System

Abstract

The system according to the embodiment is intended to integrate a plurality of media formats to generate and manage comprehensive content.SOLUTION: A system according to an embodiment includes a speech to text unit, a speech conversion unit, an image generation unit, a classification analysis unit, and a music combination unit. The speech-to-text converter converts speech into text. The speech conversion unit converts the text converted by the speech-to-text unit into speech. The image generation unit generates an image from the text or the speech generated by the speech-to-text unit or the speech conversion unit. The classification analysis unit performs classification and relevance analysis on the content generated by the speech-to-text unit, the speech conversion unit, and the image generation unit. The music combining unit combines the content generated by the speech-to-text transcriber, the speech converter, the image generator, and the classification analyzer with music.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult to integrate multiple media formats to generate and manage comprehensive content.

[0005] The system according to the embodiment aims to integrate multiple media formats to generate and manage comprehensive content. [Means for solving the problem]

[0006] The system according to the embodiment includes a speech-to-text unit, a speech conversion unit, an image generation unit, a classification and analysis unit, and a music combination unit. The speech-to-text unit converts speech into text. The speech conversion unit converts the text converted by the speech-to-text unit into speech. The image generation unit generates an image from the text or speech generated by the speech-to-text unit or the speech conversion unit. The classification and analysis unit classifies and analyzes relevance of content generated by the speech-to-text unit, speech conversion unit, and image generation unit. The music combination unit combines the content generated by the speech-to-text unit, speech conversion unit, image generation unit, and classification and analysis unit with music. [Effects of the Invention]

[0007] An embodiment of the system can integrate multiple media formats to create and manage comprehensive content. [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) The Creative Symphony system according to an embodiment of the present invention is a system for comprehensively creating and managing content that combines multiple media formats, such as voice, text, images, and music. This enables the Creative Symphony system to comprehensively create and manage content that combines multiple media formats.

[0029] A Creative Symphony system according to an embodiment includes a speech-to-text unit, a speech conversion unit, an image generation unit, a classification and analysis unit, and a music combination unit. The speech-to-text unit converts speech into text. For example, the speech-to-text unit converts speech data into text data using a speech recognition algorithm. The speech-to-text unit may use Whisper API as software. The speech-to-text unit analyzes speech data and generates corresponding text data. For example, when a user says, "Hello, the weather is nice today," the speech is converted into text. The speech conversion unit converts the text converted by the speech-to-text unit into speech. For example, the speech conversion unit converts text data into natural-sounding speech using speech synthesis technology. The speech conversion unit may use Text-to-Speech API as software. The speech conversion unit analyzes text data and converts it into natural-sounding speech. For example, the text "Hello, the weather is nice today" is reproduced in a natural-sounding voice. The image generation unit generates an image from the text or speech generated by the speech-to-text unit or the speech conversion unit. For example, the image generation unit may use natural language processing technology to analyze the content of text or audio and generate related images. The image generation unit may use the DALL-E API as its software. The image generation unit may analyze the content of text or audio and generate related images. For example, an image of a blue sky and white clouds is generated from the text "blue sky and white clouds." The classification and analysis unit classifies and analyzes the relevance of the content generated by the speech-to-text unit, speech conversion unit, and image generation unit. For example, the classification and analysis unit may use a machine learning algorithm to analyze the generated text, audio, and image data and classify the content and analyze the relevance of the content. The classification and analysis unit may use the Embedding API as its software. The classification and analysis unit may identify that the generated text and images are related to "weather" and classify them into the same category.The music combination unit combines the content generated by the speech-to-text unit, speech conversion unit, image generation unit, and classification and analysis unit with music. For example, the music combination unit combines text, audio, and image data generated using music editing software with music. The music combination unit also harmonizes the generated content with music using synchronization technology. The music combination unit also combines the generated text, audio, and image data with music. For example, the music combination unit selects upbeat music to match a landscape image on a sunny day. This enables the Creative Symphony system according to the embodiment to comprehensively generate and manage content that combines multiple media formats. For example, educators can create multimedia teaching materials for classes, entertainment companies can produce promotional videos, marketers can generate a variety of content for advertising campaigns, and content creators can create original works.

[0030] The speech-to-text conversion unit can automatically remove background noise from speech and convert clear speech data into text. For example, the speech-to-text conversion unit inputs speech data into a noise removal algorithm to automatically remove background noise. The clear speech data is then converted into text. Furthermore, when removing noise from speech data, the speech-to-text conversion unit filters a specific frequency band to reduce noise. As a result, clear speech data is converted into text. Furthermore, the speech-to-text conversion unit uses noise removal technology to remove background noise from speech data in real time and convert clear speech data into text. For example, noise during a meeting is removed. In this way, background noise can be removed and clear speech data can be converted into text.

[0031] The speech-to-text unit can convert speech data into text in multiple languages ​​simultaneously to generate multilingual content. The speech-to-text unit, for example, builds a system that converts speech data into text in multiple languages ​​simultaneously. For example, it converts speech into English, Japanese, French, etc. simultaneously. The speech-to-text unit also inputs speech data into a multilingual text conversion algorithm to convert speech into text in multiple languages. For example, it converts speech from an international conference into text in multiple languages. The speech-to-text unit also converts speech data into text in multiple languages ​​in real time to generate multilingual content. For example, it converts speech from a live event into text in multiple languages. This makes it possible to convert speech into text in multiple languages ​​simultaneously and generate multilingual content.

