System

Abstract

An object of the system according to the embodiment is to efficiently generate multimedia content by integrating voice, text, image, and music.SOLUTION: A system includes a conversion unit, an analysis unit, a generation unit, and a music generation unit. The conversion unit converts the voice into a text. The analysis unit analyzes the text converted by the conversion unit. The generation unit generates an image on the basis of the text analyzed by the analysis unit. The music generation unit generates music based on the image generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that the process of integrating voice, text, images, and music to generate multimedia content is complicated and difficult to carry out efficiently.

[0005] The system according to the embodiment aims to efficiently generate multimedia content by integrating voice, text, images, and music. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversion unit, an analysis unit, a generation unit, and a music generation unit. The conversion unit converts speech into text. The analysis unit analyzes the text converted by the conversion unit. The generation unit generates an image based on the text analyzed by the analysis unit. The music generation unit generates music based on the image generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate multimedia content by integrating voice, text, images, and music. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A media generation system according to an embodiment of the present invention is a system that converts speech into text, analyzes the text, generates images, and generates music. The media generation system includes a conversion unit that converts speech into text, an analysis unit that analyzes the converted text, a generation unit that generates images based on the analyzed text, and a music generation unit that generates music based on the generated images. For example, the media generation system allows a user to record the audio of a lecture and convert it into text to create lecture notes. It can also convert text into speech to create audio guides. The media generation system can also generate images tailored to a specific theme when a user is creating marketing materials. It can also generate images suitable for explanations when creating educational materials. The media generation system can also analyze the content of text entered by a user and extract important information. For example, a user can input a large amount of text data and extract important keywords and phrases from it. It can also perform sentiment analysis of the text to determine whether the content is positive or negative. The media generation system can also generate music tailored to a user's preferences. For example, it can generate relaxing music when a user wants to relax. It can also generate music tailored to a specific event. This allows users to easily create and share multimedia content. This allows users to easily create and share multimedia content. For example, when creating an online educational course, users can convert lecture audio into text, extract key points using text analysis, create explanatory images using customizable image generation, and add background music using music generation. When developing marketing materials, users can analyze market research results using text analysis, create graphs and charts using customizable image generation, and add narration using bidirectional speech-to-text conversion.For social media content generation, you can create captions with two-way speech-to-text conversion, create visual content with customizable image generation, and add background music with music generation.

[0029] A media generation system according to an embodiment includes a conversion unit, an analysis unit, a generation unit, and a music generation unit. The conversion unit converts speech into text. For example, the conversion unit converts speech into text using speech recognition technology. The conversion unit can also select an appropriate conversion model depending on the type and format of the speech. For example, the conversion unit can convert speech, singing, noise, and other types of speech into text. The analysis unit analyzes the text converted by the conversion unit. For example, the analysis unit analyzes the text using natural language processing technology to extract important information. The analysis unit can also perform sentiment analysis of the text to determine whether the content is positive or negative. For example, the analysis unit can extract important information from the text using keyword extraction technology. The generation unit generates images based on the text analyzed by the analysis unit. For example, the generation unit generates images using a generation AI. The generation unit can generate images tailored to user needs. For example, the generation unit can generate images tailored to a specific theme. The music generation unit generates music based on the images generated by the generation unit. For example, the music generation unit generates music using a generation AI. The music generation unit can generate music tailored to the user's preferences. For example, the music generation unit can generate relaxing music or music tailored to a specific event. This allows the media generation system according to the embodiment to realize a series of processes for generating music from audio to text, from text to images, and from images.

[0030] The conversion unit can convert speech into text. The conversion unit converts speech into text using, for example, speech recognition technology. The conversion unit can also select an appropriate conversion model depending on the type and format of speech. For example, the conversion unit can convert speech, singing, noise, and other speech into text. This makes it possible to provide a speech-to-text conversion function. Some or all of the above-described processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit can input speech data into a generation AI and have the generation AI convert the speech data into text data.

[0031] The analysis unit can analyze text and extract important information. The analysis unit can analyze text and extract important information using, for example, natural language processing technology. The analysis unit can also perform sentiment analysis of text and determine whether the content is positive or negative. For example, the analysis unit can extract important information from text using keyword extraction technology. This can provide a function for extracting important information from text. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input text data into a generation AI and have the generation AI extract important information.

[0032] The generation unit can generate an image based on the extracted information. The generation unit generates an image using, for example, a generation AI. The generation unit can generate an image tailored to the needs of a user. For example, the generation unit can generate an image tailored to a specific theme. This makes it possible to provide a function for generating an image based on the extracted information. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the extracted information into a generation AI and cause the generation AI to generate an image.

[0033] The music generation unit can generate music based on the generated image. The music generation unit generates music using, for example, a generation AI. The music generation unit can generate music tailored to the user's preferences. For example, the music generation unit can generate relaxing music or music tailored to a specific event. This makes it possible to provide a function for generating music based on the generated image. Some or all of the above-described processing in the music generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the music generation unit can input the generated image data to the generation AI and cause the generation AI to generate music.

[0034] The conversion unit can convert text into speech. The conversion unit converts text into speech using, for example, speech synthesis technology. The conversion unit can also select an appropriate speech model depending on the type and format of the text. For example, the conversion unit can convert text such as a character string, a document, or tagged text into speech. This makes it possible to provide a text-to-speech conversion function. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit can input text data to a generation AI and have the generation AI convert the text data into speech data.

[0035] The analysis unit can perform sentiment analysis of the text. The analysis unit can perform sentiment analysis of the text using, for example, natural language processing technology. The analysis unit can determine sentiment, such as positive, negative, or neutral, based on the content of the text. For example, the analysis unit can analyze the sentiment of the text using a sentiment analysis algorithm. This can provide a function for performing sentiment analysis of the text. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input text data into a generation AI and have the generation AI perform sentiment analysis.

[0036] The generation unit can generate images tailored to a specific theme. The generation unit can generate images tailored to a specific theme using, for example, a generation AI. The generation unit can generate images tailored to user needs. For example, the generation unit can generate images tailored to a theme such as a season, an event, or an emotion. This makes it possible to provide a function for generating images tailored to a specific theme. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input information related to a specific theme into the generation AI and cause the generation AI to generate an image.

[0037] The music generation unit can generate music tailored to a specific event. The music generation unit generates music tailored to a specific event using, for example, a generation AI. The music generation unit can generate music tailored to the needs of a user. For example, the music generation unit can generate music tailored to events such as birthdays, weddings, and Christmas. This makes it possible to provide a function for generating music tailored to a specific event. Some or all of the above-described processing in the music generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the music generation unit can input information about a specific event to the generation AI and cause the generation AI to generate music.

[0038] The conversion unit can remove background noise from the audio and convert clear audio data into text. For example, the conversion unit uses a generation AI to filter background noise in real time to obtain clear audio data. The conversion unit can also analyze the audio data in advance to identify and remove noise components. The conversion unit can also combine multiple noise removal algorithms to perform optimal noise removal. This allows clear audio data to be converted into text by removing background noise. Some or all of the above-mentioned processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input audio data to the generation AI and have the generation AI perform noise removal and text conversion.

