System

Abstract

An object of a system according to an embodiment is to improve usability while inheriting dialect culture.SOLUTION: A system includes a dialect data collection part, a dialect learning part, a dialect selection part, a screen display part, and a voice output part. The dialect data collector may collect dialect data. The dialect learner may learn dialect data collected by the dialect data collector. The dialect selector selects a dialect to be used by the user. A screen display part performs screen display based on the dialect selected by the dialect selection part. The speech output unit performs speech output based on the dialect selected by the dialect selection 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, systems were created in standard Japanese, which made it difficult to preserve dialect culture.

[0005] The system according to the embodiment aims to improve usability while preserving dialect culture. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialect data collection unit, a dialect learning unit, a dialect selection unit, a screen display unit, and an audio output unit. The dialect data collection unit collects dialect data. The dialect learning unit learns the dialect data collected by the dialect data collection unit. The dialect selection unit selects a dialect to be used by the user. The screen display unit displays a screen based on the dialect selected by the dialect selection unit. The audio output unit outputs audio based on the dialect selected by the dialect selection unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve usability while preserving dialect culture. [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 dialect-accommodating application according to an embodiment of the present invention is a system that uses AI technology to learn dialects from around Japan and displays screens and outputs audio in the selected dialect. This allows the dialect-accommodating application to display screens and output audio in the dialect selected by the user, achieving both the preservation of dialect culture and usability.

[0029] A dialect-compatible application according to an embodiment includes a dialect data collection unit, a dialect learning unit, a dialect selection unit, a screen display unit, and an audio output unit. The dialect data collection unit collects dialect data. For example, the dialect data collection unit collects speech data and text data from people speaking dialects in various regions. The dialect data collection unit can also collect speech data from local festivals and events. The dialect data collection unit can also collect interview audio from elderly people in the region. For example, the dialect data collection unit collects dialect data from various regions, such as Kansai dialect, Tohoku dialect, and Kyushu dialect. The dialect learning unit learns the dialect data collected by the dialect data collection unit. For example, the dialect learning unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to analyze and learn dialect characteristics based on the collected dialect data. The dialect learning unit can also use an emotion estimation function to extract emotionally rich expressions from the dialect data and have the generation AI learn them. Furthermore, the dialect learning unit can learn audio data from local folk tales and legends. For example, the generation AI learns intonations and accents unique to each region. The dialect selection unit selects the dialect used by the user. For example, the dialect selection unit provides a function on the dialect selection screen that automatically suggests a dialect for that region when the user inputs their hometown or current location. The dialect selection unit can also use an emotion estimation function to suggest a dialect that matches the user's emotional state. Furthermore, the dialect selection unit can provide a function on the dialect selection screen that allows the user to listen to sample dialect audio before selecting. The screen display unit displays the screen based on the dialect selected by the dialect selection unit. For example, the screen display unit trains the generation AI to learn expressions and phrases unique to each region when dialectizing the screen display. The screen display unit can also use the emotion estimation function to generate dialect expressions that match the user's emotional state. Furthermore, the screen display unit can incorporate traditional fonts and designs of the region. The audio output unit outputs audio based on the dialect selected by the dialect selection unit. For example, when converting speech output into a dialect, the voice output unit trains the generation AI to learn the intonation and accent specific to each region.The voice output unit can also use the emotion estimation function to generate dialect voices corresponding to the user's emotional state. Furthermore, the voice output unit can incorporate traditional music and sound effects from the region. This allows the dialect-enabled application according to the embodiment to display screens and output voices in a dialect selected by the user. For example, the user can use the system in a dialect that they are familiar with, thereby contributing to the preservation of dialect culture.

[0030] The dialect data collection unit collects audio data from regional festivals or events and trains the generation AI on it. For example, the dialect data collection unit collects audio data from regional festivals and events and trains the generation AI on it. For example, it collects audio data from Aomori's Nebuta Festival and Okinawa's Eisa Festival to learn the nuances and intonation of the dialect. The dialect data collection unit also collects audio data from regional festivals and events and trains the generation AI on it to reproduce the unique rhythm and intonation of the dialect. For example, it collects audio data from Kyoto's Gion Festival and Fukuoka's Hakata Gion Yamakasa Festival. The dialect data collection unit also collects audio data from regional festivals and events and trains the generation AI on it to reflect the cultural background of the dialect. For example, it collects audio data from Akita's Kanto Festival and Ehime's Niihama Taiko Festival. This allows for more accurate reproduction of the nuances and intonation of the dialect.

[0031] The dialect data collection unit collects interview audio from local elderly people and trains the generation AI. For example, the dialect data collection unit collects interview audio from local elderly people and trains the generation AI. For example, interview audio from elderly people in the Tohoku region is collected to preserve dialects that are in danger of disappearing. The dialect data collection unit also collects interview audio from local elderly people and trains the generation AI to reflect the history and culture of the dialect. For example, interview audio from elderly people in the Kyushu region is collected. The dialect data collection unit also collects interview audio from local elderly people and trains the generation AI to preserve dialect diversity. For example, interview audio from elderly people in the Kansai region is collected. This helps preserve dialects that are in danger of disappearing.

[0032] The dialect data collection unit collects audio data of regional folk tales or legends and allows the generation AI to learn from it. For example, the dialect data collection unit collects audio data of regional folk tales and legends and allows the generation AI to learn from it. For example, audio data of folk tales and legends from the Tohoku region is collected to reflect the cultural background. The dialect data collection unit also collects audio data of regional folk tales and legends and allows the generation AI to learn from it, thereby reflecting the cultural background of the dialect. For example, audio data of folk tales and legends from the Kyushu region is collected. The dialect data collection unit also collects audio data of regional folk tales and legends and allows the generation AI to learn from it, thereby reflecting the cultural background of the dialect. For example, audio data of folk tales and legends from the Kansai region is collected. This makes it possible to output dialects that include cultural background.

