of the user's voice and generate a melody line that is optimal for that. For example, the songwriting unit analyzes the tone of the user's voice and generates a melody line that matches that tone. For example, if the user's voice is strong in the high range, the songwriting unit composes a melody that makes use of that high range. The songwriting unit can also analyze the user's sense of rhythm and generate a melody line that is optimal for that rhythmic sense. For example, the songwriting unit can compose a melody that incorporates a rhythmic pattern that the user is good at. The songwriting unit can also analyze the characteristics of the user's voice and generate a melody line that makes use of those characteristics. For example, the songwriting unit can compose a melody that makes use of the resonance of the user's voice. In this way, a melody line based on the tone and rhythmic sense of the user's voice can be generated.

[0066] The songwriting unit can analyze the user's past musical works and performances of cover songs to compose lyrics and music that fit the user's individual musical style. For example, the songwriting unit can analyze the user's past musical works and compose lyrics and music that fit their style and theme. For example, if their past works were rock, the unit can compose a new song in the same style. The songwriting unit can also analyze the performance of the user's cover songs and compose lyrics and music that fit that performance. For example, it can compose an original song that incorporates the characteristics of the cover song. The songwriting unit can also compose lyrics and music that fit the user's individual musical style based on the performance data of the user's past musical works and cover songs. For example, it can compose a song that reflects the user's specialty genre or theme. This makes it possible to compose lyrics and music based on the user's past musical works and performances of cover songs.

[0067] The voice training unit can use the emotion estimation function to suggest a voice training menu according to the user's emotional state. For example, the voice training unit uses the emotion estimation function to analyze the user's emotional state in real time and suggest a voice training menu according to that state. For example, when the user is relaxed, the voice training unit suggests vocal exercises that have a relaxing effect. The voice training unit also suggests a voice training menu according to the user's emotional state based on the user's emotion estimation data. For example, when the user is nervous, the voice training unit suggests vocal exercises to relieve tension. The voice training unit also uses the emotion estimation function to suggest a voice training menu according to the user's emotional state. For example, when the user is feeling positive, the voice training unit suggests vocal exercises to further enhance that emotion. In this way, a voice training menu according to the user's emotional state can be provided.

[0068] The voice training unit can analyze the health of the user's vocal cords and suggest optimal vocal training and rest. The voice training unit, for example, analyzes the health of the user's vocal cords and suggests optimal vocal training. For example, it measures the fatigue level of the vocal cords and sets an appropriate rest period. The voice training unit also monitors the health of the user's vocal cords over a long period of time and evaluates the effectiveness of vocal training. For example, it adjusts a training menu according to the recovery status of the vocal cords. The voice training unit also analyzes the health of the user's vocal cords and suggests vocal training and rest to protect and promote recovery of the vocal cords. For example, it teaches vocal techniques that do not put strain on the vocal cords. This makes it possible to provide vocal training and rest based on the health of the user's vocal cords.

[0069] The voice training unit can analyze the characteristics of the user's voice and provide training that suits the user's individual vocal technique and singing style. For example, the voice training unit analyzes the characteristics of the user's voice and provides vocal technique that suits those characteristics. For example, if the user's voice is strong in the low range, the voice training unit will suggest vocal training that makes use of that range. The voice training unit also analyzes the user's singing style and provides a training menu that suits that style. For example, if the user is good at jazz, the voice training unit will suggest vocal training specialized for jazz. The voice training unit also analyzes the user's vocal characteristics over a long period of time and provides a training menu that suits changes in the voice. For example, the training content can be adjusted according to the growth and changes in the voice. This makes it possible to provide training that suits the user's vocal technique and singing style based on the user's vocal characteristics.

[0070] The voice training unit can analyze the user's physical movements and posture and suggest training to improve factors that affect vocalization. The voice training unit, for example, analyzes the user's physical movements and posture and identifies factors that affect vocalization. For example, it provides guidance on improving posture and breathing techniques. The voice training unit also monitors the user's physical movements and posture over a long period of time and suggests optimal posture and movements for vocalization. For example, it analyzes posture during vocalization using a camera and provides feedback in real time. The voice training unit also analyzes the user's physical movements and posture and provides a training menu to improve factors that affect vocalization. For example, it suggests vocal exercises that incorporate stretching and muscle training. This makes it possible to provide vocal improvement training based on the user's physical movements and posture.