[0039] The conversion unit can improve conversion accuracy by taking into account the characteristics of the speaker of the voice. For example, the conversion unit uses a generation AI to analyze the speaker's accent and select an appropriate conversion model. The conversion unit can also learn the speaker's speaking habits and improve conversion accuracy. The conversion unit can also analyze the speaker's voice patterns in real time and adjust conversion accuracy. This improves conversion accuracy by taking the speaker's characteristics into account. Some or all of the above-mentioned processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input voice data to the generation AI and have the generation AI analyze the speaker's characteristics and adjust the conversion accuracy.

[0040] The conversion unit can automatically recognize technical terms and proper nouns according to the content of the speech and accurately convert them into text. For example, the conversion unit uses a generation AI to analyze speech data and automatically add technical terms and proper nouns to a dictionary. The conversion unit can also understand the context of the speech and accurately recognize technical terms and proper nouns. The conversion unit can also accurately convert technical terms and proper nouns by referring to the user's past input history. This improves conversion accuracy by accurately recognizing technical terms and proper nouns and converting them into text. Some or all of the above-mentioned processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input speech data into the generation AI and have the generation AI perform recognition of technical terms and proper nouns and text conversion.

[0041] The conversion unit can accurately convert region-specific expressions into text by taking into account the geographical background of the speaker of the audio. For example, the conversion unit uses a generation AI to analyze the speaker's geographical background and add region-specific expressions to a dictionary. The conversion unit can also select an appropriate conversion model by taking into account the speaker's geographical background. The conversion unit can also detect the speaker's geographical background in real time and accurately convert region-specific expressions. This improves conversion accuracy by accurately converting region-specific expressions into text. Some or all of the above-mentioned processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input audio data to the generation AI and have the generation AI analyze the geographical background and convert region-specific expressions.

[0042] The conversion unit can improve conversion accuracy by selecting an appropriate voice model based on the age and gender of the speaker of the voice. For example, the conversion unit uses a generation AI to analyze the speaker's age and select an appropriate voice model. The conversion unit can also analyze the speaker's gender and select an appropriate voice model. The conversion unit can also detect the speaker's age and gender in real time to improve conversion accuracy. This improves conversion accuracy by selecting a voice model based on the speaker's age and gender. Some or all of the above-mentioned processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input voice data to the generation AI and have the generation AI analyze the age and gender and select a voice model.

[0043] The analysis unit can perform context-dependent analysis to understand the context of the text and extract important information. For example, the analysis unit has a generation AI analyze the context of the text and extract important information. The analysis unit can also extract important information using a context-dependent analysis algorithm. The analysis unit can also analyze the context of the text in real time and dynamically extract important information. This allows important information to be accurately extracted by performing context-dependent analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input text data to the generation AI and have the generation AI perform context-dependent analysis and extract important information.

[0044] The analysis unit can analyze the linguistic features of the text and extract deeper meaning. For example, the analysis unit uses a generative AI to analyze metaphors and similes in the text and extract deeper meaning. The analysis unit can also analyze the meaning of the text by taking linguistic features into account. The analysis unit can also analyze the linguistic features of the text in real time and dynamically extract deeper meaning. This makes it possible to extract deeper meaning from the text by analyzing linguistic features. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generative AI. For example, the analysis unit can input text data into the generative AI and have the generative AI analyze the linguistic features and extract deeper meaning.

[0045] The analysis unit can analyze the structure of the text and clarify the hierarchical structure of the information. For example, the analysis unit causes the generation AI to analyze paragraphs and headings of the text and clarify the hierarchical structure of the information. The analysis unit can also analyze the hierarchical structure of the information taking the structure of the text into consideration. The analysis unit can also analyze the structure of the text in real time and dynamically clarify the hierarchical structure of the information. In this way, the hierarchical structure of the information can be clarified by analyzing the structure of the text. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input text data to the generation AI and have the generation AI analyze the structure and clarify the hierarchical structure of the information.

[0046] The analysis unit can switch analysis algorithms depending on the genre of the text. For example, when the generation AI analyzes technical documents, the analysis unit uses an algorithm specialized in technical terminology. When analyzing novels, the analysis unit can also use an algorithm specialized in narrative structure. The analysis unit can also detect the genre of the text in real time and select an appropriate analysis algorithm. This enables more appropriate analysis by switching the analysis algorithm depending on the genre of the text. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input text data to the generation AI and have the generation AI detect the genre and switch the analysis algorithm.

[0047] The analysis unit can adjust the level of detail of the analysis according to the length of the text. For example, when the generation AI analyzes short text, the analysis unit performs a detailed analysis. When analyzing long text, the analysis unit can also perform an analysis that focuses on the main points. The analysis unit can also detect the length of the text in real time and dynamically adjust the level of detail of the analysis. This enables more appropriate analysis by adjusting the level of detail of the analysis according to the length of the text. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input text data to the generation AI and have the generation AI detect the length and adjust the level of detail of the analysis.

[0048] The analysis unit can change the analysis method depending on the language of the text. For example, when the generation AI analyzes English text, the analysis unit uses an analysis method specialized for English. When analyzing Japanese text, the analysis unit can also use an analysis method specialized for Japanese. The analysis unit can also select an appropriate analysis method when analyzing multilingual text. This enables more appropriate analysis by changing the analysis method depending on the language of the text. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input text data to the generation AI and have the generation AI detect the language and change the analysis method.

[0049] The generation unit can automatically optimize the layout and design of the image based on the extracted information. For example, the generation unit selects an optimal layout based on the extracted information using a generation AI. The generation unit can also automatically arrange design elements based on the extracted information. The generation unit can also analyze the extracted information in real time and dynamically adjust the optimal layout and design. This enables more effective image generation by optimizing the layout and design of the image based on the extracted information. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input the extracted information to the generation AI and have the generation AI optimize the layout and design.

[0050] The generation unit can customize visual elements such as colors and fonts for the generated image. For example, the generation unit uses a generation AI to customize colors according to the user's preferences. The generation unit can also customize fonts according to the user's preferences. The generation unit can also customize visual elements based on the user's past selection history. This enables more personalized image generation by customizing visual elements such as colors and fonts. Some or all of the above-described processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input user preferences and history data into the generation AI and have the generation AI customize the visual elements.

[0051] The generation unit can add dynamic animation effects to the image to be generated. For example, the generation unit adds dynamic animation effects to the image using a generation AI. The generation unit can also customize animation effects according to the user's preferences. The generation unit can also select the optimal animation effect according to the content of the image. By adding dynamic animation effects, more attractive images can be generated. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input image data to the generation AI and have the generation AI add animation effects.

[0052] The generation unit can customize the generated image by reflecting the user's past preferences and history. For example, the generation unit uses a generation AI to customize the theme and style of the image based on the user's past selection history. The generation unit can also customize colors and design elements according to the user's preferences. The generation unit can also analyze the user's past usage history and generate an optimal image. This enables more personalized image generation by reflecting the user's past preferences and history. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's preferences and history data into the generation AI and have the generation AI customize the image.