[0033] The dialect data collection unit works with local schools or educational institutions to collect speech data of children who speak dialects and have the generation AI learn from it. For example, the dialect data collection unit works with local schools or educational institutions to collect speech data of children who speak dialects and have the generation AI learn from it. For example, the dialect data collection unit collects speech data of children who speak dialects at schools in the Tohoku region. The dialect data collection unit also works with local schools and educational institutions to collect speech data of children who speak dialects and have the generation AI learn from it, thereby reflecting the diversity of dialects. For example, the dialect data collection unit collects speech data of children who speak dialects at schools in the Kyushu region. The dialect data collection unit also works with local schools and educational institutions to collect speech data of children who speak dialects and have the generation AI learn from it, thereby reflecting the voices of the generation that will lead the future of dialects. For example, the dialect data collection unit collects speech data of children who speak dialects at schools in the Kansai region. This makes it possible to reflect the diversity of dialects.

[0034] The dialect selection unit can display information introducing the characteristics or history of regional dialects on the dialect selection screen. For example, the dialect selection unit displays information introducing the characteristics and history of regional dialects on the dialect selection screen. For example, it displays information introducing the characteristics and history of Kansai dialect. The dialect selection unit also displays information introducing the characteristics and history of regional dialects on the dialect selection screen to help the user select a dialect. For example, it displays information introducing the characteristics and history of Tohoku dialect. The dialect selection unit also displays information introducing the characteristics and history of regional dialects on the dialect selection screen to help the user select a dialect. For example, it displays information introducing the characteristics and history of Kyushu dialect. This helps the user select a dialect.

[0035] The dialect selection unit can add a function that automatically suggests a regional dialect when the user inputs their hometown or current location on the dialect selection screen. For example, the dialect selection unit adds a function that automatically suggests a regional dialect when the user inputs their hometown or current location on the dialect selection screen. For example, if the user inputs Osaka, Kansai dialect is suggested. The dialect selection unit also adds a function that automatically suggests a regional dialect when the user inputs their hometown or current location on the dialect selection screen. For example, if the user inputs Sendai, Tohoku dialect is suggested. The dialect selection unit also adds a function that automatically suggests a regional dialect when the user inputs their hometown or current location on the dialect selection screen. For example, if the user inputs Fukuoka, Kyushu dialect is suggested. This allows the user to easily select the dialect of their region.

[0036] The dialect selection unit can add a function to the dialect selection screen that allows the user to select a combination of multiple dialects. For example, the dialect selection unit adds a function to the dialect selection screen that allows the user to select a combination of multiple dialects. For example, the combination of Kansai dialect and Tohoku dialect is selected. The dialect selection unit also adds a function to the dialect selection screen that allows the user to select a combination of multiple dialects, allowing the user to enjoy dialects from multiple regions at the same time. For example, the combination of Kyushu dialect and Kansai dialect is selected. The dialect selection unit also adds a function to the dialect selection screen that allows the user to select a combination of multiple dialects, allowing the user to enjoy dialects from multiple regions at the same time. For example, the combination of Tohoku dialect and Kyushu dialect is selected. This allows the user to enjoy dialects from multiple regions at the same time.

[0037] The dialect selection unit can add a function to the dialect selection screen that allows the user to select a dialect after listening to sample audio of the dialect. For example, the dialect selection unit adds a function to the dialect selection screen that allows the user to select a dialect after listening to sample audio of the dialect. For example, the user may select a dialect after listening to sample audio of Kansai dialect. The dialect selection unit also adds a function to the dialect selection screen that allows the user to select a dialect after listening to sample audio of the dialect, so that the user can check the nuances of the dialect before making a selection. For example, the user may select a dialect after listening to sample audio of Tohoku dialect. The dialect selection unit also adds a function to the dialect selection screen that allows the user to select a dialect after listening to sample audio of the dialect, so that the user can check the nuances of the dialect before making a selection. For example, the user may select a dialect after listening to sample audio of Kyushu dialect. This allows the user to check the nuances of the dialect before making a selection.

[0038] The screen display unit can make the generation AI learn expressions or phrases specific to each region when dialectalizing the screen display. For example, the screen display unit makes the generation AI learn expressions and phrases specific to each region when dialectalizing the screen display. For example, it makes it learn expressions and phrases specific to the Kansai dialect. Furthermore, the screen display unit can make the generation AI learn expressions and phrases specific to each region when dialectalizing the screen display, thereby realizing a more natural dialect display. For example, it makes it learn expressions and phrases specific to the Tohoku dialect. Furthermore, the screen display unit can make the generation AI learn expressions and phrases specific to each region when dialectalizing the screen display, thereby realizing a more natural dialect display. For example, it makes it learn expressions and phrases specific to the Kyushu dialect. This makes it possible to realize a more natural dialect display.

[0039] The screen display unit can incorporate a traditional regional font or design when converting the screen display into a dialect. For example, the screen display unit incorporates a traditional regional font or design when converting the screen display into a dialect. For example, traditional fonts and designs from the Kansai region are incorporated. Furthermore, the screen display unit incorporates a traditional regional font or design when converting the screen display into a dialect, thereby visually reflecting the regional culture. For example, traditional fonts and designs from the Tohoku region are incorporated. Furthermore, the screen display unit incorporates a traditional regional font or design when converting the screen display into a dialect, thereby visually reflecting the regional culture. For example, traditional fonts and designs from the Kyushu region are incorporated. This makes it possible to visually reflect the regional culture.