[0071] The voice training unit can use the emotion estimation function to provide vocal practice to bring out a specific emotion. The voice training unit, for example, uses the emotion estimation function to provide vocal practice to bring out a specific emotion for the user. For example, it suggests vocal practice to help the user gain confidence. The voice training unit also provides vocal practice to bring out a specific emotion based on the emotion estimation data of the user. For example, it suggests vocal practice to help the user relax. The voice training unit also uses the emotion estimation function to provide vocal practice to bring out a specific emotion for the user. For example, it suggests vocal practice to help the user express emotion. This makes it possible to provide vocal practice to bring out a specific emotion for the user.

[0072] The voice training unit can analyze the user's diet and lifestyle habits and provide advice to improve voice quality. The voice training unit, for example, analyzes the user's diet and lifestyle habits and provides advice to improve voice quality. For example, it can suggest foods and nutrients that are good for the vocal cords. The voice training unit can also monitor the user's lifestyle habits over a long period of time and provide specific advice to improve voice quality. For example, it can suggest sleep time and stress management methods. The voice training unit can also analyze the user's diet and lifestyle habits and provide customized advice to improve voice quality. For example, it can suggest individual nutritional plans and exercise plans. This makes it possible to provide advice to improve voice quality based on the user's diet and lifestyle habits.

[0073] The producing unit can use the emotion estimation function to make performance improvement suggestions based on the user's emotions. The producing unit, for example, uses the emotion estimation function to make performance improvement suggestions based on the user's emotions. For example, it provides stage direction and performance advice to help the user gain confidence. The producing unit also makes performance improvement suggestions based on emotions based on the user's emotion estimation data. For example, it provides stage direction and performance advice to help the user relax. The producing unit also uses the emotion estimation function to make performance improvement suggestions based on the user's emotions. For example, it provides stage direction and performance advice to help the user express emotion. In this way, it is possible to provide performance improvement suggestions based on the user's emotions.

[0074] The production department can analyze the user's performance data and propose optimal stage direction and costumes. The production department, for example, analyzes the user's performance data and proposes optimal stage direction. For example, it sets lighting and sound settings to match the user's movements and facial expressions. The production department also monitors the user's performance data over a long period of time and evaluates the effectiveness of the stage direction. For example, it adjusts the direction based on the audience's reaction and the quality of the performance. The production department also analyzes the user's performance data and proposes optimal costumes. For example, it selects designs and materials that match the user's body type and movements. This makes it possible to propose stage direction and costumes based on the user's performance data.

[0075] The production department can analyze the preferences of the user's fan base and provide a production plan based on that. The production department, for example, analyzes the preferences of the user's fan base and provides a production plan based on that. For example, it incorporates songs and performance styles that fans like. The production department also monitors the preferences of the user's fan base over a long period of time and evaluates the effectiveness of the production plan. For example, it adjusts the plan based on fans' reactions and feedback. The production department also analyzes the preferences of the user's fan base and suggests content and events that fans want. For example, it plans events that fans want to attend and merchandise that fans want to purchase. This makes it possible to provide a production plan based on the preferences of the user's fan base.

[0076] The producing unit can use the emotion estimation function to propose stage performances that will bring out a specific emotion in the user. For example, the producing unit uses the emotion estimation function to propose stage performances that will bring out a specific emotion in the user. For example, the producing unit sets up lighting and sound so that the user can express their emotions. The producing unit also proposes stage performances that will bring out a specific emotion based on the emotion estimation data of the user. For example, it proposes stage performances that will help the user relax. The producing unit also uses the emotion estimation function to propose stage performances that will bring out a specific emotion in the user. For example, it proposes stage performances that will help the user gain confidence. In this way, it is possible to provide stage performances that will bring out a specific emotion in the user.

[0077] The production department can analyze a user's performance from different perspectives and suggest multiple areas for improvement. For example, the production department can analyze a user's performance from different perspectives and suggest multiple areas for improvement. For example, it can evaluate each element individually, such as singing ability, dancing, and facial expressions. The production department can also monitor a user's performance over a long period of time and evaluate the effectiveness of the areas for improvement. For example, it can evaluate based on the quality of the performance after the improvements and the audience's reaction. The production department can also analyze a user's performance from different perspectives and provide a comprehensive improvement plan. For example, it can propose a plan that includes improving singing ability, dancing technique, and improving stage direction. This allows the production department to analyze a user's performance from multiple angles and suggest areas for improvement.