[0053] The generation unit can apply a design that matches a specific event or season to the image to be generated. For example, the generation unit uses a generation AI to apply a design that matches a specific event (e.g., Christmas or Halloween). The generation unit can also select colors or themes that match the season (e.g., spring or summer). The generation unit can also refer to the user's calendar information to generate an image that matches an event. This makes it possible to generate more appropriate images by applying a design that matches a specific event or season. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input information about the event or season into the generation AI and have the generation AI apply the design.

[0054] The generation unit can incorporate region-specific elements into the generated image by taking into account the user's geographical background. For example, the generation unit uses a generation AI to analyze the user's geographical background and incorporate region-specific scenery and buildings into the image. The generation unit can also select region-specific colors and design elements by taking into account the user's geographical background. The generation unit can also detect the user's geographical background in real time and reflect optimal region-specific elements in the image. This makes it possible to generate images that incorporate region-specific elements by taking the user's geographical background into account. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input information about the geographical background into the generation AI and cause the generation AI to incorporate region-specific elements.

[0055] The music generation unit can adjust the atmosphere and tone of the music based on the color and design of the generated image. For example, the generation AI analyzes the color of the image and adjusts the atmosphere of the music. The music generation unit can also select the tone of the music taking into account the design elements of the image. The music generation unit can also analyze the content of the image in real time and dynamically adjust the atmosphere and tone of the music. This enables more appropriate music generation by adjusting the atmosphere and tone of the music based on the color and design of the generated image. Some or all of the above-mentioned processing in the music generation unit may be performed using or without the generation AI. For example, the music generation unit can input image data to the generation AI and have the generation AI adjust the atmosphere and tone of the music.

[0056] The music generation unit can customize specific instruments and tones for the music to be generated. For example, the generation AI of the music generation unit selects a specific instrument according to the user's preferences. The music generation unit can also customize tones according to the user's preferences. The music generation unit can also select optimal instruments and tones based on the user's past selection history. This allows for customizing specific instruments and tones, making it possible to generate more personalized music. Some or all of the above-described processing in the music generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the music generation unit can input the user's preferences and history data into the generation AI and have the generation AI customize the instruments and tones.

[0057] The music generation unit can add dynamic effects and filters to the music it generates. For example, the music generation unit uses a generation AI to add dynamic effects to the music. The music generation unit can also customize effects and filters according to the user's preferences. The music generation unit can also select optimal effects and filters according to the content of the music. By adding dynamic effects and filters, more attractive music can be generated. Some or all of the above-described processing in the music generation unit may be performed using or without the generation AI. For example, the music generation unit can input music data into the generation AI and have the generation AI add effects and filters.

[0058] The music generation unit can customize the music to be generated by reflecting the user's past musical preferences and history. For example, the music generation unit customizes the genre and style of music using a generation AI based on the user's past musical preferences. The music generation unit can also analyze the user's past playback history to generate optimal music. The music generation unit can also customize the tempo and melody of the music according to the user's preferences. This enables more personalized music to be generated by reflecting the user's past musical preferences and history. Some or all of the above-described processing in the music generation unit may be performed using or without the generation AI. For example, the music generation unit can input the user's preferences and history data into the generation AI and have the generation AI customize the music.

[0059] The music generation unit can apply a theme that matches a specific event or season to the music to be generated. For example, the music generation unit uses a generation AI to generate music that matches a specific event (e.g., Christmas or Halloween). The music generation unit can also generate music that matches a season (e.g., spring or summer). The music generation unit can also reference the user's calendar information to generate music that matches an event. This makes it possible to generate more appropriate music by applying a theme that matches a specific event or season. Some or all of the above-mentioned processing in the music generation unit may be performed using or without the generation AI. For example, the music generation unit can input information about an event or season to the generation AI and have the generation AI apply a theme.

[0060] The music generation unit can incorporate regional musical elements into the music to be generated, taking into account the user's geographical background. For example, the music generation unit uses a generation AI to analyze the user's geographical background and incorporate regional instruments and rhythms into the music. The music generation unit can also select a regional musical style taking into account the user's geographical background. The music generation unit can also detect the user's geographical background in real time and reflect optimal regional musical elements in the music. This makes it possible to generate music that incorporates regional musical elements by taking the user's geographical background into account. Some or all of the above-described processing in the music generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the music generation unit can input information about the geographical background into the generation AI and cause the generation AI to incorporate regional musical elements.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The media generation system may further include a speaker characteristic analysis unit that analyzes speaker characteristics from the user's voice data. The speaker characteristic analysis unit may analyze, for example, the speaker's age, gender, accent, speaking habits, etc., and improve the accuracy of speech-to-text conversion based on these characteristics. For example, if the speaker is elderly, the speaker characteristic analysis unit may adjust the conversion model taking into account the pronunciation and speaking habits unique to elderly people. Also, if the speaker has a regional accent, it may select a conversion model corresponding to that accent. Furthermore, it may be possible to learn the speaker's speaking habits and adjust the conversion accuracy in real time. This allows for more accurate text conversion by taking the speaker's characteristics into account.

[0063] The media generation system may further include a genre determination unit that automatically determines the genre of the user's input text. The genre determination unit may, for example, use natural language processing technology to analyze the content of the text and determine its genre, such as technical document, novel, or news article. Based on the determined genre, the analysis unit may select an appropriate analysis algorithm to analyze the text. For example, in the case of technical documents, an algorithm that extracts technical terms and analyzes the technical content may be used. In addition, in the case of novels, an algorithm that analyzes the story structure and the relationships between characters may be used. This enables appropriate analysis according to the text genre.

[0064] The media generation system can further include a context analysis unit that understands the context of the user's input text and performs context-dependent analysis. The context analysis unit, for example, uses generative AI to analyze the context of the text and extract important information. Using a context-dependent analysis algorithm enables analysis that takes the context of the text into account. For example, if the same word has different meanings depending on the context, it can extract the appropriate meaning according to the context. The context analysis unit can also analyze the context of the text in real time and dynamically extract important information. This context-dependent analysis enables more accurate information extraction.

[0065] The media generation system can further include a linguistic feature analysis unit that analyzes the linguistic features of the user's input text and extracts deeper meaning. The linguistic feature analysis unit, for example, uses generative AI to analyze metaphors and similes in the text and extracts deeper meaning. Analyzing the meaning of the text by taking linguistic features into account enables more accurate information extraction. For example, in the case of text containing metaphorical or similes, the meaning behind the expressions can be accurately understood. The linguistic feature analysis unit can also analyze the linguistic features of the text in real time and dynamically extract deeper meaning. This makes it possible to extract deeper meaning from the text by analyzing linguistic features.