[0040] The screen display unit can add a function to display information about regional landmarks or specialty products in addition to the dialect of each region when converting the screen display into a dialect. For example, the screen display unit adds a function to display information about regional landmarks and specialty products in addition to the dialect of each region when converting the screen display into a dialect. For example, information about landmarks and specialty products of the Kansai region is displayed. Furthermore, the screen display unit can add a function to display information about regional landmarks and specialty products in addition to the dialect of each region when converting the screen display into a dialect, thereby conveying the charm of the region. For example, information about landmarks and specialty products of the Tohoku region is displayed. Furthermore, the screen display unit can add a function to display information about regional landmarks and specialty products in addition to the dialect of each region when converting the screen display into a dialect, thereby conveying the charm of the region. For example, information about landmarks and specialty products of the Kyushu region is displayed. This makes it possible to convey the charm of the region.

[0041] The screen display unit can add a function that allows the user to customize the dialect expressions themselves when converting the screen display into a dialect. For example, the screen display unit adds a function that allows the user to customize the dialect expressions themselves when converting the screen display into a dialect. For example, the user customizes the expressions for Kansai dialect. The screen display unit also adds a function that allows the user to customize the dialect expressions themselves when converting the screen display into a dialect, thereby realizing a dialect display that suits each individual user. For example, the user customizes the expressions for Tohoku dialect. The screen display unit also adds a function that allows the user to customize the dialect expressions themselves when converting the screen display into a dialect, thereby realizing a dialect display that suits each individual user. For example, the user customizes the expressions for Kyushu dialect. This makes it possible to realize a dialect display that suits each individual user.

[0042] The voice output unit can train the generation AI to regionally specific intonations or accents when dialectalizing the voice output. For example, the voice output unit can train the generation AI to regionally specific intonations and accents when dialectalizing the voice output. For example, the voice output unit can train the generation AI to regionally specific intonations and accents when dialectalizing the voice output, thereby realizing a more natural dialect voice. For example, the voice output unit can train the generation AI to regionally specific intonations and accents when dialectalizing the voice output, thereby realizing a more natural dialect voice. For example, the voice output unit can train the generation AI to regionally specific intonations and accents when dialectalizing the voice output, thereby realizing a more natural dialect voice. For example, the voice output unit can train the generation AI to regionally specific intonations and accents when dialectalizing the voice output. This can realize a more natural dialect voice.

[0043] The audio output unit can incorporate traditional music or sound effects of the region when converting the audio output into a dialect. For example, the audio output unit incorporates traditional music and sound effects of the region when converting the audio output into a dialect. For example, traditional music and sound effects from the Kansai region are incorporated. Furthermore, the audio output unit incorporates traditional music and sound effects of the region when converting the audio output into a dialect, thereby reflecting the culture of the region auditorily as well. For example, traditional music and sound effects from the Tohoku region are incorporated. Furthermore, the audio output unit incorporates traditional music and sound effects of the region when converting the audio output into a dialect, thereby reflecting the culture of the region auditorily as well. For example, traditional music and sound effects from the Kyushu region are incorporated. In this way, the culture of the region can be reflected auditorily as well.

[0044] The audio output unit can add a function to provide information about the history or culture of a region by voice in addition to the dialect of the region when converting the audio output into a dialect. For example, the audio output unit adds a function to provide information about the history or culture of a region by voice in addition to the dialect of the region when converting the audio output into a dialect. For example, information about the history and culture of the Kansai region is provided by voice. Furthermore, the audio output unit adds a function to provide information about the history and culture of a region by voice in addition to the dialect of the region when converting the audio output into a dialect, thereby conveying the charm of the region. For example, information about the history and culture of the Tohoku region is provided by voice. Furthermore, the audio output unit adds a function to provide information about the history and culture of a region by voice in addition to the dialect of the region when converting the audio output into a dialect, thereby conveying the charm of the region. For example, information about the history and culture of the Kyushu region is provided by voice. This makes it possible to convey the charm of the region.

[0045] The audio output unit can add a function that allows the user to customize the dialect voice themselves when dialectalizing the audio output. For example, the audio output unit adds a function that allows the user to customize the dialect voice themselves when dialectalizing the audio output. For example, the user customizes the voice of Kansai dialect. The audio output unit also adds a function that allows the user to customize the dialect voice themselves when dialectalizing the audio output, thereby realizing a dialect voice that suits each individual user. For example, the user customizes the voice of Tohoku dialect. The audio output unit also adds a function that allows the user to customize the dialect voice themselves when dialectalizing the audio output, thereby realizing a dialect voice that suits each individual user. For example, the user customizes the voice of Kyushu dialect. This makes it possible to realizing a dialect voice that suits each individual user.

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

[0047] Dialect-enabled applications can also provide customization features based on user preferences. For example, users can register their favorite expressions and phrases within a particular dialect and prioritize their display and output. Users can also record dialects in their own voice and import them into the application for use. Furthermore, a feature can be added that allows users to share dialect data with other users. This allows users to enjoy their own customized dialect experience.

[0048] Dialect-enabled applications can also add features that provide local tourist information. For example, they can display information about local tourist attractions and events related to the dialect selected by the user. They can also provide information about local specialties and gourmet food. They can also provide information about the history and culture of the region, allowing users to learn more about the region. This allows users to rediscover the charms of the region through the dialect.

[0049] Dialect-enabled applications can also add features to support users' learning. For example, they can provide quizzes and games to help users learn words and phrases in the dialect they select. They can also add features to support dialect pronunciation practice, helping users master correct pronunciation. They can also provide explanations of dialect grammar and expressions, helping users gain a deeper understanding of the dialect. This allows users to learn the dialect while having fun.

[0050] Dialect-enabled applications can also add features to monitor the user's health. For example, they can measure the user's heart rate and blood pressure to check their health. They can also monitor the user's sleep and provide advice on how to improve the quality of their sleep. They can also record the user's exercise volume and provide advice on how to improve their exercise. This helps users manage their health.

[0051] Dialect-aware applications can also add features to support users' schedule management. For example, they can input the user's schedule and set reminders. They can also provide advice on optimal time management based on the user's schedule. They can also provide notifications in the user's dialect according to the user's schedule. This allows users to manage their schedules efficiently.