[0078] The production department can analyze the user's cultural background and regional trends and provide a production plan based on that. For example, the production department can analyze the user's cultural background and provide a production plan rooted in that culture. For example, if the user is familiar with Japanese culture, the production department can suggest Japanese-style stage productions and costumes. The production department can also analyze the user's regional trends and provide a production plan that reflects those trends. For example, if the user is from Latin America, the production department can suggest stage productions that incorporate elements of Latin music. The production department can also analyze the user's cultural background and regional trends and provide a production plan based on that. For example, if the user is interested in traditional African music, the production department can suggest stage productions that incorporate African rhythms. This makes it possible to provide a production plan based on the user's cultural background and regional trends.

[0079] The debut support unit can use the emotion estimation function to propose a debut support plan according to the user's emotional state. For example, the debut support unit uses the emotion estimation function to analyze the user's emotional state in real time and propose a debut support plan according to that state. For example, it provides a schedule and promotion strategy that will help the user gain confidence. The debut support unit also proposes a debut support plan according to the user's emotional state based on the user's emotion estimation data. For example, it provides a schedule and promotion strategy that will help the user relax. The debut support unit also uses the emotion estimation function to propose a debut support plan according to the user's emotional state. For example, it provides a schedule and promotion strategy that will help the user express their emotions. In this way, it is possible to provide a debut support plan according to the user's emotional state.

[0080] The debut support unit can analyze the user's skills and experience and propose optimal debut timing and promotion strategies. The debut support unit, for example, analyzes the user's skills and experience and proposes optimal debut timing. For example, it recommends debuting when the user's singing ability or performance quality reaches a certain level. The debut support unit also proposes optimal promotion strategies based on the user's past experience and track record. For example, it provides promotion plans tailored to the user's specialty genres and styles. The debut support unit also monitors the user's skills and experience over a long period of time and evaluates growth toward debut. For example, it adjusts the debut timing according to improvement in skills and accumulation of experience. This makes it possible to provide debut timing and promotion strategies based on the user's skills and experience.

[0081] The debut support unit can analyze the user's performance data and provide specific advice for improving skills toward a debut. The debut support unit, for example, analyzes the user's performance data and provides specific advice for improving skills toward a debut. For example, it proposes a training menu for improving singing ability or dance technique. The debut support unit also monitors the user's performance data over a long period of time and evaluates the effectiveness of skill improvement. For example, it proposes the next step based on the results of training. The debut support unit also analyzes the user's performance data and provides customized advice for improving skills toward a debut. For example, it proposes a training plan that takes individual weaknesses and strengths into consideration. This makes it possible to provide specific advice for improving skills based on the user's performance data.

[0082] The debut support unit can use the emotion estimation function to propose a debut strategy for eliciting a specific emotion from the user. For example, the debut support unit uses the emotion estimation function to propose a debut strategy for eliciting a specific emotion from the user. For example, it proposes promotional activities or events that will help the user gain confidence. The debut support unit also proposes a debut strategy for eliciting a specific emotion based on the emotion estimation data of the user. For example, it proposes debut events or schedules that will help the user relax. The debut support unit also uses the emotion estimation function to propose a debut strategy for eliciting a specific emotion from the user. For example, it proposes debut events or promotional activities that will help the user express their emotions. In this way, a debut strategy for eliciting a specific emotion from the user can be provided.

[0083] The debut support unit can analyze the user's schedule for debut from different perspectives and propose multiple debut scenarios. For example, the debut support unit analyzes the user's schedule for debut and proposes multiple debut scenarios. For example, it provides a scenario that combines different timing and promotion strategies. The debut support unit also monitors the user's schedule for debut over a long period of time and proposes an optimal debut scenario. For example, it adjusts the scenario according to skill improvements and market trends. The debut support unit also analyzes the user's schedule for debut from different perspectives and proposes a comprehensive debut scenario. For example, it provides a scenario that takes the user's strengths and weaknesses into consideration. In this way, the user's schedule for debut can be analyzed from multiple angles and multiple debut scenarios can be proposed.

[0084] The debut support unit can analyze the user's cultural background and regional market trends and provide a debut support plan based on that. For example, the debut support unit can analyze the user's cultural background and provide a debut support plan rooted in that culture. For example, if the user is familiar with Japanese culture, the debut support unit can propose a promotion strategy specialized for the Japanese market. The debut support unit can also analyze the market trends in the user's region and provide a debut support plan that reflects those trends. For example, if the user is from Latin America, the debut support unit can propose a promotion strategy specialized for the Latin American market. The debut support unit can also analyze the user's cultural background and regional market trends and provide a debut support plan based on those trends. For example, if the user is interested in traditional African music, the debut support unit can propose a promotion strategy specialized for the African market. This makes it possible to provide a debut support plan based on the user's cultural background and regional market trends.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The analysis unit can analyze the user's diet and lifestyle habits and provide advice to improve voice quality. For example, it can analyze the user's diet and suggest ingredients and nutrients that are good for the vocal cords. It can also monitor the user's lifestyle habits over a long period of time and provide specific advice to improve voice quality. For example, it can suggest sleep time and stress management methods. It can also analyze the user's diet and lifestyle habits and provide customized advice to improve voice quality. For example, it can suggest individual nutritional plans and exercise plans. This makes it possible to provide advice to improve voice quality based on the user's diet and lifestyle habits.

