system

Abstract

The system according to the embodiment aims to realize an emotional reading aloud that matches the preferences of the user. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a voice conversion unit, and an adjustment unit. The reception unit receives input from a user. The generation unit generates text to be read aloud in an emotional way based on the information received by the reception unit. The voice conversion unit converts the text generated by the generation unit into voice. The adjustment unit adjusts the voice generated by the voice conversion unit to suit the user's preferences.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to provide emotionally rich reading that matches the user's preferences.

[0005] The system according to the embodiment aims to realize an emotional reading aloud that matches the preferences of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a voice conversion unit, and an adjustment unit. The reception unit receives input from a user. The generation unit generates text to be read aloud in an emotional way based on the information received by the reception unit. The voice conversion unit converts the text generated by the generation unit into voice. The adjustment unit adjusts the voice generated by the voice conversion unit to suit the user's preferences. [Effects of the Invention]

[0007] The system according to the embodiment can realize emotional reading that matches the preferences of the user. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) A voice assistant system according to an embodiment of the present invention accepts user input, generates text for emotional reading using a generation AI, converts it into speech, and adjusts it to suit the user's preferences. In the voice assistant system, the user inputs the title and content of a story, and the generation AI analyzes the content and generates text for emotional reading. This generated text is converted into speech using an automated text-to-speech app, and the speech is adjusted to suit the user's preferences. For example, in a voice assistant system, a user inputs the title and content of a story into an app. The generation AI then analyzes the content and generates text for emotional reading. The generated text is converted into speech using an automated text-to-speech app. The speech can be adjusted to suit the user's preferences, allowing for selection of various voice tones, such as a gentle voice or a lively voice. Furthermore, the voice assistant system also has the ability to customize the content of stories based on the child's age and interests. For example, a story can be told in simple language for a 3-year-old child and in slightly more difficult language for a 10-year-old child. The user can also select specific characters or themes, and the generation AI generates stories based on those characters or themes. This allows the voice assistant system to allow the user to easily have their child read an emotionally rich and easy-to-understand story.This allows the voice assistant system to easily have their child read an emotionally rich and easy-to-understand story.For example, when a user reads a story to their child before bed, the voice assistant system can read a story selected by the owner to the owner in an easy-to-understand and emotional way.

[0029] A voice assistant system according to an embodiment includes a reception unit, a generation unit, a voice conversion unit, and an adjustment unit. The reception unit receives input from a user. The user input includes, but is not limited to, the title and content of a story. For example, the reception unit receives input of the title and content of a story from the user into an app. The reception unit can receive various input methods, such as voice input and text input. The generation unit uses a generation AI to generate text for emotional reading based on the information received by the reception unit. For example, the generation AI analyzes the content of the story and generates text for emotional reading. The generation unit can also generate a story based on a specific character or theme based on the user input. For example, the generation AI generates a story based on the personality of a specific character or theme. The voice conversion unit converts the text generated by the generation unit into voice. For example, the voice conversion unit converts the generated text into voice using an automatic text-to-voice app. The voice conversion unit can also adjust the tone and speed of the voice to suit the user's preferences. For example, the voice conversion unit can select various voice tones, such as a gentle voice or a lively voice. The adjustment unit adjusts the voice generated by the voice conversion unit to suit the user's preferences. The adjustment unit can adjust, for example, the tone and speed of the voice. The adjustment unit can also adjust the tone and speed of the voice to suit the user's preferences. For example, the adjustment unit can select various voice tones, such as a gentle voice or a lively voice. This allows the voice assistant system according to the embodiment to generate and adjust an emotionally rich voice based on a user's input.

[0030] The generation unit can generate a story that fits a character or theme based on user input. For example, the generation unit generates a story that fits a specific character or theme based on user input using a generation AI. For example, the generation unit generates a story based on the personality of a specific character or the setting of a theme. The generation unit can also generate a story based on a character or theme selected by the user. For example, the generation unit generates a story based on the personality of a character or the setting of a theme selected by the user. This allows the generation unit to generate a story that fits a specific character or theme based on user input.

[0031] The adjustment unit can adjust the tone or speed of the voice to suit the user's preferences. The adjustment unit can, for example, adjust the tone or speed of the voice. For example, the adjustment unit can select various voice tones, such as a gentle voice or a lively voice. The adjustment unit can also adjust the speed of the voice. For example, the adjustment unit can select various speeds, such as a slow speed or a fast speed. In this way, the adjustment unit can adjust the tone or speed of the voice to suit the user's preferences.

[0032] The generation unit can customize the content of the story according to the child's age or interests. For example, the generation AI customizes the content of the story according to the child's age and interests. For example, the generation unit generates a story using simple language for a 3-year-old child and slightly more difficult language for a 10-year-old child. The generation unit can also customize the content of the story based on the child's interests. For example, the generation unit customizes the content of the story based on the child's interests. This allows the generation unit to customize the content of the story according to the child's age and interests.

[0033] The reception unit can have a function of allowing the user to interactively ask questions in accordance with the progress of the conversation. The reception unit, for example, has a function of allowing the user to interactively ask questions in accordance with the progress of the conversation. For example, the reception unit asks questions to the user in accordance with the progress of the conversation. The reception unit can also allow the user to ask questions in accordance with the progress of the conversation. For example, the reception unit asks questions to the user in accordance with the progress of the conversation. This allows the reception unit to interactively ask questions to the user in accordance with the progress of the conversation.

[0034] The generation unit can have a function of changing the story based on a child's reaction to the story. The generation unit can have a function of changing the story based on a child's reaction to the story, for example, when the generation AI receives a child's reaction to the story. For example, the generation unit changes the story based on the child's reaction. Also, the generation unit can change the story based on a child's reaction to the story. For example, the generation unit changes the story based on the child's reaction. This allows the generation unit to change the story based on a child's reaction to the story.

[0035] The reception unit can analyze the user's past input history and suggest an appropriate input method. The reception unit, for example, analyzes the user's past input history and suggests the optimal input method. For example, the reception unit automatically displays the titles and contents of stories that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests the content of stories to be used in a specific time period from the user's past input history. This allows the reception unit to suggest the optimal input method based on the user's past input history.