[0032] The speech-to-text conversion unit performs speaker identification of the speech data, and when multiple speakers are included, can convert the data into text for each speaker. The speech-to-text conversion unit, for example, performs speaker identification of the speech data, and when multiple speakers are included, builds a system that converts the data into text for each speaker. For example, the speech of a meeting is converted into text for each speaker. The speech-to-text conversion unit also uses a speaker identification algorithm to identify the speaker of the speech data and converts the data into text for each speaker. For example, the speech of an interview is converted into text for each speaker. The speech-to-text conversion unit also performs speaker identification of the speech data in real time, and when multiple speakers are included, a system is developed that converts the data into text for each speaker. For example, the speech of a discussion is converted into text for each speaker. As a result, when multiple speakers are included, it is possible to convert the data into text for each speaker.

[0033] The speech conversion unit can understand the context of the text and add appropriate intonation and accent. For example, the speech conversion unit builds a system that analyzes the context of text data and adds appropriate intonation and accent. For example, it adds a rising intonation to questions. The speech conversion unit also uses a context analysis algorithm to understand the context of the text data and selects appropriate intonation and accent. For example, it adds a strong accent to parts that need emphasis. The speech conversion unit also develops a system that dynamically adjusts intonation and accent based on the results of context analysis of the text data. For example, it adjusts intonation and accent every time the context changes. This makes it possible to add intonation and accent according to the context.

[0034] The speech conversion unit can convert text into multiple speech styles (e.g., male voice, female voice, child voice) to allow the user to select. The speech conversion unit, for example, builds a system that converts text data into multiple speech styles. For example, speech styles such as male voice, female voice, child voice, etc. are provided. The speech conversion unit also converts text data into various speech styles to allow the user to select. For example, it provides an interface that allows the user to select their preferred speech style. The speech conversion unit also develops a system that converts text data into multiple speech styles in real time to allow the user to select. For example, it provides speech from a live event in various styles. This allows conversion into multiple speech styles to allow the user to select.

[0035] The speech conversion unit can automatically add background sounds and sound effects according to the content of the text. For example, the speech conversion unit builds a system that analyzes the content of text data and automatically adds appropriate background sounds and sound effects. For example, birdsong is added to text describing natural scenery. The speech conversion unit also understands the context of the text data and selects background sounds and sound effects according to the content. For example, tense music is added to tense scenes. The speech conversion unit also develops a system that dynamically adds background sounds and sound effects based on the results of analyzing the text data. For example, the background sounds and sound effects are adjusted every time the content of the text changes. This makes it possible to automatically add background sounds and sound effects according to the content of the text.

[0036] The image generation unit can understand the context of the text or audio, generate multiple related images, and provide options. The image generation unit, for example, builds a system that analyzes the context of text or audio data and generates multiple related images. For example, it generates multiple images corresponding to scenes in a story and provides options to the user. The image generation unit also uses a context analysis algorithm to understand the content of the text or audio data and generate multiple related images. For example, it generates multiple images of tourist spots for travelogue text. The image generation unit also develops a system that dynamically generates related images based on the results of context analysis of the text or audio data and provides options to the user. For example, it generates multiple images suitable for presentation slides. This makes it possible to generate multiple related images based on the context and provide options.

[0037] The image generation unit can generate images from text or audio in different art styles (e.g., oil painting, watercolor painting). The image generation unit builds a system that generates images based on text or audio data in different art styles. For example, it converts images with the same content into an oil painting style or a watercolor painting style. The image generation unit also uses an art style conversion algorithm to convert the generated images into different art styles. For example, it converts a photographic-style image into a painting-style image. The image generation unit also develops a system that selects an appropriate art style depending on the content of the text or audio data and generates an image. For example, a classical art style is applied to a historical theme. This makes it possible to generate images in different art styles.

[0038] The image generation unit can generate animations and GIF format images based on the content of text and audio. For example, the image generation unit builds a system that analyzes the content of text and audio data and generates animations and GIF format images. For example, it animates story scenes. The image generation unit also uses content analysis algorithms to generate animations and GIF format images based on the content of text and audio data. For example, it animates explanations that involve actions. The image generation unit also develops a system that dynamically generates animations and GIF format images based on the results of the analysis of text and audio data. For example, it generates dynamic visuals for presentations. This makes it possible to generate animations and GIF format images based on the content.

[0039] The classification and analysis unit can track changes in content over time and perform trend analysis. The classification and analysis unit, for example, builds a system that tracks changes in content data over time and performs trend analysis. For example, it analyzes the increase or decrease in content related to a specific theme. The classification and analysis unit also analyzes content data along a time axis and develops an algorithm that identifies trends. For example, it analyzes seasonal trends. The classification and analysis unit also builds a system that tracks changes in content data over time in real time and performs trend analysis. For example, it immediately notifies users when a new trend occurs. This makes it possible to track changes in content over time and perform trend analysis.

[0040] The classification analysis unit can automatically analyze the relevance between different media formats (e.g., video, audio, text) and perform integrated classification. The classification analysis unit, for example, analyzes content data of different media formats and builds a system that automatically analyzes the relevance. For example, it identifies the relevance between video and text. The classification analysis unit also develops an algorithm that analyzes the relevance between media formats and performs integrated classification. For example, it sets categories based on the relevance between audio and text. The classification analysis unit also analyzes content data of different media formats in real time and builds a system that automatically analyzes the relevance. For example, it identifies the relevance between audio and text in live streaming. This makes it possible to automatically analyze the relevance between different media formats and perform integrated classification.

[0041] The classification analysis unit can visualize the relevance of content, allowing the user to intuitively understand it. For example, the classification analysis unit builds a system that visualizes the relevance of content data, allowing the user to intuitively understand it. For example, related content is displayed in graphs or charts. The classification analysis unit also develops an algorithm for visualizing the relevance, allowing the user to easily grasp the relevance. For example, related content is displayed as nodes and links. The classification analysis unit also builds a system that visualizes the relevance of content data in real time, allowing the user to intuitively understand it. For example, the visualization is updated every time the relevance changes. In this way, the relevance of content is visualized, allowing the user to intuitively understand it.