[0066] The media generation system can further include a structural analysis unit that analyzes the structure of the user's input text and clarifies the hierarchical structure of the information. The structural analysis unit, for example, uses generative AI to analyze the paragraphs and headings of the text and clarifies the hierarchical structure of the information. Analyzing the hierarchical structure of the information while taking the structure of the text into consideration enables more accurate information extraction. For example, if different themes are addressed in different paragraphs, the information can be organized by those themes. The structural analysis unit can also analyze the structure of the text in real time and dynamically clarify the hierarchical structure of the information. In this way, the hierarchical structure of the information can be clarified by analyzing the structure of the text.

[0067] The media generation system can further include a length analysis unit that adjusts the level of analysis detail according to the length of the user's input text. The length analysis unit, for example, uses generative AI to detect the length of the text in real time and dynamically adjusts the level of analysis detail. Adjusting the level of analysis detail according to the length of the text enables more appropriate information extraction. For example, for short text, detailed analysis can be performed, and for long text, analysis that focuses on the main points can be performed. The length analysis unit can also select an analysis algorithm based on the length of the text to perform the optimal analysis. This allows for appropriate analysis according to the length of the text.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The conversion unit converts speech to text. The conversion unit uses speech recognition technology to convert speech to text and can select an appropriate conversion model depending on the type and format of the speech. For example, speech, singing, noise, and other speech can be converted to text. Step 2: The analysis unit analyzes the text converted by the conversion unit. The analysis unit uses natural language processing technology to analyze the text and extract important information. It can also perform sentiment analysis of the text to determine whether the content is positive or negative. For example, it can use keyword extraction technology to extract important information from the text. Step 3: The generator generates an image based on the text analyzed by the analyzer. The generator uses AI to generate an image tailored to the user's needs. For example, it can generate an image tailored to a specific theme. Step 4: The music generation unit generates music based on the images generated by the generation unit. The music generation unit generates music using a generative AI and can generate music tailored to the user's preferences. For example, it can generate relaxing music or music tailored to a specific event.

[0070] (Example 2) A media generation system according to an embodiment of the present invention is a system that converts speech into text, analyzes the text, generates images, and generates music. The media generation system includes a conversion unit that converts speech into text, an analysis unit that analyzes the converted text, a generation unit that generates images based on the analyzed text, and a music generation unit that generates music based on the generated images. For example, the media generation system allows a user to record the audio of a lecture and convert it into text to create lecture notes. It can also convert text into speech to create audio guides. The media generation system can also generate images tailored to a specific theme when a user is creating marketing materials. It can also generate images suitable for explanations when creating educational materials. The media generation system can also analyze the content of text entered by a user and extract important information. For example, a user can input a large amount of text data and extract important keywords and phrases from it. It can also perform sentiment analysis of the text to determine whether the content is positive or negative. The media generation system can also generate music tailored to a user's preferences. For example, it can generate relaxing music when a user wants to relax. It can also generate music tailored to a specific event. This allows users to easily create and share multimedia content. This allows users to easily create and share multimedia content. For example, when creating an online educational course, users can convert lecture audio into text, extract key points using text analysis, create explanatory images using customizable image generation, and add background music using music generation. When developing marketing materials, users can analyze market research results using text analysis, create graphs and charts using customizable image generation, and add narration using bidirectional speech-to-text conversion.For social media content generation, you can create captions with two-way speech-to-text conversion, create visual content with customizable image generation, and add background music with music generation.

[0071] A media generation system according to an embodiment includes a conversion unit, an analysis unit, a generation unit, and a music generation unit. The conversion unit converts speech into text. For example, the conversion unit converts speech into text using speech recognition technology. The conversion unit can also select an appropriate conversion model depending on the type and format of the speech. For example, the conversion unit can convert speech, singing, noise, and other types of speech into text. The analysis unit analyzes the text converted by the conversion unit. For example, the analysis unit analyzes the text using natural language processing technology to extract important information. The analysis unit can also perform sentiment analysis of the text to determine whether the content is positive or negative. For example, the analysis unit can extract important information from the text using keyword extraction technology. The generation unit generates images based on the text analyzed by the analysis unit. For example, the generation unit generates images using a generation AI. The generation unit can generate images tailored to user needs. For example, the generation unit can generate images tailored to a specific theme. The music generation unit generates music based on the images generated by the generation unit. For example, the music generation unit generates music using a generation AI. The music generation unit can generate music tailored to the user's preferences. For example, the music generation unit can generate relaxing music or music tailored to a specific event. This allows the media generation system according to the embodiment to realize a series of processes for generating music from audio to text, from text to images, and from images.

[0072] The conversion unit can convert speech into text. The conversion unit converts speech into text using, for example, speech recognition technology. The conversion unit can also select an appropriate conversion model depending on the type and format of speech. For example, the conversion unit can convert speech, singing, noise, and other speech into text. This makes it possible to provide a speech-to-text conversion function. Some or all of the above-described processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit can input speech data into a generation AI and have the generation AI convert the speech data into text data.

[0073] The analysis unit can analyze text and extract important information. The analysis unit can analyze text and extract important information using, for example, natural language processing technology. The analysis unit can also perform sentiment analysis of text and determine whether the content is positive or negative. For example, the analysis unit can extract important information from text using keyword extraction technology. This can provide a function for extracting important information from text. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input text data into a generation AI and have the generation AI extract important information.

[0074] The generation unit can generate an image based on the extracted information. The generation unit generates an image using, for example, a generation AI. The generation unit can generate an image tailored to the needs of a user. For example, the generation unit can generate an image tailored to a specific theme. This makes it possible to provide a function for generating an image based on the extracted information. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the extracted information into a generation AI and cause the generation AI to generate an image.

[0075] The music generation unit can generate music based on the generated image. The music generation unit generates music using, for example, a generation AI. The music generation unit can generate music tailored to the user's preferences. For example, the music generation unit can generate relaxing music or music tailored to a specific event. This makes it possible to provide a function for generating music based on the generated image. Some or all of the above-described processing in the music generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the music generation unit can input the generated image data to the generation AI and cause the generation AI to generate music.

[0076] The conversion unit can convert text into speech. The conversion unit converts text into speech using, for example, speech synthesis technology. The conversion unit can also select an appropriate speech model depending on the type and format of the text. For example, the conversion unit can convert text such as a character string, a document, or tagged text into speech. This makes it possible to provide a text-to-speech conversion function. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit can input text data to a generation AI and have the generation AI convert the text data into speech data.

[0077] The analysis unit can perform sentiment analysis of the text. The analysis unit can perform sentiment analysis of the text using, for example, natural language processing technology. The analysis unit can determine sentiment, such as positive, negative, or neutral, based on the content of the text. For example, the analysis unit can analyze the sentiment of the text using a sentiment analysis algorithm. This can provide a function for performing sentiment analysis of the text. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input text data into a generation AI and have the generation AI perform sentiment analysis.

[0078] The generation unit can generate images tailored to a specific theme. The generation unit can generate images tailored to a specific theme using, for example, a generation AI. The generation unit can generate images tailored to user needs. For example, the generation unit can generate images tailored to a theme such as a season, an event, or an emotion. This makes it possible to provide a function for generating images tailored to a specific theme. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input information related to a specific theme into the generation AI and cause the generation AI to generate an image.