[0036] The reception unit can automatically acquire related additional information based on the input content and present it to the user. The reception unit automatically acquires related additional information based on the input content, for example, and presents it to the user. For example, when a user inputs a story title, the reception unit automatically acquires and displays related images and videos. Furthermore, when a user inputs a specific character, the reception unit can automatically acquire and display background information about the character. For example, when a user inputs the content of a story, the reception unit automatically acquires and displays related educational information and reference materials. In this way, the reception unit can automatically acquire related additional information based on the input content and present it to the user.

[0037] The reception unit can select an appropriate input means depending on the user's input method. For example, the reception unit selects the optimal input means depending on the user's input method (voice, text, image, etc.). For example, when a user inputs a story title by voice, the reception unit converts it into text using voice recognition technology. Also, when a user uploads an image, the reception unit can automatically extract the content of the related story using image recognition technology. For example, when a user inputs the content of the story in text, the reception unit provides the optimal input assistance function based on the input content. This allows the reception unit to select the optimal input means depending on the user's input method.

[0038] The reception unit can prioritize acquiring highly relevant information based on the input content, taking into account the user's geographical location information. The reception unit, for example, prioritizes acquiring highly relevant information based on the input content, taking into account the user's geographical location information. For example, if the user inputs a story about a specific area, the reception unit prioritizes acquiring information related to that area. Furthermore, if the user inputs a story about a trip, the reception unit can also prioritize acquiring tourist spots and historical background related to the current location. For example, if the user inputs a story about a specific place, the reception unit prioritizes acquiring maps and photos related to that place. This allows the reception unit to prioritize acquiring highly relevant information based on the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity based on the input content and acquire related information. The reception unit can, for example, analyze the user's social media activity based on the input content and acquire related information. For example, the reception unit can acquire related information based on the content of stories shared by the user on social media. The reception unit can also analyze the content of posts made by the user on social media and suggest related story content. For example, the reception unit can suggest related story content by referring to the activity of the user's friends on social media. In this way, the reception unit can analyze the user's social media activity and acquire related information.

[0040] The reception unit can adjust the input method based on the input content, reflecting the user's past feedback. The reception unit adjusts the input method based on, for example, the input content, reflecting the user's past feedback. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. For example, the reception unit analyzes the user's past feedback and customizes the input interface. In this way, the reception unit can customize the input method, reflecting the user's past feedback.

[0041] The generator can adjust the detail of the text based on the importance of the story when generating text. For example, when the generation AI generates text, the generator adjusts the level of detail of the text based on the importance of the story. For example, in the case of an important story, the generator generates text including a detailed explanation. In addition, in the case of a simple story, the generator can generate concise text that hits the main points. For example, in the case of an educational story, the generator generates text including detailed background information. This allows the generator to adjust the level of detail of the text based on the importance of the story.

[0042] The generation unit can apply different generation algorithms depending on the category of the story when generating text. For example, when the generation AI generates text, the generation unit applies different generation algorithms depending on the category of the story. For example, in the case of an adventure story, the generation unit generates text using expressions that create a sense of tension. In addition, in the case of an educational story, the generation unit can also generate text that includes easy-to-understand explanations. For example, in the case of a fantasy story, the generation unit generates text using expressions that stimulate the imagination. This allows the generation unit to apply different generation algorithms depending on the category of the story.

[0043] The generation unit can improve the accuracy of text generation by referring to the user's past generation results. For example, when a generation AI generates text, the generation unit improves the accuracy of generation by referring to the user's past generation results. For example, the generation unit generates optimal text based on expressions that the user has preferred in the past. The generation unit can also preferentially use specific expression methods from the user's past generation results. For example, the generation unit analyzes the user's past generation results and optimizes the generation algorithm. This allows the generation unit to improve the accuracy of generation by referring to the user's past generation results.

[0044] The generation unit can determine the generation priority based on the time of submission of the story when generating text. For example, when the generation AI generates text, the generation unit determines the generation priority based on the time of submission of the story. For example, in the case of an urgent story, the generation unit generates text with the highest priority. In addition, in the case of a regular story, the generation unit can also generate text based on a schedule. For example, the generation unit adjusts the generation priority based on the submission time specified by the user. This allows the generation unit to determine the generation priority based on the time of submission of the story.

[0045] The generation unit can adjust the order of generation based on the relevance of the story when generating text. For example, when the generation AI generates text, the generation unit adjusts the order of generation based on the relevance of the story. For example, the generation unit generates text with the highest priority for an important story. The generation unit can also generate text with the highest priority for a highly relevant story. For example, the generation unit adjusts the order of generation based on the relevance specified by the user. This allows the generation unit to adjust the order of generation based on the relevance of the story.

[0046] The generation unit can adjust the use of technical terminology in the generated text according to the user's level of expertise when generating the text. For example, when the generation AI generates text, the generation unit adjusts the use of technical terminology in the generated text according to the user's level of expertise. For example, if the user has technical knowledge, the generation unit generates text that uses a lot of technical terminology. In addition, if the user is a beginner, the generation unit can also generate text that uses easy-to-understand expressions. For example, the generation unit selects the optimal expression method based on the user's level of expertise. This allows the generation unit to adjust the use of technical terminology in the generated text according to the user's level of expertise.

[0047] The speech conversion unit can adjust the detail of the speech based on the importance of the text during speech conversion. For example, the speech conversion unit can adjust the detail of the speech based on the importance of the text during speech conversion. For example, the speech conversion unit can generate speech including detailed explanations for important stories. The speech conversion unit can also generate concise speech that focuses on the main points for simple stories. For example, the speech conversion unit can generate speech including detailed background information for educational stories. This allows the speech conversion unit to adjust the detail of the speech based on the importance of the text.

[0048] The speech conversion unit can apply different conversion algorithms depending on the category of the text during speech conversion. For example, the speech conversion unit applies different conversion algorithms depending on the category of the text during speech conversion. For example, in the case of an adventure story, the speech conversion unit generates speech using expressions that create a sense of tension. In addition, in the case of an educational story, the speech conversion unit can also generate speech including easy-to-understand explanations. For example, in the case of a fantasy story, the speech conversion unit generates speech using expressions that stimulate the imagination. This allows the speech conversion unit to apply different conversion algorithms depending on the category of the text.