[0052] Dialect-enabled applications can also add a function to provide content that matches the user's hobbies and interests. For example, if a user is interested in cooking, they can provide recipes in the dialect. Or, if a user is interested in traveling, they can provide travel guides in the dialect. They can also provide content customized to the user's hobbies and interests. This allows users to enjoy content that matches their hobbies and interests.

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

[0054] Step 1: The dialect data collection unit collects dialect data. For example, it collects audio and text data from people who speak the dialects of each region, audio data from local festivals and events, and audio interviews with elderly people in the region. Specifically, it collects dialect data from each region, such as Kansai dialect, Tohoku dialect, and Kyushu dialect. Step 2: The dialect learning unit learns the dialect data collected by the dialect data collection unit. For example, the generation AI can analyze and learn dialect characteristics based on the collected dialect data. It can also use the emotion estimation function to extract emotionally rich expressions and train the generation AI. It also learns audio data from local folk tales and legends. Step 3: The dialect selection unit selects the dialect to be used by the user. For example, a function is provided on the dialect selection screen that automatically suggests the dialect of that region when the user inputs their hometown or current location. It is also possible to suggest dialects that correspond to the user's emotional state using an emotion estimation function. Furthermore, a function is provided on the dialect selection screen that allows the user to listen to sample audio of the dialect before making a selection. Step 4: The screen display unit displays the screen based on the dialect selected by the dialect selection unit. For example, the generation AI can learn expressions and phrases specific to each region. It can also generate dialect expressions according to the user's emotional state using an emotion estimation function. It can also incorporate traditional fonts and designs from the region. Step 5: The voice output unit outputs voice based on the dialect selected by the dialect selection unit. For example, the generation AI can learn the intonation and accent specific to each region. It can also generate dialect voice according to the user's emotional state using an emotion estimation function. It can also incorporate traditional music and sound effects from the region.

[0055] (Example 2) The dialect-accommodating application according to an embodiment of the present invention is a system that uses AI technology to learn dialects from around Japan and displays screens and outputs audio in the selected dialect. This allows the dialect-accommodating application to display screens and output audio in the dialect selected by the user, achieving both the preservation of dialect culture and usability.

[0056] A dialect-compatible application according to an embodiment includes a dialect data collection unit, a dialect learning unit, a dialect selection unit, a screen display unit, and an audio output unit. The dialect data collection unit collects dialect data. For example, the dialect data collection unit collects speech data and text data from people speaking dialects in various regions. The dialect data collection unit can also collect speech data from local festivals and events. The dialect data collection unit can also collect interview audio from elderly people in the region. For example, the dialect data collection unit collects dialect data from various regions, such as Kansai dialect, Tohoku dialect, and Kyushu dialect. The dialect learning unit learns the dialect data collected by the dialect data collection unit. For example, the dialect learning unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to analyze and learn dialect characteristics based on the collected dialect data. The dialect learning unit can also use an emotion estimation function to extract emotionally rich expressions from the dialect data and have the generation AI learn them. Furthermore, the dialect learning unit can learn audio data from local folk tales and legends. For example, the generation AI learns intonations and accents unique to each region. The dialect selection unit selects the dialect used by the user. For example, the dialect selection unit provides a function on the dialect selection screen that automatically suggests a dialect for that region when the user inputs their hometown or current location. The dialect selection unit can also use an emotion estimation function to suggest a dialect that matches the user's emotional state. Furthermore, the dialect selection unit can provide a function on the dialect selection screen that allows the user to listen to sample dialect audio before selecting. The screen display unit displays the screen based on the dialect selected by the dialect selection unit. For example, the screen display unit trains the generation AI to learn expressions and phrases unique to each region when dialectizing the screen display. The screen display unit can also use the emotion estimation function to generate dialect expressions that match the user's emotional state. Furthermore, the screen display unit can incorporate traditional fonts and designs of the region. The audio output unit outputs audio based on the dialect selected by the dialect selection unit. For example, when converting speech output into a dialect, the voice output unit trains the generation AI to learn the intonation and accent specific to each region.The voice output unit can also use the emotion estimation function to generate dialect voices corresponding to the user's emotional state. Furthermore, the voice output unit can incorporate traditional music and sound effects from the region. This allows the dialect-enabled application according to the embodiment to display screens and output voices in a dialect selected by the user. For example, the user can use the system in a dialect that they are familiar with, thereby contributing to the preservation of dialect culture.

[0057] The dialect data collection unit collects audio data from regional festivals or events and trains the generation AI on it. For example, the dialect data collection unit collects audio data from regional festivals and events and trains the generation AI on it. For example, it collects audio data from Aomori's Nebuta Festival and Okinawa's Eisa Festival to learn the nuances and intonation of the dialect. The dialect data collection unit also collects audio data from regional festivals and events and trains the generation AI on it to reproduce the unique rhythm and intonation of the dialect. For example, it collects audio data from Kyoto's Gion Festival and Fukuoka's Hakata Gion Yamakasa Festival. The dialect data collection unit also collects audio data from regional festivals and events and trains the generation AI on it to reflect the cultural background of the dialect. For example, it collects audio data from Akita's Kanto Festival and Ehime's Niihama Taiko Festival. This allows for more accurate reproduction of the nuances and intonation of the dialect.

[0058] The dialect data collection unit collects interview audio from local elderly people and trains the generation AI. For example, the dialect data collection unit collects interview audio from local elderly people and trains the generation AI. For example, interview audio from elderly people in the Tohoku region is collected to preserve dialects that are in danger of disappearing. The dialect data collection unit also collects interview audio from local elderly people and trains the generation AI to reflect the history and culture of the dialect. For example, interview audio from elderly people in the Kyushu region is collected. The dialect data collection unit also collects interview audio from local elderly people and trains the generation AI to preserve dialect diversity. For example, interview audio from elderly people in the Kansai region is collected. This helps preserve dialects that are in danger of disappearing.