[0079] The music generation unit can generate music tailored to a specific event. The music generation unit generates music tailored to a specific event using, for example, a generation AI. The music generation unit can generate music tailored to the needs of a user. For example, the music generation unit can generate music tailored to events such as birthdays, weddings, and Christmas. This makes it possible to provide a function for generating music tailored to a specific event. Some or all of the above-described processing in the music generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the music generation unit can input information about a specific event to the generation AI and cause the generation AI to generate music.

[0080] The conversion unit can estimate the user's emotion and adjust the speech-to-text conversion accuracy based on the estimated user's emotion. The conversion unit estimates the user's emotion using, for example, an emotion estimation algorithm. The conversion unit adjusts the speech-to-text conversion accuracy based on the user's emotion. For example, if the user is nervous, the generation AI can improve the conversion accuracy by taking into account the intonation and speed of the voice. Also, if the user is relaxed, the generation AI can adjust the conversion accuracy by emphasizing the natural flow of the voice. Also, if the user is excited, the generation AI can accurately capture the emphasized parts of the voice and improve the conversion accuracy. This enables more accurate text conversion by adjusting the conversion accuracy based on the user's emotion. Emotion estimation is achieved using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the conversion unit may be performed using the generation AI, or may be performed without the generation AI. For example, the conversion unit can input voice data to the generation AI and have the generation AI perform emotion estimation and adjustment of the conversion accuracy.

[0081] The conversion unit can remove background noise from the audio and convert clear audio data into text. For example, the conversion unit uses a generation AI to filter background noise in real time to obtain clear audio data. The conversion unit can also analyze the audio data in advance to identify and remove noise components. The conversion unit can also combine multiple noise removal algorithms to perform optimal noise removal. This allows clear audio data to be converted into text by removing background noise. Some or all of the above-mentioned processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input audio data to the generation AI and have the generation AI perform noise removal and text conversion.

[0082] The conversion unit can improve conversion accuracy by taking into account the characteristics of the speaker of the voice. For example, the conversion unit uses a generation AI to analyze the speaker's accent and select an appropriate conversion model. The conversion unit can also learn the speaker's speaking habits and improve conversion accuracy. The conversion unit can also analyze the speaker's voice patterns in real time and adjust conversion accuracy. This improves conversion accuracy by taking the speaker's characteristics into account. Some or all of the above-mentioned processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input voice data to the generation AI and have the generation AI analyze the speaker's characteristics and adjust the conversion accuracy.

[0083] The conversion unit can automatically recognize technical terms and proper nouns according to the content of the speech and accurately convert them into text. For example, the conversion unit uses a generation AI to analyze speech data and automatically add technical terms and proper nouns to a dictionary. The conversion unit can also understand the context of the speech and accurately recognize technical terms and proper nouns. The conversion unit can also accurately convert technical terms and proper nouns by referring to the user's past input history. This improves conversion accuracy by accurately recognizing technical terms and proper nouns and converting them into text. Some or all of the above-mentioned processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input speech data into the generation AI and have the generation AI perform recognition of technical terms and proper nouns and text conversion.

[0084] The conversion unit can estimate the user's emotion and adjust the text-to-speech conversion speed based on the estimated user emotion. The conversion unit, for example, estimates the user's emotion using an emotion estimation algorithm. The conversion unit adjusts the text-to-speech conversion speed based on the user's emotion. For example, if the user is in a hurry, the generation AI can speed up the text-to-speech conversion speed. Also, if the user is relaxed, the generation AI can slow down the text-to-speech conversion speed. Also, if the user is excited, the generation AI can adjust the text-to-speech conversion speed to maintain a natural rhythm. This enables more natural voice conversion by adjusting the conversion speed based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the conversion unit may be performed using the generation AI, or may be performed without the generation AI. For example, the conversion unit can input text data to the generation AI and have the generation AI perform emotion estimation and adjust the conversion speed.

[0085] The conversion unit can estimate the emotion of the speaker of the audio and adjust the text expression method based on the emotion. The conversion unit, for example, estimates the speaker's emotion using an emotion estimation algorithm. The conversion unit adjusts the text expression method based on the speaker's emotion. For example, the generation AI analyzes the speaker's emotion and selects an expression method according to the emotion. The generation AI can also adjust the tone and style of the text taking the speaker's emotion into consideration. The generation AI can also detect the speaker's emotion in real time and dynamically change the text expression method. This enables more appropriate text conversion by adjusting the text expression method based on the speaker's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the conversion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the conversion unit can input audio data to the generation AI and have the generation AI perform emotion estimation and adjust the text expression method.

[0086] The conversion unit can accurately convert region-specific expressions into text by taking into account the geographical background of the speaker of the audio. For example, the conversion unit uses a generation AI to analyze the speaker's geographical background and add region-specific expressions to a dictionary. The conversion unit can also select an appropriate conversion model by taking into account the speaker's geographical background. The conversion unit can also detect the speaker's geographical background in real time and accurately convert region-specific expressions. This improves conversion accuracy by accurately converting region-specific expressions into text. Some or all of the above-mentioned processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input audio data to the generation AI and have the generation AI analyze the geographical background and convert region-specific expressions.

[0087] The conversion unit can improve conversion accuracy by selecting an appropriate voice model based on the age and gender of the speaker of the voice. For example, the conversion unit uses a generation AI to analyze the speaker's age and select an appropriate voice model. The conversion unit can also analyze the speaker's gender and select an appropriate voice model. The conversion unit can also detect the speaker's age and gender in real time to improve conversion accuracy. This improves conversion accuracy by selecting a voice model based on the speaker's age and gender. Some or all of the above-mentioned processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input voice data to the generation AI and have the generation AI analyze the age and gender and select a voice model.

[0088] The analysis unit can estimate the user's emotions and adjust the text analysis method based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions using an emotion estimation algorithm. The analysis unit adjusts the text analysis method based on the user's emotions. For example, if the user is nervous, the generation AI can simplify the text analysis method. Alternatively, if the user is relaxed, the generation AI can perform a detailed analysis. Alternatively, if the user is excited, the generation AI can emphasize important points in the text for analysis. This enables more appropriate text analysis by adjusting the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without the generation AI. For example, the analysis unit can input text data into the generation AI and have the generation AI perform emotion estimation and adjust the analysis method.

[0089] The analysis unit can perform context-dependent analysis to understand the context of the text and extract important information. For example, the analysis unit has a generation AI analyze the context of the text and extract important information. The analysis unit can also extract important information using a context-dependent analysis algorithm. The analysis unit can also analyze the context of the text in real time and dynamically extract important information. This allows important information to be accurately extracted by performing context-dependent analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input text data to the generation AI and have the generation AI perform context-dependent analysis and extract important information.