[0049] The speech conversion unit can improve the accuracy of speech conversion by referring to the user's past conversion results. For example, the speech conversion unit can improve the accuracy of speech conversion by referring to the user's past conversion results. For example, the speech conversion unit generates an optimal voice based on the user's preferred tones of voice in the past. The speech conversion unit can also preferentially use a specific expression method based on the user's past conversion results. For example, the speech conversion unit can analyze the user's past conversion results and optimize the conversion algorithm. This allows the speech conversion unit to improve the accuracy of speech conversion by referring to the user's past conversion results.

[0050] The speech conversion unit can determine the priority of conversion based on the time of submission of text during speech conversion. For example, the speech conversion unit determines the priority of conversion based on the time of submission of text during speech conversion. For example, in the case of an urgent conversation, the speech conversion unit generates speech with the highest priority. In addition, in the case of a regular conversation, the speech conversion unit can also generate speech based on a schedule. For example, the speech conversion unit adjusts the priority of conversion based on the submission time specified by the user. This allows the speech conversion unit to determine the priority of conversion based on the time of submission of text.

[0051] The speech conversion unit can adjust the order of conversion based on the relevance of the text during speech conversion. For example, the speech conversion unit adjusts the order of conversion based on the relevance of the text during speech conversion. For example, the speech conversion unit generates speech with the highest priority for important topics. The speech conversion unit can also generate speech with the highest priority for highly relevant topics. For example, the speech conversion unit adjusts the order of conversion based on the relevance specified by the user. This allows the speech conversion unit to adjust the order of conversion based on the relevance of the text.

[0052] The speech conversion unit can adjust the use of technical terms in the conversion according to the user's level of expertise during speech conversion. For example, the speech conversion unit adjusts the use of technical terms in the conversion according to the user's level of expertise during speech conversion. For example, if the user has specialized knowledge, the speech conversion unit generates speech that uses a lot of technical terms. Furthermore, if the user is a beginner, the speech conversion unit can also generate speech that uses easy-to-understand expressions. For example, the speech conversion unit selects an optimal expression method based on the user's level of expertise. This allows the speech conversion unit to adjust the use of technical terms in the conversion according to the user's level of expertise.

[0053] The adjustment unit can select an appropriate adjustment method by referring to the user's past adjustment history during adjustment. For example, the adjustment unit selects the optimal adjustment method by referring to the user's past adjustment history during adjustment. For example, the adjustment unit generates optimal audio based on the tones and speeds that the user has previously preferred. The adjustment unit can also preferentially use specific tones and speeds from the user's past adjustment history. For example, the adjustment unit analyzes the user's past adjustment history and optimizes the adjustment algorithm. This allows the adjustment unit to select the optimal adjustment method by referring to the user's past adjustment history.

[0054] The adjustment unit can customize the adjustment content based on the user's current situation during adjustment. For example, the adjustment unit customizes the adjustment content based on the user's current situation during adjustment. For example, if the user is relaxed, the adjustment unit generates voice at a slow speed with a gentle tone. Also, if the user is in a hurry, the adjustment unit can generate voice at a fast speed with a concise tone. For example, if the user is excited, the adjustment unit generates voice at a fast speed with a lively tone. This allows the adjustment unit to customize the adjustment content based on the user's current situation.

[0055] The adjustment unit can improve the adjustment method by reflecting user feedback during adjustment. For example, the adjustment unit can improve the adjustment method by reflecting user feedback during adjustment. For example, the adjustment unit can select an optimal tone or speed based on feedback provided by the user. The adjustment unit can also preferentially use a specific tone or speed based on user feedback. For example, the adjustment unit can analyze user feedback and optimize the adjustment algorithm. In this way, the adjustment unit can improve the adjustment method by reflecting user feedback.

[0056] The adjustment unit can select an appropriate adjustment method in consideration of the user's geographical location information when making adjustments. For example, the adjustment unit selects the optimal adjustment method in consideration of the user's geographical location information when making adjustments. For example, when the user is in a specific area, the adjustment unit selects the adjustment method based on information related to the area. Furthermore, when the user makes adjustments while traveling, the adjustment unit can also select the adjustment method based on information related to the user's current location. For example, when the user is in a specific location, the adjustment unit selects the adjustment method based on information related to the location. This allows the adjustment unit to select the optimal adjustment method in consideration of the user's geographical location information.

[0057] The adjustment unit can analyze the user's social media activity and suggest an adjustment method when making an adjustment. For example, the adjustment unit can analyze the user's social media activity and suggest an adjustment method when making an adjustment. For example, the adjustment unit can suggest an optimal adjustment method based on information shared by the user on social media. The adjustment unit can also analyze the content of the user's social media posts and suggest a related adjustment method. For example, the adjustment unit can refer to the activity of the user's friends on social media and suggest a related adjustment method. In this way, the adjustment unit can analyze the user's social media activity and suggest an adjustment method.

[0058] The adjustment unit can adjust the adjustment method by reflecting the user's past feedback during adjustment. For example, the adjustment unit customizes the adjustment method by reflecting the user's past feedback during adjustment. For example, the adjustment unit suggests an optimal adjustment method based on feedback provided by the user in the past. The adjustment unit can also preferentially suggest a specific adjustment method based on the user's past feedback. For example, the adjustment unit analyzes the user's past feedback and customizes the adjustment interface. This allows the adjustment unit to customize the adjustment method by reflecting the user's past feedback.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can automatically search for related past data based on the user's input and present it to the user. For example, if the user inputs a specific story title, the reception unit searches for stories created in the past with similar titles and suggests them to the user. Also, if the user inputs a specific theme, the reception unit can automatically search for past stories related to that theme and display them to the user. Furthermore, the reception unit can also suggest new related stories based on the content the user has input in the past. This allows the reception unit to automatically search for related past data based on the user's input and present it to the user.