[0042] The music combination unit can automatically adjust the tempo and rhythm of music to suit other media formats. For example, the music combination unit builds a system that automatically adjusts the tempo and rhythm of music data to suit other media formats. For example, the tempo of music is adjusted to match a video scene. The music combination unit also uses a tempo and rhythm adjustment algorithm to adjust music data to suit other media formats. For example, the rhythm of music is adjusted to match the speed of narration. The music combination unit also develops a system that adjusts the tempo and rhythm of music data to suit other media formats in real time. For example, music is adjusted to match the video of a live event. This allows the tempo and rhythm of music to be automatically adjusted to suit other media formats.

[0043] The music combination unit can combine different genres of music to provide a unique musical experience. For example, the music combination unit builds a system that combines different genres of music to provide a unique musical experience. For example, classical music and electronica are combined. The music combination unit also uses a music genre combination algorithm to automatically combine different genres of music. For example, jazz and rock are combined. The music combination unit also develops a system that combines different genres of music in real time to provide a unique musical experience. For example, different genres of music are combined in a live performance. This allows different genres of music to be combined to provide a unique musical experience.

[0044] The music combination unit can synchronize music with other media formats to generate interactive content. For example, the music combination unit builds a system that synchronizes music data with other media formats to generate interactive content. For example, it creates interactive videos in which images change in sync with music. The music combination unit also synchronizes music with other media formats in real time using a synchronization algorithm. For example, it generates content in which animation moves in sync with the rhythm of music. The music combination unit also develops a system that dynamically synchronizes music with other media formats to generate interactive content. For example, it creates content in which music and images are linked in response to user operations. This makes it possible to synchronize music with other media formats to generate interactive content.

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

[0046] The Creative Symphony system can also analyze a user's behavioral history and suggest optimal content based on past usage patterns. For example, it can analyze the trends in content a user has viewed in the past and suggest similar content. It can also provide content that is most suitable for a specific time of day based on the user's behavioral history. For example, it can suggest relaxing music and videos at night. It can also analyze a user's behavioral history in real time and dynamically suggest content based on current usage. This allows it to suggest optimal content based on the user's behavioral history.

[0047] The Creative Symphony system can also collect the user's health data and provide content tailored to their health condition. For example, it can monitor the user's heart rate and stress level and provide relaxing music and videos. It can also suggest music and videos suitable for exercise based on the user's health data. For example, it can provide music with a good tempo while jogging. It can also analyze the user's health data in real time and dynamically provide content tailored to their current health condition. This allows it to provide content tailored to the user's health condition.

[0048] The Creative Symphony system can also obtain the user's location information and provide content based on that location. For example, if the user is in a specific location, it can provide content related to the history and culture of that location. It can also suggest content that is best suited to the user's current location based on the user's location information. For example, if the user is in a tourist spot, it can provide tourist guides and information on recommended spots. It can also analyze the user's location information in real time and dynamically provide content based on the user's current location. This allows it to provide content based on the user's location information.

[0049] The Creative Symphony system can also analyze a user's social media activity to provide content based on their interests. For example, it can analyze the content and comments a user has shared on social media and suggest relevant content. It can also provide trending content based on a user's social media activity. For example, it can provide content related to currently trending topics. It can also analyze a user's social media activity in real time to dynamically provide content based on their current interests. This allows it to provide content based on a user's social media activity.

[0050] The Creative Symphony system can also analyze a user's purchasing history and provide content based on their purchasing trends. For example, it can analyze products and services that a user has purchased in the past and suggest related content. It can also provide content related to specific products and services based on a user's purchasing history. For example, it can provide instructions on how to use and reviews of purchased products. It can also analyze a user's purchasing history in real time and dynamically provide content based on their current purchasing trends. This makes it possible to provide content based on a user's purchasing history.

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

[0052] Step 1: The speech-to-text unit converts speech into text. For example, it uses a speech recognition algorithm to convert speech data into text data. It also uses the Whisper API to analyze the speech data and generate corresponding text data. For example, when a user says, "Hello, the weather is nice today," the speech is converted into text. Step 2: The speech conversion unit converts the text converted by the speech-to-text unit into speech. For example, it uses speech synthesis technology to convert the text data into natural-sounding speech. It also uses a Text-to-Speech API to analyze the text data and convert it into natural-sounding speech. For example, the text "Hello, the weather is nice today" is played back in a natural-sounding voice. Step 3: The image generation unit generates an image from the text or audio generated by the speech-to-text unit or speech conversion unit. For example, it uses natural language processing technology to analyze the content of the text or audio and generates an image related to it. It also uses the DALL-E API to analyze the content of the text or audio and generates an image related to it. For example, an image of a blue sky and white clouds is generated from the text "blue sky and white clouds." Step 4: The classification and analysis unit classifies and analyzes the content generated by the speech-to-text unit, speech conversion unit, and image generation unit. For example, it uses machine learning algorithms to analyze the generated text, audio, and image data, and classifies the content and analyzes its relevance. It also uses the Embedding API to identify generated text and images as being related to "weather" and classify them into the same category. Step 5: The music combination unit combines the content generated by the speech-to-text unit, speech conversion unit, image generation unit, and classification and analysis unit with music. For example, music editing software is used to combine the generated text, audio, and image data with music. Synchronization technology is also used to harmonize the generated content with music. For example, upbeat music is selected to match an image of a sunny day landscape.

[0053] (Example 2) The Creative Symphony system according to an embodiment of the present invention is a system for comprehensively creating and managing content that combines multiple media formats, such as voice, text, images, and music. This enables the Creative Symphony system to comprehensively create and manage content that combines multiple media formats.