[0059] The dialect data collection unit uses the emotion estimation function to extract emotionally rich expressions from the dialect data and have the generation AI learn them. The dialect data collection unit, for example, uses the emotion estimation function to extract emotionally rich expressions from the dialect data and have the generation AI learn them. For example, expressions with strong emotions of joy and sadness are extracted. The dialect data collection unit also uses the emotion estimation function to extract emotionally rich expressions from the dialect data and have the generation AI learn them, thereby realizing emotionally rich dialect output. For example, expressions with strong emotions of surprise and anger are extracted. The dialect data collection unit also uses the emotion estimation function to extract emotionally rich expressions from the dialect data and have the generation AI learn them, thereby realizing emotionally rich dialect output. For example, expressions with strong emotions of emotion and excitement are extracted. This makes it possible to realize emotionally rich dialect output.

[0060] The dialect data collection unit collects audio data of regional folk tales or legends and allows the generation AI to learn from it. For example, the dialect data collection unit collects audio data of regional folk tales and legends and allows the generation AI to learn from it. For example, audio data of folk tales and legends from the Tohoku region is collected to reflect the cultural background. The dialect data collection unit also collects audio data of regional folk tales and legends and allows the generation AI to learn from it, thereby reflecting the cultural background of the dialect. For example, audio data of folk tales and legends from the Kyushu region is collected. The dialect data collection unit also collects audio data of regional folk tales and legends and allows the generation AI to learn from it, thereby reflecting the cultural background of the dialect. For example, audio data of folk tales and legends from the Kansai region is collected. This makes it possible to output dialects that include cultural background.

[0061] The dialect data collection unit works with local schools or educational institutions to collect speech data of children who speak dialects and have the generation AI learn from it. For example, the dialect data collection unit works with local schools or educational institutions to collect speech data of children who speak dialects and have the generation AI learn from it. For example, the dialect data collection unit collects speech data of children who speak dialects at schools in the Tohoku region. The dialect data collection unit also works with local schools and educational institutions to collect speech data of children who speak dialects and have the generation AI learn from it, thereby reflecting the diversity of dialects. For example, the dialect data collection unit collects speech data of children who speak dialects at schools in the Kyushu region. The dialect data collection unit also works with local schools and educational institutions to collect speech data of children who speak dialects and have the generation AI learn from it, thereby reflecting the voices of the generation that will lead the future of dialects. For example, the dialect data collection unit collects speech data of children who speak dialects at schools in the Kansai region. This makes it possible to reflect the diversity of dialects.

[0062] The dialect data collection unit uses the emotion estimation function to extract phrases that express specific emotions from the dialect data and have the generation AI learn them. The dialect data collection unit, for example, uses the emotion estimation function to extract phrases that express specific emotions from the dialect data and have the generation AI learn them. For example, phrases that express emotions of joy and sadness are extracted. The dialect data collection unit also uses the emotion estimation function to extract phrases that express specific emotions from the dialect data and have the generation AI learn them, thereby realizing dialect output that corresponds to the emotion. For example, phrases that express emotions of surprise and anger are extracted. The dialect data collection unit also uses the emotion estimation function to extract phrases that express specific emotions from the dialect data and have the generation AI learn them, thereby realizing dialect output that corresponds to the emotion. For example, phrases that express emotions of emotion and excitement are extracted. This makes it possible to realize dialect output that corresponds to the emotion.

[0063] The dialect selection unit can display information introducing the characteristics or history of regional dialects on the dialect selection screen. For example, the dialect selection unit displays information introducing the characteristics and history of regional dialects on the dialect selection screen. For example, it displays information introducing the characteristics and history of Kansai dialect. The dialect selection unit also displays information introducing the characteristics and history of regional dialects on the dialect selection screen to help the user select a dialect. For example, it displays information introducing the characteristics and history of Tohoku dialect. The dialect selection unit also displays information introducing the characteristics and history of regional dialects on the dialect selection screen to help the user select a dialect. For example, it displays information introducing the characteristics and history of Kyushu dialect. This helps the user select a dialect.

[0064] The dialect selection unit can add a function that automatically suggests a regional dialect when the user inputs their hometown or current location on the dialect selection screen. For example, the dialect selection unit adds a function that automatically suggests a regional dialect when the user inputs their hometown or current location on the dialect selection screen. For example, if the user inputs Osaka, Kansai dialect is suggested. The dialect selection unit also adds a function that automatically suggests a regional dialect when the user inputs their hometown or current location on the dialect selection screen. For example, if the user inputs Sendai, Tohoku dialect is suggested. The dialect selection unit also adds a function that automatically suggests a regional dialect when the user inputs their hometown or current location on the dialect selection screen. For example, if the user inputs Fukuoka, Kyushu dialect is suggested. This allows the user to easily select the dialect of their region.

[0065] The dialect selection unit can add a function to suggest a dialect according to the emotional state of the user using the emotion estimation function. For example, the dialect selection unit adds a function to suggest a dialect according to the emotional state of the user using the emotion estimation function. For example, if the user is happy, Kansai dialect is suggested. The dialect selection unit also adds a function to suggest a dialect according to the emotional state of the user using the emotion estimation function, making it possible to select a dialect that matches the user's emotion. For example, Tohoku dialect is suggested if the user is sad. The dialect selection unit also adds a function to suggest a dialect according to the emotional state of the user using the emotion estimation function, making it possible to select a dialect that matches the user's emotion. For example, Kyushu dialect is suggested if the user is surprised. This makes it possible to select a dialect that matches the user's emotion.