[0061] The generation unit can provide feedback on the content of the speech in real time based on the user's input. For example, the generation unit suggests appropriate expressions and wording for the content of the speech input by the user. The generation unit can also provide advice on the flow and structure of the speech based on the content input by the user. Furthermore, the generation unit can also make suggestions for adding emotionally rich expressions to the content input by the user. In this way, the generation unit can provide feedback on the content of the speech in real time based on the user's input.

[0062] The adjustment unit can add background sounds and sound effects to the audio according to the user's preferences. For example, if the user wants to relax, the adjustment unit can add calming music or nature sounds to the background. If the user wants to get excited, the adjustment unit can add lively music and sound effects. Furthermore, the adjustment unit can also allow the user to select background sounds that match a specific theme. This allows the adjustment unit to add background sounds and sound effects to the audio according to the user's preferences.

[0063] The generation unit can generate story content in multiple languages ​​based on user input. For example, the generation unit generates story content input by the user in multiple languages, such as English, Japanese, and Spanish. The generation unit can also use appropriate expressions and wording based on the language selected by the user. Furthermore, the generation unit can automatically translate between languages ​​when the user generates a story in multiple languages. This allows the generation unit to generate story content in multiple languages ​​based on user input.

[0064] The reception unit can retrieve information from a related external database based on the user's input and present it to the user. For example, if the user inputs the title of a particular story, the reception unit can retrieve information about that title from the related external database and display it to the user. Also, if the user inputs a particular theme, the reception unit can retrieve information from an external database related to that theme and provide it to the user. Furthermore, the reception unit can automatically update information from the related external database based on the user's input. This allows the reception unit to retrieve information from the related external database based on the user's input and present it to the user.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit receives input from the user. The input from the user includes, but is not limited to, the title and content of the talk. The reception unit receives input of the title and content of the talk by the user into the app. The reception unit can also receive input via various methods, such as voice input and text input. Step 2: The generator uses AI to generate text for emotional reading based on the information received by the receiver. The generator analyzes the content of the story and generates text for emotional reading. It can also generate stories based on specific characters or themes based on user input. Step 3: The speech converter converts the text generated by the generator into speech. The speech converter uses an automatic text-to-speech app to convert the generated text into speech. The speech converter can also adjust the tone and speed of the speech to suit the user's preferences. Step 4: The adjustment unit adjusts the voice generated by the voice conversion unit to suit the user's preferences. The adjustment unit can adjust the tone and speed of the voice. For example, various voice tones can be selected, such as a gentle voice or a lively voice.

[0067] (Example 2) A voice assistant system according to an embodiment of the present invention accepts user input, generates text for emotional reading using a generation AI, converts it into speech, and adjusts it to suit the user's preferences. In the voice assistant system, the user inputs the title and content of a story, and the generation AI analyzes the content and generates text for emotional reading. This generated text is converted into speech using an automated text-to-speech app, and the speech is adjusted to suit the user's preferences. For example, in a voice assistant system, a user inputs the title and content of a story into an app. The generation AI then analyzes the content and generates text for emotional reading. The generated text is converted into speech using an automated text-to-speech app. The speech can be adjusted to suit the user's preferences, allowing for selection of various voice tones, such as a gentle voice or a lively voice. Furthermore, the voice assistant system also has the ability to customize the content of stories based on the child's age and interests. For example, a story can be told in simple language for a 3-year-old child and in slightly more difficult language for a 10-year-old child. The user can also select specific characters or themes, and the generation AI generates stories based on those characters or themes. This allows the voice assistant system to allow the user to easily have their child read an emotionally rich and easy-to-understand story.This allows the voice assistant system to easily have their child read an emotionally rich and easy-to-understand story.For example, when a user reads a story to their child before bed, the voice assistant system can read a story selected by the owner to the owner in an easy-to-understand and emotional way.

[0068] A voice assistant system according to an embodiment includes a reception unit, a generation unit, a voice conversion unit, and an adjustment unit. The reception unit receives input from a user. The user input includes, but is not limited to, the title and content of a story. For example, the reception unit receives input of the title and content of a story from the user into an app. The reception unit can receive various input methods, such as voice input and text input. The generation unit uses a generation AI to generate text for emotional reading based on the information received by the reception unit. For example, the generation AI analyzes the content of the story and generates text for emotional reading. The generation unit can also generate a story based on a specific character or theme based on the user input. For example, the generation AI generates a story based on the personality of a specific character or theme. The voice conversion unit converts the text generated by the generation unit into voice. For example, the voice conversion unit converts the generated text into voice using an automatic text-to-voice app. The voice conversion unit can also adjust the tone and speed of the voice to suit the user's preferences. For example, the voice conversion unit can select various voice tones, such as a gentle voice or a lively voice. The adjustment unit adjusts the voice generated by the voice conversion unit to suit the user's preferences. The adjustment unit can adjust, for example, the tone and speed of the voice. The adjustment unit can also adjust the tone and speed of the voice to suit the user's preferences. For example, the adjustment unit can select various voice tones, such as a gentle voice or a lively voice. This allows the voice assistant system according to the embodiment to generate and adjust an emotionally rich voice based on a user's input.

[0069] The generation unit can generate a story that fits a character or theme based on user input. For example, the generation unit generates a story that fits a specific character or theme based on user input using a generation AI. For example, the generation unit generates a story based on the personality of a specific character or the setting of a theme. The generation unit can also generate a story based on a character or theme selected by the user. For example, the generation unit generates a story based on the personality of a character or the setting of a theme selected by the user. This allows the generation unit to generate a story that fits a specific character or theme based on user input.

[0070] The adjustment unit can adjust the tone or speed of the voice to suit the user's preferences. The adjustment unit can, for example, adjust the tone or speed of the voice. For example, the adjustment unit can select various voice tones, such as a gentle voice or a lively voice. The adjustment unit can also adjust the speed of the voice. For example, the adjustment unit can select various speeds, such as a slow speed or a fast speed. In this way, the adjustment unit can adjust the tone or speed of the voice to suit the user's preferences.