[0054] A Creative Symphony system according to an embodiment includes a speech-to-text unit, a speech conversion unit, an image generation unit, a classification and analysis unit, and a music combination unit. The speech-to-text unit converts speech into text. For example, the speech-to-text unit converts speech data into text data using a speech recognition algorithm. The speech-to-text unit may use Whisper API as software. The speech-to-text unit analyzes speech data and generates corresponding text data. For example, when a user says, "Hello, the weather is nice today," the speech is converted into text. The speech conversion unit converts the text converted by the speech-to-text unit into speech. For example, the speech conversion unit converts text data into natural-sounding speech using speech synthesis technology. The speech conversion unit may use Text-to-Speech API as software. The speech conversion unit analyzes text data and converts it into natural-sounding speech. For example, the text "Hello, the weather is nice today" is reproduced in a natural-sounding voice. The image generation unit generates an image from the text or speech generated by the speech-to-text unit or the speech conversion unit. For example, the image generation unit may use natural language processing technology to analyze the content of text or audio and generate related images. The image generation unit may use the DALL-E API as its software. The image generation unit may analyze the content of text or audio and generate related images. For example, an image of a blue sky and white clouds is generated from the text "blue sky and white clouds." The classification and analysis unit classifies and analyzes the relevance of the content generated by the speech-to-text unit, speech conversion unit, and image generation unit. For example, the classification and analysis unit may use a machine learning algorithm to analyze the generated text, audio, and image data and classify the content and analyze the relevance of the content. The classification and analysis unit may use the Embedding API as its software. The classification and analysis unit may identify that the generated text and images are related to "weather" and classify them into the same category.The music combination unit combines the content generated by the speech-to-text unit, speech conversion unit, image generation unit, and classification and analysis unit with music. For example, the music combination unit combines text, audio, and image data generated using music editing software with music. The music combination unit also harmonizes the generated content with music using synchronization technology. The music combination unit also combines the generated text, audio, and image data with music. For example, the music combination unit selects upbeat music to match a landscape image on a sunny day. This enables the Creative Symphony system according to the embodiment to comprehensively generate and manage content that combines multiple media formats. For example, educators can create multimedia teaching materials for classes, entertainment companies can produce promotional videos, marketers can generate a variety of content for advertising campaigns, and content creators can create original works.

[0055] The speech-to-text conversion unit performs emotional analysis of the speech data and can adjust the accuracy of the conversion to text based on the emotion. For example, the speech-to-text conversion unit inputs the speech data into an emotion analysis tool and calculates an emotion score. Based on the score, the speech-to-text conversion unit adjusts the text conversion algorithm and converts the speech data to text according to the intensity of the emotion. The speech-to-text conversion unit also performs emotional analysis of the speech data and identifies the type of emotion (joy, sadness, anger, etc.). Based on the results, the speech-to-text conversion unit dynamically adjusts the accuracy of the conversion to text. The speech-to-text conversion unit also uses emotion analysis to convert the speech data to text while preserving the emotional nuances. For example, parts with strong emotions are emphasized when converted to text. This allows the accuracy of the conversion to text to be adjusted based on the emotion.

[0056] The speech-to-text conversion unit can automatically remove background noise from speech and convert clear speech data into text. For example, the speech-to-text conversion unit inputs speech data into a noise removal algorithm to automatically remove background noise. The clear speech data is then converted into text. Furthermore, when removing noise from speech data, the speech-to-text conversion unit filters a specific frequency band to reduce noise. As a result, clear speech data is converted into text. Furthermore, the speech-to-text conversion unit uses noise removal technology to remove background noise from speech data in real time and convert clear speech data into text. For example, noise during a meeting is removed. In this way, background noise can be removed and clear speech data can be converted into text.

[0057] The speech-to-text unit can use the emotion estimation function to select a text style (e.g., formal, casual) that corresponds to the user's emotion. For example, the speech-to-text unit performs emotion analysis of the speech data and selects a text style that corresponds to the user's emotion. For example, if the user is emotional, a casual style is selected. The speech-to-text unit also uses the emotion estimation function to automatically adjust the text style based on the user's emotion. For example, a formal style is selected in a formal situation. The speech-to-text unit also builds a system that dynamically changes the text style based on the emotion analysis results of the speech data. For example, the style is adjusted each time the emotion changes. This makes it possible to select a text style that corresponds to the user's emotion.

[0058] The speech-to-text unit can convert speech data into text in multiple languages ​​simultaneously to generate multilingual content. The speech-to-text unit, for example, builds a system that converts speech data into text in multiple languages ​​simultaneously. For example, it converts speech into English, Japanese, French, etc. simultaneously. The speech-to-text unit also inputs speech data into a multilingual text conversion algorithm to convert speech into text in multiple languages. For example, it converts speech from an international conference into text in multiple languages. The speech-to-text unit also converts speech data into text in multiple languages ​​in real time to generate multilingual content. For example, it converts speech from a live event into text in multiple languages. This makes it possible to convert speech into text in multiple languages ​​simultaneously and generate multilingual content.

[0059] The speech-to-text conversion unit performs speaker identification of the speech data, and when multiple speakers are included, can convert the data into text for each speaker. The speech-to-text conversion unit, for example, performs speaker identification of the speech data, and when multiple speakers are included, builds a system that converts the data into text for each speaker. For example, the speech of a meeting is converted into text for each speaker. The speech-to-text conversion unit also uses a speaker identification algorithm to identify the speaker of the speech data and converts the data into text for each speaker. For example, the speech of an interview is converted into text for each speaker. The speech-to-text conversion unit also performs speaker identification of the speech data in real time, and when multiple speakers are included, a system is developed that converts the data into text for each speaker. For example, the speech of a discussion is converted into text for each speaker. As a result, when multiple speakers are included, it is possible to convert the data into text for each speaker.