[0066] The dialect selection unit can add a function to the dialect selection screen that allows the user to select a combination of multiple dialects. For example, the dialect selection unit adds a function to the dialect selection screen that allows the user to select a combination of multiple dialects. For example, the combination of Kansai dialect and Tohoku dialect is selected. The dialect selection unit also adds a function to the dialect selection screen that allows the user to select a combination of multiple dialects, allowing the user to enjoy dialects from multiple regions at the same time. For example, the combination of Kyushu dialect and Kansai dialect is selected. The dialect selection unit also adds a function to the dialect selection screen that allows the user to select a combination of multiple dialects, allowing the user to enjoy dialects from multiple regions at the same time. For example, the combination of Tohoku dialect and Kyushu dialect is selected. This allows the user to enjoy dialects from multiple regions at the same time.

[0067] The dialect selection unit can add a function to the dialect selection screen that allows the user to select a dialect after listening to sample audio of the dialect. For example, the dialect selection unit adds a function to the dialect selection screen that allows the user to select a dialect after listening to sample audio of the dialect. For example, the user may select a dialect after listening to sample audio of Kansai dialect. The dialect selection unit also adds a function to the dialect selection screen that allows the user to select a dialect after listening to sample audio of the dialect, so that the user can check the nuances of the dialect before making a selection. For example, the user may select a dialect after listening to sample audio of Tohoku dialect. The dialect selection unit also adds a function to the dialect selection screen that allows the user to select a dialect after listening to sample audio of the dialect, so that the user can check the nuances of the dialect before making a selection. For example, the user may select a dialect after listening to sample audio of Kyushu dialect. This allows the user to check the nuances of the dialect before making a selection.

[0068] The dialect selection unit uses the emotion estimation function to collect emotional responses to the dialect selected by the user, and can improve the accuracy of dialect selection based on the data. The dialect selection unit, for example, uses the emotion estimation function to collect emotional responses to the dialect selected by the user. For example, it collects emotional responses when the user selects Kansai dialect. The dialect selection unit also uses the emotion estimation function to collect emotional responses to the dialect selected by the user, and improves the accuracy of dialect selection based on the data. For example, it collects emotional responses when the user selects Tohoku dialect. The dialect selection unit also uses the emotion estimation function to collect emotional responses to the dialect selected by the user, and improves the accuracy of dialect selection based on the data. For example, it collects emotional responses when the user selects Kyushu dialect. This improves the accuracy of dialect selection.

[0069] The screen display unit can make the generation AI learn expressions or phrases specific to each region when dialectalizing the screen display. For example, the screen display unit makes the generation AI learn expressions and phrases specific to each region when dialectalizing the screen display. For example, it makes it learn expressions and phrases specific to the Kansai dialect. Furthermore, the screen display unit can make the generation AI learn expressions and phrases specific to each region when dialectalizing the screen display, thereby realizing a more natural dialect display. For example, it makes it learn expressions and phrases specific to the Tohoku dialect. Furthermore, the screen display unit can make the generation AI learn expressions and phrases specific to each region when dialectalizing the screen display, thereby realizing a more natural dialect display. For example, it makes it learn expressions and phrases specific to the Kyushu dialect. This makes it possible to realize a more natural dialect display.

[0070] The screen display unit can incorporate a traditional regional font or design when converting the screen display into a dialect. For example, the screen display unit incorporates a traditional regional font or design when converting the screen display into a dialect. For example, traditional fonts and designs from the Kansai region are incorporated. Furthermore, the screen display unit incorporates a traditional regional font or design when converting the screen display into a dialect, thereby visually reflecting the regional culture. For example, traditional fonts and designs from the Tohoku region are incorporated. Furthermore, the screen display unit incorporates a traditional regional font or design when converting the screen display into a dialect, thereby visually reflecting the regional culture. For example, traditional fonts and designs from the Kyushu region are incorporated. This makes it possible to visually reflect the regional culture.

[0071] The screen display unit can use the emotion estimation function to generate a dialect expression according to the emotional state of the user. The screen display unit, for example, uses the emotion estimation function to generate a dialect expression according to the emotional state of the user. For example, if the user is happy, an expression in Kansai dialect is generated. The screen display unit also uses the emotion estimation function to generate a dialect expression according to the emotional state of the user, thereby realizing a screen display that matches the emotion. For example, if the user is sad, an expression in Tohoku dialect is generated. The screen display unit also uses the emotion estimation function to generate a dialect expression according to the emotional state of the user, thereby realizing a screen display that matches the emotion. For example, if the user is surprised, an expression in Kyushu dialect is generated. This makes it possible to realize a screen display that matches the emotion.

[0072] The screen display unit can add a function to display information about regional landmarks or specialty products in addition to the dialect of each region when converting the screen display into a dialect. For example, the screen display unit adds a function to display information about regional landmarks and specialty products in addition to the dialect of each region when converting the screen display into a dialect. For example, information about landmarks and specialty products of the Kansai region is displayed. Furthermore, the screen display unit can add a function to display information about regional landmarks and specialty products in addition to the dialect of each region when converting the screen display into a dialect, thereby conveying the charm of the region. For example, information about landmarks and specialty products of the Tohoku region is displayed. Furthermore, the screen display unit can add a function to display information about regional landmarks and specialty products in addition to the dialect of each region when converting the screen display into a dialect, thereby conveying the charm of the region. For example, information about landmarks and specialty products of the Kyushu region is displayed. This makes it possible to convey the charm of the region.

[0073] The screen display unit can add a function that allows the user to customize the dialect expressions themselves when converting the screen display into a dialect. For example, the screen display unit adds a function that allows the user to customize the dialect expressions themselves when converting the screen display into a dialect. For example, the user customizes the expressions for Kansai dialect. The screen display unit also adds a function that allows the user to customize the dialect expressions themselves when converting the screen display into a dialect, thereby realizing a dialect display that suits each individual user. For example, the user customizes the expressions for Tohoku dialect. The screen display unit also adds a function that allows the user to customize the dialect expressions themselves when converting the screen display into a dialect, thereby realizing a dialect display that suits each individual user. For example, the user customizes the expressions for Kyushu dialect. This makes it possible to realize a dialect display that suits each individual user.