[0087] The information collection unit can also analyze the social media posts of the user's friends and followers and utilize information obtained from the user's social network. For example, the information collection unit can analyze the social media posts of the user's friends and followers to identify common interests. For example, the information collection unit can propose a training plan tailored to the user based on music and event information posted by friends. The information collection unit can also analyze the user's social network to understand relationships with friends and followers. For example, the information collection unit can adjust a training plan based on events and activities in which close friends participate. The information collection unit can also analyze emotion estimation data of the user's friends and followers to propose a training plan that takes social influence into account. For example, the information collection unit can encourage friends to participate in activities that have positive emotions. This makes it possible to utilize information obtained from the user's social network.

[0088] The songwriting unit can analyze the tone and rhythmic sense of the user's voice and generate a melody line that is optimal for that. For example, it can analyze the tone of the user's voice and generate a melody line that matches that tone. For example, if the user's voice is strong in the high range, it can compose a melody that makes use of that range. It can also analyze the user's sense of rhythm and generate a melody line that is optimal for that rhythmic sense. For example, it can compose a melody that incorporates the rhythmic patterns that the user is good at. It can also analyze the characteristics of the user's voice and generate a melody line that makes use of those characteristics. For example, it can compose a melody that makes use of the resonance of the user's voice. In this way, it is possible to generate a melody line based on the tone and rhythmic sense of the user's voice.

[0089] The production department can analyze the preferences of a user's fan base and provide a production plan based on that. For example, it can analyze the preferences of a user's fan base and provide a production plan based on that. For example, it can incorporate songs and performance styles that fans like. It can also monitor the preferences of a user's fan base over a long period of time and evaluate the effectiveness of the production plan. For example, it can adjust the plan based on fans' reactions and feedback. It can also analyze the preferences of a user's fan base and suggest content and events that fans want. For example, it can plan events that fans want to attend and merchandise that fans want to purchase. This makes it possible to provide a production plan based on the preferences of a user's fan base.

[0090] The debut support unit can use the emotion estimation function to propose a debut support plan according to the user's emotional state. For example, the emotion estimation function can be used to analyze the user's emotional state in real time and propose a debut support plan according to that state. For example, a schedule or promotion strategy that will help the user gain confidence can be provided. Furthermore, based on the user's emotion estimation data, a debut support plan according to the emotional state can be proposed. For example, a schedule or promotion strategy that will help the user relax can be provided. Furthermore, the emotion estimation function can be used to propose a debut support plan according to the user's emotional state. For example, a schedule or promotion strategy that will help the user express their emotions can be provided. In this way, a debut support plan according to the user's emotional state can be provided.

[0091] The voice training unit can analyze the user's physical movements and posture and suggest training to improve factors that affect vocalization. For example, it can analyze the user's physical movements and posture and identify factors that affect vocalization. For example, it can provide guidance on improving posture and breathing techniques. It can also monitor the user's physical movements and posture over a long period of time and suggest optimal posture and movements for vocalization. For example, it can analyze the user's posture during vocalization using a camera and provide feedback in real time. It can also analyze the user's physical movements and posture and provide a training menu to improve factors that affect vocalization. For example, it can suggest vocal exercises that incorporate stretching and strength training. This makes it possible to provide vocal improvement training based on the user's physical movements and posture.

[0092] The producing department can use the emotion estimation function to propose stage performances that will bring out specific emotions in the user. For example, the producing department uses the emotion estimation function to propose stage performances that will bring out specific emotions in the user. For example, the producing department sets up lighting and sound settings that will allow the user to express their emotions. Furthermore, based on the emotion estimation data of the user, the producing department proposes stage performances that will bring out specific emotions. For example, the producing department proposes stage performances that will help the user relax. Furthermore, the producing department uses the emotion estimation function to propose stage performances that will bring out specific emotions in the user. For example, the producing department proposes stage performances that will help the user gain confidence. In this way, it is possible to provide stage performances that will bring out specific emotions in the user.