[0060] The speech-to-text unit can use the emotion estimation function to analyze the emotion a user feels when speaking in real time and provide feedback according to the emotion. For example, the speech-to-text unit uses the emotion estimation function to analyze the emotion a user feels when speaking in real time and build a system that provides feedback based on the results. For example, feedback that elicits positive emotions is provided. The speech-to-text unit also analyzes the user's emotion in real time and provides feedback according to the emotion. For example, if the user is nervous, feedback to encourage relaxation is provided. The speech-to-text unit also develops a system that analyzes the emotion a user feels when speaking in real time based on the emotion estimation data and provides advice according to the emotion. For example, advice to calm down is provided if the user is emotional. This makes it possible to provide feedback according to the user's emotion in real time.

[0061] The speech conversion unit can perform emotional analysis of the text and automatically adjust the tone of the voice according to the emotion. The speech conversion unit, for example, performs emotional analysis of the text data and builds a system that automatically adjusts the tone of the voice according to the emotion. For example, if the emotion of joy is strong, the speech conversion unit converts the voice to a brighter tone. The speech conversion unit also uses an emotion analysis algorithm to identify the emotion of the text data and selects a tone of the voice according to that emotion. For example, if the emotion of sadness is strong, the speech conversion unit converts the voice to a calmer tone. The speech conversion unit also develops a system that dynamically adjusts the tone of the voice based on the results of the emotional analysis of the text data. For example, the tone of the voice is adjusted every time the emotion changes. This makes it possible to automatically adjust the tone of the voice according to the emotion.

[0062] The speech conversion unit can understand the context of the text and add appropriate intonation and accent. For example, the speech conversion unit builds a system that analyzes the context of text data and adds appropriate intonation and accent. For example, it adds a rising intonation to questions. The speech conversion unit also uses a context analysis algorithm to understand the context of the text data and selects appropriate intonation and accent. For example, it adds a strong accent to parts that need emphasis. The speech conversion unit also develops a system that dynamically adjusts intonation and accent based on the results of context analysis of the text data. For example, it adjusts intonation and accent every time the context changes. This makes it possible to add intonation and accent according to the context.

[0063] The voice conversion unit can use the emotion estimation function to select a voice style (e.g., encouraging, comforting) that matches the user's emotion. The voice conversion unit, for example, uses the emotion estimation function to build a system that selects a voice style that matches the user's emotion. For example, if the user is depressed, a comforting voice style is selected. The voice conversion unit also analyzes the user's emotion in real time and selects a voice style based on the results. For example, if the user is happy, an encouraging voice style is selected. The voice conversion unit also develops a system that dynamically adjusts the voice style according to the user's emotion based on the emotion estimation data. For example, the voice style is adjusted every time the emotion changes. This makes it possible to select a voice style that matches the user's emotion.

[0064] The speech conversion unit can convert text into multiple speech styles (e.g., male voice, female voice, child voice) to allow the user to select. The speech conversion unit, for example, builds a system that converts text data into multiple speech styles. For example, speech styles such as male voice, female voice, child voice, etc. are provided. The speech conversion unit also converts text data into various speech styles to allow the user to select. For example, it provides an interface that allows the user to select their preferred speech style. The speech conversion unit also develops a system that converts text data into multiple speech styles in real time to allow the user to select. For example, it provides speech from a live event in various styles. This allows conversion into multiple speech styles to allow the user to select.

[0065] The speech conversion unit can automatically add background sounds and sound effects according to the content of the text. For example, the speech conversion unit builds a system that analyzes the content of text data and automatically adds appropriate background sounds and sound effects. For example, birdsong is added to text describing natural scenery. The speech conversion unit also understands the context of the text data and selects background sounds and sound effects according to the content. For example, tense music is added to tense scenes. The speech conversion unit also develops a system that dynamically adds background sounds and sound effects based on the results of analyzing the text data. For example, the background sounds and sound effects are adjusted every time the content of the text changes. This makes it possible to automatically add background sounds and sound effects according to the content of the text.

[0066] The speech conversion unit uses the emotion estimation function to learn the speech style most preferred by the user and can reflect this in subsequent conversions. The speech conversion unit, for example, uses the emotion estimation function to build a system that learns the speech style most preferred by the user. For example, it selects the optimal speech style based on the user's emotional response. The speech conversion unit also analyzes the user's emotional response data and reflects the results in subsequent speech conversions. For example, it preferentially selects the speech style preferred by the user. The speech conversion unit also develops a system that learns the user's preferences based on the emotion estimation data and reflects this in subsequent speech conversions. For example, it adjusts the speech style each time the user's emotion changes. This allows the speech style most preferred by the user to be learned and reflected in subsequent conversions.

[0067] The image generation unit can perform emotion analysis of text and audio and generate an image style based on the emotion. The image generation unit, for example, performs emotion analysis of text and audio data and builds a system that generates an image style based on the emotion. For example, if the emotion of joy is strong, an image with bright colors is generated. The image generation unit also uses an emotion analysis algorithm to identify the emotion of the text or audio data and select an image style according to that emotion. For example, if the emotion of sadness is strong, an image with dark colors is generated. The image generation unit also develops a system that dynamically adjusts the image style based on the emotion analysis results of the text and audio data. For example, the image style is adjusted every time the emotion changes. In this way, an image style based on the emotion can be generated.

[0068] The image generation unit can understand the context of the text or audio, generate multiple related images, and provide options. The image generation unit, for example, builds a system that analyzes the context of text or audio data and generates multiple related images. For example, it generates multiple images corresponding to scenes in a story and provides options to the user. The image generation unit also uses a context analysis algorithm to understand the content of the text or audio data and generate multiple related images. For example, it generates multiple images of tourist spots for travelogue text. The image generation unit also develops a system that dynamically generates related images based on the results of context analysis of the text or audio data and provides options to the user. For example, it generates multiple images suitable for presentation slides. This makes it possible to generate multiple related images based on the context and provide options.