[0074] The screen display unit uses the emotion estimation function to analyze the emotion the user feels toward the screen display, and can improve the accuracy of the dialect display based on the data. The screen display unit, for example, uses the emotion estimation function to analyze the emotion the user feels toward the screen display. For example, it analyzes the emotion the user feels toward a screen display in Kansai dialect. The screen display unit also uses the emotion estimation function to analyze the emotion the user feels toward the screen display, and improves the accuracy of the dialect display based on the data. For example, it analyzes the emotion the user feels toward a screen display in Tohoku dialect. The screen display unit also uses the emotion estimation function to analyze the emotion the user feels toward the screen display, and improves the accuracy of the dialect display based on the data. For example, it analyzes the emotion the user feels toward a screen display in Kyushu dialect. This improves the accuracy of the dialect display.

[0075] The voice output unit can train the generation AI to regionally specific intonations or accents when dialectalizing the voice output. For example, the voice output unit can train the generation AI to regionally specific intonations and accents when dialectalizing the voice output. For example, the voice output unit can train the generation AI to regionally specific intonations and accents when dialectalizing the voice output, thereby realizing a more natural dialect voice. For example, the voice output unit can train the generation AI to regionally specific intonations and accents when dialectalizing the voice output, thereby realizing a more natural dialect voice. For example, the voice output unit can train the generation AI to regionally specific intonations and accents when dialectalizing the voice output, thereby realizing a more natural dialect voice. For example, the voice output unit can train the generation AI to regionally specific intonations and accents when dialectalizing the voice output. This can realize a more natural dialect voice.

[0076] The audio output unit can incorporate traditional music or sound effects of the region when converting the audio output into a dialect. For example, the audio output unit incorporates traditional music and sound effects of the region when converting the audio output into a dialect. For example, traditional music and sound effects from the Kansai region are incorporated. Furthermore, the audio output unit incorporates traditional music and sound effects of the region when converting the audio output into a dialect, thereby reflecting the culture of the region auditorily as well. For example, traditional music and sound effects from the Tohoku region are incorporated. Furthermore, the audio output unit incorporates traditional music and sound effects of the region when converting the audio output into a dialect, thereby reflecting the culture of the region auditorily as well. For example, traditional music and sound effects from the Kyushu region are incorporated. In this way, the culture of the region can be reflected auditorily as well.

[0077] The voice output unit can use the emotion estimation function to generate dialect voice according to the emotional state of the user. The voice output unit, for example, uses the emotion estimation function to generate dialect voice according to the emotional state of the user. For example, if the user is happy, a Kansai dialect voice is generated. The voice output unit also uses the emotion estimation function to generate dialect voice according to the emotional state of the user, thereby realizing voice output that matches the emotion. For example, if the user is sad, a Tohoku dialect voice is generated. The voice output unit also uses the emotion estimation function to generate dialect voice according to the emotional state of the user, thereby realizing voice output that matches the emotion. For example, if the user is surprised, a Kyushu dialect voice is generated. This makes it possible to realize voice output that matches the emotion.

[0078] The audio output unit can add a function to provide information about the history or culture of a region by voice in addition to the dialect of the region when converting the audio output into a dialect. For example, the audio output unit adds a function to provide information about the history or culture of a region by voice in addition to the dialect of the region when converting the audio output into a dialect. For example, information about the history and culture of the Kansai region is provided by voice. Furthermore, the audio output unit adds a function to provide information about the history and culture of a region by voice in addition to the dialect of the region when converting the audio output into a dialect, thereby conveying the charm of the region. For example, information about the history and culture of the Tohoku region is provided by voice. Furthermore, the audio output unit adds a function to provide information about the history and culture of a region by voice in addition to the dialect of the region when converting the audio output into a dialect, thereby conveying the charm of the region. For example, information about the history and culture of the Kyushu region is provided by voice. This makes it possible to convey the charm of the region.

[0079] The audio output unit can add a function that allows the user to customize the dialect voice themselves when dialectalizing the audio output. For example, the audio output unit adds a function that allows the user to customize the dialect voice themselves when dialectalizing the audio output. For example, the user customizes the voice of Kansai dialect. The audio output unit also adds a function that allows the user to customize the dialect voice themselves when dialectalizing the audio output, thereby realizing a dialect voice that suits each individual user. For example, the user customizes the voice of Tohoku dialect. The audio output unit also adds a function that allows the user to customize the dialect voice themselves when dialectalizing the audio output, thereby realizing a dialect voice that suits each individual user. For example, the user customizes the voice of Kyushu dialect. This makes it possible to realizing a dialect voice that suits each individual user.

[0080] The voice output unit uses the emotion estimation function to analyze the emotion the user feels toward the voice output, and can improve the accuracy of the dialect voice based on the data. The voice output unit, for example, uses the emotion estimation function to analyze the emotion the user feels toward the voice output. For example, it analyzes the emotion the user feels toward a voice output in Kansai dialect. The voice output unit also uses the emotion estimation function to analyze the emotion the user feels toward the voice output, and improves the accuracy of the dialect voice based on the data. For example, it analyzes the emotion the user feels toward a voice output in Tohoku dialect. The voice output unit also uses the emotion estimation function to analyze the emotion the user feels toward the voice output, and improves the accuracy of the dialect voice based on the data. For example, it analyzes the emotion the user feels toward a voice output in Kyushu dialect. This improves the accuracy of the dialect voice.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] Dialect-enabled applications can also provide customization features based on user preferences. For example, users can register their favorite expressions and phrases within a particular dialect and prioritize their display and output. Users can also record dialects in their own voice and import them into the application for use. Furthermore, a feature can be added that allows users to share dialect data with other users. This allows users to enjoy their own customized dialect experience.