[0093] The debut support unit can analyze the user's skills and experience and propose optimal debut timing and promotion strategies. For example, it can analyze the user's skills and experience and propose optimal debut timing. For example, it can recommend debuting when the user's singing ability or performance quality reaches a certain level. It can also propose optimal promotion strategies based on the user's past experience and track record. For example, it can provide promotion plans tailored to the user's specialty genres and styles. It can also monitor the user's skills and experience over a long period of time and evaluate growth toward debut. For example, it can adjust the debut timing according to improvements in skills and accumulated experience. This makes it possible to provide debut timing and promotion strategies based on the user's skills and experience.

[0094] The voice training unit can use the emotion estimation function to provide vocal practice to bring out a specific emotion. For example, the emotion estimation function is used to provide vocal practice to bring out a specific emotion to the user. For example, vocal practice to help the user gain confidence is suggested. Furthermore, vocal practice to bring out a specific emotion is provided based on the emotion estimation data of the user. For example, vocal practice to help the user relax is suggested. Furthermore, the emotion estimation function is used to provide vocal practice to bring out a specific emotion to the user. For example, vocal practice to help the user express emotion is suggested. In this way, vocal practice to bring out a specific emotion to the user can be provided.

[0095] The production department can analyze the user's cultural background and regional trends and provide a production plan based on that. For example, the production department can analyze the user's cultural background and provide a production plan rooted in that culture. For example, if the user is familiar with Japanese culture, the production department can suggest Japanese-style stage productions and costumes. The production department can also analyze the user's regional trends and provide a production plan that reflects those trends. For example, if the user is from Latin America, the production department can suggest stage productions that incorporate elements of Latin music. The production department can also analyze the user's cultural background and regional trends and provide a production plan based on those analysis results. For example, if the user is interested in traditional African music, the production department can suggest stage productions that incorporate African rhythms. This makes it possible to provide a production plan based on the user's cultural background and regional trends.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The information collection unit collects at least one of the following information: daily conversations, social media, and diary entries of the user. For example, audio data and text data can be collected. The information collection unit can also automatically collect social media posts and acquire digital diary entries. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit uses text analysis technology to analyze the user's emotions and interests. The analysis unit can also use a sentiment analysis algorithm to analyze the user's emotional fluctuation patterns. The analysis unit can also use topic modeling technology to identify topics of interest to the user. Step 3: The songwriting unit composes lyrics and music based on the information analyzed by the analysis unit. For example, the generation AI generates lyrics that reflect keywords and emotions obtained from the user's daily conversations and social media posts. The generation AI can also compose melodies based on the user's emotions. The generation AI can also select a musical genre based on the user's interests and compose music that suits that genre. Step 4: The voice training department performs voice training based on the lyrics and melody generated by the songwriting department. For example, the generation AI analyzes the user's vocal characteristics and vocal habits, and based on that, suggests a training menu for vocal practice and improving singing technique. The generation AI can also generate an appropriate voice training plan based on recording data of the user's voice. The generation AI can also adjust the training menu according to changes in the user's voice. Step 5: The production department produces the user's performance trained by the voice training department. For example, the generation AI analyzes the user's singing and dancing performance and suggests areas for improvement and strengthening. The generation AI can also suggest costumes, makeup, and stage direction that match the user's image. The generation AI can also generate a production plan based on the user's performance data. Step 6: The debut support department supports the debut of the user produced by the production department. For example, the generation AI proposes schedule management and promotion strategies for the debut. The generation AI can also evaluate the user's performance and provide advice on how to acquire the skills and experience necessary for the debut. The generation AI can also generate a debut support plan based on information about the user's debut.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

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

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

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

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

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0142] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0143] 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.

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information collection unit that collects at least one piece of information from a user's daily conversations, SNS, and diary; an analysis unit that analyzes the information collected by the information collection unit; a songwriting unit that writes lyrics and composes music based on the information analyzed by the analysis unit; a voice training unit that performs voice training based on the lyrics and melody created by the songwriting unit; a producing unit that produces the performance of the user trained by the voice training unit; a debut support unit that supports the debut of the user produced by the production unit. A system characterized by:

2. The information collecting unit Analyzing the emotions in the user's daily conversations or SNS posts and generating an individual training plan based on the emotional fluctuation patterns 2. The system of claim 1.

3. The analysis unit Analyzing the user's visual data and inferring emotions or interests from facial expressions or postures 2. The system of claim 1.

4. The songwriting department: The lyrics and the melody are generated based on the emotion of the user, and musical expression is performed according to the intensity of the emotion.

2. The system of claim 1.

5. The voice training section Proposing a voice training menu according to the emotional state of the user 2. The system of claim 1.

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