[0069] The image generation unit can use the emotion estimation function to apply an image filter (e.g., warm colors, cool colors) according to the user's emotion. The image generation unit, for example, uses the emotion estimation function to build a system that applies an image filter according to the user's emotion. For example, a warm color filter is applied to positive emotions. The image generation unit also analyzes the user's emotion in real time and selects an image filter based on the results. For example, a cool color filter is applied to negative emotions. The image generation unit also develops a system that dynamically adjusts the image filter according to the user's emotion based on the emotion estimation data. For example, the filter is adjusted every time the emotion changes. This makes it possible to apply an image filter according to the user's emotion.

[0070] The image generation unit can generate images from text or audio in different art styles (e.g., oil painting, watercolor painting). The image generation unit builds a system that generates images based on text or audio data in different art styles. For example, it converts images with the same content into an oil painting style or a watercolor painting style. The image generation unit also uses an art style conversion algorithm to convert the generated images into different art styles. For example, it converts a photographic-style image into a painting-style image. The image generation unit also develops a system that selects an appropriate art style depending on the content of the text or audio data and generates an image. For example, a classical art style is applied to a historical theme. This makes it possible to generate images in different art styles.

[0071] The image generation unit can generate animations and GIF format images based on the content of text and audio. For example, the image generation unit builds a system that analyzes the content of text and audio data and generates animations and GIF format images. For example, it animates story scenes. The image generation unit also uses content analysis algorithms to generate animations and GIF format images based on the content of text and audio data. For example, it animates explanations that involve actions. The image generation unit also develops a system that dynamically generates animations and GIF format images based on the results of the analysis of text and audio data. For example, it generates dynamic visuals for presentations. This makes it possible to generate animations and GIF format images based on the content.

[0072] The image generation unit uses the emotion estimation function to learn the image style that the user most emotionally empathizes with and can reflect this in subsequent generations. The image generation unit, for example, uses the emotion estimation function to build a system that learns the image style that the user most emotionally empathizes with. For example, it selects the optimal image style based on the user's emotional response. The image generation unit also analyzes the user's emotional response data and reflects the results in subsequent image generation. For example, it preferentially selects the image style that the user prefers. The image generation unit also develops a system that learns the user's preferences based on the emotion estimation data and reflects this in subsequent image generation. For example, it adjusts the image style each time the user's emotion changes. This allows the image style that the user most emotionally empathizes with to be learned and reflected in subsequent generations.

[0073] The classification analysis unit can perform sentiment analysis of content and add categories based on the sentiment. For example, the classification analysis unit builds a system that performs sentiment analysis of content data and adds categories based on the sentiment. For example, it classifies content with positive sentiment into a "joy" category. The classification analysis unit also uses a sentiment analysis algorithm to identify the sentiment of the content data and set a category according to that sentiment. For example, it classifies content with negative sentiment into a "sadness" category. The classification analysis unit also develops a system that dynamically adds categories based on the results of the sentiment analysis of the content data. For example, it adjusts categories every time the sentiment changes. This makes it possible to add categories based on the sentiment.

[0074] The classification and analysis unit can track changes in content over time and perform trend analysis. The classification and analysis unit, for example, builds a system that tracks changes in content data over time and performs trend analysis. For example, it analyzes the increase or decrease in content related to a specific theme. The classification and analysis unit also analyzes content data along a time axis and develops an algorithm that identifies trends. For example, it analyzes seasonal trends. The classification and analysis unit also builds a system that tracks changes in content data over time in real time and performs trend analysis. For example, it immediately notifies users when a new trend occurs. This makes it possible to track changes in content over time and perform trend analysis.

[0075] The classification analysis unit can use the emotion estimation function to evaluate the relevance of content according to the user's emotion. The classification analysis unit, for example, uses the emotion estimation function to build a system for evaluating the relevance of content according to the user's emotion. For example, it prioritizes displaying content for which the user has positive emotions. The classification analysis unit also analyzes the user's emotion in real time and evaluates the relevance of content based on the results. For example, it prioritizes displaying content that the user is interested in. The classification analysis unit also develops a system that dynamically evaluates the relevance of content according to the user's emotion based on the emotion estimation data. For example, it adjusts the relevance each time the emotion changes. This makes it possible to evaluate the relevance of content according to the user's emotion.

[0076] The classification analysis unit can automatically analyze the relevance between different media formats (e.g., video, audio, text) and perform integrated classification. The classification analysis unit, for example, analyzes content data of different media formats and builds a system that automatically analyzes the relevance. For example, it identifies the relevance between video and text. The classification analysis unit also develops an algorithm that analyzes the relevance between media formats and performs integrated classification. For example, it sets categories based on the relevance between audio and text. The classification analysis unit also analyzes content data of different media formats in real time and builds a system that automatically analyzes the relevance. For example, it identifies the relevance between audio and text in live streaming. This makes it possible to automatically analyze the relevance between different media formats and perform integrated classification.

[0077] The classification analysis unit can visualize the relevance of content, allowing the user to intuitively understand it. For example, the classification analysis unit builds a system that visualizes the relevance of content data, allowing the user to intuitively understand it. For example, related content is displayed in graphs or charts. The classification analysis unit also develops an algorithm for visualizing the relevance, allowing the user to easily grasp the relevance. For example, related content is displayed as nodes and links. The classification analysis unit also builds a system that visualizes the relevance of content data in real time, allowing the user to intuitively understand it. For example, the visualization is updated every time the relevance changes. In this way, the relevance of content is visualized, allowing the user to intuitively understand it.