[0090] The analysis unit can analyze the linguistic features of the text and extract deeper meaning. For example, the analysis unit uses a generative AI to analyze metaphors and similes in the text and extract deeper meaning. The analysis unit can also analyze the meaning of the text by taking linguistic features into account. The analysis unit can also analyze the linguistic features of the text in real time and dynamically extract deeper meaning. This makes it possible to extract deeper meaning from the text by analyzing linguistic features. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generative AI. For example, the analysis unit can input text data into the generative AI and have the generative AI analyze the linguistic features and extract deeper meaning.

[0091] The analysis unit can analyze the structure of the text and clarify the hierarchical structure of the information. For example, the analysis unit causes the generation AI to analyze paragraphs and headings of the text and clarify the hierarchical structure of the information. The analysis unit can also analyze the hierarchical structure of the information taking the structure of the text into consideration. The analysis unit can also analyze the structure of the text in real time and dynamically clarify the hierarchical structure of the information. In this way, the hierarchical structure of the information can be clarified by analyzing the structure of the text. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input text data to the generation AI and have the generation AI analyze the structure and clarify the hierarchical structure of the information.

[0092] The analysis unit can estimate the user's emotion and adjust the sentiment analysis result of the text based on the estimated user's emotion. The analysis unit estimates the user's emotion using, for example, an emotion estimation algorithm. The analysis unit adjusts the sentiment analysis result of the text based on the user's emotion. For example, if the user has a positive emotion, the generation AI can adjust the sentiment analysis result of the text to be positive. Also, if the user has a negative emotion, the generation AI can adjust the sentiment analysis result of the text to be negative. Also, if the user has a neutral emotion, the generation AI can adjust the sentiment analysis result of the text to be neutral. This enables more appropriate sentiment analysis by adjusting the sentiment analysis result based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input text data into the generation AI and have the generation AI perform emotion estimation and adjustment of the emotion analysis results.

[0093] The analysis unit can switch analysis algorithms depending on the genre of the text. For example, when the generation AI analyzes technical documents, the analysis unit uses an algorithm specialized in technical terminology. When analyzing novels, the analysis unit can also use an algorithm specialized in narrative structure. The analysis unit can also detect the genre of the text in real time and select an appropriate analysis algorithm. This enables more appropriate analysis by switching the analysis algorithm depending on the genre of the text. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input text data to the generation AI and have the generation AI detect the genre and switch the analysis algorithm.

[0094] The analysis unit can adjust the level of detail of the analysis according to the length of the text. For example, when the generation AI analyzes short text, the analysis unit performs a detailed analysis. When analyzing long text, the analysis unit can also perform an analysis that focuses on the main points. The analysis unit can also detect the length of the text in real time and dynamically adjust the level of detail of the analysis. This enables more appropriate analysis by adjusting the level of detail of the analysis according to the length of the text. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input text data to the generation AI and have the generation AI detect the length and adjust the level of detail of the analysis.

[0095] The analysis unit can change the analysis method depending on the language of the text. For example, when the generation AI analyzes English text, the analysis unit uses an analysis method specialized for English. When analyzing Japanese text, the analysis unit can also use an analysis method specialized for Japanese. The analysis unit can also select an appropriate analysis method when analyzing multilingual text. This enables more appropriate analysis by changing the analysis method depending on the language of the text. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input text data to the generation AI and have the generation AI detect the language and change the analysis method.

[0096] The generation unit can estimate the user's emotion and adjust the style of the generated image based on the estimated user emotion. The generation unit estimates the user's emotion using, for example, an emotion estimation algorithm. The generation unit adjusts the style of the generated image based on the user's emotion. For example, if the user is relaxed, the generation AI can generate an image with soft colors. Alternatively, if the user is excited, the generation AI can generate an image with vivid colors. Alternatively, if the user is calm, the generation AI can generate an image with a simple and sophisticated style. This enables more appropriate image generation by adjusting the image style based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input emotion data to the generation AI and cause the generation AI to perform emotion estimation and image style adjustment.

[0097] The generation unit can automatically optimize the layout and design of the image based on the extracted information. For example, the generation unit selects an optimal layout based on the extracted information using a generation AI. The generation unit can also automatically arrange design elements based on the extracted information. The generation unit can also analyze the extracted information in real time and dynamically adjust the optimal layout and design. This enables more effective image generation by optimizing the layout and design of the image based on the extracted information. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input the extracted information to the generation AI and have the generation AI optimize the layout and design.

[0098] The generation unit can customize visual elements such as colors and fonts for the generated image. For example, the generation unit uses a generation AI to customize colors according to the user's preferences. The generation unit can also customize fonts according to the user's preferences. The generation unit can also customize visual elements based on the user's past selection history. This enables more personalized image generation by customizing visual elements such as colors and fonts. Some or all of the above-described processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input user preferences and history data into the generation AI and have the generation AI customize the visual elements.

[0099] The generation unit can add dynamic animation effects to the image to be generated. For example, the generation unit adds dynamic animation effects to the image using a generation AI. The generation unit can also customize animation effects according to the user's preferences. The generation unit can also select the optimal animation effect according to the content of the image. By adding dynamic animation effects, more attractive images can be generated. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input image data to the generation AI and have the generation AI add animation effects.

[0100] The generation unit can estimate the user's emotions and select a theme for the image to be generated based on the estimated user's emotions. The generation unit, for example, estimates the user's emotions using an emotion estimation algorithm. The generation unit selects a theme for the image to be generated based on the user's emotions. For example, if the user is relaxed, the generation AI can generate images themed around nature or landscapes. Alternatively, if the user is excited, the generation AI can generate images themed around sports or action. Alternatively, if the user is calm, the generation AI can generate images themed around art or design. This enables more appropriate image generation by selecting an image theme based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input emotion data into the generation AI and have the generation AI perform emotion estimation and image theme selection.

[0101] The generation unit can customize the generated image by reflecting the user's past preferences and history. For example, the generation unit uses a generation AI to customize the theme and style of the image based on the user's past selection history. The generation unit can also customize colors and design elements according to the user's preferences. The generation unit can also analyze the user's past usage history and generate an optimal image. This enables more personalized image generation by reflecting the user's past preferences and history. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's preferences and history data into the generation AI and have the generation AI customize the image.

[0102] The generation unit can apply a design that matches a specific event or season to the image to be generated. For example, the generation unit uses a generation AI to apply a design that matches a specific event (e.g., Christmas or Halloween). The generation unit can also select colors or themes that match the season (e.g., spring or summer). The generation unit can also refer to the user's calendar information to generate an image that matches an event. This makes it possible to generate more appropriate images by applying a design that matches a specific event or season. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input information about the event or season into the generation AI and have the generation AI apply the design.

[0103] The generation unit can incorporate region-specific elements into the generated image by taking into account the user's geographical background. For example, the generation unit uses a generation AI to analyze the user's geographical background and incorporate region-specific scenery and buildings into the image. The generation unit can also select region-specific colors and design elements by taking into account the user's geographical background. The generation unit can also detect the user's geographical background in real time and reflect optimal region-specific elements in the image. This makes it possible to generate images that incorporate region-specific elements by taking the user's geographical background into account. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input information about the geographical background into the generation AI and cause the generation AI to incorporate region-specific elements.