[0083] Dialect-enabled applications can also add features that provide local tourist information. For example, they can display information about local tourist attractions and events related to the dialect selected by the user. They can also provide information about local specialties and gourmet food. They can also provide information about the history and culture of the region, allowing users to learn more about the region. This allows users to rediscover the charms of the region through the dialect.

[0084] Dialect-enabled applications can also add features to support users' learning. For example, they can provide quizzes and games to help users learn words and phrases in the dialect they select. They can also add features to support dialect pronunciation practice, helping users master correct pronunciation. They can also provide explanations of dialect grammar and expressions, helping users gain a deeper understanding of the dialect. This allows users to learn the dialect while having fun.

[0085] Dialect-enabled applications can also add a function to provide music that corresponds to the user's emotional state. For example, cheerful music can be provided if the user is happy, and calm music can be provided if the user is sad. It can also provide relaxation music if the user wants to relax. Furthermore, it can adjust the genre and tempo of the music according to the user's emotional state. This allows the user to enjoy music that matches their emotions.

[0086] Dialect-enabled applications can also add a function to provide advice according to the user's emotional state. For example, if the user is feeling stressed, they can provide advice to relax. If the user is feeling down, they can provide words of encouragement. Furthermore, if the user is excited, they can provide advice to stay calm. In this way, the user can receive appropriate advice according to their emotions.

[0087] Dialect-enabled applications can also add features to monitor the user's health. For example, they can measure the user's heart rate and blood pressure to check their health. They can also monitor the user's sleep and provide advice on how to improve the quality of their sleep. They can also record the user's exercise volume and provide advice on how to improve their exercise. This helps users manage their health.

[0088] Dialect-enabled applications can also add a function to provide entertainment content according to the user's emotional state. For example, if the user is happy, a comedy video can be provided, and if the user is sad, an inspiring movie can be provided. Or, if the user wants to relax, a relaxation video can be provided. Furthermore, the genre and content of entertainment content can be adjusted according to the user's emotional state. This allows the user to enjoy entertainment that matches their emotions.

[0089] Dialect-aware applications can also add features to support users' schedule management. For example, they can input the user's schedule and set reminders. They can also provide advice on optimal time management based on the user's schedule. They can also provide notifications in the user's dialect according to the user's schedule. This allows users to manage their schedules efficiently.

[0090] Dialect-enabled applications can also add a function that provides relaxation methods according to the user's emotional state. For example, if the user is feeling stressed, they can provide deep breathing or meditation techniques. Or, if the user is tense, they can provide stretching techniques to help them relax. Furthermore, they can customize relaxation methods according to the user's emotional state. This allows the user to practice relaxation methods that suit their emotions.

[0091] Dialect-enabled applications can also add a function to provide content that matches the user's hobbies and interests. For example, if a user is interested in cooking, they can provide recipes in the dialect. Or, if a user is interested in traveling, they can provide travel guides in the dialect. They can also provide content customized to the user's hobbies and interests. This allows users to enjoy content that matches their hobbies and interests.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The dialect data collection unit collects dialect data. For example, it collects audio and text data from people who speak the dialects of each region, audio data from local festivals and events, and audio interviews with elderly people in the region. Specifically, it collects dialect data from each region, such as Kansai dialect, Tohoku dialect, and Kyushu dialect. Step 2: The dialect learning unit learns the dialect data collected by the dialect data collection unit. For example, the generation AI can analyze and learn dialect characteristics based on the collected dialect data. It can also use the emotion estimation function to extract emotionally rich expressions and train the generation AI. It also learns audio data from local folk tales and legends. Step 3: The dialect selection unit selects the dialect to be used by the user. For example, a function is provided on the dialect selection screen that automatically suggests the dialect of that region when the user inputs their hometown or current location. It is also possible to suggest dialects that correspond to the user's emotional state using an emotion estimation function. Furthermore, a function is provided on the dialect selection screen that allows the user to listen to sample audio of the dialect before making a selection. Step 4: The screen display unit displays the screen based on the dialect selected by the dialect selection unit. For example, the generation AI can learn expressions and phrases specific to each region. It can also generate dialect expressions according to the user's emotional state using an emotion estimation function. It can also incorporate traditional fonts and designs from the region. Step 5: The voice output unit outputs voice based on the dialect selected by the dialect selection unit. For example, the generation AI can learn the intonation and accent specific to each region. It can also generate dialect voice according to the user's emotional state using an emotion estimation function. It can also incorporate traditional music and sound effects from the region.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

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

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a dialect data collection unit that collects dialect data; a dialect learning unit that learns the dialect data collected by the dialect data collection unit; a dialect selection unit for selecting a dialect to be used by a user; a screen display unit that displays a screen based on the dialect selected by the dialect selection unit; a voice output unit that outputs voice based on the dialect selected by the dialect selection unit. A system characterized by:

2. The dialect data collection unit Collect audio data from local festivals or events and train the AI ​​to generate it.

2. The system of claim 1.

3. The dialect selection unit On the dialect selection screen, display information introducing the characteristics or history of each region's dialect.

2. The system of claim 1.

4. The screen display unit When converting screen displays into dialects, the AI ​​is trained to learn expressions or phrases specific to each region.

2. The system of claim 1.

5. The audio output unit For dialectalization of speech output, the AI ​​is trained to understand regional intonations or accents.

2. The system of claim 1.

6. The dialect data collection unit Extracting emotive expressions from dialect data and training the generative AI 2. The system of claim 1.

7. The dialect selection unit Add a feature to suggest dialects based on the user's emotional state 2. The system of claim 1.

8. The screen display unit Generating dialect expressions according to the user's emotional state 2. The system of claim 1.

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