[0078] The classification and analysis unit can use the emotion estimation function to preferentially display content that is of most interest to the user. For example, the classification and analysis unit uses the emotion estimation function to build a system that preferentially displays content that is of most interest to the user. For example, it ranks content based on the user's emotion score. The classification and analysis unit also analyzes the user's emotions in real time and preferentially displays content that is of interest to the user based on the results. For example, it preferentially displays content that the user has positive emotions about. The classification and analysis unit also develops a system that dynamically displays content that is of interest to the user based on the emotion estimation data. For example, it adjusts the content to be displayed each time the emotion changes. This makes it possible to preferentially display content that is of most interest to the user.

[0079] The music combination unit can perform emotional analysis of music and select music that is emotionally in harmony with other media formats. For example, the music combination unit performs emotional analysis of music data and builds a system for selecting music that is emotionally in harmony with other media formats. For example, it selects music with positive emotions. The music combination unit also uses an emotion analysis algorithm to identify the emotion of the music data and selects music according to that emotion. For example, it selects calm music for sad emotions. The music combination unit also develops a system that dynamically selects music that is in harmony with other media formats based on the results of the emotional analysis of the music data. For example, it adjusts the music each time the emotion changes. This makes it possible to select music that is emotionally in harmony with other media formats.

[0080] The music combination unit can automatically adjust the tempo and rhythm of music to suit other media formats. For example, the music combination unit builds a system that automatically adjusts the tempo and rhythm of music data to suit other media formats. For example, the tempo of music is adjusted to match a video scene. The music combination unit also uses a tempo and rhythm adjustment algorithm to adjust music data to suit other media formats. For example, the rhythm of music is adjusted to match the speed of narration. The music combination unit also develops a system that adjusts the tempo and rhythm of music data to suit other media formats in real time. For example, music is adjusted to match the video of a live event. This allows the tempo and rhythm of music to be automatically adjusted to suit other media formats.

[0081] The music combination unit can use the emotion estimation function to select a music style (e.g., relaxing, energetic) that corresponds to the user's emotion. For example, the music combination unit uses the emotion estimation function to build a system that selects a music style that corresponds to the user's emotion. For example, if the user wants to relax, relaxing music is selected. The music combination unit also analyzes the user's emotion in real time and selects a music style based on the results. For example, energetic music is selected when the user is feeling energetic. The music combination unit also develops a system that dynamically adjusts the music style that corresponds to the user's emotion based on the emotion estimation data. For example, the music style is adjusted every time the emotion changes. This makes it possible to select a music style that corresponds to the user's emotion.

[0082] The music combination unit can combine different genres of music to provide a unique musical experience. For example, the music combination unit builds a system that combines different genres of music to provide a unique musical experience. For example, classical music and electronica are combined. The music combination unit also uses a music genre combination algorithm to automatically combine different genres of music. For example, jazz and rock are combined. The music combination unit also develops a system that combines different genres of music in real time to provide a unique musical experience. For example, different genres of music are combined in a live performance. This allows different genres of music to be combined to provide a unique musical experience.

[0083] The music combination unit can synchronize music with other media formats to generate interactive content. For example, the music combination unit builds a system that synchronizes music data with other media formats to generate interactive content. For example, it creates interactive videos in which images change in sync with music. The music combination unit also synchronizes music with other media formats in real time using a synchronization algorithm. For example, it generates content in which animation moves in sync with the rhythm of music. The music combination unit also develops a system that dynamically synchronizes music with other media formats to generate interactive content. For example, it creates content in which music and images are linked in response to user operations. This makes it possible to synchronize music with other media formats to generate interactive content.

[0084] The music combination unit uses the emotion estimation function to learn the music style that the user most emotionally empathizes with and can reflect this in subsequent combinations. The music combination unit, for example, uses the emotion estimation function to build a system that learns the music style that the user most emotionally empathizes with. For example, it selects the optimal music style based on the user's emotional response. The music combination unit also analyzes the user's emotional response data and reflects the results in subsequent music combinations. For example, it prioritizes the selection of music styles that the user prefers. The music combination unit also learns the user's preferences based on the emotion estimation data and develops a system that reflects this in subsequent music combinations. For example, it adjusts the music style each time the user's emotions change. This allows the music style that the user most emotionally empathizes with to be learned and reflected in subsequent combinations.

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

[0086] The Creative Symphony system can also analyze a user's behavioral history and suggest optimal content based on past usage patterns. For example, it can analyze the trends in content a user has viewed in the past and suggest similar content. It can also provide content that is most suitable for a specific time of day based on the user's behavioral history. For example, it can suggest relaxing music and videos at night. It can also analyze a user's behavioral history in real time and dynamically suggest content based on current usage. This allows it to suggest optimal content based on the user's behavioral history.

[0087] The Creative Symphony system can also collect the user's health data and provide content tailored to their health condition. For example, it can monitor the user's heart rate and stress level and provide relaxing music and videos. It can also suggest music and videos suitable for exercise based on the user's health data. For example, it can provide music with a good tempo while jogging. It can also analyze the user's health data in real time and dynamically provide content tailored to their current health condition. This allows it to provide content tailored to the user's health condition.

[0088] The Creative Symphony system can also obtain the user's location information and provide content based on that location. For example, if the user is in a specific location, it can provide content related to the history and culture of that location. It can also suggest content that is best suited to the user's current location based on the user's location information. For example, if the user is in a tourist spot, it can provide tourist guides and information on recommended spots. It can also analyze the user's location information in real time and dynamically provide content based on the user's current location. This allows it to provide content based on the user's location information.