[0104] The music generation unit can estimate the user's emotion and adjust the tempo and melody of the music to be generated based on the estimated user's emotion. The music generation unit estimates the user's emotion using, for example, an emotion estimation algorithm. The music generation unit adjusts the tempo and melody of the music to be generated based on the user's emotion. For example, if the user is relaxed, the generation AI can generate music with a slow tempo. Alternatively, if the user is excited, the generation AI can generate music with an up-tempo. Alternatively, if the user is calm, the generation AI can generate music with a gentle melody. This enables more appropriate music generation by adjusting the tempo and melody of the music based on the user's emotion. Emotion estimation is realized using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the music generation unit may be performed using the generation AI, or may be performed without the generation AI. For example, the music generation unit can input emotion data to the generation AI and have the generation AI perform emotion estimation and adjust the tempo and melody of the music.

[0105] The music generation unit can adjust the atmosphere and tone of the music based on the color and design of the generated image. For example, the generation AI analyzes the color of the image and adjusts the atmosphere of the music. The music generation unit can also select the tone of the music taking into account the design elements of the image. The music generation unit can also analyze the content of the image in real time and dynamically adjust the atmosphere and tone of the music. This enables more appropriate music generation by adjusting the atmosphere and tone of the music based on the color and design of the generated image. Some or all of the above-mentioned processing in the music generation unit may be performed using or without the generation AI. For example, the music generation unit can input image data to the generation AI and have the generation AI adjust the atmosphere and tone of the music.

[0106] The music generation unit can customize specific instruments and tones for the music to be generated. For example, the generation AI of the music generation unit selects a specific instrument according to the user's preferences. The music generation unit can also customize tones according to the user's preferences. The music generation unit can also select optimal instruments and tones based on the user's past selection history. This allows for customizing specific instruments and tones, making it possible to generate more personalized music. Some or all of the above-described processing in the music generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the music generation unit can input the user's preferences and history data into the generation AI and have the generation AI customize the instruments and tones.

[0107] The music generation unit can add dynamic effects and filters to the music it generates. For example, the music generation unit uses a generation AI to add dynamic effects to the music. The music generation unit can also customize effects and filters according to the user's preferences. The music generation unit can also select optimal effects and filters according to the content of the music. By adding dynamic effects and filters, more attractive music can be generated. Some or all of the above-described processing in the music generation unit may be performed using or without the generation AI. For example, the music generation unit can input music data into the generation AI and have the generation AI add effects and filters.

[0108] The music generation unit can estimate the user's emotion and select the genre of music to be generated based on the estimated user emotion. The music generation unit estimates the user's emotion using, for example, an emotion estimation algorithm. The music generation unit selects the genre of music to be generated based on the user's emotion. For example, if the user is relaxed, the generation AI can select classical or jazz. Alternatively, if the user is excited, the generation AI can select rock or pop. Alternatively, if the user is calm, the generation AI can select ambient or new age. This enables more appropriate music generation by selecting a music genre based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the music generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the music generation unit can input emotion data to the generation AI and have the generation AI perform emotion estimation and music genre selection.

[0109] The music generation unit can customize the music to be generated by reflecting the user's past musical preferences and history. For example, the music generation unit customizes the genre and style of music using a generation AI based on the user's past musical preferences. The music generation unit can also analyze the user's past playback history to generate optimal music. The music generation unit can also customize the tempo and melody of the music according to the user's preferences. This enables more personalized music to be generated by reflecting the user's past musical preferences and history. Some or all of the above-described processing in the music generation unit may be performed using or without the generation AI. For example, the music generation unit can input the user's preferences and history data into the generation AI and have the generation AI customize the music.

[0110] The music generation unit can apply a theme that matches a specific event or season to the music to be generated. For example, the music generation unit uses a generation AI to generate music that matches a specific event (e.g., Christmas or Halloween). The music generation unit can also generate music that matches a season (e.g., spring or summer). The music generation unit can also reference the user's calendar information to generate music that matches an event. This makes it possible to generate more appropriate music by applying a theme that matches a specific event or season. Some or all of the above-mentioned processing in the music generation unit may be performed using or without the generation AI. For example, the music generation unit can input information about an event or season to the generation AI and have the generation AI apply a theme.

[0111] The music generation unit can incorporate regional musical elements into the music to be generated, taking into account the user's geographical background. For example, the music generation unit uses a generation AI to analyze the user's geographical background and incorporate regional instruments and rhythms into the music. The music generation unit can also select a regional musical style taking into account the user's geographical background. The music generation unit can also detect the user's geographical background in real time and reflect optimal regional musical elements in the music. This makes it possible to generate music that incorporates regional musical elements by taking the user's geographical background into account. Some or all of the above-described processing in the music generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the music generation unit can input information about the geographical background into the generation AI and cause the generation AI to incorporate regional musical elements. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned conversion unit, analysis unit, generation unit, and music generation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the conversion unit is realized by the processor 46 of the smart device 14 or the processor 28 of the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart device 14 or the processor 28 of the data processing device 12. For example, the generation unit is realized by the processor 46 of the smart device 14 or the processor 28 of the data processing device 12. For example, the music generation unit is realized by the processor 46 of the smart device 14 or the processor 28 of the data processing device 12. The data processing device 12 may be, for example, a device in the cloud. For example, the data processing device 12 may be a device that realizes the above-mentioned conversion unit, analysis unit, generation unit, and music generation unit in cooperation with the cloud, and some elements may be included in the cloud. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned conversion unit, analysis unit, generation unit, and music generation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the conversion unit is realized by the processor 46 of the smart glasses 214 or the processor 28 of the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214 or the processor 28 of the data processing device 12. For example, the generation unit is realized by the processor 46 of the smart glasses 214 or the processor 28 of the data processing device 12. For example, the music generation unit is realized by the processor 46 of the smart glasses 214 or the processor 28 of the data processing device 12. The data processing device 12 is, for example, a device in the cloud. For example, the data processing device 12 may be a device that realizes the above-mentioned conversion unit, analysis unit, generation unit, and music generation unit in cooperation with the cloud, and some elements may be included in the cloud. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned conversion unit, analysis unit, generation unit, and music generation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the conversion unit is realized by the processor 46 of the headset type terminal 314 or the processor 28 of the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset type terminal 314 or the processor 28 of the data processing device 12. For example, the generation unit is realized by the processor 46 of the headset type terminal 314 or the processor 28 of the data processing device 12. For example, the music generation unit is realized by the processor 46 of the headset type terminal 314 or the processor 28 of the data processing device 12. The data processing device 12 is, for example, a device in the cloud. For example, the data processing device 12 may be a device that realizes the above-mentioned conversion unit, analysis unit, generation unit, and music generation unit in cooperation with the cloud, and some of the elements may be included in the cloud. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned conversion unit, analysis unit, generation unit, and music generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the conversion unit is realized by the processor 46 of the robot 414 or the processor 28 of the data processing device 12. For example, the analysis unit is realized by the processor 46 of the robot 414 or the processor 28 of the data processing device 12. For example, the generation unit is realized by the processor 46 of the robot 414 or the processor 28 of the data processing device 12. For example, the music generation unit is realized by the processor 46 of the robot 414 or the processor 28 of the data processing device 12. The data processing device 12 is, for example, a device in the cloud. For example, the data processing device 12 may be a device that realizes the above-mentioned conversion unit, analysis unit, generation unit, and music generation unit in cooperation with the cloud, and some elements may be included in the cloud.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] The media generation system may further include a speaker characteristic analysis unit that analyzes speaker characteristics from the user's voice data. The speaker characteristic analysis unit may analyze, for example, the speaker's age, gender, accent, speaking habits, etc., and improve the accuracy of speech-to-text conversion based on these characteristics. For example, if the speaker is elderly, the speaker characteristic analysis unit may adjust the conversion model taking into account the pronunciation and speaking habits unique to elderly people. Also, if the speaker has a regional accent, it may select a conversion model corresponding to that accent. Furthermore, it may be possible to learn the speaker's speaking habits and adjust the conversion accuracy in real time. This allows for more accurate text conversion by taking the speaker's characteristics into account.