[0071] The generation unit can customize the content of the story according to the child's age or interests. For example, the generation AI customizes the content of the story according to the child's age and interests. For example, the generation unit generates a story using simple language for a 3-year-old child and slightly more difficult language for a 10-year-old child. The generation unit can also customize the content of the story based on the child's interests. For example, the generation unit customizes the content of the story based on the child's interests. This allows the generation unit to customize the content of the story according to the child's age and interests.

[0072] The reception unit can have a function of allowing the user to interactively ask questions in accordance with the progress of the conversation. The reception unit, for example, has a function of allowing the user to interactively ask questions in accordance with the progress of the conversation. For example, the reception unit asks questions to the user in accordance with the progress of the conversation. The reception unit can also allow the user to ask questions in accordance with the progress of the conversation. For example, the reception unit asks questions to the user in accordance with the progress of the conversation. This allows the reception unit to interactively ask questions to the user in accordance with the progress of the conversation.

[0073] The generation unit can have a function of changing the story based on a child's reaction to the story. The generation unit can have a function of changing the story based on a child's reaction to the story, for example, when the generation AI receives a child's reaction to the story. For example, the generation unit changes the story based on the child's reaction. Also, the generation unit can change the story based on a child's reaction to the story. For example, the generation unit changes the story based on the child's reaction. This allows the generation unit to change the story based on a child's reaction to the story.

[0074] The reception unit can estimate the user's emotion and select input content based on the estimated user's emotion. For example, the reception unit can estimate the user's emotion and select input content based on the estimated user's emotion. For example, if the user is feeling stressed, the reception unit can provide a simple input interface and avoid complex input. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input. In this way, the reception unit can filter input content based on the user's emotion.

[0075] The reception unit can analyze the user's past input history and suggest an appropriate input method. The reception unit, for example, analyzes the user's past input history and suggests the optimal input method. For example, the reception unit automatically displays the titles and contents of stories that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests the content of stories to be used in a specific time period from the user's past input history. This allows the reception unit to suggest the optimal input method based on the user's past input history.

[0076] The reception unit can automatically acquire related additional information based on the input content and present it to the user. The reception unit automatically acquires related additional information based on the input content, for example, and presents it to the user. For example, when a user inputs a story title, the reception unit automatically acquires and displays related images and videos. Furthermore, when a user inputs a specific character, the reception unit can automatically acquire and display background information about the character. For example, when a user inputs the content of a story, the reception unit automatically acquires and displays related educational information and reference materials. In this way, the reception unit can automatically acquire related additional information based on the input content and present it to the user.

[0077] The reception unit can select an appropriate input means depending on the user's input method. For example, the reception unit selects the optimal input means depending on the user's input method (voice, text, image, etc.). For example, when a user inputs a story title by voice, the reception unit converts it into text using voice recognition technology. Also, when a user uploads an image, the reception unit can automatically extract the content of the related story using image recognition technology. For example, when a user inputs the content of the story in text, the reception unit provides the optimal input assistance function based on the input content. This allows the reception unit to select the optimal input means depending on the user's input method.

[0078] The reception unit can estimate the user's emotion and determine the priority of input content based on the estimated user's emotion. For example, the reception unit can estimate the user's emotion and determine the priority of input content based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit can prioritize displaying important input items to allow the user to complete input quickly. Furthermore, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. For example, when the user is in a hurry, the reception unit can prioritize displaying the most important input items to allow the user to complete input quickly. In this way, the reception unit can prioritize the input content based on the user's emotion.

[0079] The reception unit can prioritize acquiring highly relevant information based on the input content, taking into account the user's geographical location information. The reception unit, for example, prioritizes acquiring highly relevant information based on the input content, taking into account the user's geographical location information. For example, if the user inputs a story about a specific area, the reception unit prioritizes acquiring information related to that area. Furthermore, if the user inputs a story about a trip, the reception unit can also prioritize acquiring tourist spots and historical background related to the current location. For example, if the user inputs a story about a specific place, the reception unit prioritizes acquiring maps and photos related to that place. This allows the reception unit to prioritize acquiring highly relevant information based on the user's geographical location information.

[0080] The reception unit can analyze the user's social media activity based on the input content and acquire related information. The reception unit can, for example, analyze the user's social media activity based on the input content and acquire related information. For example, the reception unit can acquire related information based on the content of stories shared by the user on social media. The reception unit can also analyze the content of posts made by the user on social media and suggest related story content. For example, the reception unit can suggest related story content by referring to the activity of the user's friends on social media. In this way, the reception unit can analyze the user's social media activity and acquire related information.

[0081] The reception unit can adjust the input method based on the input content, reflecting the user's past feedback. The reception unit adjusts the input method based on, for example, the input content, reflecting the user's past feedback. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. For example, the reception unit analyzes the user's past feedback and customizes the input interface. In this way, the reception unit can customize the input method, reflecting the user's past feedback.

[0082] The generation unit can estimate the user's emotions and adjust the expression of the text based on the estimated user's emotions. For example, the generation AI estimates the user's emotions and adjusts the way the text is expressed based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates text using calm expressions. Also, if the user is excited, the generation unit can generate text using lively expressions. For example, if the user is sad, the generation unit generates text using comforting expressions. This allows the generation unit to adjust the way the text is expressed based on the user's emotions.

[0083] The generator can adjust the detail of the text based on the importance of the story when generating text. For example, when the generation AI generates text, the generator adjusts the level of detail of the text based on the importance of the story. For example, in the case of an important story, the generator generates text including a detailed explanation. In addition, in the case of a simple story, the generator can generate concise text that hits the main points. For example, in the case of an educational story, the generator generates text including detailed background information. This allows the generator to adjust the level of detail of the text based on the importance of the story.

[0084] The generation unit can apply different generation algorithms depending on the category of the story when generating text. For example, when the generation AI generates text, the generation unit applies different generation algorithms depending on the category of the story. For example, in the case of an adventure story, the generation unit generates text using expressions that create a sense of tension. In addition, in the case of an educational story, the generation unit can also generate text that includes easy-to-understand explanations. For example, in the case of a fantasy story, the generation unit generates text using expressions that stimulate the imagination. This allows the generation unit to apply different generation algorithms depending on the category of the story.