[0089] The Creative Symphony system can also analyze a user's social media activity to provide content based on their interests. For example, it can analyze the content and comments a user has shared on social media and suggest relevant content. It can also provide trending content based on a user's social media activity. For example, it can provide content related to currently trending topics. It can also analyze a user's social media activity in real time to dynamically provide content based on their current interests. This allows it to provide content based on a user's social media activity.

[0090] The Creative Symphony system can also analyze a user's purchasing history and provide content based on their purchasing trends. For example, it can analyze products and services that a user has purchased in the past and suggest related content. It can also provide content related to specific products and services based on a user's purchasing history. For example, it can provide instructions on how to use and reviews of purchased products. It can also analyze a user's purchasing history in real time and dynamically provide content based on their current purchasing trends. This makes it possible to provide content based on a user's purchasing history.

[0091] The Creative Symphony system can also estimate the user's emotions and dynamically change the content display order based on the estimated emotions. For example, if the user is feeling positive, it can prioritize displaying upbeat content. On the other hand, if the user is feeling negative, it can prioritize displaying relaxing content. Furthermore, it can analyze the user's emotions in real time and dynamically change the display order according to changes in emotions. This allows it to provide a content display order that matches the user's emotions.

[0092] The Creative Symphony system can also estimate the user's emotions and adjust the difficulty of the content based on the estimated emotions. For example, if the user is relaxed, it can provide more difficult content. On the other hand, if the user is stressed, it can provide less difficult content. Furthermore, it can analyze the user's emotions in real time and dynamically adjust the difficulty of the content according to changes in emotions. This allows it to provide content of varying difficulty according to the user's emotions.

[0093] The Creative Symphony system can also estimate the user's emotions and dynamically change the color tone and design of the content based on the estimated emotions. For example, if the user has positive emotions, it can use bright colors and designs. On the other hand, if the user has negative emotions, it can use muted colors and designs. Furthermore, it can analyze the user's emotions in real time and dynamically change the color tone and design according to changes in emotions. This allows it to provide colors and designs that match the user's emotions.

[0094] The Creative Symphony system can also estimate the user's emotions and dynamically adjust the volume and speed of the content based on the estimated emotions. For example, if the user is relaxed, the volume can be lowered and the speed can be slowed down. On the other hand, if the user is feeling energetic, the volume can be increased and the speed can be increased. Furthermore, the system can analyze the user's emotions in real time and dynamically adjust the volume and speed according to changes in emotions. This allows the system to provide volume and speed that correspond to the user's emotions.

[0095] The Creative Symphony system can also estimate the user's emotions and dynamically change the content theme based on the estimated emotions. For example, if the user has positive emotions, it can provide content with a fun theme. On the other hand, if the user has negative emotions, it can provide content with a relaxing theme. Furthermore, it can analyze the user's emotions in real time and dynamically change the theme according to changes in emotions. This allows it to provide content with a theme that suits the user's emotions.

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

[0097] Step 1: The speech-to-text unit converts speech into text. For example, it uses a speech recognition algorithm to convert speech data into text data. It also uses the Whisper API to analyze the speech data and generate corresponding text data. For example, when a user says, "Hello, the weather is nice today," the speech is converted into text. Step 2: The speech conversion unit converts the text converted by the speech-to-text unit into speech. For example, it uses speech synthesis technology to convert the text data into natural-sounding speech. It also uses a Text-to-Speech API to analyze the text data and convert it into natural-sounding speech. For example, the text "Hello, the weather is nice today" is played back in a natural-sounding voice. Step 3: The image generation unit generates an image from the text or audio generated by the speech-to-text unit or speech conversion unit. For example, it uses natural language processing technology to analyze the content of the text or audio and generates an image related to it. It also uses the DALL-E API to analyze the content of the text or audio and generates an image related to it. For example, an image of a blue sky and white clouds is generated from the text "blue sky and white clouds." Step 4: The classification and analysis unit classifies and analyzes the content generated by the speech-to-text unit, speech conversion unit, and image generation unit. For example, it uses machine learning algorithms to analyze the generated text, audio, and image data, and classifies the content and analyzes its relevance. It also uses the Embedding API to identify generated text and images as being related to "weather" and classify them into the same category. Step 5: The music combination unit combines the content generated by the speech-to-text unit, speech conversion unit, image generation unit, and classification and analysis unit with music. For example, music editing software is used to combine the generated text, audio, and image data with music. Synchronization technology is also used to harmonize the generated content with music. For example, upbeat music is selected to match an image of a sunny day landscape.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (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.

[0100] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0102] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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 AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

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

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0126] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0130] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0139] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0142] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0157] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

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

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

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

Claims

1. a speech-to-text unit that converts speech into text; a speech conversion unit that converts the text converted by the speech-to-text unit into speech; an image generation unit that generates an image from the text or voice generated by the voice-to-text unit or the voice conversion unit; a classification and analysis unit that classifies and analyzes relevance of the content generated by the speech-to-text unit, the speech conversion unit, and the image generation unit; a music combination unit that combines the content generated by the speech-to-text unit, the speech conversion unit, the image generation unit, and the classification and analysis unit with music. A system characterized by:

2. The speech-to-text unit Perform sentiment analysis on voice data and adjust the accuracy of transcription based on sentiment 2. The system of claim 1.

3. The speech-to-text unit Automatically remove background noise from the audio and convert clear audio data into text 2. The system of claim 1.

4. The speech-to-text unit Selecting a transcription style according to the user's emotions 2. The system of claim 1.

5. The speech-to-text unit Converting voice data into text in multiple languages ​​simultaneously to generate multilingual content 2. The system of claim 1.

6. The speech-to-text unit Speaker identification is performed on the audio data, and if multiple speakers are included, the data is converted into text for each speaker.

2. The system of claim 1.

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