[0114] The media generation system may further include a genre determination unit that automatically determines the genre of the user's input text. The genre determination unit may, for example, use natural language processing technology to analyze the content of the text and determine its genre, such as technical document, novel, or news article. Based on the determined genre, the analysis unit may select an appropriate analysis algorithm to analyze the text. For example, in the case of technical documents, an algorithm that extracts technical terms and analyzes the technical content may be used. In addition, in the case of novels, an algorithm that analyzes the story structure and the relationships between characters may be used. This enables appropriate analysis according to the text genre.

[0115] The media generation system can further estimate the user's emotions and select the theme of the images to be generated based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate images themed around nature or landscapes. If the user is excited, it can generate images themed around sports or action. Furthermore, if the user is calm, it can generate images themed around art or design. In this way, by selecting the theme of the image based on the user's emotions, more appropriate image generation can be achieved.

[0116] The media generation system can further include a context analysis unit that understands the context of the user's input text and performs context-dependent analysis. The context analysis unit, for example, uses generative AI to analyze the context of the text and extract important information. Using a context-dependent analysis algorithm enables analysis that takes the context of the text into account. For example, if the same word has different meanings depending on the context, it can extract the appropriate meaning according to the context. The context analysis unit can also analyze the context of the text in real time and dynamically extract important information. This context-dependent analysis enables more accurate information extraction.

[0117] The media generation system can further estimate the user's emotions and adjust the tempo and melody of the music to be generated based on the estimated emotions. For example, if the user is relaxed, the music generation unit can generate music with a slow tempo. If the user is excited, it can also generate music with an upbeat tempo. Furthermore, if the user is calm, it can also generate music with a gentle melody. In this way, by adjusting the tempo and melody of the music based on the user's emotions, more appropriate music can be generated.

[0118] The media generation system can further include a linguistic feature analysis unit that analyzes the linguistic features of the user's input text and extracts deeper meaning. The linguistic feature analysis unit, for example, uses generative AI to analyze metaphors and similes in the text and extracts deeper meaning. Analyzing the meaning of the text by taking linguistic features into account enables more accurate information extraction. For example, in the case of text containing metaphorical or similes, the meaning behind the expressions can be accurately understood. The linguistic feature analysis unit can also analyze the linguistic features of the text in real time and dynamically extract deeper meaning. This makes it possible to extract deeper meaning from the text by analyzing linguistic features.

[0119] The media generation system can further estimate the user's emotions and adjust the style of the generated image based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate an image with soft colors. If the user is excited, the generation unit can generate an image with vivid colors. Furthermore, if the user is calm, the generation unit can generate an image with a simple and sophisticated style. In this way, by adjusting the image style based on the user's emotions, more appropriate image generation can be achieved.

[0120] The media generation system can further include a structural analysis unit that analyzes the structure of the user's input text and clarifies the hierarchical structure of the information. The structural analysis unit, for example, uses generative AI to analyze the paragraphs and headings of the text and clarifies the hierarchical structure of the information. Analyzing the hierarchical structure of the information while taking the structure of the text into consideration enables more accurate information extraction. For example, if different themes are addressed in different paragraphs, the information can be organized by those themes. The structural analysis unit can also analyze the structure of the text in real time and dynamically clarify the hierarchical structure of the information. In this way, the hierarchical structure of the information can be clarified by analyzing the structure of the text.

[0121] The media generation system can further estimate the user's emotions and select the genre of music to be generated based on the estimated emotions. For example, if the user is relaxed, the music generation unit can select classical or jazz. If the user is excited, it can select rock or pop. Furthermore, if the user is calm, it can select ambient or new age. In this way, more appropriate music can be generated by selecting the genre of music based on the user's emotions.

[0122] The media generation system can further include a length analysis unit that adjusts the level of analysis detail according to the length of the user's input text. The length analysis unit, for example, uses generative AI to detect the length of the text in real time and dynamically adjusts the level of analysis detail. Adjusting the level of analysis detail according to the length of the text enables more appropriate information extraction. For example, for short text, detailed analysis can be performed, and for long text, analysis that focuses on the main points can be performed. The length analysis unit can also select an analysis algorithm based on the length of the text to perform the optimal analysis. This allows for appropriate analysis according to the length of the text.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The conversion unit converts speech to text. The conversion unit uses speech recognition technology to convert speech to text and can select an appropriate conversion model depending on the type and format of the speech. For example, speech, singing, noise, and other speech can be converted to text. Step 2: The analysis unit analyzes the text converted by the conversion unit. The analysis unit uses natural language processing technology to analyze the text and extract important information. It can also perform sentiment analysis of the text to determine whether the content is positive or negative. For example, it can use keyword extraction technology to extract important information from the text. Step 3: The generator generates an image based on the text analyzed by the analyzer. The generator uses AI to generate an image tailored to the user's needs. For example, it can generate an image tailored to a specific theme. Step 4: The music generation unit generates music based on the images generated by the generation unit. The music generation unit generates music using a generative AI and can generate music tailored to the user's preferences. For example, it can generate relaxing music or music tailored to a specific event.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] [Explanation of symbols]

[0197] 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 converter for converting speech to text; an analysis unit that analyzes the text converted by the conversion unit; a generation unit that generates an image based on the text analyzed by the analysis unit; a music generation unit that generates music based on the image generated by the generation unit. A system characterized by:

2. The conversion unit Convert speech to text 2. The system of claim 1.

3. The analysis unit Analyze text and extract important information 2. The system of claim 1.

4. The generation unit Generate an image based on the extracted information 2. The system of claim 1.

5. The music generation unit Generate music based on generated images 2. The system of claim 1.

6. The conversion unit Convert text to speech 2. The system of claim 1.

7. The analysis unit Perform sentiment analysis on text 2. The system of claim 1.

8. The generation unit Generate images tailored to a specific theme 2. The system of claim 1.

Images