[0085] The generation unit can improve the accuracy of text generation by referring to the user's past generation results. For example, when a generation AI generates text, the generation unit improves the accuracy of generation by referring to the user's past generation results. For example, the generation unit generates optimal text based on expressions that the user has preferred in the past. The generation unit can also preferentially use specific expression methods from the user's past generation results. For example, the generation unit analyzes the user's past generation results and optimizes the generation algorithm. This allows the generation unit to improve the accuracy of generation by referring to the user's past generation results.

[0086] The generation unit can estimate the user's emotions and adjust the length of the text based on the estimated user's emotions. For example, the generation AI estimates the user's emotions and adjusts the length of the text based on the estimated user's emotions. For example, if the user is in a hurry, the generation unit generates short, to-the-point text. Alternatively, if the user is relaxed, the generation unit can generate longer text with detailed explanations. For example, if the user is excited, the generation unit generates text with visually stimulating effects. This allows the generation unit to adjust the length of the text based on the user's emotions.

[0087] The generation unit can determine the generation priority based on the time of submission of the story when generating text. For example, when the generation AI generates text, the generation unit determines the generation priority based on the time of submission of the story. For example, in the case of an urgent story, the generation unit generates text with the highest priority. In addition, in the case of a regular story, the generation unit can also generate text based on a schedule. For example, the generation unit adjusts the generation priority based on the submission time specified by the user. This allows the generation unit to determine the generation priority based on the time of submission of the story.

[0088] The generation unit can adjust the order of generation based on the relevance of the story when generating text. For example, when the generation AI generates text, the generation unit adjusts the order of generation based on the relevance of the story. For example, the generation unit generates text with the highest priority for an important story. The generation unit can also generate text with the highest priority for a highly relevant story. For example, the generation unit adjusts the order of generation based on the relevance specified by the user. This allows the generation unit to adjust the order of generation based on the relevance of the story.

[0089] The generation unit can adjust the use of technical terminology in the generated text according to the user's level of expertise when generating the text. For example, when the generation AI generates text, the generation unit adjusts the use of technical terminology in the generated text according to the user's level of expertise. For example, if the user has technical knowledge, the generation unit generates text that uses a lot of technical terminology. In addition, if the user is a beginner, the generation unit can also generate text that uses easy-to-understand expressions. For example, the generation unit selects the optimal expression method based on the user's level of expertise. This allows the generation unit to adjust the use of technical terminology in the generated text according to the user's level of expertise.

[0090] The voice conversion unit can estimate the user's emotion and adjust the voice expression based on the estimated user's emotion. The voice conversion unit, for example, estimates the user's emotion and adjusts the voice expression method based on the estimated user's emotion. For example, the voice conversion unit generates voice in a calm voice when the user is relaxed. Furthermore, the voice conversion unit can also generate voice in a lively voice when the user is excited. For example, the voice conversion unit generates voice in a comforting voice when the user is sad. In this way, the voice conversion unit can adjust the voice expression method based on the user's emotion.

[0091] The speech conversion unit can adjust the detail of the speech based on the importance of the text during speech conversion. For example, the speech conversion unit can adjust the detail of the speech based on the importance of the text during speech conversion. For example, the speech conversion unit can generate speech including detailed explanations for important stories. The speech conversion unit can also generate concise speech that focuses on the main points for simple stories. For example, the speech conversion unit can generate speech including detailed background information for educational stories. This allows the speech conversion unit to adjust the detail of the speech based on the importance of the text.

[0092] The speech conversion unit can apply different conversion algorithms depending on the category of the text during speech conversion. For example, the speech conversion unit applies different conversion algorithms depending on the category of the text during speech conversion. For example, in the case of an adventure story, the speech conversion unit generates speech using expressions that create a sense of tension. In addition, in the case of an educational story, the speech conversion unit can also generate speech including easy-to-understand explanations. For example, in the case of a fantasy story, the speech conversion unit generates speech using expressions that stimulate the imagination. This allows the speech conversion unit to apply different conversion algorithms depending on the category of the text.

[0093] The speech conversion unit can improve the accuracy of speech conversion by referring to the user's past conversion results. For example, the speech conversion unit can improve the accuracy of speech conversion by referring to the user's past conversion results. For example, the speech conversion unit generates an optimal voice based on the user's preferred tones of voice in the past. The speech conversion unit can also preferentially use a specific expression method based on the user's past conversion results. For example, the speech conversion unit can analyze the user's past conversion results and optimize the conversion algorithm. This allows the speech conversion unit to improve the accuracy of speech conversion by referring to the user's past conversion results.

[0094] The voice conversion unit can estimate the user's emotion and adjust the length of the voice based on the estimated user's emotion. The voice conversion unit, for example, estimates the user's emotion and adjusts the length of the voice based on the estimated user's emotion. For example, if the user is in a hurry, the voice conversion unit generates short, to-the-point voice. Also, if the user is relaxed, the voice conversion unit can generate longer voice with detailed explanations. For example, if the user is excited, the voice conversion unit generates voice with visually stimulating effects. In this way, the voice conversion unit can adjust the length of the voice based on the user's emotion.

[0095] The speech conversion unit can determine the priority of conversion based on the time of submission of text during speech conversion. For example, the speech conversion unit determines the priority of conversion based on the time of submission of text during speech conversion. For example, in the case of an urgent conversation, the speech conversion unit generates speech with the highest priority. In addition, in the case of a regular conversation, the speech conversion unit can also generate speech based on a schedule. For example, the speech conversion unit adjusts the priority of conversion based on the submission time specified by the user. This allows the speech conversion unit to determine the priority of conversion based on the time of submission of text.

[0096] The speech conversion unit can adjust the order of conversion based on the relevance of the text during speech conversion. For example, the speech conversion unit adjusts the order of conversion based on the relevance of the text during speech conversion. For example, the speech conversion unit generates speech with the highest priority for important topics. The speech conversion unit can also generate speech with the highest priority for highly relevant topics. For example, the speech conversion unit adjusts the order of conversion based on the relevance specified by the user. This allows the speech conversion unit to adjust the order of conversion based on the relevance of the text.

[0097] The speech conversion unit can adjust the use of technical terms in the conversion according to the user's level of expertise during speech conversion. For example, the speech conversion unit adjusts the use of technical terms in the conversion according to the user's level of expertise during speech conversion. For example, if the user has specialized knowledge, the speech conversion unit generates speech that uses a lot of technical terms. Furthermore, if the user is a beginner, the speech conversion unit can also generate speech that uses easy-to-understand expressions. For example, the speech conversion unit selects an optimal expression method based on the user's level of expertise. This allows the speech conversion unit to adjust the use of technical terms in the conversion according to the user's level of expertise.

[0098] The adjustment unit can estimate the user's emotion and adjust the tone or speed of the voice based on the estimated user's emotion. The adjustment unit, for example, estimates the user's emotion and adjusts the tone or speed of the voice based on the estimated user's emotion. For example, the adjustment unit can generate voice with a calm tone and a slow speed when the user is relaxed. Also, the adjustment unit can generate voice with a lively tone and a fast speed when the user is excited. For example, the adjustment unit can generate voice with a comforting tone and a slow speed when the user is sad. In this way, the adjustment unit can adjust the tone or speed of the voice based on the user's emotion.

[0099] The adjustment unit can select an appropriate adjustment method by referring to the user's past adjustment history during adjustment. For example, the adjustment unit selects the optimal adjustment method by referring to the user's past adjustment history during adjustment. For example, the adjustment unit generates optimal audio based on the tones and speeds that the user has previously preferred. The adjustment unit can also preferentially use specific tones and speeds from the user's past adjustment history. For example, the adjustment unit analyzes the user's past adjustment history and optimizes the adjustment algorithm. This allows the adjustment unit to select the optimal adjustment method by referring to the user's past adjustment history.

[0100] The adjustment unit can customize the adjustment content based on the user's current situation during adjustment. For example, the adjustment unit customizes the adjustment content based on the user's current situation during adjustment. For example, if the user is relaxed, the adjustment unit generates voice at a slow speed with a gentle tone. Also, if the user is in a hurry, the adjustment unit can generate voice at a fast speed with a concise tone. For example, if the user is excited, the adjustment unit generates voice at a fast speed with a lively tone. This allows the adjustment unit to customize the adjustment content based on the user's current situation.

[0101] The adjustment unit can improve the adjustment method by reflecting user feedback during adjustment. For example, the adjustment unit can improve the adjustment method by reflecting user feedback during adjustment. For example, the adjustment unit can select an optimal tone or speed based on feedback provided by the user. The adjustment unit can also preferentially use a specific tone or speed based on user feedback. For example, the adjustment unit can analyze user feedback and optimize the adjustment algorithm. In this way, the adjustment unit can improve the adjustment method by reflecting user feedback.

[0102] The adjustment unit can estimate the user's emotion and determine the priority of adjustments based on the estimated user's emotion. For example, the adjustment unit can estimate the user's emotion and determine the priority of adjustments based on the estimated user's emotion. For example, when the user is feeling stressed, the adjustment unit can prioritize displaying important adjustment items and quickly completing the adjustments. Furthermore, when the user is relaxed, the adjustment unit can provide detailed adjustment options and suggest a customizable adjustment method. For example, when the user is in a hurry, the adjustment unit can prioritize displaying the most important adjustment items and quickly completing the adjustments. In this way, the adjustment unit can determine the priority of adjustments based on the user's emotion.

[0103] The adjustment unit can select an appropriate adjustment method in consideration of the user's geographical location information when making adjustments. For example, the adjustment unit selects the optimal adjustment method in consideration of the user's geographical location information when making adjustments. For example, when the user is in a specific area, the adjustment unit selects the adjustment method based on information related to the area. Furthermore, when the user makes adjustments while traveling, the adjustment unit can also select the adjustment method based on information related to the user's current location. For example, when the user is in a specific location, the adjustment unit selects the adjustment method based on information related to the location. This allows the adjustment unit to select the optimal adjustment method in consideration of the user's geographical location information.

[0104] The adjustment unit can analyze the user's social media activity and suggest an adjustment method when making an adjustment. For example, the adjustment unit can analyze the user's social media activity and suggest an adjustment method when making an adjustment. For example, the adjustment unit can suggest an optimal adjustment method based on information shared by the user on social media. The adjustment unit can also analyze the content of the user's social media posts and suggest a related adjustment method. For example, the adjustment unit can refer to the activity of the user's friends on social media and suggest a related adjustment method. In this way, the adjustment unit can analyze the user's social media activity and suggest an adjustment method.

[0105] The adjustment unit can adjust the adjustment method by reflecting the user's past feedback during adjustment. For example, the adjustment unit customizes the adjustment method by reflecting the user's past feedback during adjustment. For example, the adjustment unit suggests an optimal adjustment method based on feedback provided by the user in the past. The adjustment unit can also preferentially suggest a specific adjustment method based on the user's past feedback. For example, the adjustment unit analyzes the user's past feedback and customizes the adjustment interface. This allows the adjustment unit to customize the adjustment method by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, voice conversion unit, and adjustment unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives input from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates text to be read aloud with emotion using a generation AI. The voice conversion unit is realized, for example, by the output device 40 of the smart device 14 and converts the generated text into voice. The adjustment unit is realized, for example, by the control unit 46A of the smart device 14 and adjusts the tone and speed of the voice to suit the user's preferences. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, generation unit, voice conversion unit, and adjustment unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives input from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates text to be read aloud with emotion using a generation AI. The voice conversion unit is realized, for example, by the speaker 240 of the smart glasses 214 and converts the generated text into voice. The adjustment unit is realized, for example, by the control unit 46A of the smart glasses 214 and adjusts the tone and speed of the voice to suit the user's preferences. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, voice conversion unit, and adjustment unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives input from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates text to be read aloud in an emotionally rich manner using a generation AI. The voice conversion unit is realized, for example, by the speaker 240 of the headset-type terminal 314 and converts the generated text into voice. The adjustment unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and adjusts the tone and speed of the voice to suit the user's preferences. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, voice conversion unit, and adjustment unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives input from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates text to be read aloud in an emotionally rich manner using a generation AI. The voice conversion unit is realized, for example, by the speaker 240 of the robot 414 and converts the generated text into voice. The adjustment unit is realized, for example, by the control unit 46A of the robot 414 and adjusts the tone and speed of the voice to suit the user's preferences.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The reception unit can automatically search for related past data based on the user's input and present it to the user. For example, if the user inputs a specific story title, the reception unit searches for stories created in the past with similar titles and suggests them to the user. Also, if the user inputs a specific theme, the reception unit can automatically search for past stories related to that theme and display them to the user. Furthermore, the reception unit can also suggest new related stories based on the content the user has input in the past. This allows the reception unit to automatically search for related past data based on the user's input and present it to the user.

[0108] The generation unit can provide feedback on the content of the speech in real time based on the user's input. For example, the generation unit suggests appropriate expressions and wording for the content of the speech input by the user. The generation unit can also provide advice on the flow and structure of the speech based on the content input by the user. Furthermore, the generation unit can also make suggestions for adding emotionally rich expressions to the content input by the user. In this way, the generation unit can provide feedback on the content of the speech in real time based on the user's input.

[0109] The adjustment unit can add background sounds and sound effects to the audio according to the user's preferences. For example, if the user wants to relax, the adjustment unit can add calming music or nature sounds to the background. If the user wants to get excited, the adjustment unit can add lively music and sound effects. Furthermore, the adjustment unit can also allow the user to select background sounds that match a specific theme. This allows the adjustment unit to add background sounds and sound effects to the audio according to the user's preferences.

[0110] The generation unit can generate story content in multiple languages ​​based on user input. For example, the generation unit generates story content input by the user in multiple languages, such as English, Japanese, and Spanish. The generation unit can also use appropriate expressions and wording based on the language selected by the user. Furthermore, the generation unit can automatically translate between languages ​​when the user generates a story in multiple languages. This allows the generation unit to generate story content in multiple languages ​​based on user input.

[0111] The reception unit can retrieve information from a related external database based on the user's input and present it to the user. For example, if the user inputs the title of a particular story, the reception unit can retrieve information about that title from the related external database and display it to the user. Also, if the user inputs a particular theme, the reception unit can retrieve information from an external database related to that theme and provide it to the user. Furthermore, the reception unit can automatically update information from the related external database based on the user's input. This allows the reception unit to retrieve information from the related external database based on the user's input and present it to the user.

[0112] The generation unit can estimate the user's emotion and dynamically change the content of the story based on the estimated user's emotion. For example, if the user is sad, the generation unit can generate a story with comforting content. Also, if the user is excited, the generation unit can generate a story with lively content. Furthermore, if the user is relaxed, the generation unit can generate a story with calm content. In this way, the generation unit can dynamically change the content of the story based on the user's emotion.

[0113] The reception unit can estimate the user's emotion and dynamically change the input interface based on the estimated user's emotion. For example, the reception unit can provide a simple and intuitive interface when the user is stressed. Alternatively, the reception unit can provide an interface including detailed options when the user is relaxed. Furthermore, the reception unit can provide a visually stimulating interface when the user is excited. This allows the reception unit to dynamically change the input interface based on the user's emotion.

[0114] The generation unit can estimate the user's emotions and change the ending of the story based on the estimated user's emotions. For example, the generation unit can generate a story with a happy ending when the user is sad. Also, the generation unit can generate a story with a thrilling ending when the user is excited. Furthermore, the generation unit can generate a story with a calm ending when the user is relaxed. In this way, the generation unit can change the ending of the story based on the user's emotions.

[0115] The voice conversion unit can estimate the user's emotion and dynamically change the voice effect based on the estimated user's emotion. For example, the voice conversion unit can add a gentle echo effect when the user is relaxed. Also, the voice conversion unit can add a dynamic effect when the user is excited. Furthermore, the voice conversion unit can add a comforting effect when the user is sad. In this way, the voice conversion unit can dynamically change the voice effect based on the user's emotion.

[0116] The adjustment unit can estimate the user's emotion and dynamically adjust the volume of the audio based on the estimated user's emotion. For example, the adjustment unit can play audio at a gentle volume when the user is relaxed. Also, the adjustment unit can play audio at a higher volume when the user is excited. Furthermore, the adjustment unit can play audio at a gentle volume when the user is sad. In this way, the adjustment unit can dynamically adjust the volume of the audio based on the user's emotion.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit receives input from the user. The input from the user includes, but is not limited to, the title and content of the talk. The reception unit receives input of the title and content of the talk by the user into the app. The reception unit can also receive input via various methods, such as voice input and text input. Step 2: The generator uses AI to generate text for emotional reading based on the information received by the receiver. The generator analyzes the content of the story and generates text for emotional reading. It can also generate stories based on specific characters or themes based on user input. Step 3: The speech converter converts the text generated by the generator into speech. The speech converter uses an automatic text-to-speech app to convert the generated text into speech. The speech converter can also adjust the tone and speed of the speech to suit the user's preferences. Step 4: The adjustment unit adjusts the voice generated by the voice conversion unit to suit the user's preferences. The adjustment unit can adjust the tone and speed of the voice. For example, various voice tones can be selected, such as a gentle voice or a lively voice.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 reception unit that receives input from a user; a generating unit that generates text to be read aloud in an emotional way based on the information received by the receiving unit; a speech conversion unit that converts the text generated by the generation unit into speech; an adjustment unit that adjusts the voice generated by the voice conversion unit to suit the user's preferences. A system characterized by:

2. The generation unit Generate stories based on characters and themes based on user input 2. The system of claim 1.

3. The adjustment unit Adjust the tone or speed of the speech to suit your preferences 2. The system of claim 1.

4. The generation unit Customize the content of your talk based on your child's age or interests 2. The system of claim 1.

5. The reception unit Provides a function that allows users to ask questions interactively as the conversation progresses 2. The system of claim 1.

6. The generation unit It has the ability to change the story based on the child's reaction to it.

2. The system of claim 1.

7. The reception unit Estimate the user's emotions and select input content based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests appropriate input methods 2. The system of claim 1.

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